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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Re-Envisioning RCx: Achieving Max Potential HVAC Controls Retrofits through Modernized BAS Hardware and Software

Most existing buildings have sub-optimal heating, ventilation, and air conditioning (HVAC) controls, resulting in wasted energy and occupant discomfort. Retro-commissioning (RCx) addresses many of these issues, but it is a lengthy and highly customized process. Limited capabilities of existing building automation system hardware restricts the scope of most RCx projects. Incentive programs consider building automation system (BAS) hardware retrofits to be high-capital investments and do not allow them in typical RCx programs. This paper describes work that the authors are leading to facilitate technical and market innovation in the BAS industry to unlock large savings in existing commercial buildings through deep retrofits of BAS hardware and software. California and New York research projects are demonstrating BAS retrofits leveraging the American Society of Heating, Refrigeration, and Air Conditioning Engineers’ (ASHRAE) new Guideline 36 high performance sequences of operation to achieve greater than 20 percent whole building energy savings, while saving costs and reducing risk through streamlined processes and standardization across BAS manufacturer product lines and across implementation practices. This paper describes market barriers that impede achieving deep savings from BAS retrofits in custom incentive and traditional RCx programs and presents a new maximum potential BAS retrofit model that addresses these barriers. The new model leverages the authors’ efforts in market enablement through open standards, BAS industry partnerships, and tools for cost-effective scaling that includes tools for project screening, savings calculations, and measurement and verification (M&V). This approach is widely applicable and will be ready for at-scale implementation within two years.

Paliaga, Gwelen↗
DOE OSTI2022

Nuclear Data: What, Why How, Who? [Slides]

Nuclear data is data that describes physics in forms and formats that computer code can read to perform simulations of nuclear processes. The data is put into ”evaluations” following the combination of experiment and theory. Various physics are applied to these evaluations by the NJOY nuclear data processing code to produce application files, which simulation codes (e.g., MCNP, Partisn, etc.) can use to model various scenarios. The Nuclear Data Team at Los Alamos National Laboratory—in partnership with a variety of national and international organizations—provides nuclear data for use at LANL and throughout the world. This data is verified and validated to ensure that it performs as expected and accurately represents Mother Nature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Computationally Accelerated Discovery and Experimental Demonstration of High-Performance Materials for Advanced Solar Thermochemical Hydrogen Production

