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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Ablator Response Model Development: From Flight Data Back to Fundamental Experiments

The successful Mars atmospheric entry by the Mars Science Laboratory (MSL-Curiosity) combined with the success of the Earth atmospheric entry by the Stardust capsule have established PICA as a major Thermal Protection Systems (TPS) material. We expect that this class of materials will be on the short list selected by NASA for any atmospheric entry missions and that it will be the lead of that list of materials in any planning, feasibility studies or flight readiness studies. In addition to NASAs successes, the Dragon capsule, the successful commercial space vehicle built by SpaceX, uses PICA-X, while the European Space Agency is considering ASTERM for its exploration missions that involve atmospheric entries, both of these materials are of the same family as PICA. In the talk, a high-fidelity model will be detailed and discussed. The model tracks the chemical composition of the gases produced during pyrolysis. As in the conventional models, it uses equilibrium chemistry to determine the recession rate at high temperatures but switches to in-volume finite-rate ablation for lower temperatures. It also tracks the time evolution of the porosity of the material. Progress in implementing this high-fidelity model in a code will be presented. In addition, a set of basic experimental data being supported for model validation will be summarized. The validation process for the model development will be discussed. Preliminary results will be presented for a case where detailed pyrolysis product chemistry is computed. Finally, a wish list for a set of validation experiments will be outlined and discussed.

Mansour, Nagi N.↗
DOE OSTI2026

Dual particle imaging using time-of-flight neutron classification

Fast-neutron imaging technology is well-suited for passive nuclear material monitoring, secondary inspection of flagged cargo, and wide-area search for lost neutron sources. However, imaging systems that use pulse shape discrimination for event classification require complex pulse waveform analysis. In this work, we evaluate time-of-flight (TOF) based particle classification as an alternative solution for fast-neutron imaging by classifying all events with a TOF above a maximum threshold as neutrons. We measured a Cf-252 source next to Cs-137 using a 12-bar organic-glass scintillator array. By varying the TOF thresholds for neutron identification, we demonstrate a clear trade-off between event yield and backprojection image fidelity, with stricter thresholds improving precision at the cost of statistics, TOF thresholded data generated an image that predicted the neutron source direction with 20% reduced mean central angle prediction error compared to a traditional pulse shape discrimination (PSD) method with comparable event count. Time-of-flight particle classification shows promise as an alternative to pulse shape discrimination systems for fast neutron imaging systems looking to minimize costs and size of electronics with comparable imaging quality. The sources used demonstrate that the method is effective in classifying measured neutrons in a measurement environment with 150 μCi Cs-137 and 1.6 × 10 6 n/s Cf-252 sources positioned at distances of 66 cm and 81 cm from the detector. Additionally, the method classifies low-energy neutron events that pulse shape discrimination removes, so a combination of both methods would result in a higher overall neutron event efficiency.

Heriot, William [Univ. of Michigan, Ann Arbor, MI ↗
DOE OSTI2026

Combined effects of horizontal and vertical resolution on reliable turbulence prediction at tidal energy sites: A systematic study in the Salish Sea, WA

Predicting turbulence characteristics with coastal ocean models is essential for tidal energy converter deployment. While large eddy simulation provides a detailed turbulence representation, computational limitations restrict its use to smaller domains. We systematically evaluate whether well-configured coastal models can provide reliable turbulence prediction through progressive refinement of 3D model representation. We implemented four model configurations (Levels 1–4) using terrain-following coordinates, isolating the impacts of horizontal resolution, vertical resolution, and layer distribution. We validated all configurations against field measurements from the Salish Sea, WA. Results show that tidal current velocity predictions remain unchanged regarding model configuration, but turbulence properties are sensitive to resolution refinement. Increasing vertical resolution alone proved insufficient; even with vertical sigma-levels rising from 11 to 41, significant underprediction persisted until finer horizontal resolution better captured bathymetric variations. The Level 4 configuration, incorporating geometric sigma-levels distribution, achieved turbulence prediction skill scores exceeding 0.90. Turbulence closure comparison revealed Mellor- Yamada 2.5 outperformed k-epsilon in TKE prediction (skill scores 0.84–0.94 versus 0.72–0.81) due to better boundary layer parameterization. This study shows that well-configured coastal models effectively bridge the gap between simplified tools and costly high-fidelity modeling, offering the tidal energy industry practical and cost-effective turbulence data at commercially relevant scales.

