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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗
DOE OSTI2025

LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages

The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the LLaMA 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's capacity to generate code, documentation, and unit tests, as well as its ability to translate existing code between commonly used programming languages. Our comprehensive analysis evaluates the compilation, runtime behavior, and correctness of the generated and translated code. Additionally, we assess the quality of automatically generated code, documentation, and unit tests. Here, our results indicate that while LLaMA 2-70B frequently generates syntactically correct and functional code for simpler numerical tasks, it encounters substantial difficulties with more complex, parallelized, or distributed computations, requiring considerable manual corrections. We identify key limitations and suggest areas for future improvements to better leverage AI-driven automation in scientific computing workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2023

Simulation, Challenge Testing & Validation of Occupancy Recognition & CO 2 Technologies (Final Project Report)

This final report covers the results of the development of testing methods of occupancy sensor systems connected to HVAC controls. This project focused on the development of test methods to evaluate the performance of HVAC-connected occupancy sensor systems, including occupancy presence, occupancy counting, and CO 2 sensor systems in commercial and residential buildings. This included evaluation of the reliability, ease of commissioning, and energy savings potential of these sensor systems. The results of this work help to standardize the methods used to evaluate performance, to enable the ability to compare sensor system performance following the same methods, to understand which sensor systems perform better or worse compared to others. The U.S. building stock’s energy usage can benefit substantially from having building system operations informed by reliable occupancy recognition and CO 2 measurements, however to date, standard methods have not been in place to ensure that sensor systems’ reported performance is uniformly evaluated. The results of this work support the development of a standard or guideline that outlines the developed methods of testing. This project also included the testing of both off-the-shelf and novel SENSOR team-developed low-cost occupancy sensor systems, using the developed methods of testing. The results of this testing helped to inform further development and improvement of new low-cost sensor systems that will benefit from being used in residential and commercial buildings throughout the country.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Performance Metrics to Evaluate Utility Resilience Investments

In 2019, Sandia National Laboratories (Sandia) contracted Synapse Energy Economics (Synapse) to research the integration of community and electric grid resilience investment planning as part of the Designing Resilient Communities (DRC): A Consequence-Based Approach for Grid Investment project. Synapse produced a series of reports to explore the challenges and opportunities in several key areas, including benefit-cost analysis (BCA), performance metrics, microgrids, and regulatory mechanisms. This report focuses on BCA. BCA is an approach that electric utilities, electric utility regulators, and communities can use to evaluate the costs and benefits of a wide range of grid resilience investments in a comprehensive and consistent way. While BCA is regularly applied to some types of grid investments, application of BCA to grid resilience investments is in the early stages of development. Though resilience is increasingly cited in connection with grid investment proposals and plans, the resilience- related costs and benefits of grid resilience investments are typically not fully identified, infrequently quantified, and almost never monetized. Without complete assessments of costs and benefits, regulators can be hesitant to approve some types of grid resilience investments. This report provides the first application of the framework developed in the 2020 National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources (NSPM for DERs) to grid resilience investments. We provide guidance on next steps for implementation to enable grid resilience investments to receive due consideration. We suggest developing BCA principles and standards for jurisdiction-specific BCA tests. We also recommend identifying the resilience impacts of the investments and quantification of these impacts by establishing utility performance metrics for resilience. Proactive integration of grid resilience investments into existing regulatory processes and practices can increase the capacity of jurisdictions to respond to and recover from the consequences of extreme events. 1 National Energy Screening Project. 2020. National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Evaluation of the Performance of the SAVY Container Subjected to Various Impact Events using Finite Element Methods

