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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 OSTI2021

SiPM-matrix readout of two-phase argon detectors using electroluminescence in the visible and near infrared range

Proportional electroluminescence (EL) in noble gases is used in two-phase detectors for dark matter searches to record (in the gas phase) the ionization signal induced by particle scattering in the liquid phase. The “standard” EL mechanism is considered to be due to noble gas excimer emission in the vacuum ultraviolet (VUV). In addition, there are two alternative mechanisms, producing light in the visible and near infrared (NIR) ranges. The first is due to bremsstrahlung of electrons scattered on neutral atoms (“neutral bremsstrahlung”, NBrS). The second, responsible for electron avalanche scintillation in the NIR at higher electric fields, is due to transitions between excited atomic states. In this work, we have for the first time demonstrated two alternative techniques of the optical readout of two-phase argon detectors, in the visible and NIR range, using a silicon photomultiplier matrix and electroluminescence due to either neutral bremsstrahlung or avalanche scintillation. The amplitude yield and position resolution were measured for these readout techniques, which allowed to assess the detection threshold for electron and nuclear recoils in two-phase argon detectors for dark matter searches. To the best of our knowledge, this is the first practical application of the NBrS effect in detection science.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Toward quantum Monte Carlo forces on heavier ions: Scaling properties

Quantum Monte Carlo (QMC) forces have been studied extensively in recent decades because of their importance with spectroscopic observables and geometry optimization. Here, we benchmark the accuracy and computational cost of QMC forces. The zero-variance zero-bias (ZVZB) force estimator is used in standard variational and diffusion Monte Carlo simulations with mean-field based trial wavefunctions and atomic pseudopotentials. Statistical force uncertainties are obtained with a recently developed regression technique for heavy tailed QMC data [P. Lopez Rios and G. J. Conduit, Phys. Rev. E 99, 063312 (2019)]. By considering selected atoms and dimers with elements ranging from H to Zn (1 ≤ Z eff ≤ 20), we assess the accuracy and the computational cost of ZVZB forces as the effective pseudopotential valence charge, Z eff , increases. We find that the costs of QMC energies and forces approximately follow simple power laws in Zeff. The force uncertainty grows more rapidly, leading to a best case cost scaling relationship of approximately Z e ff 6.5 ( 3 ) for diffusion Monte Carlo. We find that the accessible system size at fixed computational cost scales as Z e ff − 2 , insensitive to model assumptions or the use of the “space warp” variance-reduction technique. Our results predict the practical cost of obtaining forces for a range of materials, such as transition metal oxides where QMC forces have yet to be applied, and underscore the importance of further developing force variance-reduction techniques, particularly for atoms with high Z eff .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Third-Order Møller–Plesset Theory Made More Useful? The Role of Density Functional Theory Orbitals

The practical utility of Møller–Plesset (MP) perturbation theory is severely constrained by the use of Hartree–Fock (HF) orbitals. It has recently been shown that the use of regularized orbital-optimized MP2 orbitals and scaling of MP3 energy could lead to a significant reduction in MP3 error. In this work, we examine whether density functional theory (DFT)-optimized orbitals can be similarly employed to improve the performance of MP theory at both the MP2 and MP3 levels. We find that the use of DFT orbitals leads to significantly improved performance for prediction of thermochemistry, barrier heights, noncovalent interactions, and dipole moments relative to the standard HF-based MP theory. Indeed, MP3 (with or without scaling) with DFT orbitals is found to surpass the accuracy of coupled-cluster singles and doubles (CCSD) for several data sets. We also found that the results are not particularly functional sensitive in most cases (although range-separated hybrid functionals with low delocalization error perform the best). As such, MP3 based on DFT orbitals thus appears to be an efficient, noniterative O(N 6 ) scaling wave-function approach for single-reference electronic structure computations. Scaled MP2 with DFT orbitals is also found to be quite accurate in many cases, although modern double hybrid functionals are likely to be considerably more accurate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Review and Comparison of Different Sensitivity Analysis Techniques in Practice

