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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Algorithm Performance Dataset from NASA Open-Source Software

NASA Langley Research Center has recently developed and released the open-source software Multi Model Monte Carlo with Python (MXMCPy- LAR-19756-1) as a general capability for computing the statistics of outputs from an expensive, high-fidelity model by leveraging faster, low-fidelity models for speedup. Given a fixed computational budget and a collection of models with varying cost/accuracy, multi model Monte Carlo (MC) seeks a sample allocation strategy across the models that results in an estimator with optimal variance reduction. MXMCPy is a versatile tool that enables convenient access to many existing multi-model MC approaches (over a dozen algorithms available) within one modular and extensible package [1]. With MXMCPy, users can easily compare existing methods to determine the best choice for their particular problem,while developers have a basis for implementing and sharing new variance reduction approaches. However,there is currently very little understanding about which algorithm will perform best for a given problem (defined by the correlation between and relative cost of the available models) without a brute force search.

Geoffrey F Bomarito↗
NASA NTRS2010

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Kiely, Aaron B.↗
DOE OSTI2025

Quantum algorithm to simulate Lindblad master equations

We present a quantum algorithm for simulating a family of Markovian master equations that can be realized through a probabilistic application of unitary channels and state preparation. Our approach employs a second-order product formula for the Lindblad master equation, achieved by decomposing the dynamics into dissipative and Hamiltonian components and replacing the dissipative segments with randomly compiled, easily implementable elements. The sampling approach eliminates the need for ancillary qubits to simulate the dissipation process and reduces the gate complexity in terms of the number of jump operators. We provide a rigorous performance analysis of the algorithm. We also extend the algorithm to time-dependent Lindblad equations, generalize the family of Markovian master equations it can be applied to, and explore applications beyond the Markovian noise model. A new error bound, in terms of the diamond norm, for second-order product formulas for time-dependent Liouvillians is provided that might be of independent interest. Published by the American Physical Society 2025

Borras, Evan (ORCID:000900017709037X)↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
NASA NTRSDate not supplied

Joint Retrieval of Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The latest geostationary sensors like GOES 16/17 ABI and Himawari 8/9 AHI provide high frequent observations of the Earth surface with continuously changing solar illumination geometries, which allow us to retrieve the surface Bidirectional Reflectance Distribution Function (BRDF) with dedicated atmospheric correction algorithms like MAIAC (Multi-Angle Implementation of Atmospheric Correction). However, because the viewing geometry of a specific location from the geostationary satellites are fixed, the angular sampling of surface BRDF by GEO (Geostationary Earth Orbit) sensors is far from comprehensive. This study tries to address this issue by exploring a GEO-LEO (Low-Earth-Orbit) synergy, in particular, jointly retrieving surface BRDF parameters with concurrent ABI/AHI and VIIRS top-of-atmosphere (TOA) reflectance for the near-infrared (NIR) band. The NIR band is chosen because the ABI, AHI, and VIIRS instruments have very similar spectral response functions in this band and therefore simplifies the requirements for cross-sensor radiometric calibration. We compile ABI/AHI and VIIRS TOA data with the best quality (cloud-free and low aerosol loading) at chosen AERONET sites. We then run the GeoNEX MAIAC algorithm to retrieve the Ross-Thick-Li-Sparse (RTLS) surface BRDF parameters with or without the AEORNET measured atmospheric aerosol optical depth (AOD) as inputs. The joint retrieval results are considered the best estimate of surface BRDF. We compare the joint BRDF retrievals with the corresponding MAIAC BRDF products, retrieved with ABI/AHI or VIIRS separately, to evaluate their differences. We expect that the jointly retrieved BRDF data are more robust than the standard products and may help us reduce uncertainties in higher-level earth observation satellite products.

Remote Sensing↗
DOE OSTI2024

Phase retrieval for refraction-enhanced x-ray radiography using a deep neural network

X-ray refraction-enhanced radiography (RER) or phase contrast imaging is widely used to study internal discontinuities within materials. The resulting radiograph captures both the decrease in intensity caused by material absorption along the x-ray path, as well as the phase shift, which is highly sensitive to gradients in density. A significant challenge lies in effectively analyzing the radiographs to decouple the intensity and phase information and accurately ascertain the density profile. Conventional algorithms often yield ambiguous and unrealistic results due to difficulties in including physical constraints and other relevant information. We have developed an algorithm that uses a deep neural network to address these issues and applied it to extract the detailed density profile from an experimental RER. To generalize the applicability of our algorithm, we have developed a technique that quantitatively evaluates the complexity of the phase retrieval process based on the characteristics of the sample and the configuration of the experiment. Accordingly, this evaluation aids in the selection of the neural network architecture for each specific case. Beyond RER, the model has potential applications for other diagnostics where phase retrieval analysis is required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2025

