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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 OSTI2022

Task 5 Field Scale Stress Modeling Scripts and Data

Project data and Matlab scripts for Task 5 of the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO2 Storage”. Project includes the static earth models, grids and other data used for simulation and calibration of the two case studies addressed in the project, referred to herein as the “FutureGen2” model (Illinois) and the “Perch” model (Otsego county, Michigan). Large Dataset, please contact EDXSupport@netl.doe.gov

FutureGen2↗
DOE OSTI2015

A Pilot Evaluation of a 4-Dimensional Cone-Beam Computed Tomographic Scheme Based on Simultaneous Motion Estimation and Image Reconstruction

Purpose: To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies. Methods and Materials: The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm. Results: The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients. Conclusion: The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

BlueCRAB Multiphysics in Support of the U.S.NRC

This presentation provides an overview of the nonLWR modeling and simulation project at INL. It includes a discussion of the various reference plant models developed since 2019 and a more detailed view at results from a monolith-based heat pipe cooled micro reactor.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

CMIP6-based Multi-model Streamflow Projections over the Conterminous US

This dataset presents an ensemble of streamflow projections based on the hydroclimate projections dataset supporting the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). The six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6) downscaled using statistical (i.e., DBCCA) and dynamical (i.e., RegCM) and bias-corrected using two meteorological reference observations (Daymet & Livneh) are driven through two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. This leads to the production of an ensemble of hydroclimate projections, including total runoff (surface runoff and baseflow) for 1980–2019 baseline and 2020–2059 near-term future periods under multiple emission scenarios at 1/24° (~4 km) spatial resolution across the CONUS. The total runoff projections are routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model to produce an ensemble of streamflow projections for both periods across 2.7 million NHDPlusV2 stream reaches in the CONUS.

13 HYDRO ENERGY↗
DOE OSTI2022

How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase in the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Simulation of CO 2 Storage

This report is a compilation of Lawrence Livermore National Laboratory’s (LLNL) accomplishments on CO 2 storage simulation and modeling research, performed for the US-­China Clean Energy Research Center (CERC). Within the CERC project management structure, this work is referred to as Subtask 6.4.a Simulation and Modeling. The task falls under CERC’s Advanced Coal Technology Consortium (ACTC) Research Theme 6—CO 2 Sequestration Capacity and Near-­Term Opportunities. The goals of the task were to develop new CO 2 sequestration simulation approaches and tools, then apply them to CO 2 storage projects in the U.S. and China. Work on this task paused when funding was redirected to CERC’s other efforts. Two sections of this report provide valuable snapshot of LLNL’s progress when funding was curtailed: 1) Section 5.2.2 is a 14-­page presentation written January 8, 2013; and 2) Section 5.1.3 is a progress report from the first quarter of Fiscal year 2013.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Exact sum rules with approximate ground states

Electromagnetic and weak transitions tell us a great deal about the structure of atomic nuclei. Yet modeling transitions can be difficult: it is often easier to compute the ground state, if only as an approximation, than excited states. One alternative is through transition sum rules, in particular the non-energy-weighted and energy-weighted sum rules, which can be computed as expectation values of operators. We investigate by computing sum rules for a variety of nuclei, comparing the numerically exact full configuration-interaction shell model, as a reference, to Hartree-Fock, projected Hartree-Fock, and the nucleon pair approximation. Furthermore, these approximations yield reasonable agreement, which we explain by prior work on the systematics of transition moments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US

We present a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on a six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two different downscaling approaches (statistical-based DBCCA & dynamical-based RegCM) based on two meteorological reference observations (Daymet & Livneh), and then fed to two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. Each ensemble member covers 1980–2019 in the historic period and 2020–2059 in the near-term future period under the high-end (SSP585) emission scenario. Major variables such as daily maximum temperature (tmax), daily minimum temperature (tmin), daily total precipitation (prcp), daily average wind speed (wind), and daily total runoff (runoff) at 1/24° (~4 km) spatial resolution across the CONUS are provided through this data portal. This dataset is derived to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Further details of this dataset can be referred to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2025

CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implemented as mosartwmpy-power, and WRES) to evaluate how climate change may affect future hydropower production for both federal and non-federal hydropower fleets. This dataset was developed to support the SECURE Water Act Section 9505 Assessment for the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, see Broman et al. (2024), Thurber et al. (2024), Kao et al. (2022), and Zhou et al. (2023).