There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question – How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? – different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desired sensitivity analysis method judiciously, making sure to match the selected approach to the specifics of their problem and to their desired objectives. In this chapter, we provide a practical overview of a collection of widely used, widely available sensitivity analysis methods. We focus on global sensitivity approaches, which seek to characterize how uncertainty in the model output may be allocated to sources of uncertainty in model inputs across the entire input space. Generally, this will require the practitioner to specify a probability distribution over the input space. On the other hand, methods for local sensitivity analysis do not require this specification but they have more limited utility, providing insight into sources of uncertainty associated only with a particular, specified location in the input space. Our hope is that this chapter may serve as a decision-making tool for practitioners, helping to guide the selection of a sensitivity analysis approach that will best fit their needs. To support this goal, we have selected a suite of approaches to cover, which, while not exhaustive, we believe provides a flexible and robust sensitivity analysis toolkit. All methods included are widely used and available in standard software packages.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Offshore Wind Farm Turbine and Energy Storage Optimization

Abstract This paper evaluates the technical and economic feasibility of repurposing decommissioned offshore oil and gas platforms as electrical substations for offshore wind projects in the U.S. Gulf of America, a region characterized by relatively low and highly variable wind speeds, extensive legacy offshore infrastructure, and exposure to merchant electricity markets. A unified techno-economic framework is developed using the Repurposing Offshore Infrastructure for Continued Energy (ROICE) Economic Model (REM) to integrate Gulfspecific wind resource assessment, commercial wind turbine performance, offshore infrastructure cost modeling, and wholesale electricity market exposure. Gulf wind speed data are vertically extrapolated to turbine hub height and combined with manufacturer power curves to compute annual energy production and capacity factors across a broad portfolio of commercial turbines, enabling identification of turbine designs best suited for low-wind offshore environments. Hourly electricity price data from the Midcontinent Independent System Operator (MISO) day-ahead market are incorporated to characterize revenue potential, price volatility, and the temporal alignment between wind generation and market conditions. In addition, a conceptual framework for offshore battery energy storage system (BESS) integration is developed to support future investigation of market-responsive energy shifting at repurposed platforms. Results from the turbine evaluation demonstrate that machines with lower cut-in wind speeds and earlier ‘rated-power’ characteristics significantly outperform larger, industry-standard offshore turbines for the same net power under Gulf wind conditions, underscoring the need for region-specific technology selection. Market analysis further reveals substantial price variability and limited intrinsic alignment between wind production and high-price periods, motivating consideration of operational flexibility mechanisms. While storage optimization is not implemented in this study, the REM framework establishes a transparent and replicable foundation for co-evaluating turbine selection, infrastructure constraints, and market exposure, providing a practical pathway for assessing the potential role of repurposed offshore platforms in enabling economically viable offshore wind development in the Gulf of America.

02 PETROLEUM↗
DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗
DOE OSTI2021

Recommended Practices for Managing Induced Seismicity Risk Associated with Geologic Carbon Storage

