Results for “Design decisions” — Page 13

DOE OSTI2026

DASSH-F: Subchannel Based Thermal Analysis

The DASSH thermal analysis code is designed to rapidly allow a reactor design engineer to obtain flow rates requirements that satisfy peak temperature constraints in the domain. The advantage of using DASSH over a hand calculation is that it has a more rigorous treatment of the pin power distribution and coolant heat transfer within an assembly and between assemblies. The advantage of using DASSH over a conventional 3D subchannel code or a computational fluid dynamics code (CFD) is that it can obtain the desired solution in a matter of minutes in serial with minor computer memory needs. The DASSH methodology for pin lattice models is virtually identical to SUPERENERGY-2 with additional functionalities taken from follow on work to SUPERENERGY-2 done at ANL in the 1980s. DASSH today is an integral component of the Argonne Fast Reactor analysis suite for reactor design work. DASSH obtains the power distribution from a coupled neutron-gamma heating calculation in GAMSOR (including DIF3D) at each time point of a companion fuel cycle analysis calculation with REBUS. The domain in DASSH assumes a hexagonal grid typical for fast reactors with much of the geometry information taken from the DIF3D model. DASSH assumes the assemblies that are loaded into each grid position are ducted to control the coolant flow. The user can alternatively provide their own geometry and power profile instead of inheriting it from DIF3D. Considerable detail is given on the subchannel formulation of DASSH in this document. Much of the formulation and design of the code builds upon research done by previous authors with little new investigation. Thus the decisions made in developing the subchannel model used in DASSH have their origins over 50 years ago. Much of the heat transfer methodology in DASSH is built upon correlations for both the coolant mixing and heat transfer coefficients for pins and ducts. DASSH is thus not a rigorous treatment of a given problem, but a rapid assessment of the temperature field that has known limitations with respect to an experimental measurement or CFD calculation. The DASSH input and output are detailed along with usage of the software. The DASSH output provides tables of evaluated material properties and key coolant and pin temperature results. DASSH can create Python scripts that generate domain summary pictures. DASSH can also generate assembly temperature maps and VTK output files which allow the DASSH solution to be visualized. As the primary purpose of the DASSH software is to compute the coolant and fuel pin temperature distribution for a given model of a reactor, much of the output focus is giving the user quick summary tables needed to assess the performance of a given orifice flow specification. The present version of DASSH has a crude orifice search capability and a sufficient orifice flow search capability. The flow search tries to meet user specified constraints for 1) peak 2-sigma clad temperature, 2) peak coolant temperature, and 3) desired bulk outlet temperature. This document serves as the manual for the Fortran based DASSH software that was developed to replace the Python version of DASSH developed as part of the VTR program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Thermal Reservoir Networks for Modularly Expandable Thermal Microgrids

The Department of Defense (DoD) faces the substantial challenge of cost-effectively retrofitting one to two installations per month, each comprising approximately 1,000 buildings, to improve resilience, reduce energy consumption, and enhance energy supply security. Achieving these objectives requires optimal system selection and effective risk mitigation during system integration. To address this need, we introduce Platform-Based Design (PBD), a structured, hierarchical methodology adapted from other industrial sectors to the domain of energy system retrofits. We demonstrate the effectiveness of PBD through a techno-economic feasibility study comparing geothermal-coupled thermal energy networks (TENs) with conventional energy systems for heating, cooling, and powering 17 buildings at Joint Base Andrews (JBA) in Maryland. Our analysis illustrates that the PBD approach enables rigorous, data-driven, sequential decision making, resulting in a family of Pareto-optimal systems, among which the TEN emerged as the most promising solution. The selected TEN design integrates geothermal borefields, heat recovery heat pumps, photovoltaic (PV) arrays, and battery storage. Compared to the baseline system – gas heating combined with air-source chillers – the proposed TEN reduces annual imported energy by 74% and peak electricity demand by 45%, achieves a levelized cost of energy of $\$0.210$/kWh, and substantially enhances resilience. Life-cycle costs increase by approximately 6%, and initial investment costs are about 2.5 times higher than the baseline. However, if central plant infrastructure, district loops, and utility-scale PV and battery systems are privately funded and operated, the initial investment would fall below the baseline system cost. Critical to achieving these significant performance improvements were detailed nonlinear dynamic simulations coupling geothermal heat transfer, energy system operation, and realistic feedback control logic. These simulations identified essential design modifications and control strategy refinements that substantially reduced energy use, peak demand, and compressor shortcycling, thereby improving durability and reliability—issues that would have been significantly more expensive to resolve during operation. Additionally, the verification step highlighted sensitivities to key design parameters that could reduce initial investment by approximately $\$2$ million and reduce annual life-cycle costs more than $\$300,000$. We recommend adopting the PBD methodology for future feasibility studies and TEN pilot projects to gain valuable operational experience. Furthermore, we recommend that DoD invest in transferring and scaling the PBD methodology to other installations. This entails developing standardized computational frameworks and component libraries as well as training industry in conducting PBD. Such investments would enable rapid, robust, reliable, and cost-effective retrofits, supporting DoD’s ambitious energy system modernization goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