The SAVY container is a Safety Class Design Feature utilized both inside engineered features and outside engineered features (e.g. gloveboxes) at TA-55 Plutonium Facility Building 4. These containers were certified as low damage ratio containers by demonstrating their ability to retain material after exposure to impacts and elevated temperatures resulting from a design basis seismic event. Original testing of the SAVY did not evaluate the effects of various facility specific insults to the SAVY while within a DOT Type A Container, caused either by design basis accident or an operational mishap. These insults include: 1. Impact of a structural I-beam (drum upright & drum on side); 2. Impact of a section of Positive Pressure Chilled Water Pipe (PPCWS); 3. Forklift fork tine impact; 4. Fuel truck impact. Abaqus Explicit Dynamics Finite Element Analysis was performed on a nested SAVY/DOT Type 7A container configuration to assess the structural integrity of the SAVY under the accident conditions listed.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Evaluation of the Performance and Exhaust Emissions of a 4 Cylinder CI Engine Operating With Dimethyl Ether (DME) and Propane Blends

In response to stringent emissions regulations and the need for higher efficiency engines, the utilization of DME and propane fuel blends in compression ignition (CI) engines has gained interest in the automotive industry. In this study, a range of DME-propane blends are explored in a CI combustion strategy at high injection pressures. A GT-Power model of a 2.2 L Hyundai CI engine was developed to facilitate evaluation of the impacts of variations of DME and propane blends at a light and medium engine speed-torque-load operating condition; specifically at 1500 rpm, 50 Nm and 2.84 bar brake mean effective pressure (BMEP); and 2000 rpm, 150 Nm, 8.53 bar BMEP speed-torque-load combination. Here, the GT-Power model was validated using Computational Fluid Dynamics (CFD) simulations. The results indicate that high diesel-like efficiencies can be achieved with a 100% DME mass substitution and up to 50% propane-DME blends could be implemented without a significant penalty on engine performance indicators. Significant brake specific nitrogen oxides (BSNOx) reductions were also observed along with reductions in carbon dioxide (CO2) and soot when leveraging these fuel blends.

computational fluid dynamics↗
DOE OSTI2020

Comparative Approach for Evaluating the Containment Performance of Legacy NM Storage Containers

Many different types of nuclear material storage containers are used throughout TA-55 and specifically PF-4, in some instances these containers are used outside of engineering control barriers in most cases these barriers are gloveboxes. When a container is used outside of a glovebox or open front hood the container itself is relied upon as the primary engineering control barrier for the user. For this reason a process must be established in order to evaluate Non- Manual Compliant or NMC containers used outside of a glovebox in a comparative approach against Manual compliant containers. Legacy containers are typically understood to be non-standards packaged prior to 1998. For the purposes of this document “legacy” refers to any non-Manual compliant containers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Performance and Economic Evaluation of sCO2 Bottoming Cycles for Natural Gas Combined Cycle Plants with Capture

Natural gas combined cycles (NGCCs) with carbon capture are expected to play a significant role in decarbonization of the power generation sector. NGCC plants generally use triple pressure reheat steam Rankine power cycles for the bottoming cycle. Some studies in the literature have investigated the application of recompression and cascade style supercritical CO2 (sCO2) cycles for NGCC bottoming cycle applications but these studies have focused on power plants without carbon capture. However, NGCC plants fitted with post-combustion solvent-based CO2 capture systems will require a significant amount of steam for solvent regeneration and this can have a major impact on the optimal sCO2 bottoming cycle design. This study investigates the performance and economic potential of sCO2 bottoming cycles for H-class gas turbine based NGCC plants with a post-combustion capture system. A portion of the gas turbine exhaust heat is used for generation of steam required for solvent-based capture system while the rest of the waste heat is utilized in an sCO2 bottoming cycle for power generation. Overall, the performance and LCOE of investigated sCO2 bottoming cycles is similar to that of a state-of-the-art triple pressure reheat steam Rankine cycle. As the gas turbine exhaust temperature increases (beyond 630oC), sCO2 bottoming cycles begin to show greater performance and economic benefits compared to a steam Rankine cycle.