The geologic storage of carbon dioxide (CO 2 ) is one method to help reduce or eliminate atmospheric CO 2 emissions. The sequestered CO 2 is originally captured from the atmosphere or from a stationary industrial source and subsequently injected into a deep subsurface porous rock formation. To facilitate the successful deployment of field scale carbon storage projects, the U.S. Department of Energy (DOE) is developing tools and protocols for defensible, science-based frameworks to quantify and mitigate risks associated with the long-term storage of CO 2 . This protocol specifically addresses the risk of induced seismicity due to injection in a geologic carbon storage (GCS) site. This integrated and risk-based protocol is a product of the U.S. DOE Fossil Energy’s National Risk Assessment Partnership (NRAP), a multi-year collaborative research effort of Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Energy Technology Laboratory (NETL), and Pacific Northwest National Laboratory (PNNL). These recommended practices describe a set of 7 steps to evaluate, manage, communicate, and mitigate the risk of induced seismicity at GCS sites. The base methodology of the recommended practices follows a framework similar to the Protocol for Addressing Induced Seismicity Associated with Enhanced Geothermal Systems (Majer et al., 2012), developed for the Geothermal Technology Office of the U.S. DOE. These recommended practices present a framework to systematically assess the induced seismicity risk and quantify the associated uncertainties. These recommendations are based on current research and are sufficiently general to allow for modification and application to a variety of different types of sites. The substance of the recommended practices contained herein includes both technical and non-technical issues, and covers all operational stages of the GCS project lifecycle. They start at the preliminary risk assessment phase, continue through site assessment and characterization, include best practice communication and seismic monitoring plan methodologies, discuss the evaluation and mitigation of seismic hazard and risk, and closes with an exploration of operational management plans, which conclude when the induced seismicity risk abates back to background level. The focus of these recommendations is on actively managing the risks associated with induced seismicity by developing an actionable risk management plan that starts at the project proposal stage and continues through site closure through an iterative assessment and improvement process. The audience of this document is expected to include all interested stakeholders (e.g., operators, project developers, regulators, and the general public) and is expressly written to be accessible to this broad range of partners. This document is intended to disseminate knowledge gained through recent advances in the science of induced seismicity hazard and risk assessments, to provide updates based on recent experience gained by similar corollary injection-induced seismicity cases, and most importantly to establish a uniform framework to carry out a successful induced seismicity risk management plan for carbon storage projects in the future. These recommendations do not directly address any domestic or international regulations or standards. A complementary NRAP report makes recommendations for the assessment and management of environmental subsurface risks associated with unwanted fluid migration at GCS sites (Thomas et al., 2021) and should be referred to in order to address those additional GCS site risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recommended Practices for Managing Induced Seismicity Risk Associated with Geologic Carbon Storage

The geologic storage of carbon dioxide (CO 2 ) is one method to help reduce or eliminate atmospheric CO 2 emissions. The sequestered CO 2 is originally captured from the atmosphere or from a stationary industrial source and subsequently injected into a deep subsurface porous rock formation. To facilitate the successful deployment of field scale carbon storage projects, the U.S. Department of Energy (DOE) is developing tools and protocols for defensible, science-based frameworks to quantify and mitigate risks associated with the long-term storage of CO 2 . This protocol specifically addresses the risk of induced seismicity due to injection in a geologic carbon storage (GCS) site. This integrated and risk-based protocol is a product of the U.S. DOE Fossil Energy’s National Risk Assessment Partnership (NRAP), a multi-year collaborative research effort of Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Energy Technology Laboratory (NETL), and Pacific Northwest National Laboratory (PNNL). These recommended practices describe a set of 7 steps to evaluate, manage, communicate, and mitigate the risk of induced seismicity at GCS sites. The base methodology of the recommended practices follows a framework similar to the $\textit{Protocol for Addressing Induced Seismicity Associated with Enhanced Geothermal Systems}$ (Majer et al., 2012), developed for the Geothermal Technology Office of the U.S. DOE. These recommended practices present a framework to systematically assess the induced seismicity risk and quantify the associated uncertainties. These recommendations are based on current research and are sufficiently general to allow for modification and application to a variety of different types of sites. The substance of the recommended practices contained herein includes both technical and non-technical issues, and covers all operational stages of the GCS project lifecycle. They start at the preliminary risk assessment phase, continue through site assessment and characterization, include best practice communication and seismic monitoring plan methodologies, discuss the evaluation and mitigation of seismic hazard and risk, and closes with an exploration of operational management plans, which conclude when the induced seismicity risk abates back to background level. The focus of these recommendations is on actively managing the risks associated with induced seismicity by developing an actionable risk management plan that starts at the project proposal stage and continues through site closure through an iterative assessment and improvement process. The audience of this document is expected to include all interested stakeholders (e.g., operators, project developers, regulators, and the general public) and is expressly written to be accessible to this broad range of partners. This document is intended to disseminate knowledge gained through recent advances in the science of induced seismicity hazard and risk assessments, to provide updates based on recent experience gained by similar corollary injection-induced seismicity cases, and most importantly to establish a uniform framework to carry out a successful induced seismicity risk management plan for carbon storage projects in the future. These recommendations do not directly address any domestic or international regulations or standards. A complementary NRAP report makes recommendations for the assessment and management of environmental subsurface risks associated with unwanted fluid migration at GCS sites (Thomas et al., 2021) and should be referred to in order to address those additional GCS site risks

