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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 NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRS2019

A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

With the advancement in high performance computing and numerical optimization techniques,engineering design optimization problems are becoming more complex, larger scale,higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed anew optimization framework, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previouslyperceived. However, the original version of this framework, in the process of adaptive samplingto explore and exploit the design space, finds only a single sample point per iteration. The efforthere builds upon this previously developed optimization framework to include multiple infillsampling capability that combines the concept of generalized expected improvement function,unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGOwith the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficultengineering design optimization problems. The results reveal the performance of the newapproach is problem dependent. When applied to a ten-bar truss problem, the newly proposedmultiple infill strategy consistently leads to a better design solutions when compared to theexisting CPTV method (implemented with the context of the AMIEGO framework). On theother hand, when applied to a mixed-integer high fidelity wing topology optimization problem- MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferiorsolution as compared to CPTV approach. These results also reveal that a small number ofstarting points, in general, are sufficient to lead to a good overall solution.

Mixed-integer optimization↗
NASA NTRS2024

Starling Formation-Flying Optical Experiment: Initial Operations and Flight Results

This paper presents initial flight results for angles-only navigation of a swarm of small spacecraft, conducted during the Starling Formation-Flying Optical Experiment (StarFOX). StarFOX is one of four experiments aboard the NASA Starling mission, which consists of four CubeSats launched in July 2023. Angles-only methods apply inter-satellite bearing angles obtained by on-board cameras for navigation, increasing satellite autonomy and enabling new mission concepts. Nevertheless, prior flight demonstrations have only featured one observer and target and have relied upon a-priori target orbit knowledge for initialization, translational maneuvers to resolve target range, and external absolute orbit updates to maintain convergence. StarFOX overcomes these limitations by applying the angles-only Absolute and Relative Trajectory Measurement System (ARTMS), which integrates three novel algorithms. Image Processing detects and tracks multiple targets in images, using multi-hypothesis methods and domain-specific kinematic modeling, and computes target bearing angles. Batch Orbit Determination computes initial swarm orbit estimates from bearing angle batches, via iterative batch least squares and sampling of the weakly observable target range. Sequential Orbit Determination leverages an adaptive, efficient unscented Kalman filter with nonlinear models to refine swarm state estimates over time. Multi-observer measurements shared over an intersatellite link are seamlessly fused to enable robust absolute and relative orbit determination. Initial StarFOX experiments are the first demonstrations of autonomous angles-only navigation for a satellite swarm, including multi-target and multi-observer relative navigation; autonomous initialization of navigation for unknown targets; and simultaneous absolute and relative orbit determination. Relative navigation accuracy of 1% (single observer) and 0.2% (multi-observer) of the inter-satellite range is achieved under challenging measurement conditions. Results demonstrate promising performance with regards to ongoing StarFOX campaigns and the application of angles-only navigation to future distributed missions.

Relative Navigation↗
NASA NTRS1994

A fundamental aeroservoelastic study combining unsteady CFD with adaptive control

This paper describes a two-dimensional aeroservoelastic study in the time domain. The model, which is based on exact inviscid aerodynamics, correctly represents the large amplitude motions and the associated strong shock dynamics in the transonic regime. The aeroservoelastic system consists of a two degree-of-freedom airfoil with a trailing edge control surface. Using first-order actuator dynamics, a digital adaptive controller is applied to provide active flutter suppression. Comparisons between time-responses of the open-loop and closed loop systems show the ability of the trailing edge control surface to suppress non-linear transonic aeroelastic phenomena. A relation between actuator dynamics, sampling time-step and limits on the flap deflection angle to guarantee the effectiveness of the adaptive controller was demonstrated by the results generated.

Friedmann, P.↗
NASA NTRS2010

Mars Science Laboratory CHIMRA: A Device for Processing Powdered Martian Samples

The CHIMRA is an extraterrestrial sample acquisition and processing device for the Mars Science Laboratory that emphasizes robustness and adaptability through design configuration. This work reviews the guidelines utilized to invent the initial CHIMRA and the strategy employed in advancing the design; these principles will be discussed in relation to both the final CHIMRA design and similar future devices. The computational synthesis necessary to mature a boxed-in impact-generating mechanism will be presented alongside a detailed mechanism description. Results from the development testing required to advance the design for a highly-loaded, long-life and high-speed bearing application will be presented. Lessons learned during the assembly and testing of this subsystem as well as results and lessons from the sample-handling development test program will be reviewed.

