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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Microstructural Engineering and Accelerated Test Method Development to Achieve Low Cost, High Performance Solutions for Hydrogen Storage and Delivery

This project made advancements in developing lower cost steel alloys with novel microstructural design for use in hydrogen refueling infrastructure such as storage, compressors, and dispensing components, and utilizing accelerated test methods to efficiently evaluate variations in alloy and microstructure design. The project specifically sought to design alloys with lower nickel contents to reduce alloy cost, which was accomplished through substituting manganese for nickel along with other alloy additions to control deformation characteristics known to be important for hydrogen embrittlement resistance. Through Mn substitutions for nickel, austenitic and duplex austenite-ferrite steels were successfully developed with lower cost than currently available commercial stainless steel products that are employed for hydrogen refueling infrastructure. The steels were processed to achieve comparable strength and toughness in hydrogen environments as the commercially available steels containing high Ni contents. To evaluate mechanical performance in hydrogen, a testing methodology was employed to compare ubiquitous laboratory testing using electrochemical hydrogen charging in a liquid electrolyte to less accessible high pressure gaseous testing. While the application of these steels is in high pressure gaseous environments, the electrochemical hydrogen charging tests produced comparable results. Additionally, the Los Alamos Neutron Scatting Center enabled characterization of deformation mechanisms of the steel alloys in the presence of hydrogen, which has been associated with steel alloy characteristics associated with hydrogen embrittlement. Finally, a fracture mechanics based test bed model was developed to predict the influence of hydrogen gas pressure and fatigue conditions on fatigue lifetimes of pressure vessel steels. Together, these developments can be employed to enable lower cost hydrogen fueling infrastructure and more reliable prediction of steel alloy components in hydrogen service conditions. In particular, the newly alloys have the potential to replace stainless steels, having demonstrated comparable performance at substantially reduced cost.

08 HYDROGEN↗
DOE OSTI2012

Improved charge breeding efficiency of light ions with an electron cyclotron resonance ion source

The Californium Rare Isotope Breeder Upgrade is a new radioactive beam facility for the Argonne Tandem Linac Accelerator System (ATLAS). The facility utilizes a {sup 252}Cf fission source coupled with an electron cyclotron resonance ion source to provide radioactive beam species for the ATLAS experimental program. The californium fission fragment distribution provides nuclei in the mid-mass range which are difficult to extract from production targets using the isotope separation on line technique and are not well populated by low-energy fission of uranium. To date the charge breeding program has focused on optimizing these mid-mass beams, achieving high charge breeding efficiencies of both gaseous and solid species including 14.7% for the radioactive species {sup 143}Ba{sup 27+}. In an effort to better understand the charge breeding mechanism, we have recently focused on the low-mass species sodium and potassium which up to present have been difficult to charge breed efficiently. Unprecedented charge breeding efficiencies of 10.1% for {sup 23}Na{sup 7+} and 17.9% for {sup 39}K{sup 10+} were obtained injecting stable Na{sup +} and K{sup +} beams from a surface ionization source.

43 PARTICLE ACCELERATORS↗
NASA NTRS1993

Multiwavelength observations of unidentified high energy gamma ray sources

As was the case for COS B, the majority of high-energy (greater than 100 MeV) gamma-ray sources detected by the EGRET instrument on GRO are not immediately identifiable with cataloged objects at other wavelengths. These persistent gamma-ray sources are, next to the gamma-ray bursts, the least understood objects in the universe. Even a rudimentary understanding of their nature awaits identifications and follow-up work at other wavelengths to tell us what they are. The as yet unidentified sources are potentially the most interesting, since they may represent unrecognized new classes of astronomical objects, such as radio-quiet pulsars or new types of active galactic nuclei (AGN's). This two-year investigation is intended to support the analysis, correlation, and theoretical interpretation of data that we are obtaining at x ray, optical, and radio wavelengths in order to render the gamma-ray data interpretable. According to plan, in the first year concentration was on the identification and study of Geminga. The second year will be devoted to studies of similar unidentified gamma-ray sources which will become available in the first EGRET catalogs. The results obtained so far are presented in the two papers which are reproduced in the Appendix. In these papers, we discuss the pulse profiles of Geminga, the geometry and efficiency of the magnetospheric accelerator, the distance to Geminga, the implications for theories of polar cap heating, the effect of the magnetic field on the surface emission and environment of the neutron star, and possible interpretations of a radio-quiet Geminga. The implications of the other gamma-ray pulsars which were discovered to have high gamma-ray efficiency are also discussed, and the remaining unidentified COS B sources are attributed to a population of efficient gamma-ray sources, some of which may be radio quiet.

