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DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

MO-FG-213-04: Managing the Relationship

Academic-industrial collaborations are playing an increasingly prominent role in medical physics research. Successful partnerships have enabled truly translational, benchtop-to-bedside research that has significantly and positively impacted our field. Given the current flat or depleting trends in federal funding, and the increasing emphasis in the medical physics community on demonstrating translatability, efficacy, and safety prior to widespread clinical use, it is anticipated that such partnerships will become ever more important in advancing our field. In the future, it is likely that neither academia alone, nor industry alone, will be able to bring significant innovation to successfully impact patient care in radiotherapy. Such innovation will require partnership and symbiosis through the lifetime of the project. This symposium is aimed at professionals from academia and the industry who are interested in either initiating new collaborations or maintaining and improving existing partnerships. We will hear from four thought leaders from industry and academia on critical aspects of building and maintaining successful academic-industrial research partnerships. The following topics will be addressed: 1. Motivations: The industry perspective [Kevin Brown, Elekta] What goes into the development of a company’s R&D roadmap What are the motivations and expectations of the industry from academic partners How can academics make a successful pitch by identifying projects aligned with a company’s roadmap Why are some relationships successful and some not Milestones and progress reports - Why do we need them 2. Motivations: The academic perspective [T. Rock Mackie, Univ. of Madison, WI; Tomotherapy] What are the motivations and expectations of academic partners from the industry What are the cultural differences in academia vs industry on sharing information, exchanging ideas Partnership or entrepreneurship? When does each option make sense 3. Industrial Grants and Research Contracts – a Primer [Norbert Pelc; Stanford University] What topics are suited to industrial grants vs. academic grants How do resources (personnel effort, tuition) get allocated and charged in academia What are reasonable resource and timeline requests for small and big projects; from small and big companies “Fair market value” considerations Sections of a research agreement (IP including software confidential information, publications, data, exclusivity, support) 4. Managing the relationship [John Wong, Johns Hopkins University] How to effectively collaborate with each other How to adapt to changes in the relationship How to renew funding with the industry Moderated Panel Discussion [All speakers + moderators] Learning Objectives: 1. Understand the motivations of each partner 2. Elements that contribute to a successful partnership 3. Avoiding pitfalls - Challenges on either side that may derail a project 4. How to frame a research grant and agreement 5. How to sustain the collaborative relationship Amit Sawant: Funding from: NIH. Varian, Elekta and VisionRT; Michelle Svatos: Employee and shareholder: Varian, Board Member, TopasMC; Kevin Brown: Employee, Elekta; T. Rock Mackie: Consultant, Accuray; Norbert Pelc: Research support: Philips Healthcare, GE Healthcare; John Wong: Funding from NIH, Xstrahl and Elekta. Co-founder and advisor for JPLC Associates, LLC.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Are Lock-Free Concurrent Algorithms Practically Wait-Free?

Lock-free concurrent algorithms guarantee that some concurrent operation will always make progress in a finite number of steps. Yet programmers prefer to treat concurrent code as if it were wait-free, guaranteeing that all operations always make progress. Unfortunately, designing wait-free algorithms is generally a very complex task, and the resulting algorithms are not always efficient. Although obtaining efficient wait-free algorithms has been a long-time goal for the theory community, most nonblocking commercial code is only lock-free. This article suggests a simple solution to this problem. We show that for a large class of lock-free algorithms, under scheduling conditions that approximate those found in commercial hardware architectures, lock-free algorithms behave as if they are wait-free. In other words, programmers can continue to design simple lock-free algorithms instead of complex wait-free ones, and in practice, they will get wait-free progress. Our main contribution is a new way of analyzing a general class of lock-free algorithms under a stochastic scheduler. Our analysis relates the individual performance of processes to the global performance of the system using Markov chain lifting between a complex per-process chain and a simpler system progress chain. We show that lock-free algorithms are not only wait-free with probability 1 but that in fact a general subset of lock-free algorithms can be closely bounded in terms of the average number of steps required until an operation completes. Here, to the best of our knowledge, this is the first attempt to analyze progress conditions, typically stated in relation to a worst-case adversary, in a stochastic model capturing their expected asymptotic behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Proposed guidance for preparing and reviewing a molten salt non-power production or utilization facility application

