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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

In-Flight Edge Response Measurements for High Spatial Resolution Remote Sensing Systems

In-flight measurement of spatial resolution were conducted as part of the ASA Scientific Data Purchase (SDP) Validation and Verification (V&V) process. Characterization included remote sensing systems with ground sample distance (GSD) of 1 meter or less, such as the panchromatic imager on-board the ICONOS satellite and the airborne ADAR System 5500 multispectral instrument. Final image products were used to evaluate the effect of both the image acquisition system (e.g., optics, electronics, motion, jitter, atmosphere) and image post-processing (e.g., resampling, modulation trasfer function (MTF) compensator). Spatial resolution was characterized by full width at half maximum (FWHM) of an edge response-derived line spread function. This was found to be a more robust measure of spatial resolution than the value of NTF at Nyquist frequency The edge responses were analysed using the tilted-edge technique that ovecomes the spatial sampling limitations of the digital imaging systems. As an enhancement to existing algorithms, the slope of the edge response and the orientation of the edge target were determined by a single computational process. Adjacent black and white square panels, either painted on a flat surface or deployed as traps, formed the ground-based edge targets used in the tests. Orientation of the deployable tarps was optimized beforehand, based on simulations of the imaging system. Numerous edge target images were analyzed for each of the tested sensors. The effect of such factors as acquisition geometry, temporal variability, MTF compensation, and GSD on spatial resolution were investigated.

Blonski, Slawomir↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1983

Laboratory simulation of tunable diode laser remote measurement of atmospheric gases using topographic targets

In order to test the possibility that tunable diode laser (TDL) systems using topographic targets can be applied to the measurement of a variety of gases at concentrations and distances characteristic of industrial gas leaks, a single-ended TDL system was assembled for use with selected topographic targets to detect NO2 at atmospheric pressure and NO2 and NH3 at low pressures. In both cases, the intensity distribution of radiation backscattered from selected targets was found to be heavily weighted to a specular rather than a Lambertian distribution. A sensitivity of 5 ppm-m has been demonstrated for the case of NO2.

Webster, C. R.↗
NASA NTRS1987

Hubble space telescope pointing control system simulation

A time domain simulation of the Hubble Space Telescope (HST) pointing control system is described. The simulation was developed to aid in the evaluation of moving target pointing algorithms and to serve as an analysis tool. The HST pointing control system is briefly described, and the time domain simulation is discussed. A typical simulation result for moving target tracking is presented.

Strohbehn, K.↗
DOE OSTI2025

Modeling and verification of dynamic field ionization for laser-target interactions

Integrating field ionization models into kinetic plasma simulations is required for a variety of applications, especially when field strengths vary from low to high regimes, such as in laser-target interactions. The introduction of new physics models into kinetic codes requires a rigorous verification of their accuracy through well-defined verification problems. In this work, the field ionization model that has been included in the kinetic plasma code VPIC is presented, along with the detailed approach adopted for its integration. This model includes a comprehensive range of field ionization mechanisms: multiphoton ionization, tunneling ionization, and barrier suppression ionization. New verification problems employed to evaluate the ionization model's fidelity are outlined, and the simulation parameters that affect the accuracy of simulation results are explored. Additionally, this work addresses the impact of field ionization on computational performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1974

Antenna performance and resolution

The performance of the antenna throughout SL-2, SL-3, and SL-4 was investigated along with the antenna resolution of brightness temperature during flight. The target area selected for the test flights was the Gulf of California, as it offered land/water interface. The coordinate transformations and antenna orientation, flight path simulation, and integration over the radiometric target are discussed.

Carney, J. J.↗
DOE OSTI2024

Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis

Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2020

CITADEL: an icy worlds simulation testbed

Icy Worlds present an exciting target for in-situ sample acquisition and analysis of surface samples for their potential to contain conditions necessary to support life. The unknown surface composition and topography of icy worlds present a challenging environment for which to develop effective sampling systems. Testing such sampling systems in a relevant environment is a critical part of validating and refining their design. We have developed the Cryogenic Ice Testing, Acquisition Development, and Excavation Laboratory (CITADEL) to enable sample acquisition and handling operations in an icy world or primitive body representative environment of <70K and 10-5 Pa.