Halpern, Jules P.↗
NASA NTRS1995

Ion propulsion

An ion engine is a plasma thruster which produces thrust by extracting ions from the plasma and accelerating them to high velocity with an electrostatic field. The ions are then neutralized and leave the engine as high velocity neutral particles. The advantages of ion engines are high specific impulse and efficiency and their ability to operate over a wide range of input powers. In comparison with other electric thrusters, the ion engine has higher efficiency and specific impulse than thermal electric devices such as the arcjet, microwave, radiofrequency and laser heated thrusters and can operate at much lower current levels than the MPD thruster. However, the thrust level for an ion engine may be lower than a thermal electric thruster of the same operating power, consistent with its higher specific impulse, and therefore ion engines are best suited for missions which can tolerate longer duration propulsive phases. The critical issue for the ion engine is lifetime, since the prospective missions may require operation for several thousands of hours. The critical components of the ion engine, with respect to engine lifetime, are the screen and accelerating grid structures. Typically, these are large metal screens that must support a large voltage difference and maintain a small gap between them. Metallic whisker growth, distortion and vibration can lead to arcing, and over a long period of time ion sputtering will erode the grid structures and change their geometry. In order to study the effects of long time operation of the grid structure, we are developing computer codes based on the Particle-In-Cell (PIC) technique and Laser Induced Fluorescence (LIF) diagnostic techniques to study the physical processes which control the performance and lifetime of the grid structures.

Meserole, J. S.↗
NASA NTRS1989

Ion propulsion

An ion engine is a plasma thruster which produces thrust by extracting ions from the plasma and accelerating them to high velocity with an electrostatic field. The ions are then neutralized and leave the engine as high velocity neutral particles. The advantages of ion engines are high specific impulse and efficiency and their ability to operate over a wide range of input powers. In comparison with other electric thrusters, the ion engine has higher efficiency and specific impulse than thermal electric devices such as the arcjet, microwave, radio frequency, and laser heated thrusters and can operate at much lower current levels than the MPD thruster. However, the thrust level for an ion engine may be lower than a thermal electric thruster of the same operating power, consistent with its higher specific impulse, and therefore ion engines are best suited for missions which can tolerate longer duration propulsive phases. The critical issue for the ion engine is lifetime, since the prospective missions may require operation for several thousands of hours. The critical components of the ion engine, with respect to engine lifetime, are the screen and accelerating grid structures. Typically, these are large metal screens that must support a large voltage difference and maintain a small gap between them. Metallic whisker growth, distortion, and vibration can lead to arcing, and over a long period of time ion sputtering will erode the grid structures and change their geometry. In order to study the effects of long time operation of the grid structure, we are developing computer codes based on the particle-in-cell (PIC) technique and laser induced fluorescence (LIF) diagnostic techniques to study the physical processes which control the performance and lifetime of the grid structures.

Meserole, J. S.↗
DOE OSTI2023

Particle-in-cell Simulations of Relativistic Magnetic Reconnection with Advanced Maxwell Solver Algorithms

