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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation

Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$ galaxy-galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $Ω_{m}$ from lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $Λ$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $Ω_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.

Jarugula, Sreevani [Fermilab] (ORCID:0000000253867↗
NASA NTRS2003

Comparisons of Gas-phase Temperature Measurements in a Flame Using Thin-Filament Pyrometry and Thermocouples

Less-intrusive, fast-responding, and full-field temperature measurements have long been a desired tool for the research community. Recently, the emission of a silicon-carbide (SiC) fiber placed in a flowing hot (or reacting) gas has been used to measure the temperature profile along the length of the fiber. The relationship between the gas and fiber temperature comes from an energy balance on the fiber. In the present work, we compared single point flame temperature measurements using thin-filament pyrometry (TFP) and thermocouples. The data was from vertically traversing a thermocouple and a SiC fiber through a methanol/air diffusion flame of a porous-metal wick burner. The results showed that the gas temperature using the TFP technique agreed with the thermocouple measurements (25.4 m diameter wire) within 3.5% for temperatures above 1200 K. Additionally, we imaged the entire SiC fiber (with a spatial resolution of 0.14 mm) while it was in the flame using a high resolution CCD camera. The intensity level along the fiber length is a function of the temperature. This results in a one-dimensional temperature profiles at various heights above the burner wick. This temperature measurement technique, while having a precision of less than 1 K, showed data scatter as high as 38 K. Finally, we discuss the major sources of uncertainty in gas temperature measurement using TFP.

Struk, Peter↗
DOE OSTI2014

Correlating electroluminescence characterization and physics-based models of InGaN/GaN LEDs: Pitfalls and open issues

Electroluminescence (EL) characterization of InGaN/GaN light-emitting diodes (LEDs), coupled with numerical device models of different sophistication, is routinely adopted not only to establish correlations between device efficiency and structural features, but also to make inferences about the loss mechanisms responsible for LED efficiency droop at high driving currents. The limits of this investigative approach are discussed here in a case study based on a comprehensive set of current- and temperature-dependent EL data from blue LEDs with low and high densities of threading dislocations (TDs). First, the effects limiting the applicability of simpler (closed-form and/or one-dimensional) classes of models are addressed, like lateral current crowding, vertical carrier distribution nonuniformity, and interband transition broadening. Then, the major sources of uncertainty affecting state-of-the-art numerical device simulation are reviewed and discussed, including (i) the approximations in the transport description through the multi-quantum-well active region, (ii) the alternative valence band parametrizations proposed to calculate the spontaneous emission rate, (iii) the difficulties in defining the Auger coefficients due to inadequacies in the microscopic quantum well description and the possible presence of extra, non-Auger high-current-density recombination mechanisms and/or Auger-induced leakage. In the case of the present LED structures, the application of three-dimensional numerical-simulation-based analysis to the EL data leads to an explanation of efficiency droop in terms of TD-related and Auger-like nonradiative losses, with a C coefficient in the 10{sup −30} cm{sup 6}/s range at room temperature, close to the larger theoretical calculations reported so far. However, a study of the combined effects of structural and model uncertainties suggests that the C values thus determined could be overestimated by about an order of magnitude. This preliminary attempt at uncertainty quantification confirms, beyond the present case, the need for an improved description of carrier transport and microscopic radiative and nonradiative recombination mechanisms in device-level LED numerical models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Blade Heat Transfer Measurements and Prediction in a Transonic Turbine Cascade

Detailed heat transfer measurements and predictions are given for a turbine rotor with 136 deg of turning and an axial chord of 12.7 cm. Data were obtained for inlet Reynolds numbers of 0.5 and 1.0 x 10(exp 6), for isentropic exit Mach numbers of 1.0 and 1.3, and for inlet turbulence intensities of 0.25% and 7.0%. Measurements were made in a linear cascade having a highly three-dimensional flow field resulting from thick inlet boundary layers. The purpose of the work is to provide benchmark quality data for three-dimensional CFD code and model verification. Data were obtained by a steady-state technique using a heated, isothermal blade. Heat fluxes were determined from a calibrated resistance layer in conjunction with a surface temperature measured by calibrated liquid crystals. The results show the effects of strong secondary vortical flows, laminar-to-turbulent transition, shock impingement, and increased inlet turbulence on the surface heat transfer.

Giel, P. W.↗
NASA NTRS1985

The solar/interplanetary/magnetosphere/ionosphere connection - A strategy for prediction of geomagnetic storms

A physically-based strategy for the prediction of geomagnetic/ionospheric disturbances, the Solar-Terrestrial/Environmental Model (STEM 2000), is proposed, with application to the prediction of periods of spacecraft charging. Synoptic solar observations provide input for MHD models for flare occurrence, propagation of coronal disturbances, and high speed solar wind streams. A three-dimensional interplanetary MHD model determines solar wind parameters including the energy flux and the cross-magnetosphere tail electric field. Observational earth data are used to predict local time, high latitude ionospheric disturbances which have an impact on the ionospheric structure.