Pidaparti, Sandeep↗
DOE OSTI2023

A Monte-Carlo Method for Evaluating the Economic Performance of Plastics Recycling Systems Using Historical Pricing

The management of plastic waste is a considerable emerging global concern. Conventional plastics recycling rates range from 5–10% in the US, which results in considerable material being sent to landfill. New sorting technologies using artificial intelligence have the potential to dramatically increase the ability of waste management companies to sort out valuable fractions of plastic waste and create high-purity streams for secondary markets. However, several fractions of the plastics recycling stream, i.e., low-density polyethylene, polypropylene, and polystyrene, do not have well-developed markets. These materials may be sent to pyrolysis facilities for thermal recycling, but the economics of the pyrolysis industry are currently uncertain. This work aims to identify the breakeven price of the plastic fractions that would be sent to pyrolysis in order to determine the economic viability of the sorting facility. The work will use Monte Carlo analysis to evaluate several scenarios and understand how compositional variation, price variability, and facility attributes affect the breakeven price. Here, the presorting conditions in Materials Recovery Facilities strongly affects the profitability of the sorting facility. High residual high density polyethylene (HDPE) and polyethylene terephthalate (PET) content in presorted bales are important revenue drivers for the facility, even in presorted 3-7 bales. Key variables that lead to profitability include a high sale price of polyethylene terephthalate, a high sale price of high-density polyethylene, and if polypropylene can be sold to recycling markets (as opposed to pyrolysis facilities).

Plastics↗
DOE OSTI2024

Evaluating Heat Extraction Performance of Closed-Loop Geothermal Systems with Thermally Conductive Enhancements in Conduction-Only Reservoirs: Preprint

We investigated the impact of disc-shaped and linear thermal enhancements on performance of closed-loop geothermal systems using steady-state and transient COMSOL numerical simulations. Thermal enhancements refer to thermally conductive material introduced in the rocks surrounding a wellbore to compensate for the relatively low rock thermal conductivity and increase heat extraction. Materials proposed include composite cements with thermally conductive additives such as metals or graphite to obtain overall cement thermal conductivities of 10 to 100x the rock thermal conductivity. Our simulation approach was modeling a subsegment (e.g., 100 m long) of a wellbore with idealized (e.g., perfect disc-shaped) thermal enhancements, and we report results as relative increase in heat extraction with respect to systems without thermal enhancements. We did not evaluate technical feasibility or cost of installing thermal enhancements. Simulations indicate that for 5-mm thick disc-shaped thermal enhancements with 5-m radius, repeated every 1 m along the wellbore and with thermal conductivity of 100x the rock thermal conductivity, the thermal output increases roughly 20% with respect to a closed-loop system without thermal enhancements. For fishbone structure thermal enhancements, we estimate a roughly 7% increase in thermal output for 5-m long radially outward pipes with half the radius of the main wellbore, repeated every 2.5 m, with thermal conductivity of 100x the rock thermal conductivity.

advanced geothermal systems↗
DOE OSTI2020

Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model

The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗
DOE OSTI2024

Evaluating integration and performance of containerized climate applications on a Hewlett Packard Enterprise Cray system

Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs.

Abraham, Subil↗
DOE OSTI2024

Large language model evaluation for high–performance computing software development

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. Finally, we expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluation of electrochemical performance and redox activity of Fe in Ti doped layered P2-Na0.67Mn0.5Fe0.5O2 cathode for sodium ion batteries

Here, we report a wet synthesis-based titanium doping strategy to improve the structural stability and electrochemical performances, such as cycling stability and rate capability, of P2-type Na 0.67 Fe 0.5 Mn 0.5 O 2 layered oxide cathodes. Through Ti 4+ doping aimed at replacing some of the Mn and Fe atoms in the crystal structure, effective mitigation of the Jahn Teller distortion caused by active Mn 3+ before charging and Fe 4+ after charging was achieved. X-ray diffraction (XRD), Raman spectroscopy, Electrochemical Impedance Spectroscopy (EIS) and Mössbauer spectroscopy were used to investigate the effects of the Ti 4+ dopant before and after cycling. It was observed that Ti 4+ doping increased the Na layer thickness, minimized the lattice volume strain, showed better structural stability, minimally decreased Fe migration to the Na layer, and lowered charge transfer resistance in these P2-type cathodes. Overall, our reported synthesis methodology and electrochemical characterizations highlight the feasibility of Ti doping in sodium layered oxide P2-type cathodes.

25 ENERGY STORAGE↗