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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2018

Final Report: Scale-up of Algal Biofuel Production Using Waste Nutrients

Renewable fuels can be more environmentally friendly than conventional fuels and can be produced locally, supporting energy independence. Liquid renewable fuels are of special interest due to their high energy density. Algae are a promising prospective feedstock for low carbon intensity liquid biofuels, due to their high productivity and potential for high lipid or carbohydrate content. Their ability to grow on wastewater and waste nutrients and on non-arable land are cost and sustainability advantages. The organic matter content of wastewater promotes mixotrophic and/or heterotrophic growth of both algae and bacteria, boosting biomass productivity further. However, economic and technologic challenges persist that prevent the scale-up of algal biofuels to commercial relevance. These challenges include algal harvesting, drying, dewatering, and the conversion of algal biomass to usable fuels (Hannon et al 2010, Dunlap and Shaw 2009, Pienkos et al. 2009). The US Department of Energy (DOE) Bioenergy Technologies Office (BETO) supports research to overcome these challenges and outlines the research and development goals for algal biofuels in their Multi-Year Program Plan (MYPP). The long-term goals of the MYPP for algal biofuels research are briefly as follows: develop the domestic ability to produce algal biofuels at a scale of 5 billion gallons per year (BGY) by 2030 and demonstrate technologies that produce biofuel intermediates from algae at a cost of $3/gallon of gasoline equivalent (GGE) by 2022 (BETO MYPP 2016). This research reported herein was funded to investigate methods of meeting an intermediate milestone of 2,500 gallons of biofuel intermediate per acre per year for a non-integrated process by improving algal productivity, harvesting efficiency, and conversion to biofuel intermediates. Use of wastewater and wastewater nutrients was a key distinguishing characteristic of this project. In pursuit of 2,500 gallons of biofuel intermediate (BFI) per acre per year, California Polytechnic State University, San Luis Obispo (Cal Poly) sought to develop the capability to produce biofuel intermediates from microalgae grown on municipal wastewater at a 20-acre algae-based wastewater treatment facility in Delhi, California (37.43° N), which includes 7 acres of raceway ponds. Coupling algal biofuel production with wastewater treatment capitalizes on the abundant waste nutrients and carbon present in wastewater and reduces use of clean water in algal biorefinery systems. In this research, the performance of the full-scale wastewater treatment plant was characterized by measuring productivity, wastewater treatment performance, hydraulic characteristics, and energy consumption. Nine 1,000-L pilot raceways with CO2 addition were used in experiments to maximize productivity. Low-cost, energy-efficient algae bioflocculation and settling were monitored. Population genetics were monitored in an attempt to correlate taxa with superior productivity and/or settling. Algae-bacterial biomass was converted to biofuel intermediates via bench-scale hydrothermal liquefaction (HTL), and the resulting fuel quality was characterized. To develop rapid strain screening capabilities, a climate simulating photobioreactor (“LEAPS”) was developed and validated against outdoor raceways. A techno-economic analysis (TEA) and a lifecycle assessment (LCA) were performed to model process economics and sustainability. The major outcomes of this research included demonstration of 33 g/m2-day annual average algal-bacterial productivity in pilot raceways, and a biofuel intermediate yield of 0.35 g intermediate/g algae biomass via HTL, for a yield of 4,100 gallons BFI per acre per year, assuming a 90% harvest efficiency and ignoring other minor losses. This exceeded the project goal of 2,500 gallons of BFI per acre per year and nearly met the 2030 program goal, highlighting the promise of integrating wastewater treatment and biofuels production. The biocrude BFI produced by the process is refined to diesel and naphtha fuels. These fuels are normalized to the energy content of gasoline (gallons gasoline equivalent, GGE, for comparison to other fuels). Results from the TEA of the this complete “well-to-wheel” process demonstrated that, at a 400-ha scale, a minimum fuel selling price (MFSP) of $12.55/GGE, which could be decreased 57% to $7.14/GGE when including revenue from coupling the biofuel production with the co-products of wastewater treatment services and low carbon fuel credits from California and Federal programs. Due mainly to the high capital costs of thickener centrifuges and hydrothermal processing equipment, the fuel cost is sensitive to scale, with significantly lower costs expected for larger farms. In this research project, significant advances were made in improving raceway algal productivity and fuel production from algae biomass, while also improving process economics. These outcomes brought the algal biofuel technology closer to being a sustainable and marketable process. The remainder of this executive summary is organized into the following six primary project tasks: (1) to optimize biomass productivity for a selected strain at Delhi, (2) to maximize algal productivity and harvesting efficiency in Delhi pilot ponds, (3) a full-scale raceway hydraulic characterization, (4) biomass processing to biofuel intermediates, and (5) scale-up engineering analysis, modeling, and planning.