Hand, Kevin↗
NASA NTRS2005

Generic Sensor Modeling Using Pulse Method

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

Helder, Dennis L.↗
NASA NTRS2022

Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver

Rahmani, Amir↗
NASA NTRS2012

Auto Code Generation for Simulink-Based Attitude Determination Control System

This paper details the work done to auto generate C code from a Simulink-Based Attitude Determination Control System (ADCS) to be used in target platforms. NASA Marshall Engineers have developed an ADCS Simulink simulation to be used as a component for the flight software of a satellite. This generated code can be used for carrying out Hardware in the loop testing of components for a satellite in a convenient manner with easily tunable parameters. Due to the nature of the embedded hardware components such as microcontrollers, this simulation code cannot be used directly, as it is, on the target platform and must first be converted into C code; this process is known as auto code generation. In order to generate C code from this simulation; it must be modified to follow specific standards set in place by the auto code generation process. Some of these modifications include changing certain simulation models into their atomic representations which can bring new complications into the simulation. The execution order of these models can change based on these modifications. Great care must be taken in order to maintain a working simulation that can also be used for auto code generation. After modifying the ADCS simulation for the auto code generation process, it is shown that the difference between the output data of the former and that of the latter is between acceptable bounds. Thus, it can be said that the process is a success since all the output requirements are met. Based on these results, it can be argued that this generated C code can be effectively used by any desired platform as long as it follows the specific memory requirements established in the Simulink Model.

MolinaFraticelli, Jose Carlos↗
DOE OSTI2025

Phase-field modeling of diffusion bonding in 316H stainless steel: Impact of processing conditions on grain morphology and bonding quality

A novel multi-phase, multi-component phase‐field model is presented to study the diffusion bonding of 316H stainless steel. Combined with targeted experimental investigations, this model simulates the bond-growth process and predicts the bonding quality. Unlike previous models, our approach captures the simultaneous evolution of voids and grain structures, while quantifying bonding quality using defined bonding ratio. A comprehensive analysis of bond process control is performed by changing temperature, pressure and surface roughness observing the resulting bond structure, which is consistent with experimental observations and analytical predictions. Temperature is determined to be the dominant factor, with the transition from a flat to a robust bond occurring between 1000 °C and 1050 °C. At the ideal bonding temperature of 1050 °C, a surface roughness exceeding 0.6 μm or an applied stress below 4 MPa results in poor bonding quality. Beyond this, higher pressures and smoother surfaces reduce void size, accelerate void shrinkage, and lead to improved bond integrity. This diffuse-interface model can be extended to other material systems if supplied with appropriate thermodynamic and kinetic data. In conclusion, this makes it an effective modeling platform for optimizing high-temperature diffusion bonding and developing reliable bonded components such as compact heat exchangers.

Diffusion bonding↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantum Computing and Simulations for Energy Applications

While quantum computing (QC) is considered as a paradigm shift in our basic understanding of physical computation, effective implementation of QC in energy applications also depends on progress and development in the dimensions of both QC hardware and algorithms. To fully address the status and future challenges of QC applied within the energy sector, in this presentation, we firstly summarize recent advancements on the applications of QC to energy infrastructure and materials, complex energy system processes, advanced manufacturing, and energy system security. Then, we will demonstrate the results of QC performed both on a simulator and a quantum device targeting on energy-related applications.

Paudel, Hari P.↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Advanced Research on Integrated Energy Systems (ARIES) Cyber Range Overview and Threat-to-Consequence Demonstration

This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. The threat-to-consequence demonstration showcases NREL's capability to model, simulate, test, and evaluate cyberattacks targeting energy systems that coincide with natural hazards, as well as the ramifications for the energy grid as a whole.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and estimation across disciplines, and (b) the varying technical requirements for establishing trustworthy uncertainties in diverse problem contexts. In this position paper, we aim to clarify the depth of these challenges by identifying these inconsistencies and articulating how different contexts impose distinct epistemic demands. We examine the current landscape of estimation targets (e.g., prediction, inference, simulation-based inference), uncertainty constructs (e.g., frequentist, Bayesian, fiducial), and the approaches used to map between them. Drawing on the literature, we highlight and explain examples of problematic mappings. To help address these issues, we advocate for standards that promote alignment between the \textit{intent} and \textit{implementation} of uncertainty quantification (UQ) approaches. We discuss several axes of trustworthiness that are necessary (if not sufficient) for reliable UQ in ML models, and show how these axes can inform the design and evaluation of uncertainty-aware ML systems. Our practical recommendations focus on scientific ML, offering illustrative cases and use scenarios, particularly in the context of simulation-based inference (SBI).

Trivedi, Shubhendu [MIT] (ORCID:0000000312374301)↗