This project achieved its overarching goal of accelerating the discovery and validation of solar thermochemical hydrogen (STCH) materials through a tightly integrated approach that combined high-throughput computational screening, advanced machine learning (ML), and experimental testing. Guided by the objectives outlined in the Statement of Project Objectives (SOPO), our work fulfilled all major milestones across four technical tasks and delivered scientific breakthroughs and practical tools that significantly exceeded the original scope of the project. We began by addressing the challenge of predicting material phase stability through machine learning. A novel Python module was developed to generate thousands of meaningful features from composition, structure, and electronic properties, enabling rapid and reproducible ML model development. Using these tools, we trained a model to predict temperature-dependent Gibbs energies (G(T)) for inorganic crystalline materials with near-chemical accuracy—roughly 40 meV/atom—marking the first such descriptor of its kind. We also introduced a new machine-learned tolerance factor, τ, that accurately predicted perovskite formability with over 90% success, outperforming traditional heuristic models, such as the Goldschmidt tolerance factor. These capabilities allowed for rapid and accurate predictions of phase stability across a vast oxide composition space, setting the stage for high-throughput thermodynamic screening. Building on this foundation, we conducted an extensive computational screening of candidate STCH oxide materials. Over 1.1 million perovskite compositions were evaluated using the τ descriptor, leading to the identification of more than 27,000 predicted stable structures. Using density functional theory (DFT), we refined over 68,000 multinary perovskite structures and computed oxygen vacancy formation energies for over 1,300 ternary and double perovskites. These calculations enabled us to isolate compounds with redox behavior consistent with STCH requirements and resulted in a public dataset now hosted on the Materials Project. Recognizing that thermodynamic screening alone is insufficient, we addressed kinetic limitations by developing a suite of tools to estimate transition state (TS) energies for key redox reactions. We implemented a novel bounding approach that provides lower and upper estimates of TS energies with dramatically reduced computational cost, requiring less than 10% of the CPU time of a full nudged elastic band (NEB) calculation while maintaining high accuracy. This enabled rapid evaluation of over 200 reaction pathways across 90 materials. To further accelerate screening, we developed a SISSO-based ML model to predict diffusion barriers with a 96.7% success rate in classifying fast vs. slow materials, supporting a robust, data-driven framework for assessing redox kinetics. Experimental validation was critical to confirming the predictive power of our models. We synthesized and tested a wide array of candidate materials, including Mn-doped hercynite and several Gd- and La-based perovskites. Notably, Sr 0.4 Gd 0.6 Mn 0.6 Al 0.4 O 3 (SGMA) and Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF) emerged as leading STCH materials, exhibiting robust redox cycling and high hydrogen yields exceeding 150 µmol H 2 /g per cycle. These materials also retained over 50% of their hydrogen productivity under high-conversion conditions (H 2 O:H 2 = 1333:1), demonstrating strong thermodynamic favorability and promising performance under industrially relevant scenarios. Additional candidates, such as La 2 MnNiO 6 (L2MN), were found to produce even higher yields than ceria under standard STCH conditions. Our collaborators at Sandia National Laboratories confirmed these findings using high-temperature X-ray diffraction and thermogravimetric analysis, observing stable phase evolution and reversible redox activity. In several respects, the project went beyond the goals initially outlined in the SOPO. We published 17 peer-reviewed articles, including a large dataset of over 66,000 theoretical perovskites and a new structure prediction method (SPuDS-DFT) that accurately identifies ground-state structures at a fraction of the cost of traditional DFT. We demonstrated that our machine-learned G(T) model offers accuracy rivaling quasiharmonic calculations while being orders of magnitude faster. In partnership with the Materials Project, we made our datasets openly available, providing a powerful new resource for the broader materials science community. The combined computational and experimental advances of this project represent a significant advance in STCH materials discovery. By creating a robust, generalizable, and open workflow for thermodynamic and kinetic screening, and validating key findings through synthesis and reactor testing, we have provided a practical and scalable pathway for the rapid identification of new redox-active materials. The tools, data, and materials developed under this project are already supporting ongoing research and have laid the groundwork for the next generation of solar fuel technologies.

08 HYDROGEN↗
DOE OSTI2021

Deposition, Diagenesis, and Sequence Stratigraphy of the Pennsylvanian Morrowan and Atokan Intervals at Farnsworth Unit

Farnsworth Field Unit (FWU), a mature oilfield currently undergoing CO2-enhanced oil recovery (EOR) in the northeastern Texas panhandle, is the study area for an extensive project undertaken by the Southwest Regional Partnership on Carbon Sequestration (SWP). SWP is characterizing the field and monitoring and modeling injection and fluid flow processes with the intent of verifying storage of CO2 in a timeframe of 100–1000 years. Collection of a large set of data including logs, core, and 3D geophysical data has allowed us to build a detailed reservoir model that is well-grounded in observations from the field. This paper presents a geological description of the rocks comprising the reservoir that is a target for both oil production and CO2 storage, as well as the overlying units that make up the primary and secondary seals. Core descriptions and petrographic analyses were used to determine depositional setting, general lithofacies, and a diagenetic sequence for reservoir and caprock at FWU. The reservoir is in the Pennsylvanian-aged Morrow B sandstone, an incised valley fluvial deposit that is encased within marine shales. The Morrow B exhibits several lithofacies with distinct appearance as well as petrophysical characteristics. The lithofacies are typical of incised valley fluvial sequences and vary from a relatively coarse conglomerate base to an upper fine sandstone that grades into the overlying marine-dominated shales and mudstone/limestone cyclical sequences of the Thirteen Finger limestone. Observations ranging from field scale (seismic surveys, well logs) to microscopic (mercury porosimetry, petrographic microscopy, microprobe and isotope data) provide a rich set of data on which we have built our geological and reservoir models.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2021

Utility-scale Building Type Assignment Using Smart Meter Data

United States building energy use accounted for 40% of total energy use, 74% of peak demand, and $412 billion in 2019. Building energy modeling allows researchers to simulate building physics, gain insights into possible energy/demand saving opportunities, and assess cost-effective resilience amidst climate change. Many building features needed to create building energy models are readily available such as 2D footprints and LiDAR (height). A critical feature that is not generally obtainable is the building type. In partnership with a utility, a years worth of real-world, 15-minute electrical use data has been examined. The smart meter data is compared to 97 different prototype building energy models to assign building type. Real-world considerations including data preparation, quality assurance, and handling of missing values for advanced metering infrastructure data are addressed. Euclidean distance for pattern-matching of energy use, dynamic time warping, and time-window statistics with machine learning are compared for determining building type from measured electricity use.