09 BIOMASS FUELS↗
DOE OSTI2018

Innovation in Contracting Strategy for a Master Services Agreement for Remediation of Low Level Radioactive Waste - 18381

The Port Hope Area Initiative (PHAI) is a federal environmental clean-up program. Its mandate is the remediation and local, long-term, safe management of approximately 1.7 million cubic metres of historic low-level radioactive waste in the adjacent communities of Port Hope and Port Granby in Southern Ontario, Canada. The PHAI is being undertaken by Canadian Nuclear Laboratories (CNL), on behalf of Atomic Energy of Canada Limited, a federal Crown corporation. It is currently one of the largest environmental remediation projects in Canada. The safety of workers, the public and the environment while delivering the PHAI on time and within budget is CNL's number one priority. The PHAI is being carried out as two projects - the Port Hope Project and the Port Granby Project. The Port Hope Project is the larger of the two and includes the cleanup of approximately 1.2 million cubic metres of historic waste from several locations, including the Port Hope Harbour, a closed municipal landfill and approximately 800 residential properties. This complex project requires contracting the services of qualified, skilled and experienced environmental remediation contractors to meet the diverse needs of the myriad tasks that comprise the remediation and restoration of all properties. This paper will highlight how the CNL contracting services team developed an innovative approach to a contracting strategy designed to deliver the safe execution of work and keep the project on time and within budget. The approach addresses the complexities of a scope of work that consists of multiple work packages to be executed both concurrently and in tandem, and that require highly specialized trades/skills, as well as high-level project management expertise to ensure seamless coordination among the various contractors. The author will discuss CNL's strategy to engage the market with a Request for Expression of Interest to identify potential qualified suppliers and gauge their level of interest, issue a Request for Prequalification (RFPQ) to determine eligible contractors and, ultimately, issue a Request for Proposal for a Master Services Agreement (MSA) that would competitively evaluate each contractor's direct experience, technical expertise, quality management programs, safety systems compliance and track record. Using CNL's experience as a case study, the paper will highlight how an innovative approach can positively affect project timelines by describing how CNL addressed the challenge associated with the limited time available to implement procurement processes and deliver a series of executed agreements while ensuring that work would begin on time and key project milestones would be met. The author will summarize how CNL's strategy put the project back on schedule, without compromising the process guideline/requirements, and ensured that a qualified and experienced set of contractors, committed to a strong safety culture, will be available to perform the work required when the cleanup in the Port Hope community begins in 2018. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

XPRESS: eXascale PRogramming Environment and System Software

The XPRESS Project is one of four major projects of the DOE Office of Science Advanced Scientific Computing Research X-stack Program initiated in September, 2012. The purpose of XPRESS is to devise an innovative system software stack to enable practical and useful exascale computing around the end of the decade with near-term contributions to efficient and scalable operation of trans-Petaflops performance systems in the next two to three years; both for DOE mission-critical applications. To this end, XPRESS directly addresses critical challenges in computing of efficiency, scalability, and programmability through introspective methods of dynamic adaptive resource management and task scheduling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Designing and prototyping extensions to the Message Passing Interface in MPICH

As HPC system architectures and the applications running on them continue to evolve, the MPI standard itself must evolve. The trend in current and future HPC systems toward powerful nodes with multiple CPU cores and multiple GPU accelerators makes efficient support for hybrid programming critical for applications to achieve high performance. However, the support for hybrid programming in the MPI standard has not kept up with recent trends. The MPICH implementation of MPI provides a platform for implementing and experimenting with new proposals and extensions to fill this gap and to gain valuable experience and feedback before the MPI Forum can consider them for standardization. Here, in this work, we detail six extensions implemented in MPICH to increase MPI interoperability with other runtimes, with a specific focus on heterogeneous architectures. First, the extension to MPI generalized requests lets applications integrate asynchronous tasks into MPI’s progress engine. Second, the iovec extension to datatypes lets applications use MPI datatypes as a general-purpose data layout API beyond just MPI communications. Third, a new MPI object, MPIX_Stream, can be used by applications to identify execution contexts beyond MPI processes, including threads and GPU streams. MPIX stream communicators can be created to make existing MPI functions thread-aware and GPU-aware, thus providing applications with explicit ways to achieve higher performance. Fourth, MPIX Streams are extended to support the enqueue semantics for offloading MPI communications onto a GPU stream context. Fifth, thread communicators allow MPI communicators to be constructed with individual threads, thus providing a new level of interoperability between MPI and on-node runtimes such as OpenMP. Lastly, we present an extension to invoke MPI progress, which lets users spawn progress threads with fine-grained control to adapt the communication performance to their application designs. We describe the design and implementation of these extensions, provide usage examples, and highlight their expected benefits with performance results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Novel Serpentine Robot Combinations for Inspection in Hard-to-Reach Areas of Damaged or Decommissioned Structures - 17335

