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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Exploration of CIGAS Alloy System for Thin-Film Photovoltaics on Novel Lightweight and Flexible Substrates

Thin-film photovoltaics (TFPV) on lightweight and flexible substrates offer the potential for very high solar array specific power (W/kg). ITN Energy Systems, Inc. (ITN) is developing flexible TFPV blanket technology that has potential for specific power greater than 2000 W/kg (including space coatings) that could result in solar array specific power between 150 and 500 W/kg, depending on array size, when mated with mechanical support structures specifically designed to take advantage of the lightweight and flexible substrates.(1) This level of specific power would far exceed the current state of the art for spacecraft PV power generation, and meet the needs for future spacecraft missions.(2) Furthermore the high specific power would also enable unmanned aircraft applications and balloon or high-altitude airship (HAA) applications, in addition to modular and quick deploying tents for surface assets or lunar base power, as a result of the high power density (W/sq m) and ability to be integrated into the balloon, HAA or tent fabric. ITN plans to achieve the high specific power by developing single-junction and two-terminal monolithic tandem-junction PV cells using thin-films of high-efficiency and radiation resistant CuInSe2 (CIS) partnered with bandgap-tunable CIS-alloys with Ga (CIGS) or Al (CIAS) on novel lightweight and flexible substrates. Of the various thin-film technologies, single-junction and radiation resistant CIS and associated alloys with gallium, aluminum and sulfur have achieved the highest levels of TFPV device performance, with the best efficiency reaching 19.5% under AM1.5 illumination conditions and on thick glass substrates.(3) Thus, it is anticipated that single- and tandem-junction devices with flexible substrates and based on CIS and related alloys will achieve the highest levels of thin-film space and HAA solar array performance.

Woods, Lawrence M.↗
DOE OSTI2024

Co-optimization of nuclear reactor flexible power operation and maintenance scheduling

As flexible power operation of nuclear power plants becomes more attractive due to the reduction in fossil-fueled dispatchable generation on energy grids, finding optimal power production strategies that balance revenue generation with operational concerns becomes more complex. This article presents a general framework to aid operators in designing economically optimal long term dispatch strategies for nuclear power plants. The principal novelty is the linking of estimated system remaining useable life (RUL) to strategic operational decisions. It is shown that, depending on the relationship between the fixed costs from maintenance and the associated lost revenue from an outage, it can be economically optimal in the long term to delay a maintenance outage and not perform this alongside refueling. For a given relationship between power ramping and degradation, optimal strategies were found that discouraged load following in some situations while minimizing unnecessary maintenance. It is shown that heavy load following can cause maintenance and refueling outages to diverge due to their inverse relationships with respect to load following, potentially leading to a significant loss in capacity factor. As a result, this general framework can be applied to specific reactor dispatch allowing operators to adapt operational strategies as future grid conditions change.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

deep learning↗
DOE OSTI2025

Structures of respiratory syncytial virus G bound to broadly reactive antibodies provide insights into vaccine design

Abstract Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract disease in infants and older adults. The attachment glycoprotein (RSV G) binds to the chemokine receptor CX3CR1 to promote viral entry and modulate host immunity. Antibodies against RSV G are a known correlate of protection. Previously, several broadly reactive, high-affinity anti-RSV G human monoclonal antibodies were isolated from RSV-exposed individuals and were shown to be protective in vitro and in vivo. Here, we determined the structures of three of these antibodies in complex with RSV G and defined distinct conformational epitopes comprised of highly conserved RSV G residues. Binding competition and structural studies demonstrated that this highly conserved region displays two non-overlapping antigenic sites. Analyses of anti-RSV G antibody sequences reveal that antigenic site flexibility may promote the elicitation of diverse antibody germlines. Together, these findings provide a foundation for next-generation RSV prophylactics, and they expand concepts in vaccine design for the elicitation of germline lineage-diverse, broadly reactive, high-affinity antibodies.

Science & Technology - Other Topics↗
DOE OSTI2023

Progress with applications of three-ion ICRF scenarios for fusion research: A review

The viability of magnetic confinement fusion as an energy source depends on achieving the high ion temperatures required for D-T fusion. Among the available techniques, plasma heating with waves in the ion cyclotron range of frequencies (ICRF) is a prominent method for bulk ion heating in fusion plasmas. Furthermore, a detailed understanding of the non-linear physics of alpha heating and the complex impact of MeV-range fast ions on plasma dynamics becomes progressively more important. This paper provides a comprehensive overview of recent developments with the three-ion ICRF scenarios on Alcator C-Mod, ASDEX Upgrade and JET tokamaks. The results demonstrate the flexibility of these novel scenarios for heating bulk ions in D-T ≈ 50%-50% plasmas and efficient generation of MeV-range fast ions in multi-ion species plasmas. In conclusion, several key results relevant for ITER and future fusion reactors are highlighted.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Rethinking the Price Formation Problem–Part 2: Rewarding Flexibility and Managing Price Risk

In this study, part 1 of this two-part paper describes the impact that uncertainty has on the design and analysis of price formation policies in the non-convex auctions conducted by U.S. wholesale electricity market operators. Using first a toy model and then a large-scale test system, Part 2 demonstrates the difference in prices under the idealized benchmark of ex ante convex hull pricing defined in Part 1 versus existing methods, in particular documenting the potential for suppression of volatility and therefore under-compensation of flexibility by existing methods. The examples highlight that inefficient spot price formation can induce inefficient forward commitments of generators, necessitating out-of-market intervention to restore a reliable and efficient operating plan.Given the potential side effects of existing policies for investment and operation, we suggest two elements in a reoriented approach to the price formation problem: first ensuring that prices exhibit full-strength volatility, and second ensuring that risk-averse market participants have sufficient ability to manage this volatility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A-LEAF

A-LEAF is an electricity market simulation framework. It includes models for short-term market operations and long-term investment planning of generation, storage, and transmission. Model formulations include least cost and two-stage market equilibrium. Simulations can be flexibly structured over one or more years, including options for periodic samples or jumping to a single future year. Years can be simulated as 365 consecutive days at hourly or 5-minute sample rates, or they can be subsampled using a representative day group selection algorithm.