Voisin, Nathalie [Pacific Northwest National Labor↗
DOE OSTI2019

Optimal Operator Training Reference Models for Human-in-the-loop Systems

The human operator is an integral part of a stable and safe power system. While there is increasing attention paid to automation improvements, the importance of understanding and training human operators may be understated. This paper discusses a project to enhance operator training programs by evaluating human performance relative to a reference operator model identified using optimal control theory. Along with establishing a simple computer-based operator workstation for future training purpose, this paper describes the optimal control response design methodology for a human-in-the-loop power system experiment. The overall system model is presented. An optimal controller synthesis methodology is applied to the model system and the optimal controller is designed. The performance of the optimal controller is then compared to human subject performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Testing NeuralGCM's capability to simulate future heatwaves based on the 2021 Pacific Northwest heatwave event

AI-based weather and climate models are emerging as accurate and computationally efficient tools. Beyond weather forecasting, they also show promise to accelerate storyline analyses. We evaluate NeuralGCM’s ability to simulate an extreme heatwave against the Energy Exascale Earth System Model (E3SM), a physics-based climate model. NeuralGCM accurately replicates the targeted event, and generates stable and realistic mid-century projections. However, due to the absence of land feedbacks, NeuralGCM underestimates the projected warming amplitude compared to physics-based model references.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

IEA Wind TCP Task 37: Systems Engineering in Wind Energy - WP2.1 Reference Wind Turbines

This report describes two wind turbine models developed within the second work package (WP2) of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The wind turbine models aim at acting as references for future research projects on wind energy, representing a modern land-based wind turbine and a next generation offshore wind turbine. The land-based design is a class IIIA geared configuration with a rated electrical power of 3.4-megawatts, a rotor diameter of 130 meters (m), and a hub height of 110 m. The offshore design is a class IA configuration with a rated electrical power of 10.0 megawatts, a rotor diameter of 198 m, and a hub height of 119 m. The offshore turbine adopts a direct-drive generator.

17 WIND ENERGY↗
DOE OSTI2019

TRANSIENT ANALYSES OF THE VERSATILE TEST REACTOR USING RELAP5-3D

The DOE Office of Nuclear Energy established the Versatile Test Reactor (VTR) program in February 2017 in response to reports outlining the need for a fast spectrum test reactor. In this framework, Idaho National Laboratory (INL), as the proposed hosting site for the VTR, is expanding its capabilities leveraging on its staff nuclear expertise on experiments design and reactors operation, codes development and assessment, advanced safety analyses. One of the key points of this strategy is to develop capabilities necessary to perform transient analyses in order to understand the plant behavior during upset conditions and appropriately quantify margins. In this context system thermal-hydraulic models of the VTR are being developed using the SAS4A/SASSYS-1 code developed at Argonne National Laboratory as well as the INL code RELAP5-3D. The former provides reference analyses whereas the latter could provide an independent assessment of the reference analyses if deemed necessary by the project. This paper focuses on the RELAP5-3D modeling, highlighting the different solutions adopted for correctly analyzing sodium fast reactor behavior. After showing the modeling activities, we present a set of reference transient analyses for protected and unprotected transients, highlighting the intrinsic safety characteristics of the VTR design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Divergence in land surface modeling: linking spread to structure

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

58 GEOSCIENCES↗
DOE OSTI2024

CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US, Version 1.1

This dataset presents a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling approaches based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed are employed to drive two calibrated hydrologic models (VIC and PRMS), enabling the simulation of projected future hydrologic responses across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Moreover, utilizing only DBCCA and Daymet, the projections are further extended to the 2060-2099 far-future period and across three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2016

The Radiative Forcing Model Intercomparison Project (RFMIP): Experimental protocol for CMIP6