Marine Energy↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comprehensive assessment of metrology techniques for heliostat efficiency and performance evaluation

Concentrating solar power plants, specifically central receiver type systems and their heliostat field, are struggling with negative reputation in the USA, due to perceived underperformance and reliability issues. This is in part due to a lack of standards for performance assessment as well as overly simplified techno-economical models. A better understanding of influences and losses along the solar radiation path from the sun, across the solar collector to the receiver, increases the fidelity of heliostat efficiency assessment as well as solar field performance predictions. Such data are currently scarce and require a complete set of metrology capabilities to evaluate direct solar irradiance, sun shape, atmospheric attenuation, reflectance, collector shape, slope errors and total beam dispersion. In preparation for establishing a 3rd party metrology platform in collaboration with Sandia National Labs, NLR conducted a scoping study on available metrology. We present an extensive overview of techniques and commercial systems for each category. Our work includes an analysis to increase understanding of strengths and limitations of the many techniques used for surface shape and slope measurement. This applies to a controlled, indoor or outdoor laboratory environment assessing a single heliostat.

14 SOLAR ENERGY↗
DOE OSTI2025

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗
DOE OSTI2022

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scales at O(n log 2 n). We show that this approximation’s Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cooperative On-Ramp Merging with Time-Varying Vehicle-to-Vehicle Communication Delay Compensation via a Model-Free Approach

Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays-an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A co-simulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low CO2 emissions and fuel consumption, under communication delay across different communication frequencies. In conclusion, its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.

Accounting↗
DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2021

Dilute Combustion Control Using Spiking Neural Networks

Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1985

Investigation of TM Band-to-band Registration Using the JSC Registration Processor

When the Thematic Mapper (TM) imagery was first received and displayed in the fall of 1982, its radiometric lucidity and geometric fidelity was judged far superior to the multispectral scanner (MSS) imagery. The MSS data from the LANDSAT series of satellites have served well as the mainstay of satellite imagery for the remote sensing community up to now. Therefore, there is great anticipation as to the utility of this new IM imagery to advance the field of remote sensing. The thematic mapper (TM) band to band registration accuracy evaluation was discussed. In particular, the band to band registration accuracies of the TM data in the subpixel region were evaluated using the Johnson Space Center (JSC) registration processor which is ideally suited for this task. The JSC registration processor was also used to register one acquisition to another acquisition of multitemporal TM data acquired over the same ground track. The approach and the rationale behind the task, the description of the data set, the evaluation results, and the preliminary conclusions are discussed.

Yao, S. S.↗
NASA NTRS1991

Damage to metallic samples produced by measured lightning currents

A total of 10 sample disks of 2024-T3 aluminum and 4130 ferrous steel were exposed to rocket-triggered lightning currents at the Kennedy Space Center test site. The experimental configuration was arranged so that the samples were not exposed to the preliminary streamer, wire-burn, or following currents that are associated with an upward-initiated rocket-triggered flash but which are atypical of naturally initiated lightning. Return-stroke currents and continuing currents actually attaching to the sample were measured, augmented by close-up video recordings of approximately 3 feet of the channel above the sample and by 16-mm movies with 5-ms resolution. From these data it was possible to correlate individual damage spots with streamer, return-stroke, and continuing currents that produced them. Substantial penetration of 80-mil aluminum was produced by a continuing current of submedian amplitude and duration, and full penetration of a 35-mil steel sample occurred under an eightieth percentile continuing current. The primary purpose of the data acquired in these experiments is for use in improving and quantifying the fidelity of laboratory simulations of lightning burnthrough.

Fisher, Richard J.↗
NASA NTRS2003

Simulation Model Development for Icing Effects Flight Training

A high-fidelity simulation model for icing effects flight training was developed from wind tunnel data for the DeHavilland DHC-6 Twin Otter aircraft. First, a flight model of the un-iced airplane was developed and then modifications were generated to model the icing conditions. The models were validated against data records from the NASA Twin Otter Icing Research flight test program with only minimal refinements being required. The goals of this program were to demonstrate the effectiveness of such a simulator for training pilots to recognize and recover from icing situations and to establish a process for modeling icing effects to be used for future training devices.