U.S. government agencies, particularly the Dept. of Energy, need robotic tools to handle and inspect high-consequence materials - materials for which mishandling would result in great risk to both application operators and the general public. The remediation and documentation of nuclear waste has become a problem of national significance and it cannot be addressed without robotic tools. The use of robots bypasses the danger and decreases the preparation time to handle harmful materials. In the 1980's and 90's, the US Dept. of Energy (DOE) invested heavily in robotic technologies as part of a comprehensive program of research and development aimed at enabling safer and more efficient cleanup of nuclear waste sites. For the next generation of harder, more complex clean-up problems, there is a clear need for more R and D. There is a clear need for flexible, quickly deployable robotic technologies that can be configured to respond to unforeseen situations -- or can be purpose-built for planned activities -- while keeping human workers safe, yet effective. Modularity and inter-operability are core capabilities for inspection tasks in contaminated facilities with a large variety of inspection problems. With any tool, familiarity breeds ingenuity, and ingenuity leads to new innovations in efficiency, effectiveness, safety, and performance. For example, the simple screwdriver can be held fourteen different ways by an experienced machinist who knows exactly what he or she wants to accomplish at any given moment. As a tool, robots are no different. To maximize the efficiency, effectiveness, safety and performance, redeployment and reuse of familiar tools is essential. Modularity can assist with re-deployment and modularity manifests itself in both hardware and software. In some applications of inspection of high-consequence materials, a common concern has been the simplicity of the tool -- often correlated with cost (which is not always accurate) -- in case it becomes unrecoverable. In the case of robots, which have complex user interfaces and complex dynamics of interaction, the lack of familiarity can lead to clumsy operations and reduced accumulation of knowledge. As a result, while incremental successes may be achieved with sequential uses of different robots, there often is little knowledge gain that is generalizable across multiple uses. Total cost often goes down when a more sophisticated, but more familiar tool, can be used more frequently.An example is the inspection of the H-Canyon Exhaust Air Tunnel at the Savannah River Site nuclear waste facility in the United States [1]. This is a particularly nasty environment, due to the acidic gases, large amounts of erosion and debris, and wet, mucky puddles. Over the past twelve years, six inspections have been attempted with five different robotic vehicles. In fact, these inspections have been successful with their limited, but progressively increasing objectives, although most robots have been abandoned in the tunnel. While each vehicle has cost less than $75,000 (the sacrificial part), the total costs of development have been much higher. The robots used to investigate changed much from iteration to iteration and it is not clear a strong body of generalizable expertise was built up across trials. The authors of this study demonstrated the combination of two types of modular, serpentine, re-configurable robots for locomotion over complex terrains and the inspection of hard-to-reach areas. The MOTHERSHIP is a tread/limb/serpentine hybrid robot design for locomotion over complex terrains, while the CMU snake robot provides highly articulated inspection of highly confined spaces. The demonstration at the Portsmouth Gaseous Diffusion Plant involves a combination of the two whereby gross locomotion is achieved with the MOTHERSHIP and the CMU Snake deploys a sensor package to gather data from behind and under obstacles. Although statically configured in its present state, the combined robot is built of individual modules for which reconfiguration has been demonstrated in both labs at Purdue and CMU. In future work, much higher degrees of reconfiguration and re-purposing of both software and hardware are easily achievable with these unique platforms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Terry Turbopump Expanded Operating Band Modeling and Simulation Efforts in Fiscal Year 2020 - Progress Report