Development of non-power molten salt reactors (MSRs) are under consideration to further establish an MSR experience base, support the requirements of Title 10 of the Code of Federal Regulations (10 CFR) Section 50.43(e), and provide any additional analyses needed for development of a full-scale MSR. Guidance provided in this report is based on MSRs operating with liquid fuel (i.e., fuel dissolved within a molten salt). These reactors, unless owned by the DOE or DOD, will require licensing by the US Nuclear Regulatory Commission (NRC) staff. Standard review plan (SRP) guidance for large light water reactors (LWRs) is available in NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants; Light Water Reactor (LWR) Edition. However, NRC staff observed that NUREG-0800 is very cumbersome to apply to non-power reactors “because of the great differences in complexity and hazards between non-power reactors and nuclear power plants.” Therefore, a program to develop performance-based guidance applicable to non-power reactors was initiated. In 1996, NUREG-1537, Parts 1 and 2, Guidelines for Preparing and Reviewing Applications for the Licensing of Non-Power Reactors, was published. Part 1, the format and content guide, suggests a uniform format for presenting information in non-power reactor applications that is acceptable to the NRC staff, but conformance with the format and content is not required. Part 2, the SRP, ensures the quality and uniformity of the staff review of an application. Unfortunately, the application guidelines and SRP do not provide adequate guidance for all advanced non-LWR technologies and applications. This discrepancy eventually led to the 2012 development of interim staff guidance (ISG) for NUREG-1537, which includes criteria for describing and reviewing aqueous homogeneous reactors (AHRs). Specifically, NUREG-1537 ISG, 2012 expanded the original document to address three areas: 1. updated criteria for heterogeneous non-power reactors, 2. criteria for licensing AHRs, and 3. criteria for licensing a Part 50-licensed isotope production facility. In 2015, the US Department of Energy (DOE) opted to build on the AHR NUREG-1537 ISG experience by performing a gap analysis of the guidance that would be used to license a non-power MSR. MSRs represent one of the advanced non-LWR technologies selected by DOE for development through a multiyear cost share award with Southern Company Services. Under this Advanced Reactor Concepts 2015 (DOE Advanced Reactor Concepts [ARC] 15) award program, the DOE tasked Oak Ridge National Laboratory (ORNL) to evaluate the guidance changes that the NRC may need to consider when licensing an MSR non-power reactor. ORNL staff, with support from Boston Government Services, LLC, focused on five system-related chapters in NUREG-1537 that were considered most relevant to inform the effort that would be required for a non-power MSR applicant. ORNL documented this review in a technical report, ORNL/TM-2018/834, Proposed Guidance for Preparing and Reviewing Molten Salt Non-Power Reactor License Applications (NUREG-1537). The report was subsequently shared with industry and the NRC. The 2018 review was limited in scope, focusing on key system chapters based on the expected significance of each chapter relative to expected differences in addressing advanced non-LWR technologies, specifically non-power MSRs, compared with heterogeneously fueled non-power reactors. In the ORNL report, proposed generic adaptations were suggested for the following NUREG-1537 chapters: Chapter 4, “Reactor Description”; Chapter 5, “Reactor Coolant Systems”; Chapter 6, “Engineered Safety Features”; Chapter 9, “Auxiliary Systems”; Chapter 11, “Radiation Protection Program and Waste Management” The inclusion of Chapter 11 in the previous review effort was intended to provide guidance for categorizing the waste-handling process for an MSR operating with homogenous fuel. The introductions from Parts 1 and 2 of the 2012 NUREG-1537 ISG provide guidance for the application and review of production facilities. After a period of operation, non-power MSRs with homogenous fuel will include gaseous and soluble fission products. The gaseous fission products will be collected and held for decay in an off-gas system. There might also be an initiative to polish or filter the soluble fission products in the fuel salt by some mechanical or chemical means. The treatment and handling of fission products in the non-power MSR fuel salt and the description of this process in the safety analysis report (SAR) must be very precise to avoid the waste treatment facility being construed as a co-located special nuclear material (SNM) fuel cycle facility (see Section 2.3 of this report). Subsequent to the release of ORNL/TM-2018/834, NRC staff expressed a desire to continue the regulatory gap analysis that was begun in that report. This would provide additional clarity and information addressed in certain sections of the original report, while also providing new guidance on certain topics not addressed in the original report. This revision would benefit the NRC staff reviewing applications involving non-power MSR designs and would help developers understand how the NRC staff might approach the review of such applications. The focus of this report is to provide infrastructure support to the NRC staff for the regulatory review of non-power MSRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Anticipating Change - A Means to Save Time and Rethink the Implementation of the 10 Code of Federal Regulations 770 Real Property Transfer Process - 15662

