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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Research Highlight - Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage

We highlight our work from our previous publication titled "Dispatch Optimization, System Design, and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage" published in MDPI Energies 2022 (doi.org/10.3390/en15103599). Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modeling hydraulic fracture opening and closure with proppant transport and settlement

Hydraulic fracturing is a widely used reservoir stimulation technique for improving fluid circulation in rock formations with extremely low permeability, particularly in enhanced geothermal systems (EGS). To better understand the complex processes involved and improve hydraulic stimulation performance, we have developed ELK (ELectrical fracKing), a MOOSE-based 3D finite element application designed to model the behavior of proppant-fluid mixtures in propagating fractures. ELK integrates both the fluid and proppant components, incorporating particle-driven processes such as gravity settling, particle-particle interactions, and strong density and viscosity contracts, in addition to conventional fluid-driven fracture propagation. In this contribution, we extend ELK to model propped fracture closure, which occurs after the injection phase due to a dramatic drop in the effective stress on the fracture plane. During the shut-in, flowback, and production periods, the fracture width decreases, with the closure behavior depending on proppant concentration.. At low concentrations, closure follows a nonlinear joint law linked to the stiffness of asperities in the fracture walls. While at high concentrations, it is controlled by the properties of packed proppant bed. The extended ELK application is validated against several benchmark examples, including the propagation of an inclined frictional crack, fracture opening and sliding in response to fluid injection, and flowback analysis. We believe that ELK’s enhanced capabilities can serve as a valuable tool for the design and optimization of EGS deployment.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2023

Green Computing Opportunities & Strategy

Computation is critical to emerging fields of data intensive research, enabling new methodologies, approaches and tools. As the rate of hardware efficiency gains slows, computational time and energy costs increase. To match pace with computation demand, new approaches are needed to keep the opportunity for impact open. Charles Tripp, lead of the Green Computing Catalyzer, discusses research efforts to improve software efficiency to enable faster, less energy-intensive computing.

algorithmic efficiency↗
DOE OSTI2023

Adaptive basis sets for practical quantum computing

Electronic structure calculations on small systems such as H 2 , H 2 O, LiH, and BeH 2 with chemical accuracy are still a challenge for the current generation of noisy intermediate-scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H 2 molecule, in which exponents and contraction coefficients depend on the H—H distance, and apply it to the generation of H 2 potential energy surface on IBM-Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double-zeta basis sets, thus allowing one to perform double-zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Prototype Integrated Hydrogen Fuel Cell Powered Data Center (Cooperative Research and Development Final Report)

Data centers are the backbone of the modern economy-from the server rooms that power small- to medium-sized organizations, to the enterprise-class data centers that support American corporations, and the server farms that run cloud computing services. Rather than a traditional grid-centric data center approach, work under the CRADA will be directed towards implementing a proof-of-concept "hydrogen-based" data center utilizing hydrogen fuel cells, electrolyzers, and solar photovoltaics as key building blocks. This integrated proof-of-concept builds on preliminary work done by HPE and its partners Mercedes-Benz Research & Development North America, Inc. (MBRDNA) and Power Innovations, to dramatically simplify the electrical infrastructure in the data center, leverage the latest automotive-scale hydrogen fuel cell technologies, and tightly couple renewably generated DC power directly to the Information Technology (IT). While some limited aspects of this concept have been demonstrated previously the entire integrated proof of concept proposed here has not yet been realized.

08 HYDROGEN↗
NASA NTRS1974

Digital computer simulation of inductor-energy-storage dc-to-dc converters with closed-loop regulators

The simulation of converter-controller combinations by means of a flexible digital computer program which produces output to a graphic display is discussed. The procedure is an alternative to mathematical analysis of converter systems. The types of computer programming involved in the simulation are described. Schematic diagrams, state equations, and output equations are displayed for four basic forms of inductor-energy-storage dc to dc converters. Mathematical models are developed to show the relationship of the parameters.

Ohri, A. K.↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2024

Interdisciplinary Approaches to Cyber-vulnerability Impact Assessment for Energy Critical Infrastructure

As energy infrastructure becomes more interconnected, understanding cybersecurity risks to production systems requires integrating operational and computer security knowledge. We interviewed 18 experts working in the field of energy critical infrastructure to compare what information they find necessary to assess the impact of computer vulnerabilities on energy operational technology. These experts came from two groups: 1) computer security experts and 2) energy sector operations experts. We find that both groups responded similarly for general categories of information and displayed knowledge about both domains, perhaps due to their interdisciplinary work at the same organization. Yet, we found notable differences in the details of their responses and in their stated perceptions of each group’s approaches to impact assessment. Their suggestions for collaboration across domains highlighted how these two groups can work together to help each other secure the energy grid. Our findings inform the development of interdisciplinary security approaches in critical-infrastructure contexts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Developing a Hybrid Electric Vehicle Eco-Cooperative Adaptive Cruise Control System at Signalized Intersections.

This study develops an eco-driving strategy for hybrid electric vehicles (HEVs) in the vicinity of signalized intersections, entitled HEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The proposed system computes real-time, energy-optimized vehicle trajectories using HEV vehicle dynamics and energy consumption models. In the proposed system, a simple HEV energy model is used to compute the instantaneous fuel consumption. This HEV energy model is selected since it is general, transferable, and can be easily used to compute instantaneous energy consumption levels for HEVs without the additional input of vehicle engine data or complicated power control strategies. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The energy-optimum problem is formulated as an optimization problem with constraints, which is solved using a moving-horizon dynamic programming approach. The proposed HEV Eco-CACC-I system was tested to evaluate its performance for various speed limits, roadway grades, and signal timings. Lastly, the proposed HEV controller was implemented in a microscopic traffic simulation software to test its network-wide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed system can effectively reduce stop-and-go traffic in the vicinity of signalized intersections producing savings of 7.4% in energy consumption, 5.8% in traffic delay and 23% vehicle stops, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

CIO Cloud Strategy

The purpose of the Sandia CIO Cloud Strategy is to establish the strategic direction for the adoption of cloud services and technologies as the prevailing IT solution for Sandia National Laboratories. Sandia’s Chief Information Officer (CIO) will champion unified, site-wide adoption of cloud and will amplify business and mission impacts across the Labs. Sandia’s CIO Cloud Strategy aligns to the Federal Cloud Computing Strategy1 (Cloud Smart) and the Sandia Management and Operating Contract (Prime Contract).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

2022 FORCE Development Status Update

Due to the rapidly changing nature of the energy landscape in both the U.S. and abroad, including the increase in non-dispatchable variable renewable energy (VRE) deployment and the abundance of carbonbased dispatchable fuels, the Integrated Energy Systems (IES) program under the Department of Energy (DOE) has been engaged in determining whether IES could assist in providing nuclear power plants (NPPs) with a viable strategy for achieving future economic competitiveness. This includes enhanced dispatch flexibility for NPPs, thanks to thermal and electrical storage technology as well as the generation of ancillary commodities such as hydrogen, desalinated water, and synthetic fuels from the heat generated by NPPs. The technical and economic viability of these systems is of primary concern to the IES program, with the intent of providing insight to the wider nuclear energy community at large.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Enhancing high-fidelity neural network potentials through low-fidelity sampling

The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