Abstract Relativistic magnetic reconnection is a nonideal plasma process that is a source of nonthermal particle acceleration in many high-energy astrophysical systems. Particle-in-cell (PIC) methods are commonly used for simulating reconnection from first principles. While much progress has been made in understanding the physics of reconnection, especially in 2D, the adoption of advanced algorithms and numerical techniques for efficiently modeling such systems has been limited. With the GPU-accelerated PIC code WarpX, we explore the accuracy and potential performance benefits of two advanced Maxwell solver algorithms: a nonstandard finite-difference scheme (CKC) and an ultrahigh-order pseudo-spectral method (PSATD). We find that, for the relativistic reconnection problem, CKC and PSATD qualitatively and quantitatively match the standard Yee-grid finite-difference method. CKC and PSATD both admit a time step that is 40% longer than that of Yee, resulting in a ∼40% faster time to solution for CKC, but no performance benefit for PSATD when using a current deposition scheme that satisfies Gauss’s law. Relaxing this constraint maintains accuracy and yields a 30% speedup. Unlike Yee and CKC, PSATD is numerically stable at any time step, allowing for a larger time step than with the finite-difference methods. We found that increasing the time step 2.4–3 times over the standard Yee step still yields accurate results, but it only translates to modest performance improvements over CKC, due to the current deposition scheme used with PSATD. Further optimization of this scheme will likely improve the effective performance of PSATD.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: Profiling and performance optimizations

Celeritas is a GPU-optimized Monte Carlo (MC) particle transport code designed to meet the growing computational demands of next-generation high energy physics (HEP) experiments. It provides efficient simulation of electromagnetic (EM) physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

Lund, Amanda [Argonne National Laboratory (ANL)]↗
DOE OSTI2025

Experimental Studies on Enhanced Ignition for Improved Pre-Chamber Combustion in Gas Engines

Global efforts to reduce greenhouse gas emissions and mitigate climate change have intensified the demand for cleaner and more efficient combustion technologies. Heavyduty engines, which remain essential for transportation and power generation, must operate with significantly lower emissions while maintaining high efficiency and fuel flexibility. Lean combustion and exhaust gas recirculation (EGR) offer promising pathways to reduce emissions and improve thermal efficiency; however, these strategies impose severe challenges for engine operation, especially for low reactivity gaseous fuels like natural gas and ammonia. Pre-chamber combustion has emerged as a viable approach that uses turbulent jets to accelerate main chamber ignition, but its performance can degrade under high dilution or with hard-to-ignite fuels. These limitations motivate the central focus of this dissertation: understanding passive pre-chambers and improving their performance under challenging conditions.

Dhotre, Akash [Univ. of Minnesota, Minneapolis, MN↗
NASA NTRS1982

The electrothermal ramjet

An electrothermal ramjet configuration is examined as a possible alternative to rail guns and mass drivers for high acceleration launch missions. For a specific mission (earth escape) the idealized performance of the electrothermal ramjet, the electrothermal rocket and the electromagnetic acceleration system are compared. This comparison indicates that the gross performance of the ramjet compares favorably with that of the ideal electromagnetic acceleration system. A specific configuration for the ramjet is chosen and models for the dynamics, thermodynamics and fluid mechanics are presented. Results of calculations for a typical supersonic launch cycle suggest that pressure, temperature and power demand profiles associated with ramjet operation should be reasonable. A light gas gun is proposed to accelerate the vehicle to the critical velocity where efficient ramjet operation can begin. The theoretical performance of the ramjet is also shown to be substantially better than that of the light gas gun at high velocities.

Wilbur, P. J.↗
NASA NTRS1984

Monte Carlo shock-like solutions to the Boltzmann equation with collective scattering

The results of Monte Carlo simulations of steady state shocks generated by a collision operator that isotropizes the particles by means of elastic scattering in some locally defined frame of reference are presented. The simulations include both the back reaction of accelerated particles on the inflowing plasma and the free escape of high-energy particles from finite shocks. Energetic particles are found to be naturally extracted out of the background plasma by the shock process with an efficiency in good quantitative agreement with an earlier analytic approximation (Eichler, 1983 and 1984) and observations (Gosling et al., 1981) of the entire particle spectrum at a quasi-parallel interplanetary shock. The analytic approximation, which allows a self-consistent determination of the effective adiabatic index of the shocked gas, is used to calculate the overall acceleration efficiency and particle spectrum for cases where ultrarelativistic energies are obtained. It is found that shocks of the strength necessary to produce galactic cosmic rays put approximately 15 percent of the shock energy into relativistic particles.