The Department of Energy (DOE) issued 10 Code of Federal Regulations (CFR) 770 in 2000 for the 'Transfer of Real Property at Defense Nuclear Facilities for Economic Development'. The rule was issued in order to 'offset negative impacts on communities caused by unemployment from related DOE down-sizing, facility close-outs and workforce restructuring'. There is a sense of urgency implied in efforts designed to address economic conditions, especially in communities that have hosted DOE facilities and are experiencing negative impacts of mission changes. Despite the urgency, the transfer of real property from DOE or any federal agency is a lengthy, complex, iterative, but necessarily diligent effort. After all, the aim of the process is to be able to demonstrate that the property proposed for transfer is protective of human health and the environment for the intended use. Notwithstanding the efforts of the economic development organization to market, recruit, and prepare a robust proposal to request property, and the efforts of DOE to perform the many due diligence activities in response to that request, the actual results of the evaluations are an unknown until a major portion of the process is completed. Will the property be determined to be suitable for transfer? Will there be deed restrictions? Is indemnification available? Has a mission need for the property arisen while working through the reviews? There are many points along the way that can sidetrack or delay progress and, in the interim, the economic viability of the proposal can collapse. Even without the unforeseen events, the process in and of itself is lengthy. It is often heard (loudly) that it takes far too long to obtain a piece of property once a request is made; that observation/criticism is worthy of serious attention. Rather than revisit old debates, why not learn from them? Why not look at the transfer process from a different point of view and ameliorate it so it can be more responsive to community needs? Why not anticipate change, anticipate requests, and make property available before a request as well as responding to requests to make property available? There are several ways to rethink and modify the way the transfer process is implemented to reduce the time following a request. The modifications would arise from both internal information sharing, integrated and timely up-front planning and, in particular, by varying how and when certain elements of the process to enable transfer are performed. This paper will address: 1. How a unified overall vision can be created from the cleanup input of Site-Specific Advisory Boards with the Community Reuse Organizations' planning for economic development and can help inform site cleanup to end state to future use; 2. Why DOE is in the best position to know its site conditions and could determine ahead of a transfer request what property is indeed available and notify of that determination; 3. How a range of disposition paths for real property are available to DOE; and 4. Why broadening the understanding of 'best interest of the government' to one that is clearly able to recognize non-monetary benefits is in the best interest of the government. Anticipating change helps prepare DOE and their host communities for the future. It creates opportunities for flexibility and time savings. The sooner DOE's unneeded and under-utilized real property can be made available to the communities that need it most, the sooner those communities can work to moderate the adverse effects of DOE mission changes. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Licensing Modernization Project for Advanced Reactor Technologies: FY 2018 Project Status Report

The promise of new nuclear technology and the future of commercial power in the United States (U.S.) are linked to the existence of an efficient and safety-focused regulatory review and licensing process. With an appropriate regulatory framework, reactor suppliers and regulators can design, license, build, and export advanced reactor units more effectively and efficiently and thereby help meet the growing need for clean and reliable energy. To help address the challenge of maintaining compatibility between the regulatory environment and new commercial nuclear plant designs, the U.S. Department of Energy (DOE) is supporting an industry-led effort called the Licensing Modernization Project (LMP). This cost-shared initiative, started in 2016 and scheduled to continue through mid-2019, targets amendment to key elements of the U.S. nuclear power reactor regulatory framework to specifically address licensing barriers in advanced-reactor concepts. The project is focused on updating guidance for certain technical licensing requirements that are largely incompatible with non-light-water reactors (non-LWR) and establish a new pathway for design-safety evaluations and license-application development. Upon completion, the project will have created a new pathway, founded on modern probabilistic assessment techniques, that offers developers, suppliers, regulators, and owner/operatorowner-operators greater clarity and assurance in design and licensing decisions. The LMP employs a team of advanced reactor technology and licensing subject matter experts working closely with U.S. regulators, affected industries, and interested members of the public. The project is managed by Southern Company and supported through a cost share arrangement with DOE. Technical contributions are also provided to the project from the Idaho National Laboratory (INL) Regulatory Development area, the Nuclear Energy Institute (NEI), and the U.S. Nuclear Regulatory Commission (NRC). The LMP identified a suite of high priority issues that provided significant uncertainty within the regulated non-LWR community. It was determined that much of this uncertainty could be retired by issuing updated licensing guidance founded on safety performance demonstrations and modern probabilistic methodologies. The success criteria for the project were established as: • Generate sets of technology inclusive (TI), risk-informed, and performance-based (TI-RIPB) technical requirements, recommendations, and proposals that could be reviewed, amended, and adopted for use by both NRC staff and industry. The proposals would create an optional-use regulatory framework without imposing additional (i.e., mandatory) requirements. • Support LMP the recommended updates of LMP such that they can be finalized, endorsed by NRC, and used by applicants within 5 years. This also supports the DOE advanced- reactor development and deployment goal of completing licensing reviews for technically mature non-LWR concepts sufficient to allow construction to proceed by the early 2030’s. • Further overall programmatic objectives established by DOE’s Gateway for Accelerated Innovation in Nuclear (GAIN) initiative by resolving technology-inclusive policy issues that adversely impact regulatory reviews, siting, permitting, and/or licensing of non-LWRs. This would provide significantly more regulatory certainty for advanced reactor developers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