Bass, Brett↗
DOE OSTI2020

Knock Mitigation Effectiveness of EGR across the Pressure-Temperature Domain

Exhaust gas recirculation (EGR) has been shown to enable efficiency improvements in SI engines through multiple different mechanisms, including decreasing the knock propensity at high load, which allows higher compression ratio. While many of the benefits of EGR are applicable to both low and high power density engines, including reductions in pumping work and improved specific heat ratio, the knock benefits and corresponding compression ratio increases have been limited to low power density naturally aspirated engines primarily intended for hybrid vehicle architectures. An earlier study [1] indicated that there may be a kinetic limitation for the ability of EGR to mitigate knock under these conditions, but that study only considered a small number of conditions. In this investigation, we expand on that study while also providing data for model validation for the new light-duty combustion consortium from the U.S. Department of Energy: Partnership for Advancing Combustion Engines (PACE). In this investigation, the effectiveness of EGR to mitigate knock is studied with regards to the effect of engine speed (1,500 and 3,000 rpm), changing trajectory in the pressure-temperature domain by varying the intake manifold temperature (35, 60, and 90 deg C), and by considering the effect of minor species by studying the effect of untreated EGR vs. EGR that has been treated by an automotive three-way catalyst. Additionally, to increase the relevance of these data for future modeling studies, the performance of the full boiling range gasoline was compared relative to a surrogate formulation. The study found that the fuel surrogate performs well, confirmed the kinetic limitations of EGR to mitigate knock under boost, and showed improvements in EGR performance with catalyzed EGR.

42 ENGINEERING↗
DOE OSTI2020

FutureGen 2.0 Technical Data

This is a comprehensive collection of all original surface and subsurface technical data, as well as various derivative subsurface models, collected from the FutureGen 2.0 project. This collection of data has been vetted for confidential or sensitive documents.

CCUS↗
DOE OSTI2026

Partnership Center for High-fidelity Boundary Plasma Simulation

The University of Colorado Boulder contributed to the DOE Partnership Center for High‑fidelity Boundary Plasma Simulation (award DE‑SC0018271) by advancing gyrokinetic modeling of core–edge plasma interactions in tokamaks. Using the GEM and XGC codes, the project investigated turbulence spreading from the plasma edge to the core, finding that while edge‑driven trapped electron mode turbulence enhances core ion heat flux in DIII‑D L‑mode plasmas, it does not fully explain experimentally observed transport shortfalls, indicating a need for flux‑driven models. The team also demonstrated that edge micro‑instabilities are highly sensitive to small variations in magnetic equilibrium, particularly near the pedestal and edge, highlighting the importance of accurately characterizing equilibrium uncertainties. Significant progress was made in spatially coupling the core GEM and edge XGC simulations, with successful validation against standalone XGC results in realistic tokamak geometries, laying the groundwork for future extensions to kinetic electrons and electromagnetic effects.

42 ENGINEERING↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Preliminary Regional Assessment of Potential Storage Complexes, Including Capacities and Costs, Based on Application of NRAP and SCO2T (Deliverable 3.3) Storage Complexes

The Recipient will assist with the field validation of one or more risk assessment toolsets through: 1) collaborative research; 2) sharing of relevant datasets, information, and technical insights from field efforts; and 3) other modeling and simulation toolsets as they are developed. Collaborative research could include, but is not limited to: 1) demonstrating use of field data for improved leakage pathway characterization, uncertainty reduction, and risk management and uncertainty workflows; 2) testing and validation of a specific National Risk Assessment Partnership (NRAP) tool using site specific data; and 3) improved modeling or reduced-order modeling of geophysical monitoring.