58 GEOSCIENCES↗
DOE OSTI2022

Small Hydropower Interconnections: State Interconnection Processes

Small hydropower projects with rated power output between 0 to 20 MW have been the predominant source of hydropower growth over the past decade in the United States (DOE 2016; Johnson et al. 2018). However, interconnection to electricity distribution and transmission grids is a persistent barrier. Interconnection of an electricity generating unit is overseen by the distribution or transmission owner, who use interconnection standards and requirements that vary by state. The differences between standards in standards may affect the final cost, timeline, and success of a small hydropower project. Small hydropower project developers across the United States have found interconnection procedures to be fraught with cost surprises and schedule overruns. System operators have struggled to understand impacts to overburdened or rapidly evolving transmission and distribution grids. The results of these shortcomings have been stranded costs and unrealized small hydropower potential. Though regulatory actions and policy recommendations at the state level have increased the situational awareness of interconnection challenges, the remote locations of small hydropower resources and the relatively small revenues associated with energy production through small hydropower facilities continue to make interconnection processes and requirements confusing and costly. Noting these challenges, the U.S. Department of Energy Water Power Technologies Office enlisted Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to investigate the small hydropower interconnection landscape across the United States. The second in a series, this paper investigates the interconnection process in each state in the U.S. to compare their attributes. Subsequent papers in the series will analyze these interconnection processes (“Small Hydropower Interconnections: Analysis of Interconnection Processes”) and present best practices (“Small Hydropower Interconnections: Best Practices”) that will help overcome barriers to future small hydropower development. The first paper in the series examined the state of small hydropower projects in the United States (“Small Hydropower Interconnections: Small Hydropower in the United States”) to understand the industry characteristics.

13 HYDRO ENERGY↗
DOE OSTI2020

Microbiome definition re-visited: old concepts and new challenges

The field of microbiome research has evolved rapidly over the past few decades, and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term ‘microbiome’. Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and uture outlook. We propose a definition of microbiome based on the compact, clear and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota, and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Ethics-Based Review of Generative Artificial Intelligence: Assuring Responsible Use (Version 1.0)

The rapid expansion of generative artificial intelligence (GenAI) has generated excitement regarding its potential benefits and concern over its ethical implications. Governments, corporations, and standards organizations have described ethical principles to direct GenAI's development and use; however, practical guidance for implementing these principles is limited. Addressing this gap is critical, especially considering the array of risks associated with GenAI, such as legal liabilities, privacy concerns, security threats, and potential misuse. Robust policies and procedures are critical to support responsible deployment of GenAI. This report examines Pacific Northwest National Laboratory (PNNL)’s approach to promoting responsible GenAI use. Proposed initiatives include developing policies based on ethical principles, creating a governance process to review projects relative to those principles, and implementing onboarding processes for training staff. The governance framework described in this report adapts the structure and principles of Institutional Review Boards (IRBs), traditionally used in human subjects research, for GenAI ethical review, providing oversight. Ethical principles guiding responsible GenAI usage include transparency and accountability, privacy, fairness, safety, security, and validity and reliability. To operationalize these principles, we propose forming a GenAI Assurance Council (GAC) that mirrors the IRB's structure. The GAC will evaluate GenAI projects across privacy, accountability, transparency, safety, security, fairness, and validity dimensions. Complementing policy and governance is AI literacy training to support staff understanding of GenAI's ethical implications. An initial training effort for AI Incubator Chat—a GenAI tool deployed at PNNL—showed promising results, underscoring the importance of clear guidelines and user accountability. Collaborative efforts and the dissemination of best practices are also discussed. The proposed GAC model and AI literacy training provide a blueprint for establishing ethical GenAI use and governance, offering practical tools to bridge the gap between ethical principles and real-world applications. The responsible integration of GenAI at PNNL entails a multifaceted approach involving policy development, ethical governance, and AI literacy training. The positive initial feedback and collaborative opportunities position PNNL to lead by example in GenAI's responsible use, reflecting a proactive stance in addressing the ethical, legal, and societal challenges associated with this emerging technology. PNNL's systematic and ethical approach to GenAI offers a model for other institutions to emulate, promoting safe and responsible technological advancements in the AI domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Systematic Evidence Based Performance Approach to Regulation of Nuclear Sites in England and Wales - 20058