A New Appraisal- Lessons from the History of Efforts to Value Green and High-Performance Home Attributes in the United States

Rigorous consideration of green and high-performance (“green/HP”) features is rarely included in the property valuation process._ To help illuminate why this is the case, this report takes stock of the history of efforts to improve practices, and identifies barriers that have emerged and opportunities for overcoming them. Particular emphasis is placed on what energy and environmental policymakers and other stakeholders outside the appraisal community can contribute to the broader effort to advance professional practices. The history has unfolded in parallel with turbulent periods in the housing market for which appraisers and their customers are deemed to share responsibility, followed by cycles of regulations, siloing of appraisers in the name of professional integrity, and commoditization of the valuation process itself. This pattern has important ramifications for aspirations that appraisers engage more fully in identifying and valuing the green/HP characteristics of homes. On the one hand, it is legally and ethically incumbent on appraisers to do so, yet on the other hand it is perceived as a risky avenue to follow. Risks arise where findings can be challenged as either over- or under-stating value, together with a market environment in which the complexity of their assignments increases despite downward pressure on appraiser fees. While efforts to address green/HP considerations date back to the early 1980s, the vast majority of activity has taken place within the past five years. Many players have engaged in the efforts to promote improved valuation practices. These include the Appraisal Foundation, The Appraisal Institute, Colorado Energy Office, Earth Advantage, EcoBroker, Elevate Energy, Fannie Mae, Federal Housing Administration, Home Innovation Research Labs, The Institute for Market Transformation, Northwest Energy Efficiency Alliance, National Association of Homebuilders, National Association of State Energy Officials, National Association of Appraisers, RESNET, USEPA, USDOE and its National Laboratories, the U.S. Green Buildings Council, and the Vermont Green Homes Alliance. Many activities have resulted, ranging from trainings, to data-gathering instruments, and the emergence of a literature attempting to statistically isolate the effects of green/HP characteristics on home values. In some cases, the results of studies have been overgeneralized and oversold, and embodied flawed methods. Although the green/HP community has encouraged appraisers to focus on exemplary buildings (e.g., LEED or ENERGY STAR Certified), any level of green or energy performance can in fact influence value, including below-average performance (a.k.a. “brown discount”), irrespective of whether or not the building has been formally rated. This overly narrow focus represents a significant missed opportunity. Other surmountable challenges include limitations to non-appraisers’ understanding of the appraisal process (and practical constraints therein). A byproduct of this can be unrealistic expectations of what appraisers can and will do in the marketplace. These challenges notwithstanding, the environment for moving forward has improved. There is better data today (a critical need); expanded efforts to disclose energy use information (characteristics, consumption, bills); improved and more pervasive building energy codes, building rating and labeling initiatives; and a host of federal, state, and local policies that have collectively brought green/HP practices much more into the mainstream. Meanwhile, a renewed focus on professional standards of care and competency for assessing green/HP homes make it increasingly important for appraisers to consider these factors in their assignments. Despite the past four decades of studies, there is little if any discernible uptake of these practices by the appraisal practice at large. It would behoove interested parties to step back and consider what new strategies might be productive. A key element of any new plan should be to reset the nature of interactions with the industry, with the goal a more collaborative, two-way discussion to help improve outsiders’ understanding of the valuation process. It is not only the appraiser that needs to be engaged and could benefit from awareness raising. Homeowners, builders, lenders, utilities, insurance companies should also provide input on how green/HP factors impact property valuations and ways to accurately reflect these considerations in appraisals and real estate transactions more broadly. Given extensive inertia within the appraisal industry and a mixed history of interactions with the non-appraisal stakeholders, it is unlikely that the status quo will achieve much unless followed with more coordinated and persistent efforts. Workshops, studies, and memoranda of understanding will not on their own have much impact, and do not address deeper structural issues. Expectations are often unrealistic and not attentive to real-world constraints faced by appraisers. The report identifies key barriers impeding more thorough consideration of green/HP factors in residential real estate appraisals, and solutions for mitigating them. Barriers • Although industry standards of practice caution against bias of any sort, a skeptical predisposition towards “green” is reinforced by information deficiencies. • Information deficiencies result from the lack or difficulty of obtaining usable data on green/HP features in subject properties as well as valid sales comparisons or cashflow analyses. • Competency deficiencies, such as lack of conversancy in relevant technical topics, leads to oversights, and disjointed treatment of relevant information. • Time/cost pressure and process commoditization (e.g., template-based approaches) result from highly constrained budgets, quick turn-around times expected of appraisers, and standardized practices that were not developed with green/HP considerations in mind. • Professional differences between appraisers and sustainability professionals include divergent objectives, the former being market observers and the latter market influencers. • Risk aversion arises from multiple concerns including veracity, accuracy, and persistence of energy data, impacts of operational choices, new sources of appraiser liability associated with green/HP assessments, industry pressures not to over-value buildings or suggestion of bias, and concern about spending non-billable time on complex assignments. • A public policy vacuum has been created by disjointed and uncoordinated efforts from public-sector stakeholders, insufficient efforts to discuss and understand the appraisal industry and process, and a perception by some valuation professionals that green/HP is oversold. Opportunities • Elevating the competency of appraisers can be achieved through a combination of improved industry standards of care and equal-access training and professional development offerings. • Development of better information resources must focus on building-level information that provides robust documentation as well as aggregate sales-comparison data and other contextual information such as local codes, typical upgrade costs, energy prices, etc. • Improved energy benchmarking and rating tools could provide appraisers with information more well-adapted to their particular needs, which differ from those of typical audiences such as energy managers. • Better characterizing and managing risk will enable appraisers to cope with uncertainties in performance information, and help identify where risks may be introduced or mitigated by green/HP features, including higher costs or obsolescence of poorly-performing buildings. • Integrating disaster resilience and sustainability in appraisals would recognize important synergisms among these features, including durability and ability of green/HP buildings to better withstand external hazards. • Mitigating the problem of additional time/cost for performing assignments is an essential need that can be addressed by providing easier access to information and analytic procedures, perhaps coupled with new resources to defray the associated costs. • Enhancing demand for improved appraisals is a fundamental need, and depends on owners, developers, lenders, and others soliciting competent appraisers to perform scopes that expressly call out green/HP considerations, and to critically review the work product for compliance before acceptance. • Engaging new market participants, such as energy utilities and insurance companies can ensure fuller representation and participation of market stakeholders already engaged in green/HP activities and capable of furnishing valuable data and managing associated risks. Cutting across these individual activities, there is a need for outside stakeholders to formulate and follow a roadmap instead of piecemeal initiatives, bridging the professional/cultural divide between appraisers and green/HP communities, and tracking progress in order to know what is working. A more coherent communication and training strategy is needed, as the appraisal industry is highly fragmented, with two-thirds of appraisers opting out of membership in trade associations. In sum, while there is no silver bullet for advancing the practice of valuing green/HP features, there are concrete opportunities. Parties seeking solutions must identify barriers they wish to address and select from among potential initiatives that map to those barriers. Close collaboration with the appraisal community is critical, as non-appraisers have historically obtained limited traction with this industry due to lack of understanding of the nuances involved in the valuation profession. Large organizations and agencies should have a united approach; the perception or reality of a fragmented and uncoordinated strategy is unsettling for prospective partners in the appraisal industry. This requires improved communication and education within and among these communities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Sensitivity and Uncertainty Quantification of Transition Scenario Simulations