Ellison, D. C.↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2024

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Efficient monolithic all-perovskite tandem solar modules with small cell-to-module derate

All-perovskite tandem solar modules are promising to reduce the cost of photovoltaic systems with their high efficiency and solution fabrication, but their sensitivity to air still imposes a great challenge. Here a hot gas-assisted blading method is developed to accelerate the perovskite solidification, forming compact and thick narrow bandgap (NBG) perovskite films. Adding a reduction agent into NBG films followed by a short period of air exposure and a post-fabrication storage surprisingly increases carrier recombination lifetime and enables laser scribing in ambient conditions without obvious loss of device performance. This combination suppresses tin and iodide oxidation and forms a thin SnO 2 layer on the NBG film surface. Monolithic all-perovskite tandem solar modules showed a champion efficiency of 21.6% with a 14.3 cm 2 aperture area, corresponding to an active area efficiency of 23.0%. Lastly, the very small cell-to-module derate of 6.5% demonstrates the advantage of a tandem monolithic structure for solar modules.

14 SOLAR ENERGY↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Strategies for designing biocatalysts with new functions

Enzymes can be optimized to accelerate chemical transformations via a range of methods. In this review, we showcase how protein engineering and computational design techniques can be interfaced to develop highly efficient and selective biocatalysts.

09 BIOMASS FUELS↗
DOE OSTI2023

Online data-driven changepoint detection for high-dimensional dynamical systems

In this study, the detection of anomalies or transitions in complex dynamical systems is of critical importance to various applications. In this study, we propose the use of machine learning to detect changepoints for high-dimensional dynamical systems. Here, changepoints indicate instances in time when the underlying dynamical system has a fundamentally different characteristic—which may be due to a change in the model parameters or due to intermittent phenomena arising from the same model. We propose two complementary approaches to achieve this, with the first devised using arguments from probabilistic unsupervised learning and the latter devised using supervised deep learning. To accelerate the deployment of transition detection algorithms in high-dimensional dynamical systems, we introduce dimensionality reduction techniques. Our experiments demonstrate that transitions can be detected efficiently, in real-time, for the two-dimensional forced Kolmogorov flow and the Rössler dynamical system, which are characterized by anomalous regimes in phase space where dynamics are perturbed off the attractor at potentially uneven intervals. Finally, we also demonstrate how variations in the frequency of detected changepoints may be utilized to detect a significant modification to the underlying model parameters by utilizing the Lorenz-63 dynamical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Scalable FPGA Accelerator for Deep Convolutional Neural Networks with Stochastic Streaming

Here, FPGA-based heterogeneous computing platform, due to its extreme logic reconfigurability, emerges to be a strong contender as computing fabric in modern AI. As a result, various FPGA-based accelerators for deep CNN—the key driver of modern AI—have been proposed due to their advantages of high performance, reconfigurability, and fast development round, etc. In general, the consensus among researchers is that, although FPGA-based accelerator can achieve much higher energy efficiency, its raw computing performance lags behind when compared with GPUs with similar logic density. In this paper, we develop an alternative methodology to efficiently implement CNNs with FPGAs that outperform GPUs in terms of both power consumption and performance. Our key idea is to design a scalable hardware architecture and circuit design for large-scale CNNs that leverages a stochastic-based computing principle. Specifically, there are three major performance advantages. First, all key components of our deep learning CNN are designed and implemented to compute stochastically, thus achieving excellent computing performance and energy efficiency. Second, because our proposed CNN architecture enables a stream-mode computing, all of its stages can process even the partial results from preceding stages, therefore not incurring unnecessary latency due to data dependency. Finally, our FPGA-based deep CNN also provides a superior hardware scalability when compared with conventional FPGA implementations by reducing the bandwidth requirement between layers. The results show that our proposed CNN architecture significantly outperforms all previous FPGA-based deep CNN implementation approaches. It achieves 1.58x more GOPS, 6.42x more GOPS/Slice, and 10.92x more GOPS/W when compared with state-of-the-art CNN architecture. The top-5 accuracy of stochastic VGG-16 CNN is 86.77 percent with 18.91 fps frame rate.

97 MATHEMATICS AND COMPUTING↗