The Terry Turbine Expanded Operating Band Project is currently conducting testing at Texas A&M University as part of a revised experimental program meant to supplant previous full-scale testing plans under the headings of Milestone 5 and Milestone 6. In consultation with Sandia National Laboratories technical staff and with modeling and simulation support from the same, the hybrid Milestone 5&6 plan is moving forward with experiments aimed at addressing knowledge gaps regarding scale, working fluid, and turbopump self-regulation. Modeling and simulation efforts at Sandia National Laboratories in FY20 fell under the broad umbrella of Milestone 7 and consisted exclusively of MELCOR-related tasks aimed at: 1) Constructing/improving input models of Texas A&M University experiments, 2) Constructing a generic boiling water reactor input model according to best practices with systems-level Teny turbine capabilities, and 3) Adding code capability in order to leverage experimental data/findings, address bugs, and improve general code robustness Project impacts of the Covid-19 pandemic have fortunately been minimal thus far but are mentioned as necessary when discussing the hybrid Milestone 5&6 progress as well as the corresponding Milestone 7 modeling and simulation progress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

An Experimental Investigation of Human Performance Differences Depending on Simulator Complexity

As a different approach to collect human reliability analysis (HRA) data compared to the full-scope simulator studies, Idaho National Laboratory (INL) has attempted to collect HRA data based on Simplified Human Error Experimental Program (SHEEP), which uses a simplified simulator and student participants. To date, INL has considered the SHEEP approach using simplified simulators such as Rancor Microworld and Compact Nuclear Simulator to complement – not replace – full-scope studies as well as to mainly collect HRA data for estimating nominal/basic human error probabilities (HEPs) needed in the HRA quantification process. This study is a part of the project aiming to suggest how to support full-scope data collection studies based on SHEEP. This paper first introduces major tasks within the SHEEP framework. Then, as one of the major tasks, why and how we have planned to experimentally investigate human performance differences depending on simulator complexity are mainly introduced in this paper.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Authentication Protocol for ICS without Encryption

Industrial control systems (ICS) describes the different types of control systems and associated instrumentation, which includes the devices, systems, networks, and controls used to operate and/or automate industrial processes. Depending on the industry, each ICS functions differently and are built to electronically manage tasks efficiently. Historically, industrial plants have included networking, and changes have been made over time without consideration for security. Many ICS devices currently have inadequate security features. Adding encryption in ICS is undesirable because many of the new detection method involves deep packet inspection. Therefore, we propose to develop a method of providing authentication between devices without encrypting the data. This testbed design will allow rapid development and experimentation with Cyber Physical Systems. Modbus connects to a supervisory computer with a Remote Terminal Unit (RTU) in Supervisory Control and Data Acquisition (SCADA) systems. Our project seeks to enhance security in the ICS environment by adding device authentication to the lower levels of the plant floor. This approach was selected as it allows verification of the sender of data from level 1/level 0 devices up to supervisory systems. This addition is desirable as it adds enhanced verification to the integrity of system data without obscuring (via encryption) the data being transmitted across the network. This is important because many ICS systems are real time critical (decryption adds to latency) and many other good security options require deep packet inspection (which encryption completely defeats). To accomplish this, a proof of concept of adding an authentication scheme was designed and will be tested with a common ICS protocol. The setup includes a single serial cable connecting the serial ports on two devices, a Master and a Minion. Authentication will occur in the following methods: - Data layer involving Transport Layer Security/Secure Socket Layer (TLS/SSL); - TCP/IP route implementation; - Key exchange algorithm; - Raspberry Pi programming for authentication. A portion of the setup includes adding Raspberry Pi into the system as the device is handling the authentication for the network traffic. Modbus is the chosen ICS protocol. It is used to transmit signals from instrumentation and control devices back to a main controller or data gathering system. Typically, the device identity is assumed but not verified. For verifying device identity, we constructed a digital signature architecture with TLS. A novel adaptation of this is to use only the digital signature that is provided and not the encryption capability. This allows ICS traffic to flow unobscured while still providing verification for the integrity of the data. Verification of this process will involve adding additional I/O level network devices with spoofed IP addresses and detecting the authenticated versus unauthenticated traffic. Our TLS implementation will have encryption disabled but should still allow for digital signing of data. This will enable our system to distinguish between real end devices and spoofed end devices. This experiment is presently building the test setup. It is focusing on a small setting to identify authenticated and unauthenticated devices on the network. This should allow for scaling to a larger network size after the initial proof of concept. One concern with this scheme is a certificate deployment has a large scale key management. Network size will thus increase the key management task for security. In addition, we believe that further research should be done on exploring the use of this method to defeat man-in-the-middle attacks.