This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.

Feng, B.↗
DOE OSTI2015

Criticality safety analysis of spent fuel storage or handling disposals using burnup credit - loading curve calculation - 14445

MILLENNIUM and REEL SAS Companies have shared their own experience to develop a neutron and criticality calculations chain in order to perform isotopic composition predictions of spent fuel after irradiation in a reactor and criticality analysis of fuel storages taking into account burnup credit. The whole calculation sequence is divided into three steps, all calling SCALE 6.1.2 modules. The first two steps are dedicated to the evaluation of isotopic compositions of burnt fuel. As transport and depletion calculations in three dimensions appears to be way too long for the whole core (about several months), dealing with each fuel assembly one by one is the alternative solution MILLENNIUM and REEL SAS commonly chose to study. Therefore, the first step aims at determining the axial burnup profile of a given fuel assembly using a 3-D model with infinite reflections on lateral axes. The T6-depl sequence of TRITON module coupling KENO-VI and ORIGEN-S was used in this step. Sensitivity studies confirmed that addition of the 8 closest neighbors of the studied fuel assembly in the model has an insignificant impact on the axial burnup profile. However, fuel assemblies in the peripheral zone of the core must be treated separately with a model including the nearest fuel assemblies and the effect of peripheral water. Then, the second phase evaluates the isotopic compositions of each fuel assemblies at the center of the active height with a 2-D model under conservative core conditions. The T-depl sequence of the TRITON module coupling NEWT to ORIGEN-S is called in this step. Each fuel assembly must be studied separately to consider loading pattern changes occurring at each cycle. An infinite model with reflections on lateral axes is used and the closest neighbors of the fuel assembly are limited to 2 pins - which is around the size of the neutrons mean free path in the core. This is an innovative physical trick aiming at compensating for a limited computing power. Finally, isotopic compositions calculated are combined with the axial burnup profile of the first step to extrapolate the concentrations along the whole active length. Data can then be added in any criticality model, such as a spent fuel pit or any disposal handling fuel assemblies. Criticality analyses of burnt fissile material using burnup credit can be managed within a reasonable amount of time, on condition that sufficient proofs of code qualification for the studied range of parameters are provided, especially on fission products isotopic compositions calculation. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2015