42 ENGINEERING↗
DOE OSTI2023

Digital Twin Technology (“Morpheus”) for Optimized Building Operations [SWR-22-74]

The electrification of buildings is an important step to reducing greenhouse gas emissions across all industries. The management of increasingly electrified buildings is a complex pursuit, and there remains a need for cost-effective software capable of handling the computational burden required of such complexity. Through a partnership with Dallas Fort Worth (DFW) Airport, researchers at NREL have developed a digital twin modeling framework to optimize building operations, called Morpheus. Pairing predictive control with automatic fault detection and diagnostics, Morpheus decreases energy expenditures, costs, and faults for large facilities. Additionally, Morpheus employs artificial intelligence to continuously improve its performance using information provided by sensor systems, human experts with deep industry domain knowledge, and even from other similar machines or fleets of machines. Coupling this novel energy-management software with other digital twins, such as NREL’s Athena software for mobility operations, enables robust decision-making for asset and space management. The implementation of Morpheus at DFW has resulted in significantly improved HVAC system operations and reduced both peak power and overall energy consumption. This enhanced functionality comes at a more affordable price than previously developed digital twins and can be customized for other facilities’ geometries to provide optimal, individualized control of a facility’s energy consumption.

Chinde, Venkatesh↗
DOE OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Solving Hard Problems with AI: Dramatically Accelerating Drug Discovery Through A Unique Public-Private Partnership

Dramatic disruptions in technologies can result in the creation of new markets, the emergence of new paradigms, and the displacement of entrenched approaches and business models. This can be especially pronounced when multiple technologies converge to create something new. Today, artificial intelligence (“AI”) is acting as an accelerant for such technology transformations. Still, the scope and depth of its impact will depend in part on our ability to address challenging problems that are surfacing without solutions. Industries will pursue AI for their corporate missions and to create shareholder value. But the timescales and applications can be incongruent with important public needs and with the longer research horizons required to make real progress. Public-Private partnerships are an important mechanism for tackling these rapidly emerging new, complex, challenging problems. Finally, this paper examines our experience gained by creating a unique public-private partnership to apply AI to one example of a hard problem that is particularly timely today-- dramatically accelerating drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗
DOE OSTI2022

Automated Vulnerability Detection (AVUD) for Compiled Smart Grid Software

This project developed and implemented a system for conducting cybersecurity vulnerability detection of smart grid components and systems by performing static analysis of compiled software (“firmware”). The resulting system for automated vulnerability detection (AVUD) was implemented as part of Oak Ridge National Laboratory’s existing test bed for smart meters, the Sustainable Campus Initiative. The work consisted of two phases: the first phase implemented the necessary software and computational models to perform the analysis, and the second phase demonstrated the system on example firmware in partnership with smart meter manufacturer Sensus USA, Inc. The resulting system won an R&D 100 award and has been successfully commercialized, winning a National Laboratory Consortium Commercialization Award.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DuraMAT (Durable Module Materials Consortium) Annual Report FY 2020

The Durable Module Materials Consortium (DuraMAT) launched in November 2016 with five years of funding as part of the U.S. Department of Energy’s (DOE’s) Energy Materials Network. DuraMAT is a multilab consortium, led by the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (Sandia), with SLAC National Accelerator Laboratory (SLAC) and Lawrence Berkeley National Laboratory (LBNL) as core research labs. DuraMAT’s overarching goal is to discover, develop, de-risk, and enable the rapid commercialization of improved materials, designs, predictive tests, and models for PV modules that increase performance, extend lifetime, and enable new applications. We work in partnership with our 15-member Industry Advisory Board and the technical management team in DOE’s Solar Energy Technologies Office. This document describes DuraMAT activities in FY20.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

3D Modeling of Safety-Related Upgrade Pilot Project

Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper will present the development of the HFE 3D modeling execution of the Main Control Room safety upgrades. New equipment and modifications are planned for upgrading the new safety systems. 3D modeling allows for visualization of the new equipment and modifications to be shared with engineering and design teams for evaluation and review. Anthropometric considerations such as sight lines and functional reach can be evaluated directly using the 3D model. This effort has helped to provide visualizations for additional review with operations personnel and project stakeholders.

3D Modeling↗
DOE OSTI2025

Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability

Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics must be treated self-consistently with gravity for end-to-end modeling of structure formation. Exascale computing enables simulations that span survey-scale volumes while incorporating key astrophysical processes that shape complex cosmic structures. We present results from CRK-HACC, a cosmological hydrodynamics code built for extreme scalability. Using separation-of-scale techniques, GPU-resident tree solvers, in situ analysis pipelines, and multi-tiered I/O, CRK-HACCexecuted Frontier-E: a four trillion particle full-sky simulation, over an order of magnitude larger than previous efforts. The run achieved 513.1 PFLOPs peak performance, processing 46.6 billion particles per second and writing more than 100 PB of data in just over one week of runtime. Frontier-E marks a significant advance in predictive modeling for next-generation cosmological science.