Barnhart, Billy P.↗
NASA NTRS2013

Modeling Contamination Migration on the Chandra X-ray Observatory - II

During its first 14 years of operation, the cold (about -60C) optical blocking filter of the Advanced CCD Imaging Spectrometer (ACIS), aboard the Chandra X-ray Observatory, has accumulated a growing layer of molecular contamination that attenuates low-energy x rays. Over the past few years, the accumulation rate, spatial distribution, and composition have changed. This evolution has motivated further analysis of contamination migration within and near the ACIS cavity. To this end, the current study employs a higher-fidelity geometric model of the ACIS cavity, detailed thermal modeling based upon temperature data, and a refined model of the molecular transport.

O'Dell, Stephen L.↗
NASA NTRS2014

Aerodynamic Measurements of a Gulfstream Aircraft Model With and Without Noise Reduction Concepts

Steady and unsteady aerodynamic measurements of a high-fidelity, semi-span 18% scale Gulfstream aircraft model are presented. The aerodynamic data were collected concurrently with acoustic measurements as part of a larger aeroacoustic study targeting airframe noise associated with main landing gear/flap components, gear-flap interaction noise, and the viability of related noise mitigation technologies. The aeroacoustic tests were conducted in the NASA Langley Research Center 14- by 22-Foot Subsonic Wind Tunnel with the facility in the acoustically treated open-wall (jet) mode. Most of the measurements were obtained with the model in landing configuration with the flap deflected at 39º and the main landing gear on and off. Data were acquired at Mach numbers of 0.16, 0.20, and 0.24. Global forces (lift and drag) and extensive steady and unsteady surface pressure measurements were obtained. Comparison of the present results with those acquired during a previous test shows a significant reduction in the lift experienced by the model. The underlying cause was traced to the likely presence of a much thicker boundary layer on the tunnel floor, which was acoustically treated for the present test. The steady and unsteady pressure fields on the flap, particularly in the regions of predominant noise sources such as the inboard and outboard tips, remained unaffected. It is shown that the changes in lift and drag coefficients for model configurations fitted with gear/flap noise abatement technologies fall within the repeatability of the baseline configuration. Therefore, the noise abatement technologies evaluated in this experiment have no detrimental impact on the aerodynamic performance of the aircraft model.

Neuhart, Dan H.↗
NASA NTRS2016

Cardiac-Activity Measures for Assessing Airport Ramp-Tower Controller's Workload

Heart rate (HR) and heart rate variability (HRV) potentially offer objective, continuous, and non-intrusive measures of human-operator's mental workload. Such measurement capability is attractive for workload assessment in complex laboratory simulations or safety-critical field testing. The present study compares mean HR and HRV data with self-reported subjective workload ratings collected during a high-fidelity human-in-the-loop simulation of airport ramp traffic control operations, which involve complex cognitive and coordination tasks. Mean HR was found to be weakly sensitive to the workload ratings, while HRV was not sensitive or even contradictory to the assumptions. Until more knowledge on stress response mechanisms of the autonomic nervous system is obtained, it is recommended that these cardiac-activity measures be used with other workload assessment tools, such as subjective measures.

Hayashi, Miwa↗
NASA NTRS2016

Cardiac-Activity Measures for Assessing Airport Ramp-Tower Controller's Workload

Heart rate (HR) and heart rate variability (HRV) potentially offer objective, continuous, and non-intrusive measures of human-operators mental workload. Such measurement capability is attractive for workload assessment in complex laboratory simulations or safety-critical field testing. The present study compares mean HR and HRV data with self-reported subjective workload ratings collected during a high-fidelity human-in-the-loop simulation of airport ramp traffic control operations, which involve complex cognitive and coordination tasks. Mean HR was found to be weakly sensitive to the workload ratings, while HRV was not sensitive or even contradictory to the assumptions. Until more knowledge on stress response mechanisms of the autonomic nervous system is obtained, it is recommended that these cardiac-activity measures be used with other workload assessment tools, such as subjective measures.

Hayashi, Miwa↗