The Environment Agency for England has developed a systematic evidence-based approach to pursue our strategic environmental objectives for the regulation of nuclear sites. We use an annual evidence review process to ensure effective and efficient targeting of limited resources to achieve those objectives. Over the last 8 years, we have been developing and refining this approach to ensure risk-based and value driven regulation. The approach comprises nuclear site and nuclear sector review processes known as Site Environment Review (SER) and Nuclear Environment Review (NER). This approach complements our regulation of nuclear site permit holders under the Environmental Permitting Regulations (EPR). We deliver our regulation of nuclear sites in England and Wales alongside the Office for Nuclear Regulation. The SER process involves the lead regulator for each nuclear site assessing the permit holder's environmental performance across 14 themes, set within the context of the site's main activities and associated waste disposals. Our themes include environmental leadership, resources and climate change, radioactive waste management, facility management and decommissioning, groundwater, and environmental radiological protection. We use evidence from site inspections and working within our subject matter groups to grade current and predicted future environmental performance. We are particularly interested in sustainability and the application of Best Available Techniques (BAT) to prevent the creation, and minimise the disposal, of radioactive wastes. We use risk analysis (strengths, weaknesses, threats and opportunities) to examine performance against our strategic environmental objectives, which are set out, in our 5-year Nuclear Delivery Plan (NDP). The output supports the targeting of our resources at each nuclear site. We consult the relevant permit holders on the SER priorities and use their feedback to refine our plans. We expect all permit holders to take account of our priorities when considering their own programmes of work, objectives and plans. The NER process brings together what we learn and achieve through regulation across the sector. It provides input to planning priorities, supported by qualitative and semi-quantitative evidence. It covers the 28 nuclear sites in England and Wales and spans the same 14 environmental themes. During the process we collate, compile and summarise evidence from the SERs and other sources such as inspection reports and evidence from our other nuclear work programmes. The output is the NER annual report. This provides a snapshot of the status of the nuclear sector and gives insights to enable us to regulate more efficiently and effectively. It also takes account of cross-cutting issues and risks such as changes in international standards, domestic policy, regulatory framework, domestic standards and guidance, learning from experience such as incidents, events and good practice, and innovation, research and development. It provides graphics that illustrate the grading of environmental performance for the nuclear sector across the fourteen environmental themes. This analysis allows benchmarking of nuclear site's environmental performance and the visualisation provides a convenient comparison of performance across themes, sites, and over time. We use this intelligence to inform our investment in training and development of our staff, and our cross-cutting engagement on strategic issues with government, the Nuclear Decommissioning Authority (NDA) and other corporate organisations. Adopting this approach can provide benefits with organisational reputation, stakeholder participation and ensuring value from the public investment. This paper describes the history of the SER/NER process, a selection of outputs from the process and ideas for improvement. The paper will be of interest to other regulators and organisations across the world that are interested in supporting continuous improvement. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Strategies for connecting whole-building LCA to the low-carbon design process

Abstract Decarbonization is essential to meeting urgent climate goals. With the building sector in the United States accounting for 35% of total U.S. carbon emissions, reducing environmental impacts within the built environment is critical. Whole-building life cycle analysis (WBLCA) quantifies the impacts of a building throughout its life cycle. Despite being a powerful tool, WBLCA is not standard practice in the integrated design process. When WBLCA is used, it is typically either speculative and based on early design information or conducted only after design completion as an accounting measure, with virtually no opportunity to impact the actual design. This work proposes a workflow for fully incorporating WBLCA into the building design process in an iterative, recursive manner, where design decisions impact the WBLCA, which in turn informs future design decisions. We use the example of a negative-operational carbon modular building seeking negative upfront embodied carbon using bio-based materials for carbon sequestration as a case study for demonstrating the utility of the framework. Key contributions of this work include a framework of computational processes for conducting iterative WBLCA, using a combination of an existing building WBLCA tool (Tally) within the building information modeling superstructure (Revit) and a custom script (in R) for materials, life cycle stages, and workflows not available in the WBLCA tool. Additionally, we provide strategies for harmonizing the environmental impacts of novel materials or processes from various life cycle inventory sources with materials or processes in existing building WBLCA tool repositories. These strategies are useful for those involved in building design with an interest in reducing their environmental impact. For example, this framework would be useful for researchers who are conducting WBLCAs on projects that include new or unusual materials and for design teams who want to integrate WBLCA more fully into their design process in order to ensure the building materials are consciously chosen to advance climate goals, while still ensuring best performance by traditional measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Open Building Control