Scalability under a Power Bound using the GREMLINs Framework

With the move towards exascale, system and software developers will have to deal with issues of extreme parallelism. The system properties affected most by the increase in node and core count are the shared resources on node and across the system. The increase in parallelism leads to reduced memory and bandwidth when regarding individual cores. Since power is a limiting factor for supercomputers, and power is not fully utilized in current systems, overprovisioning compute resources is a viable approach to maximized power utilization. To maximize system performance in regard to these changing conditions, it is necessary to understand how resource restrictions impact performance and system behavior. For the purpose of understanding anticipated system properties the GREMLINs Framework was developed. The framework gives the opportunity to add power restrictions, hinder memory properties and introduce faults to study resilience, among others. These features give the opportunity to use current petascale technology to study problems system designers and software developers will have to face when moving towards exascale and beyond. This work describes the initial release of the GREMLINs Framework, developed for this work, and shows how it can be used to study the scaling behavior of proxy applications. These proxy applications represent a selection of HPC workloads important to the scientific community. The proxy applications studied are AMG2013, an algebraic multi-grid linear system solver, CoMD, a classical molecular dynamics proxy application and NEKBONE, an application that uses a high order spectral element method to solve the Navier-Stokes equations. The main interest of these studies lies in analysis regarding their power behavior at scale under a power bound. These findings show how the GREMLINs Framework can help systems and software designers to attain better application performance and can also be used as basis for CPU power balancing tools to use power more efficiently.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Added Value for Integrated Marine Energy Data Systems (February 2021)

Launched in 2019, the Portal and Repository for Information on Marine Renewable Energy (PRIMRE) provides centralized access, standardization, community building, and integration of United States (U.S.) databases, tools and codes, and other resources that cover a range of marine energy information. The PRIMRE universe contains a series of Knowledge Hubs that represent data and information on testing of marine energy devices (MHKDR); environmental effects (Tethys); engineering and technical papers (Tethys Engineering); descriptions of marine energy companies and technologies (Marine Energy Projects Database); codes and models (Marine Energy Software); and guidance on testing and measurements (Telesto). Content is added to PRIMRE by applying a set of Guidelines and Best Practices (PRIMRE Guidelines). An aggregate search across the PRIMRE site enables users to find data and information from all the PRIMRE Knowledge Hubs simultaneously, using a single entry-point (PRIMRE Search). In addition to providing access to a range of data and information on marine energy development, testing, and effects, PRIMRE allows for the development of valueadded products and processes that will help move the marine energy industry forward. The PRIMRE team has recently launched two key initiatives in the U.S.-Signature Projects and Lessons Learned. Outputs and outcomes from these two initiatives will be highlighted in this paper. The Signature Projects initiative is intended to bring focus to a selection of ongoing and completed marine energy projects funded by the U.S. Department of Energy's Water Power Technologies Office (WPTO), and to inform the marine energy community of what investigations have been undertaken, what tools are available, and where gaps in information persist. Each Signature Project tags papers, reports, and data from large marine energy research projects, providing easy access and attention to all the output and associated products from each project. The Lessons Learned initiative is intended to ensure that hard-won achievements are recognized and available for those who come later, that missteps and unfortunate outcomes can be prevented in future, and that efficiencies and effective shortcuts can be publicized and used as the marine energy industry moves forward. This initiative builds off the Knowledge Hubs and reaches out to members of the marine energy community, particularly technology developers and researchers, to integrate experience in the development, deployment, assessment, success, challenges while creating this new industry and field of study.