Dryer, M.↗
DOE OSTI2024

Anthropogenic effects on flood hazards in a hyper-arid watershed: The 2015 Atacama floods

An unprecedented precipitation event in the hyper-arid Atacama Desert of Northern Chile occurred in March 2015. Geomorphic alterations to the river channel and the coastal zone, coupled with the exceptional magnitude of the rainfall, caused catastrophic damage and loss of life. On the coast of the El Salado watershed, legacy mine tailings infilled the watershed-ocean connection, while the river channel was altered both by tailings and urbanization. The consequences of this event resulted from the coupling of anthropogenic geomorphic changes with an unusual climate event. Lack of field data, complex geomorphology and sediment loads influenced by human activity make analysing floods in these regions especially challenging. The objective of this work is to improve our understanding of the factors that control flood hazards by using numerical simulations to reconstruct the 2015 flood in El Salado. We carry out unsteady two-dimensional simulations fully coupled with the sediment concentration to identify the influence of tailing deposits, considering high-resolution data of the pre- and post-2015 flood topography. In conclusion, the results highlight the importance of specific event-based studies, using models that can help designing better strategies for climate change adaptation and risk mitigation, while providing information for risk reduction and channel restoration.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

A new facility and technique for two-dimensional aerodynamic testing

The design and operational characteristics of a new test leg for the High Reynolds Number Facility at Ames Research Center are presented, and the unique features of a test section for obtaining two-dimensional airfoil data are reviewed. The new facility operates at unit Reynolds numbers ranging from 1,000,000/ft to 30,000,000/ft. Boundary-layer suction panels are used in the test section (16 in. wide by 24 in. high) to minimize side-wall interference effects. Flexible, easily adjustable upper and lower walls allow test-channel area-ruling so as to nullify Mach-number changes induced by mass removal, to correct for longitudinal boundary-layer growth, and to provide contouring compatible with the streamlines of the model in free air.

Mcdevitt, J. B.↗
DOE OSTI2023

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (a) a form of Nonlinear Vector Autoregression (NVAR), and (b) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

58 GEOSCIENCES↗
DOE OSTI2020

Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. Here this work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization.

42 ENGINEERING↗
NASA NTRS1996

Aerodynamic Limitations of the UH-60A Rotor

High quality airloads data have been obtained on an instrumented UH-60A in flight and these data provide insight into the aerodynamic limiting behavior of the rotor. At moderate weight coefficients and high advance ratio limiting performance is largely caused by high drag near the blade tip on the advancing side of the rotor as supercritical flow develops on the rotor with moderate to strong, shocks on both surfaces of the blade. Drag divergence data from two-dimensional airfoil tests show good agreement with the development of the supercritical flow regions. Large aerodynamic pitching moments are observed at high advance ratio, as well, and these pitching moments are the source of high torsional moments on the blade and control system loads. These loads occur on the advancing side of the disk and are not related to blade stall which does not occur for these weight coefficients. At high weight coefficients aerodynamic and structural limits are related to dynamic stall cycles that begin on the retreating side of the blade and, for the most severe conditions, carry around to the advancing side of the blade at the presumed first frequency of the blade/control system.

Coleman, Colin P.↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗
DOE OSTI2017

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes.

47 OTHER INSTRUMENTATION↗
NASA NTRS1995

Laser Doppler velocimeter measurements and laser sheet imaging in an annular combustor model

An experimental study was conducted in annular combustor model to provide a better understanding of the flowfield. Combustor model configurations consisting of primary jets only, annular jets only, and a combination of annular and primary jets were investigated. The purpose of this research was to provide a better understanding of combustor flows and to provide a data base for comparison with computational models. The first part of this research used a laser Doppler velocimeter to measure mean velocity and statistically calculate root-mean-square velocity in two coordinate directions. From this data, one Reynolds shear stress component and a two-dimensional turbulent kinetic energy term was determined. Major features of the flowfield included recirculating flow, primary and annular jet interaction, and high turbulence. The most pronounced result from this data was the effect the primary jets had on the flowfield. The primary jets were seen to reduce flow asymmetries, create larger recirculation zones, and higher turbulence levels. The second part of this research used a technique called marker nephelometry to provide mean concentration values in the combustor. Results showed the flow to be very turbulent and unsteady. All configurations investigated were highly sensitive to alignment of the primary and annular jets in the model and inlet conditions. Any imbalance between primary jets or misalignment of the annular jets caused severe flow asymmetries.