MANN, WILLIAM↗
DOE OSTI2020

Cost and Performance Requirements for Flexible Advanced Nuclear Plants in Future U.S. Power Markets

Advanced reactor developers are at various stages of commercializing new products and must design for future market environments that will exist when their plants are available. It is therefore critical to have a clear understanding about what plants will need to cost to be attractive investments, and what performance characteristics will create the most value for plant owners. This study is among the first to model the substantial contribution that flexible advanced reactors can make towards reliable, responsive, affordable, and clean future energy systems by supplying clean dispatchable generating capacity. Advanced reactor technologies could make a major contribution to lowering the overall system cost while reducing emissions and improving the performance of future energy systems. Depending on specific market conditions, it may also be beneficial to co-locate thermal energy storage systems (ESS).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

42 ENGINEERING↗
NASA NTRS1972

Dynamics and control of escape and rescue from a tumbling spacecraft

The results of 18 months of investigations are reported. A movable mass control system to convert the tumbling motion of a spacecraft into simple spin was studied along with the optimization techniques for generating displacement profiles for a tumbling asymmetrical body. Equations of motion are discussed for two asymmetrical vehicles with flexible beams and one spacecraft with flexible solar arrays. The characteristics which allow reasonable safety and reliability in bailout are also discussed.

Kaplan, M. H.↗
NASA NTRS1978

JSC space base/power module studies

Users of the Orbiter/Spacelab combination will require both higher electrical power and longer duration than is available with the current baseline system. Present Orbiter/Spacelab mission capability is primarily constrained by the hydrogen and oxygen available to generate power in the Orbiter fuel cells. It is also necessary to assure that considerable attitude or point flexibility is retained to assure efficient operation of the Orbiter radiator cooling system. Beyond these early limitations, it is foreseen that orbital operations will eventually need even greater quantities of the basic space utilities: electrical power; heat rejection; and attitude control. Such operations, forecasted for the mid to late 1980's, will be best accommodated by a module stored in orbit that can furnish these to a docked Orbiter/Spacelab or other vehicles. The Orbital Service Module concept to provide for these services is presented.

Craig, J. W.↗
NASA NTRS1979

A simple apparatus for the experimental study of non-steady flow thrust-augmenter ejector configurations

A flexible, and easily modified, test rig is described which allows a one dimensional nonsteady flow stream to be generated, economically from a steady flow source of compressed air. This nonsteady flow is used as the primary stream in a nonsteady flow ejector constituting part of the test equipment. Standard piezo-electric pressure transducers etc. allow local pressures to be studied, as functions of time, in both the primary and secondary (mixed) flow portions of the apparatus. Provision is also made for measuring the primary and secondary mass flows and the thrust generated. Sample results obtained with the equipment are presented.

Khare, J. M.↗
NASA NTRS1983

Space Telescope pointing control

The Space Telescope Pointing Control System is used to slew the optical axis of the telescope from one target star region of the celestial sphere to the next, and to maintain precision pointing on the target star. A digital computer is employed in the processing of attitude and rate sensor data, in order to generate torque commands for the reaction wheels. The System comprises four major elements: a command generator, the electronic control system, attitude update processing, and momentum management. Emphasis is given to control-related flexibility effects of the Space Telescope's solar cell array and vehicle, and the acquisition methodology and development testing employed, in addition to Pointing Control System design features.

Dougherty, H.↗
NASA NTRS1984

High-efficiency Conventional Cells Workshop

Participants expressed more interest in GaAs cells than in Si cells. For silicon cells, the beginning of life efficiency is not a major problem but more research is needed in the end of life efficiency. The beginning of life efficiency of GaAs cells must be brought up to 20% at AMO. More proton damage tests must be conducted. Liquid phase epitaxy technology is current but chemical vapor deposition technology is more flexible. There are no obvious problems in limiting process yields. Technology transfer should occur when market demands are generated.

Fan, J. C. C.↗
NASA NTRS2000

Emergent Aerospace Designs Using Negotiating Autonomous Agents

This paper presents a distributed design methodology where designs emerge as a result of the negotiations between different stake holders in the process, such as cost, performance, reliability, etc. The proposed methodology uses autonomous agents to represent design decision makers. Each agent influences specific design parameters in order to maximize their utility. Since the design parameters depend on the aggregate demand of all the agents in the system, design agents need to negotiate with others in the market economy in order to reach an acceptable utility value. This paper addresses several interesting research issues related to distributed design architectures. First, we present a flexible framework which facilitates decomposition of the design problem. Second, we present overview of a market mechanism for generating acceptable design configurations. Finally, we integrate learning mechanisms in the design process to reduce the computational overhead.

Deshmukh, Abhijit↗