External perturbation-driven Sabatier breakthrough

The Sabatier reaction (CO 2 + 4H 2 → CH 4 + 2H 2 O) is gaining renewed interest due to its potential to reduce energy carrier storage costs, serve as a feedstock for various organic chemicals, and supply in-situ propellant and life-support resources for long-duration Mars missions. This study demonstrates that combining a modest 2 mA electric field with H 2 feed modulation markedly elevates the CO 2 hydrogenation activity of 2 wt% Ru/CeO 2 catalyst. CO 2 conversion reaches 88 % and 93 % with a CH 4 yield of 83 % and 89 % at 350 °C and 450 °C, respectively. A simple lumped kinetic model reveals that the combined external perturbations not only shift the reaction mechanism but also redistribute key surface-adsorbed intermediates such as hydrogen adatoms and hydrogen-activated CO 2 among the Ru clusters, Ru/CeO 2 interface, and ceria surface. The electric field accelerates the conversion of adsorbed CO 2 to the hydrogenated CO 2 species on Ru and boosts CH 4 formation rate constant, while simultaneously suppresses the formation of undesired, non-reactive surface intermediates. Degree-of-rate-control analysis pinpoints proton migration across the metal-support interface as the decisive lever under these coupled perturbations. In conclusion, these findings establish that rational pairing of metal-support design with well-tuned electric fields and feed oscillations can unlock unprecedented Sabatier rates, guiding the development of next-generation reactors for efficient CO 2 to CH 4 conversion.

10 - SYNTHETIC FUELS↗

DOE OSTI2026

Development and application of an environmental risk register for marine energy device and project developers

The marine energy industry is steadily advancing as more devices are deployed worldwide. However, several challenges and barriers remain, such as lingering uncertainty regarding the potential environmental effects of marine energy devices on marine animals, habitats, and ecosystems. Concerns have led to difficulty navigating permitting and consenting processes and receiving authorization to deploy devices in the marine environment, including extended timelines and costs. Based on existing risk registers, a novel marine energy environmental risk register was created to help the marine energy industry move beyond these barriers. This risk register aims to aid marine energy device and project developers identify and assess potential environmental risks early in device design or project planning, document and track potential environmental interactions, prioritize risks and determine risk responses, and make decisions throughout device or project development. It can also be used as a tool to assist in communicating with regulators and advisors during permitting processes and to inform stakeholder and community engagement efforts. This paper details the methods and process to develop a risk register specific to environmental effects of marine energy and describes two use cases (one for wave energy and another for tidal energy) to highlight example results. Due to the tool's novelty, the paper showcases its application for the marine energy industry and acknowledges limitations and possible future improvements. Overall, the environmental risk register shows promise to support marine energy developers when identifying, tracking, and addressing potential environmental risks and to help successfully navigate permitting and deploy marine energy devices responsibly.

Environmental effects↗

DOE OSTI2025

Disentangling the gap between pure and mixed-gas performance of thin film composite membranes through improved cell design and testing methods

Testing thin film composite (TFC) membrane coupons at low stage-cuts (≤5%) in a sweep-gas permeation system is a common practice to obtain mixed-gas separation properties for benchmarking performance and making scale-up decisions. However, even under these idealized conditions, mixed-gas permeance and selectivity can be more than 30% lower than their pure-gas values, partially due to concentration polarization, an effect that typically intensifies with increased membrane permeance. This study investigates the effect of cell design on mixed-gas testing using PolyActive TM TFC membranes with pure-gas CO 2 permeance of 1700 – 3100 gas permeance unit (GPU), covering the permeance range of most state-of-the-art CO 2 /N 2 separation membranes. Here, we designed and 3D-printed a counter-current permeation cell with enhanced feed and sweep flow efficiency, resulting in a 33 – 41% increase in mixed-gas CO 2 permeance compared to traditional permeation cells. Furthermore, we compared sweep-gas and vacuum permeation methods using traditional permeation cells, revealing that the latter delivers 41% higher mixed-gas CO 2 permeance, because vacuuming effectively minimizes the downstream concentration polarization. These findings highlight the importance of cell design and permeation apparatus selection in lab-scale mixed-gas testing, with strong implications for module design and process optimization at the industrial scale.

mixed gas performance↗

DOE OSTI2024

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

The U.S. Agrivoltaic Shading Tool: A National-Scale Interface for Modeling Light and Shade Patterns in Ten Common Agrivoltaic Configurations