Dwenger, Richard Dale↗
DOE OSTI2013

Joint surface reconstruction and 4D deformation estimation from sparse data and prior knowledge for marker-less Respiratory motion tracking

Purpose: The intraprocedural tracking of respiratory motion has the potential to substantially improve image-guided diagnosis and interventions. The authors have developed a sparse-to-dense registration approach that is capable of recovering the patient's external 3D body surface and estimating a 4D (3D + time) surface motion field from sparse sampling data and patient-specific prior shape knowledge.Methods: The system utilizes an emerging marker-less and laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is recovered, which describes the spatio-temporal 4D deformation of the complete patient body surface, depending on the type and state of respiration. It yields both a reconstruction of the instantaneous patient shape and a high-dimensional respiratory surrogate for respiratory motion tracking. The method is validated on a 4D CT respiration phantom and evaluated on both real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured-light scanner.Results: In the experiments, the authors estimated surface motion fields with the proposed algorithm on 256 datasets from 16 subjects and in different respiration states, achieving a mean surface reconstruction accuracy of ±0.23 mm with respect to ground truth data—down from a mean initial surface mismatch of 5.66 mm. The 95th percentile of the local residual mesh-to-mesh distance after registration did not exceed 1.17 mm for any subject. On average, the total runtime of our proof of concept CPU implementation is 2.3 s per frame, outperforming related work substantially.Conclusions: In external beam radiation therapy, the approach holds potential for patient monitoring during treatment using the reconstructed surface, and for motion-compensated dose delivery using the estimated 4D surface motion field in combination with external-internal correlation models.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Gridded daily weather data for North America with comprehensive uncertainty quantification

Abstract Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Severe Temperature Impact Study on Ferrite Electromagnetic Shielding for Wireless Electric Vehicle Charging

Wireless charging of an electric vehicle (EV) is an emerging technology that enables noncontact charging, which has advantages in terms of convenience and flexibility. However, the embedded charging pads under and on the ground expose the charging coils, ferrite shielding, and related components to a potentially severe external environment. To study the electromagnetic shielding performance under such extreme temperature conditions, ferrite shielding material's permeability characteristics are tested under temperatures as low as -50°C to as high as 240°C. Referring to the official recorded lowest and highest temperature of the United States, in this paper, it is assumed that the operation temperature ranges from -50°C to 57°C as the coldest winter and hottest summer environment for WPT application. Electromagnetic performance, shielding, and associated stray magnetic emissions are simulated by using three-dimensional (3D) transient simulations to study the impact of low and high temperature on electromagnetic shielding performance. Based on ferrite testing data, from -50°C low temperature to 57°C high temperature, the ferrite's relative permeability ranges from 2550 to 3727. The simulation study indicates that there won't be significant impact on the EM shielding performance for WPT operated under those severe environment temperature conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Tidal Stream Energy Resource Characterization in the Salish Sea

Tidal stream energy holds great potential as a renewable energy source in regions of the world where tidal currents are strong and predictable. The tidal stream energy resource of a region is strongly controlled by its tidal wave characteristics, the local bathymetry, and coastal geometric features. The Salish Sea, a large estuary on the Pacific Northwest coast, represents a great tidal stream resource because of its strong tidal currents in many tidal channels. However, the tidal resource in the Salish Sea has not been systematically characterized, primarily because of its large area and complex bathymetry and coastlines. This paper presents a modeling study conducted to characterize the tidal energy resource of the Salish Sea based on a high-resolution three-dimensional tidal hydrodynamic model of the Salish Sea, which was extensively validated using data derived from 10 tidal gauge stations and 132 Acoustic Doppler Current Profiler stations. Model validation results indicated the Salish Sea hydrodynamic model is skillful in simulating tidal wave propagation and velocity distributions in the Salish Sea. Based on model results, a total of 16 tidal channels with strong tidal currents, 9 in the San Juan Islands and Rosario Strait regions and 7 in the Puget Sound, were identified as hotspots for potential tidal energy development. Velocity probability distributions and exceedance curves of cross-channel average velocity magnitudes were calculated at all 16 channels based on the recommendations of the International Electrotechnical Commission Technical Specifications for tidal energy resource characterization. The tidal energy resource at the 16 hotspots was also characterized using power density distributions and kinetic energy fluxes. The ranking of the kinetic energy fluxes suggested that Admiralty Inlet, Rosario Strait, and Middle Channel are the top three hotspot sites in the Salish Sea for tidal stream energy development. The study demonstrated the need for a high-resolution 3-D modeling framework for accurate simulation of tidal currents in large complex estuarine systems, in the context of tidal energy resource characterization.

tidal stream energy, resource characterization, Sa↗