42 ENGINEERING↗
DOE OSTI2020

Multi-Node Program Fuzzing on High Performance Computing Resources

Significant effort is placed on tuning the internal parameters of fuzzers to explore the state space, measured as coverage, of binaries. In this work, we investigate the effects of the external environment on the resulting coverage after fuzzing two binaries with AFL for 24 hours. Parameters such as scaling to multiple nodes, node saturation, and parallel file system type on HPC resources are controlled in order to maximize coverage. It will be shown that employing a parallel file system such as IBM's General Parallel File System offers an advantage for fuzzing operations, since it contains enhancements for performance optimization. When combined with scaling to two and four nodes, while simultaneously restricting the number of coordinated AFL tasks per node on the low end (10-50% of available physical cores), coverage may be enhanced within a shorter period of time. Thus, controlling the external environment is a useful effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Creating an Interprocedural Analyst-Oriented Data Flow Representation for Binary Analysts (CIAO)

National security missions require understanding third-party software binaries, a key element of which is reasoning about how data flows through a program. However, vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. To reduce the human time burden for these analysts, we used human factors methods in a rolling discovery process to derive user-centric visual representation requirements. We encountered three main challenges: analysis projects span weeks, analysis goals significantly affect approaches and required knowledge, and analyst tools, techniques, conventions, and prioritization are based on personal preference. To address these challenges, we initially focused our human factors methods on an attack surface characterization task. We generalized our results using a two-stage modified sorting task, creating requirements for a data flow visualization. We implemented these requirements partially in manual static visualizations, which we informally evaluated, and partially in automatically generated interactive visualizations, which have yet to be integrated into workflows for evaluation. Our observations and results indicate that 1) this data flow visualization has the potential to enable novel code navigation, information presentation, and information sharing, and 2) it is an excellent time to pursue research applying human factors methods to binary analysis workflows.

42 ENGINEERING↗
DOE OSTI2021

Machine Learning Using Open Data Sources for Detection of Nuclear Proliferation Activities (U)

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics (SCAIDA) at Virginia Polytechnic Institute and State University (VT) and funded by the Department of Energy’s (DOE) Defense Nuclear Nonproliferation Research and Development, began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on large-scale open data sources to identify new, developing, and/or undeclared nuclear programs. Using the announcement in May 2018 of the proposed Savannah River Plutonium Processing Facility (SRPPF) as a test subject, the goal of this 2-year project is to forecast the SRPPF using only data prior to May 2018. The project work is split into a preliminary prototype development for the first year with an initial evaluation of viability followed by the second year of development to create an integrated prototype system and more extensive performance evaluation. This report documents the results of the preliminary-phase tasks.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

MCC On Boarding Section J-2 (Exhibit H) Quality Assurance Requirements [Slides]

Upon MATOC Award, SUBCONTRACTOR shall submit a Quality Assurance Program for evaluation in accordance with subcontract technical and quality requirements. This may be the Subcontractors corporate QA program. Acceptance by Triad of the SUBCONTRACTORS Quality Assurance Program (QAP), as prescribed in Exhibit H, QC-01A and 01B, allows the SUBCONTRACTOR to execute this contract using their approved QAP. SUBCONTRACTORS QAP as approved by CONTRACTOR must meet the applicable requirements of DOE Order 414.1D (see next slide) and must address flow down of the quality requirements to lower tier subcontractors. Triad acceptance of SUBCONTRACTOR’s QAP approval is required prior to Notice to Proceed with on-site work. A Project Specific QA plan may on rare occasions be required for a specific task

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience

Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

HEP Software Foundation Community White Paper Working Group - Data Processing Frameworks

Data processing frameworks are an essential part of HEP experiments' software stacks. Frameworks provide a means by which code developers can undertake the essential tasks of physics data processing, accessing relevant inputs and storing their outputs, in a coherent way without needing to know the details of other domains. Frameworks provide essential core services for developers and help deliver a configurable working application to the experiments' production systems. Modern HEP processing frameworks are in the process of adapting to a new computing landscape dominated by parallel processing and heterogeneity, which pose many questions regarding enhanced functionality and scaling that must be faced without compromising the maintainability of the code. In this paper we identify a program of work that can help further clarify the key concepts of frameworks for HEP and then spawn R&D activities that can focus the community's efforts in the most efficient manner to address the challenges of the upcoming experimental program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