The phrasing of the first of three questions motivating CMIP6 – “How does the Earth system respond to forcing?” – suggests that forcing is always well-known, yet the radiative forcing to which this question refers has historically been uncertain in coordinated experiments even as understanding of how best to infer radiative forcing has evolved. The Radiative Forcing Model Intercomparison Project (RFMIP) endorsed by CMIP6 seeks to provide a foundation for answering the question through three related activities: (i) accurate characterization of the effective radiative forcing relative to a near-preindustrial baseline and careful diagnosis of the components of this forcing; (ii) assessment of the absolute accuracy of clear-sky radiative transfer parameterizations against reference models on the global scales relevant for climate modeling; and (iii) identification of robust model responses to tightly specified aerosol radiative forcing from 1850 to present. Complete characterization of effective radiative forcing can be accomplished with 180 years (Tier 1) of atmosphere-only simulation using a sea-surface temperature and sea ice concentration climatology derived from the host model's preindustrial control simulation. Assessment of parameterization error requires trivial amounts of computation but the development of small amounts of infrastructure: new, spectrally detailed diagnostic output requested as two snapshots at present-day and preindustrial conditions, and results from the model's radiation code applied to specified atmospheric conditions. In conclusion, the search for robust responses to aerosol changes relies on the CMIP6 specification of anthropogenic aerosol properties; models using this specification can contribute to RFMIP with no additional simulation, while those using a full aerosol model are requested to perform at least one and up to four 165-year coupled ocean–atmosphere simulations at Tier 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Digital Architecture Planning Model

As part of the U.S. Department of Energy’s Light Water Reactor Sustainability Program, the Digital Architecture (DA) Project focuses on providing a model that nuclear utilities can refer to when planning deployment of advanced technologies. The digital architecture planning model (DAPM) is the methodology for mapping power plant operational and support activities into a DA that unifies all data sources needed by the utilities to operate their plants. The DA is defined as a collection of information technology capabilities needed to support and integrate a wide spectrum of real-time digital capabilities for performance improvements of nuclear power plants. DA can be thought of as integration of the separate instrumentation and control and information systems already in place in nuclear power plants, which are brought together for the purpose of creating new levels of automation in plant work activities. A major objective in DAPM development was to survey all key areas that needed to be reviewed in order for a utility to make knowledgeable decisions regarding needs and plans to implement a DA at the plant. The development was done in two steps. First, researchers surveyed the nuclear industry in order to learn their near-term plans for adopting new advanced capabilities and implementing a network (i.e., wireless and wire) infrastructure throughout the plant, including the power block. Secondly, a literature review covering regulatory documents, industry standards, and technical research reports and articles was conducted. The objective of the review was to identify key areas to be covered by the DAPM, which included the following: 1. The need for a DA and its benefits to the plant 2. Resources required to implement the DA 3. Challenges that need to be addressed and resolved to implement the DA 4. Roles and responsibilities of the DA implementation plan. The DAPM was developed based on results from the survey and the literature review. Model development, including the survey results and conclusions made about the key areas during the literature review, are described in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Near-Term Reliability and Resilience (NTRR) (Final Report)

The Near-Term Reliability and Resiliency (NTRR) was awarded in December 2020 as an inter-lab project to examine the reliability and resilience of the electricity grid and natural gas transportation availability. The project builds on studies conducted by The North American Electric Reliability Corporation (NERC), the U.S. Department of Energy (DOE), and other non-governmental research and operational focused on reliability and resilience analyses challenges. The research was conceived to address near-term scenarios (within 10 years), when many local and regional policy transitions could begin to impact grid reliability, resilience, and supporting infrastructure availability. To integrate the natural gas interdependency, the team began with the generating capacity and demand projections from the 2020 NERC Long-Term Reliability Assessment and the Bulk Electric System (BES) transmission topologies defined in the Western Electricity Coordinating Council (WECC) Anchor Data Set, Eastern Interconnection Reliability Assessment Group Multi-Regional Modeling Working Group (ERAG/MMWG) Data Set, the team calculated baseline regional power sector gas demands from present electricity delivery year through the end of delivery year 2030/31 by applying security constrained economic dispatch. This demand was compiled along with demand projections for regional residential, commercial, and industrial natural gas demands from the most recent Energy Information Administration (EIA) Annual Energy Outlook Reference Case into Deloitte’s MarketBuilder® North American Gas Model. Through the application of these demands, MarketBuilder® was projected the topology of natural gas flows in the natural gas pipeline network across the interconnected North American system along with regional natural gas prices that may be seen by market participants in future years Additionally, contingencies and sensitivities focused on the built models of the Eastern Interconnection (EI) and Western Interconnection (WI). They address challenges from the following with the outcomes being an identification of performance under the extreme conditions and an identification of potential grid weaknesses that should be addressed to mitigate the reduced performance and improve the resilience and reliability of the specific regions as well as the National Grid: • Weather events including extreme heat, extreme cold, high wind, no wind, wind and solar forecasting errors, and wildfires. • Gas availability, factoring in supply disruption (contractual and physical), seasonal availability constraints, and infrastructure limitations; and • Transmission availability and congestion.

03 NATURAL GAS↗