The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

Computer simulator for a mobile telephone system

A software simulator was developed to assist NASA in the design of the land mobile satellite service. Structured programming techniques were used by developing the algorithm using an ALCOL-like pseudo language and then encoding the algorithm into FORTRAN 4. The basic input data to the system is a sine wave signal although future plans call for actual sampled voice as the input signal. The simulator is capable of studying all the possible combinations of types and modes of calls through the use of five communication scenarios: single hop systems; double hop, signal gateway system; double hop, double gateway system; mobile to wireline system; and wireline to mobile system. The transmitter, fading channel, and interference source simulation are also discussed.

Schilling, D. L.↗
NASA NTRS1997

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

Valdez, P. F.↗
NASA NTRS2023

Efficient Calibration of Expensive Computational Models

Accounting for uncertainty when calibrating expensive computational models is a common challenge faced by scientists and engineers. Often Bayesian techniques are adopted to estimate a probability density function over the model parameters given noisy empirical data. The methods used to perform this type of probabilistic calibration are computationally prohibitive in that they require a large number of evaluations of the expensive model. In these cases, surrogate modeling -- that is, using a fast-to-evaluate, lower fidelity stand-in for the original computational model -- may be the only option to alleviate this computational burden. However, the upfront cost of generating training data to build a surrogate model can itself be expensive. As such, it is important to be judicious when selecting training points at which the full-fidelity model is evaluated. Here, an active learning approach is proposed that enables efficient selection of training points using approximate samples of the calibrated parameter probability density function. In this way, the training points can be concentrated in regions where the calibration algorithm requires high model accuracy.

active learning↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗
NASA NTRS2000

Full Duplex, Spread Spectrum Radio System

The goal of this project was to support the development of a full duplex, spread spectrum voice communications system. The assembly and testing of a prototype system consisting of a Harris PRISM spread spectrum radio, a TMS320C54x signal processing development board and a Zilog Z80180 microprocessor was underway at the start of this project. The efforts under this project were the development of multiple access schemes, analysis of full duplex voice feedback delays, and the development and analysis of forward error correction (FEC) algorithms. The multiple access analysis involved the selection between code division multiple access (CDMA), frequency division multiple access (FDMA) and time division multiple access (TDMA). Full duplex voice feedback analysis involved the analysis of packet size and delays associated with full loop voice feedback for confirmation of radio system performance. FEC analysis included studies of the performance under the expected burst error scenario with the relatively short packet lengths, and analysis of implementation in the TMS320C54x digital signal processor. When the capabilities and the limitations of the components used were considered, the multiple access scheme chosen was a combination TDMA/FDMA scheme that will provide up to eight users on each of three separate frequencies. Packets to and from each user will consist of 16 samples at a rate of 8,000 samples per second for a total of 2 ms of voice information. The resulting voice feedback delay will therefore be 4 - 6 ms. The most practical FEC algorithm for implementation was a convolutional code with a Viterbi decoder. Interleaving of the bits of each packet will be required to offset the effects of burst errors.

Harvey, Bruce A.↗
NASA NTRS2001

Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet

Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight architecture. Following this introduction, we focus specifically on the last topic, that being an analysis which leads to an optimal flight architecture dictated in part by science requirements but constrained by allowable orbital mechanics, instrument scan patterns, and antenna aperture properties. Because the optimal architecture involves an interplay between orbit mechanics and instrument specifications, it is important to recognize that in attempting to serve various scientific themes, the final optimal architecture will represent a compromise concerning dynamic range, spatial resolution, sampling interval, pointing, beam coincidence, and measurement uncertainty. Moreover, cost becomes a major factor in seeking the optimal architecture through the pathways of antenna and instrument scan designs, as well as propulsion requirements associated with the orbit heights of various "constellation" members. Although the results presented at the IGARSS-2001 meeting will likely not be the fully refined flight architecture specifications, they are expected to be nearly complete.

Smith, Eric A.↗
NASA NTRS2005

Path Planning Algorithms for the Adaptive Sensor Fleet

The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

Stoneking, Eric↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