data sharing↗
DOE OSTI2026

Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra: Modeling Archive

This dataset is a model archive of the paper Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra (in prep) to support a modeling study investigating how projected increases in Arctic temperature and precipitation will jointly influence hydrologic conditions in ice-rich tundra landscapes. With this dataset, this study is to address the research question: Will Arctic tundra landscapes become wetter or drier with increasing precipitation and temperature in the future when thaw-induced ground subsidence and associated microtopographic evolution are represented? The simulations focus on ice-wedge polygon tundra, a widespread form of ice-rich permafrost terrain that is highly sensitive to thaw-driven landscape change. This dataset contains model input and output data for four study watersheds in Alaska: Anaktuvuk, Utqiagvik (formerly Barrow), Brooks Foothills, and Prudhoe Bay. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.5), a physics-rich integrated surface–subsurface hydrologic model. For each watershed, ten modeling cases were performed representing two landscape evolution conditions (with subsidence and without subsidence) combined with five climate forcing scenarios derived from Shared Socioeconomic Pathways (SSP5, SSP5 with precipitation trend, SSP2, SSP2 with precipitation trend, and SSP2 with double precipitation trend). Particularly, for each watershed under the forcing SSP2 with precipitation trend, there are two additional simulations considering spatially heterogeneous subsidence distributions: one assumes randomly distributed scaling and the other includes elevation dependent distribution scaling. These simulations span 1980–2099 and include spin-up runs (1980–2009) followed by transient projections (2010–2099). To facilitate reproducibility of simulations, all datasets are organized by watershed. For each study watershed, the dataset contains: (1) Pre-partitioned mesh files for 32-core modeling (.par.32.XX), located in EACH_WATERSHED/mesh/basin; and also a non-partitioned mesh file (.exo) located in EACH_WATERSHED/mesh; (2) Climate forcings corresponding to the five SSP scenarios (.h5), located in EACH_WATERSHED/data; (3) Final states (.h5) from column spin-up modeling used to initialize historical watershed-scale spin-up runs from 1980 to 2009, located in EACH_WATERSHED/PreSpinupHistorical; (4) Final states (.h5) of historical watershed-scale spin-up runs from 1980 to 2009 used to initialize projection runs, located in EACH_WATERSHED/Spinup_daymetERA5; (5) ATS modeling input files (.xml), located in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/inputfiles; (6) ATS modeling output files (.dat), located in in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/combined_obs; (7) For the Brooks Foothills watershed, additional spatial model outputs are provided (.h5) for selected years (2033 and 2093) used to generate spatial figures in this study, located in Brooksfoothills/EACH_SIMULATION_SCENARIO/results-WITH/WITHOUT_SUBSIDENCE-year2033/2093. All data files with suffix .h5 can be accessible through Python h5py, and all data files with suffix of .dat can be imported by Python pandas. Mesh file with .exo can be visualized through Paraview or read by Python netCDF. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION↗
DOE OSTI2015

Improving public communication through environmental data transparency at Los Alamos National Laboratory - 15306

The Los Alamos National Laboratory (LANL) environmental data span a wide range of media, including air, soil, sediment, water, foodstuff, and biota as well as over 1000 analytes and time periods extending over 4 decades, resulting in over 14 million records to manage and analyze. At a time of budget cuts and rapid social and technological change, the public, regulatory and compliance agencies, and stakeholders demand accelerated data delivery and efficiency, information sharing, and transparency in the dissemination and handling of samples collected in and around LANL. Meeting these requirements, while constraining the associated costs can be challenging; however, LANL has achieved this by adopting a cloud-based database called the Environmental Information Management (EIM) system and its public database counterpart, IntellusNM (Intellus) (www.intellusnm.com). To be consistent with federal initiatives to move government computing into cloud-based services, LANL chose to partner with an existing cloud-based vendor, Locus Technologies, to implement a software-as-a-service model. As an additional cost-saving measure, local regulatory and compliance agencies collaborated with LANL to incorporate their data into EIM and Intellus database. By adopting a single platform cloud-based solution to replace 12 different pre-existing environmental data systems, LANL has streamlined environmental data processes, resulting in over $15 million cost savings through 2015. EIM, LANL's internal cloud database, provides a cradle-to-grave process for data handling. EIM supports LANL's entire data process from detailed sample planning, mobile data collection using iOS mobile devices, to automated bottle labeling, generation of chain of custody, analytical lab electronic data deliverable upload, auto data validation, sample tracking, and custom regulatory and compliance reports. EIM has functionality to assess data in various ways such as the ability to design new formatted reports, a free-form structured query language tool of the entire database, exceedance reports, calculation engine, charting, trends, and statistics. EIM also has interactive geographic information system layers and analyte concentration color contour mapping capabilities. Additionally, and of greater importance to LANL, EIM supports the ability for third-party landowners to review data before release to the public, thus ensuring transparency and confidence in LANL's stewardship of human health and the environment. Intellus, the database counterpart of EIM, is the publicly available view of the entire LANL database and requires no data feeds or transformations. Intellus allows user-friendly access to LANL's environmental data as it is collected, providing the public, scientists, remediation teams, regulators, and stakeholders within hours of receipt. Full transparency in giving the public access to environmental data information provides LANL's overall solution to environmental data management for the public. By making the data easy to obtain, search, and query for interested stakeholders, the system enables confidence in LANL's stewardship of the environment. Working together identifies key topics of improvements, develop strategies and help support innovation and substantial advances within LANL's environmental data management system. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Final Technical Report on Quantifying Dependability Attributes of Software Based Safety Critical Instrumentation and Control Systems in Nuclear Power Plants