Best practice control sequences are often not implemented correctly, or are not implemented at all, in large commercial buildings. This typically leads to 10-30 percent energy waste, along with reduced occupant productivity and unnecessary equipment wear. The current process of designing and implementing such control sequences is a manual process that starts with designers who often don’t have adequate training, then requires controls programmers to interpret and program a verbose written sequence. This process has been shown to fail to deliver high performance control sequences at scale. The Open Building Control project digitizes the current control delivery process. The project is developing tools for system designers to select control sequences, assess their energy performance and load flexibility potential using whole building simulation, specify the sequence for implementation using machine-to-machine translation by a control provider and formally testing the as-installed sequences by a commissioning agent. The project developed tools for each stage of this delivery process. The key innovation of the project is the development of the Control Description Language, a language that allows such a digitized control delivery process with end-to-end verification. Libraries of control sequences have been implemented using the Control Description Language, and their performance has been demonstrated using whole building energy simulation. An automated translation of such sequences to a commercial control product line has been conducted using a prototype translator. Tools for formal verification of as-installed control sequences relative to their specification have been developed and demonstrated. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) started the process of forming a committee to make this language an ASHRAE/ANSI Standard. This new standard will complement existing and emerging ASHRAE standards for building communication and semantic modeling by providing a standard for expressing the control logic - the actual brain of the building. We expect this language and the process it enables to be an important contribution to the deployment of high performance building control sequences at scale because it allows taming the complexity of the control delivery process, which is continually increasing due to the need for higher performance and increased load flexibility to meet goals for net zero energy and increased renewable integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

MFIX DEM Enhancement for Industry-Relevant Flows (Final Report)

The overall goal of this two-phase project is to implement performance improvements of the Multiphase Flow with Interphase Exchanges (MFIX) Discrete Element Model (DEM) code that enable a transformative shift for industrial use. Prior to this effort, the largest simulations performed using MFIX are O(10 7 ) particles. This falls short of the O(10 9 ) particle simulations that must be completed on a timescale of days or weeks (vs. months or years) to enable simulations with physically-relevant domain sizes to be incorporated into industrial design cycles within five years. This was accomplished by tailoring best-in-class practices to bear on the unique challenges posed by the MFIX-DEM algorithm and code base. Scientific simulations (e.g., in cosmology, turbulent combustion) routinely use massively parallel computing to update far more particles in short wall clock times. Results from Phase 1 (1.5 years in duration) indicated significant gains in speed were possible for a wide range of benchmark cases. Moreover, a survey sent to >35 companies indicates that the timing is ideal for such an enhanced tool, with >80% of the respondents indicating that DEM is already value-added or will be within the next 5 years, and >70% of the respondents indicating that improved speed is the top computational priority. In Phase 2 (3.5 years in duration), the two major barriers that hinder industry from effectively using multiphase Computational Fluid Dynamics (CFD) to cut costs and improve performance, namely computational overhead and confidence in predictions, continued to be addressed. Regarding the former, the results from Phase 1 to guide the effort, with enhancements focused on an improved time-stepping algorithm and particle sorting. Four target problems of 1 billion particles each and increasing complexity were identified: homogeneous cooling, tumbler with continuous particle size distribution, discharge from a rectangular hopper and a cylindrical riser. Each of these were successfully simulated for relevant time scales (on order of seconds) using less than 24 hours of wall clock time. These represent the first 1-billion particle DEM simulations performed with MFIX, namely using the MFIX-Exa code. This code is currently under development at NETL in collaboration with Lawrence Berkeley National Laboratory. Regarding the second barrier on predictive uncertainty, experiments from Phase 1 (interacting nozzles - hydrodynamics only) and Phase 2 (very small-scale segregation experiments) were used to demonstrate the ability of two simplified approaches to uncertainty quantification (UQ). By limiting the number of particles, UQ based on the simplified treatment was compared to standard UQ, which was shown to have much higher computational demands. Experiments were also performed on a pilot-scale stripper unit to provide validation data for future CFD-DEM simulations and UQ.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