Sunshine, Daniel↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
NASA NTRS2010

Simultaneous Spectral Temporal Adaptive Raman Spectrometer - SSTARS

Raman spectroscopy is a prime candidate for the next generation of planetary instruments, as it addresses the primary goal of mineralogical analysis, which is structure and composition. However, large fluorescence return from many mineral samples under visible light excitation can render Raman spectra unattainable. Using the described approach, Raman and fluorescence, which occur on different time scales, can be simultaneously obtained from mineral samples using a compact instrument in a planetary environment. This new approach is taken based on the use of time-resolved spectroscopy for removing the fluorescence background from Raman spectra in the laboratory. In the SSTARS instrument, a visible excitation source (a green, pulsed laser) is used to generate Raman and fluorescence signals in a mineral sample. A spectral notch filter eliminates the directly reflected beam. A grating then disperses the signal spectrally, and a streak camera provides temporal resolution. The output of the streak camera is imaged on the CCD (charge-coupled device), and the data are read out electronically. By adjusting the sweep speed of the streak camera, anywhere from picoseconds to milliseconds, it is possible to resolve Raman spectra from numerous fluorescence spectra in the same sample. The key features of SSTARS include a compact streak tube capable of picosecond time resolution for collection of simultaneous spectral and temporal information, adaptive streak tube electronics that can rapidly change from one sweep rate to another over ranges of picoseconds to milliseconds, enabling collection of both Raman and fluorescence signatures versus time and wavelength, and Synchroscan integration that allows for a compact, low-power laser without compromising ultimate sensitivity.

Blacksberg, Jordana↗
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Activity and adaptation of nitrilotriacetate (NTA)-degrading bacteria: field and laboratory studies

Adaptation of bacterial activity for the degradation of nitrilotriacetate (NTA) was studied using natural sediment samples and an NTA-degrading bacterium (strain ATCC 29600). Sediment samples from a river with persistent levels of NTA had much higher NTA-degradative activity than comparable samples from a less contaminated control site. When sediment from the control site was exposed to high levels of NTA a 5 day lag preceded an abrupt increase in NTA degradation while strain 29600 colonized on sand and grown in the absence of NTA became induced within eight hours. The induction of strain 29600 was compared between bacteria in suspension and cells attached to sand. The sand-associated bacteria became induced 4 to 5 h before the planktonic suspension and displayed over threefold greater specific activity. Suspensions of strain 29600 became adapted within 8 h when placed in membrane diffusion chambers that were immersed within a municipal wastewater reactor containing NTA. These findings support the concept that induction is a part of the process of bacterial adaptation to degrade NTA and sand-associated bacteria can adapt more quickly to and have a greater degradative activity for NTA than planktonic cells.

NASA Discipline Number 04-10↗
NASA NTRS2008

Pattern-Recognition System for Approaching a Known Target

A closed-loop pattern-recognition system is designed to provide guidance for maneuvering a small exploratory robotic vehicle (rover) on Mars to return to a landed spacecraft to deliver soil and rock samples that the spacecraft would subsequently bring back to Earth. The system could be adapted to terrestrial use in guiding mobile robots to approach known structures that humans could not approach safely, for such purposes as reconnaissance in military or law-enforcement applications, terrestrial scientific exploration, and removal of explosive or other hazardous items. The system has been demonstrated in experiments in which the Field Integrated Design and Operations (FIDO) rover (a prototype Mars rover equipped with a video camera for guidance) is made to return to a mockup of Mars-lander spacecraft. The FIDO rover camera autonomously acquires an image of the lander from a distance of 125 m in an outdoor environment. Then under guidance by an algorithm that performs fusion of multiple line and texture features in digitized images acquired by the camera, the rover traverses the intervening terrain, using features derived from images of the lander truss structure. Then by use of precise pattern matching for determining the position and orientation of the rover relative to the lander, the rover aligns itself with the bottom of ramps extending from the lander, in preparation for climbing the ramps to deliver samples to the lander. The most innovative aspect of the system is a set of pattern-recognition algorithms that govern a three-phase visual-guidance sequence for approaching the lander. During the first phase, a multifeature fusion algorithm integrates the outputs of a horizontal-line-detection algorithm and a wavelet-transform-based visual-area-of-interest algorithm for detecting the lander from a significant distance. The horizontal-line-detection algorithm is used to determine candidate lander locations based on detection of a horizontal deck that is part of the lander.