With the current transition from analog to digital instrumentation and control systems in nuclear power plants, the number and variety of software-based systems have significantly increased. The sophisticated nature and increasing complexity of software raises trust in these systems as a significant challenge. The trust placed in a software system is typically termed software dependability. Software dependability analysis faces uncommon challenges since software systems’ characteristics differ from those of hardware systems. The lack of systematic science-based methods for quantifying the dependability attributes in software-based instrumentation as well as control systems in safety critical applications has proved itself to be a significant inhibitor to the expanded use of modern digital technology in the nuclear industry. Dependability refers to the ability of a system to deliver a service that can be trusted. Dependability is commonly considered as a general concept that encompasses different attributes, e.g., reliability, safety, security, availability and maintainability. Dependability research has progressed significantly over the last few decades. For example, various assessment models and/or design approaches have been proposed for software reliability, software availability and software maintainability. Advances have also been made to integrate multiple dependability attributes, e.g., integrating security with other dependability attributes, measuring availability and maintainability, modeling reliability and availability, quantifying reliability and security, exploring the dependencies between security and safety and developing integrated analysis models. However, there is still a lack of understanding of the dependencies between various dependability attributes as a whole and of how such dependencies are formed. To address the need for quantification and give a more objective basis to the review process -- therefore reducing regulatory uncertainty -- measures and methods are needed to assess dependability attributes early on, as well as throughout the life-cycle process of software development. In this research, extensive expert opinion elicitation is used to identify the measures and methods for assessing software dependability. Semi-structured questionnaires were designed to elicit expert knowledge. A new notation system, Causal Mechanism Graphing, was developed to extract and represent such knowledge. The Causal Mechanism Graphs were merged, thus, obtaining the consensus knowledge shared by the domain experts. In this report, we focus on how software contributes to dependability. However, software dependability is not discussed separately from the context of systems or socio-technical systems. Specifically, this report focuses on software dependability, reliability, safety, security, availability, and maintainability. Our research was conducted in the sequence of stages found below. Each stage is further examined in its corresponding chapter. Stage 1 (Chapter 2): Elicitation of causal maps describing the dependencies between dependability attributes. These causal maps were constructed using expert opinion elicitation. This chapter describes the expert opinion elicitation process, the questionnaire design, the causal map construction method and the causal maps obtained. Stage 2 (Chapter 3): Elicitation of the causal map describing the occurrence of the event of interest for each dependability attribute. The causal mechanisms for the “event of interest” were extracted for each of the software dependability attributes. The “event of interest” for a dependability attribute is generally considered to be the “attribute failure”, e.g. security failure. The extraction was based on the analysis of expert elicitation results obtained in Stage 1. Stage 3 (Chapter 4): Identification of relevant measurements. Measures for the “events of interest” and their causal mechanisms were obtained from expert opinion elicitation for each of the software dependability attributes. The measures extracted are presented in this chapter. Stage 4 (Chapter 5): Assessment of the coverage of the causal maps via measures. Coverage was assessed to determine whether the measures obtained were sufficient to quantify software dependability, and what measures are further required. Stage 5 (Chapter 6): Identification of “missing” measures and measurement approaches for concepts not covered. New measures, for concepts that had not been covered sufficiently as determined in Stage 4, were identified using supplementary expert opinion elicitation as well as literature reviews. Stage 6 (Chapter 7): Building of a detailed quantification model based on the causal maps and measurements obtained. Ability to derive such a quantification model shows that the causal models and measurements derived from the previous stages (Stage 1 to Stage 5) can form the technical basis for developing dependability quantification models. Scope restrictions have led us to prioritize this demonstration effort. The demonstration was focused on a critical system, i.e. the reactor protection system. For this system, a ranking of the software dependability attributes by nuclear stakeholders was developed. As expected for this application, the stakeholder ranking identified safety as the most critical attribute to be quantified. A safety quantification model limited to the requirements phase of development was built. Two case studies were conducted for verification. A preliminary control gate for software safety for the requirements stage was proposed and applied to the first case study. The control gate allows a cost effective selection of the duration of the requirements phase.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