Frontiere, Nicholas [Argonne National Laboratory (↗
DOE OSTI2024

HERO CarbonSAFE Phase 2 Project in the Columbia River Basalt Group

The Hermiston, Oregon Basalt CarbonSAFE Phase II project (HERO CarbonSAFE) seeks to accelerate the deployment of commercial carbon dioxide (CO2) storage projects in basaltic rocks. Basalt CO2 storage has several advantages to conventional saline storage reservoirs including 1. The potential for rapid mineralization of CO2, 2. Associated decreases in pressure and CO2 migration risks, 3. Reduced long-term monitoring requirements with respect to plume tracking, 4. Widespread geographic distribution and, 5. Large storage potential due to thickness, porosity, and CO2 interactions with basalt. And for locations such as the Pacific Northwest, Hawaii, Iceland, India and Japan, basalts may offer the only economically feasible option for local CO2 storage. However, there are limited field-scale assessments of CO2 storage in basalt, and current carbon capture utilization and storage (CCUS) permitting and regulatory frameworks were developed for conventional saline reservoirs. HERO CarbonSAFE is designed to address research gaps and uncertainties associated with basalt storage. Specifically, the project will assess the feasibility of CO2 injection in the deep layered basalts, long-term storage (mineralization), practical approaches for large-scale implementation (50+ million metric tons of CO2 over 30 years), lithology-specific risks, and the technoeconomic potential for CO2 storage in basalts. The HERO CarbonSAFE project will assess feasibility of developing a commercial-scale (50+ million metric tons of CO2) geological storage complex within the Columbia River Basalt Group (CRBG), a layered continental flood basalt complex that underlies Calpine’s natural gas-fired Hermiston Power Project (HPP) in Hermiston, OR (Figure 1). Under this 2-year CarbonSAFE Phase II project, the HERO team will conduct a data acquisition campaign that includes drilling a stratigraphic well to a total depth of ~1,500 m into the thick layered basalts proximal to HPP. A comprehensive well logging and hydrologic testing program will be augmented with new core collected from flow zones and sealing units, and comprehensive laboratory testing to help refine the kinetic rates of mineralization. The newly acquired information will be integrated with existing data from regional wells to correlate basalt injection zone properties to develop storage hub/commercial-scale models. Using these models, the project team will evaluate injection scenarios to define the technical and economic potential for storing a minimum of 50 million metric tons of CO2 over a 30-year period, along with a robust sensitivity analysis on key parameters governing reservoir viability for sustainable injection over a commercial project lifetime. Specific technical objectives of HERO are: (1) assessing the reservoir response of a series of stacked layered reservoir flowtop sequences occurring in this area of the CRBG to commercial-scale injection volumes; (2) extending prior efforts by the project team to characterize the deep layered basalts encountered in regional studies, to leverage prior investments by U.S. Department of Energy’s (DOE) Carbon Storage program; (3) leveraging DOE’s mineralization characterization efforts to advance model parametrization for commercial scale injection of CO2 in basalts; (4) conducting risk assessments associated with scaling up to commercial storage hub injection goals, while validating DOE’s National Risk Assessment Partnership (NRAP) tools, to identify potential constraints that would prevent the CRBG from serving as a commercial-scale storage complex; (5) developing mitigation plans to address identified risks; (6) developing a commercial-scale injection and monitoring, verification and accounting (MVA) strategy; (7) utilizing computational models to define and minimize, if possible, the Area of Review (AoR) under Class VI regulations; and (8) developing a robust CO2 management strategy for CRBG that also considers a regional source/sink approach that is responsive to stakeholder needs and industrial demand. Specific institutional objectives are: (1) identifying and developing plans to mitigate the nontechnical challenges associated with the build-out of a commercial-scale storage complex within the CRBG with integrated CO2 sources; (2) implementing the community outreach plan; (3) conducting regulatory research, including a survey of issues related to pore space ownership, MVA and long-term assurance of mineralization-based storage, to support an eventual application for a UIC Class VI permit; (4) advancing the project’s plan for CO2 liability management; and (5) continuing to refine and update the project’s economic model. The final objective is the preparation of a comprehensive Site Characterization Plan that draws upon the technical and institutional feasibility assessments to prepare the project for future commercialization efforts.

58 GEOSCIENCES↗