Huntsberger, Terrance↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2025

Comparison of Nb 3 Sn Insulation Systems With as Received and De-Sized Glass

Epoxy cracking has been the main suspected contributor to training in Nb 3 Sn magnets for some time, as it leads to energy release and magnet quenching. Cracking in a superconducting composite can occur from several modes, and the likelihood of these modes is not well understood. Failure has been observed between insulation systems and the superconducting cable itself rather than within the cable or insulation. To target the behavior at the interface, samples have been fabricated and tested for Mode I fracture toughness in an adapted version of ASTM D5528. This work seeks to better characterize the fracture toughness of this interface to better understand how to model and design insulation systems.

Krave, Steven T. [Fermilab] (ORCID:000000021752145↗
NASA NTRS1971

Delta Modulation

An optimum adaptive delta modulator-demodulator configuration is derived. This device utilizes two past samples to obtain a step size which minimizes the mean square error for a Markov Gaussian source. The optimum system is compared using computer simulations with the linear delta modulator and an enhanced Abate delta modulator. In addition the performance is compared to the rate distortion bound for a Markov source. It is shown that the optimum delta modulator is neither quantization nor slope-overload limited. The results show that the output signal-to-noise ratio is independent of the input signal power and is subject only to the limitations of the hardware employed. In addition, voice was recorded using these systems. The demodulated voice indicates negligible degradation is caused by the optimum system and by the enhanced Abate system while the linear delta modulator suffers significant degradation at a sampling frequency of 56 kilobits/sec.

Schilling, D. L.↗
NASA NTRS1988

Program Collects And Analyzes Thermoelectric Data

Large gradient seebeck coefficient measurement computer program gathers many channels of data, performs analysis, and plots each set of data simutaneously. User can modify code to alter number of channels of data collected and sampling rate and equations that process data. Written in HP Basic. Program also adaptable to other types of data.

Chmielewski, Art B.↗
NASA NTRS2018

Front Delineation and Tracking with Multiple Underwater Vehicles

This work describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles. Multiple vehicles — equally-spaced along the expected frontal boundary — complete near parallel transects orthogonal to the front. Lateral gradients are used to determine the location of the front crossing from each individual vehicle transect by detecting a change in the observed water property. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in and around Monterey Bay, California in May of 2017. We compare the front detection method to previously used methods. We introduce a metric in order to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic two-dimensional ocean front using short-range Iver AUVs. This method extends to tracking gradients of different properties using a variety of vehicles.

Chavez, Francisco P.↗
NASA NTRS2010

NASA Biological Specimen Repository

The NASA Biological Specimen Repository (NBSR) was established in 2006 to collect, process, preserve and distribute spaceflight-related biological specimens from long duration ISS astronauts. This repository provides unique opportunities to study longitudinal changes in human physiology spanning may missions. The NBSR collects blood and urine samples from all participating ISS crewmembers who have provided informed consent. These biological samples are collected once before flight, during flight scheduled on flight days 15, 30, 60, 120 and within 2 weeks of landing. Postflight sessions are conducted 3 and 30 days after landing. The number of in-flight sessions is dependent on the duration of the mission. Specimens are maintained under optimal storage conditions in a manner that will maximize their integrity and viability for future research The repository operates under the authority of the NASA/JSC Committee for the Protection of Human Subjects to support scientific discovery that contributes to our fundamental knowledge in the area of human physiological changes and adaptation to a microgravity environment. The NBSR will institute guidelines for the solicitation, review and sample distribution process through establishment of the NBSR Advisory Board. The Advisory Board will be composed of representatives of all participating space agencies to evaluate each request from investigators for use of the samples. This process will be consistent with ethical principles, protection of crewmember confidentiality, prevailing laws and regulations, intellectual property policies, and consent form language. Operations supporting the NBSR are scheduled to continue until the end of U.S. presence on the ISS. Sample distribution is proposed to begin with selections on investigations beginning in 2017. The availability of the NBSR will contribute to the body of knowledge about the diverse factors of spaceflight on human physiology.

McMonigal, K. A.↗
DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