Agrivoltaic systems are dual-use configurations that co-locate agriculture and photovoltaic (PV) infrastructure and require careful design to balance crop performance and energy generation. A critical element of agrivoltaic design is the spatial and temporal distribution of irradiance and shade within and around PV arrays. To support research, planning, and stakeholder decision-making, we introduce the U.S. Agrivoltaic Shading Tool, a novel web-based application that delivers high-resolution irradiance and photosynthetically active radiation (PAR) modeling for ten standardized PV configurations across the conterminous United States. The tool leverages the National Laboratory of the Rockies (NLR) System Advisor Model (SAM) to perform detailed irradiance simulations, using meteorological data from the National Solar Radiation Database (NSRDB). Outputs include seasonal, monthly, weekly, and diurnal patterns of available sunlight, amount of shade, irradiance, and PAR at ground level within agrivoltaic system footprints. For a user's selected location, these results are visualized through interactive visualizations, heatmaps, and time-series plots, designed to be accessible to both technical and non-technical users. In addition to facilitating rapid spatial exploration of agrivoltaic light environments, the tool will offer seamless integration with the InSPIRE Agrivoltaics Design and Analysis Model (ADAM). This optional workflow will allow users to port selected site and configuration parameters into a more advanced modeling environment for further customization of structural layouts, crop-system compatibility, power generation, and technoeconomic performance. Finally, to promote open science, the entire dataset will be hosted and available for open access through the OpenEI platform. By standardizing and disseminating high-quality irradiance data and design tools, the U.S. Agrivoltaic Shading Tool supports a wide range of users, including researchers, landowners, energy developers, and policymakers, in evaluating the agronomic and energetic feasibility of agrivoltaic systems across the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Activation of Thermal Insulation and Contamination Prevention

The goals of this paper are to enhance the understanding of the potential activation and contamination hazards from the use of insulating materials in the Molten Chloride Reactor Experiment and to foster safer work environments through informed decision-making and strategic planning. It was written to resolve a gating comment from the core heating system preliminary design review. The largest dose expected from the considered insulation materials is about 0.1 mrem per year from Pyrogel. Although Cerablanket stood out among the insulation materials for encouraging ALARA practices, it has a higher thermal conductivity. At low power levels, focusing on reducing the generation of dust from ERD activities may be more effective for reducing the potential spread of contamination than changing the insulating material type. However, regardless of the material chosen, PPE and safety conscious procedural practices can help reduce the production and spread of activated dust––thus minimizing the radiological hazard to workers.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Assessing the Sensitivity of Pourbaix Diagrams to Computational Protocols: Electrochemical Stability of Ni Oxides as a Case Study

Pourbaix diagrams stand as a useful tool in assessing and visualizing materials’ electrochemical stability and are widely used for electrocatalyst design. However, their reliability hinges on the accuracy of the chemical potentials of involved phases, which may bear uncertainties and can be significantly impacted by decision-making steps in the computational protocol. Here, this study introduces a robust sensitivity analysis framework, exemplified through a detailed examination of the computational Pourbaix diagram of Ni, the oxides of which are used as high-activity and cost-friendly catalysts for many electrochemical reactions. Quantities of interest derived from the Pourbaix diagram include the appearance and stability domain of the catalytically active Ni oxide phases along with the onset electrochemical potentials of phase transitions. These metrics can guide the design of operational conditions for Ni oxide electrocatalysts. We find that the employed DFT exchange-correlation functional has the most significant influence on the computed Pourbaix diagram. Uncertainties on crystal structures, along with their related ab initio energetics, are also found to affect the size of the phase stability domain. Higher-order coupling among input parameters is found to play a crucial role in influencing the appearance and distribution of Ni phases in the diagram. Our findings suggest a need to consider variations and uncertainties associated with the computational procedures on predicted Pourbaix diagrams for materials design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

14 SOLAR ENERGY↗

DOE OSTI2027

From zonal to nodal capacity expansion planning: Spatial aggregation impacts on a realistic test-case

Solving power system capacity expansion planning (CEP) problems at realistic spatial resolutions is computationally challenging. Thus, a common practice is to solve CEP over zonal models with low spatial resolution rather than over full-scale nodal power networks. Due to improvements in solving large-scale stochastic mixed integer programs, these computational limitations are becoming less relevant, and the assumption that zonal models are realistic and useful approximations of nodal CEP is worth revisiting. Here, this work is the first to conduct a systematic computational study on the assumption that spatial aggregation can reasonably be used for ISO-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8000 buses, we find that well-designed small spatial aggregations can yield good approximations but that coarser zonal models may result in large distortions of investment decisions, e.g., capacity under-investment of up to 41% for the lowest resolution model considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