First Ever Field Pilot on Alaska's North Slope to Validate the Use of Polymer Floods for Heavy Oil EOR a.k.a Alaska North Slope Field Laboratory (ANSFL)

Alaska’s high viscosity oil resources that range between 20–30+ billion barrels represent about a third of known North Slope original oil in place (OOIP). These resources are primarily concentrated in the Schrader Bluff formation (also called West Sak on the Western North Slope) and Ugnu reservoirs and are categorized as “viscous oils” and “heavy oils” owing to their in-situ viscosities between 5–10,000 cP and up to a million+ cP respectively. The viscous oil deposits are relatively deeper (2,000 – 5,000 ft), whereas the heavy oils are somewhat shallower (2,000 – 4,000 ft). The typically shallow depths and the proximity to the continuous permafrost results in relatively lower formation temperatures and pressures, and consequently higher viscosities. The vertical depth vs. viscosity delineated in Paskvan et al. (2016) differentiates the viscous and heavy oils. As depicted in Paskvan et al. (2016), currently the main focus (referred to as “developing”) is on the viscous oils in the Schrader Bluff formation in the Milne Point Unit (MPU). Notwithstanding this Alaska North Slope (ANS) specific categorization, we use the industry adopted, all-inclusive term “heavy oil” for all high viscosity oils. Resource characterization and additional details can be found in topical publications of Paskvan et al. (2016) and Targac et al. (2005). Despite the vast resource base, the development pace, vis-à-vis the production of heavy oils has been very slow and limited due to multiple factors such as cost, logistics, challenging arctic environment, poor waterflood sweep efficiency due to mobility contrasts, and significantly high minimum miscibility pressures (MMP). Most importantly, typical or standard thermal methods that are commonplace elsewhere (Canada, California) are inapplicable due to the continuous permafrost. As a consequence, cumulative production of heavy and viscous oils is a little over 1% of OOIP slope wide and currently, there is hardly any production from Ugnu. However, on a broader level, these unfavorable factors are outweighed by the fact that (1) these resources, within the established infrastructure, are too large to ignore because of their strategic importance to the Nation and the State of Alaska and (2) Prudhoe Bay type diluent crude oil is still available for heavy oil transport through the Trans Alaska Pipeline System (TAPS). Similarly, from a reservoir standpoint, the following factors also are important offsets: (1) favorable rock characteristics of Schrader Bluff; (2) the promise demonstrated by the initial scoping studies (Seright 2010, 2011) suggesting significant increase of heavy oil recovery using polymer flooding; (3) successful field implementation in Canada, China and elsewhere in the world, and (4) availability of the existing pairs of horizontal injector-producer in Schrader Bluff The foregoing was recognized as the best readily available opportunity for significant investment by the US Department of Energy and the field operator Hilcorp Alaska LLC to conduct the first ever field scale experiment to test the polymer flooding technology to unlock the vast heavy oil resources on ANS. With this primary goal in mind, the research team embarked on a ~4.5 years long project that focused on the field polymer pilot complemented by supporting laboratory and simulation studies. As documented in this final report, over the course of the project, many lessons have been learned and valuable field and supporting laboratory data has been collected, which also is complemented by numerical reservoir simulations. We have been able to establish the injectivity of polymer solution, evidence of significant reduction in the water cut of previously waterflooded pattern, effective propagation of a hydrolyzed polyacrylamide (HPAM), benefits of low salinity water, provide practical guidance on handling of produced fluids containing breakthrough polymer, fit-for-purpose forecast-worthy history matched simulation model, polymer EOR benefit of 700-1000 bopd over waterflood, and most importantly a low polymer utilization factor of ~1.7 lb/stb. In summary this project is deemed as a scientific, technical and economic success, having met all objectives, fulfilled deliverables and within budget, providing impetus to apply polymer EOR throughout the Milne Point Field paving the way for even heavier viscosity oils in the Ugnu area, eventually extending the economic life of TAPS.

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