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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A method of wind shear detection for powered-lift STOL aircraft

A new wind shear warning system for powered-lift STOL aircraft was evaluated by using a flight simulator. Wind shear warning systems for CTOL aircraft have been designed to detect horizontal shear only. Because the approach air speed of STOL aircraft is lower than that for CTOL aircraft, STOL aircraft are more vulnerable to vertical wind due to (1) a gradient of horizontal shear that is smaller for STOL than for CTOL aircraft because of slower airspeed; (2) STOL aircraft spend longer time in a downdraft; and (3) vertical wind causes a more radical change in the STOL aircraft's flight path because of its lower airspeed. In order to detect the vertical wind, the wind shear warning system proposed calculates the difference between potential flight path measured on-board during shear traversal and trimmed flight path estimated from aircraft status. The most characteristic feature of this new system is that it utilizes only inertial information and pitot-static airspeed data; this yields a convenient means of on-board implementation. Simulation test results confirm that the new system can detect the vertical shear.

Funabiki, Kohei↗
DOE OSTI2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. In addition, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Brownian motion, and Brownian motion with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy distributions. The numerical results demonstrate the score-based SDE solver’s stability, speed, and performance across different experimental settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Scalable Interior‐Point Gauss–Newton Method for PDE‐Constrained Optimization With Bound Constraints

Here, we present a scalable approach to solve a class of partial differential equation (PDE)‐constrained optimization problems with bound constraints. This approach utilizes a robust full‐space interior‐point (IP)‐Gauss–Newton optimization method. To cope with the poorly‐conditioned IP‐Gauss–Newton saddle‐point linear systems that need to be solved approximately, once per optimization step, we propose two spectrally related preconditioners. These preconditioners leverage the limited informativeness of data in regularized PDE‐constrained optimization problems. A block Gauss–Seidel preconditioner is proposed for the GMRES‐based solution of the IP‐Gauss–Newton linear systems. It is shown, for a large‐class of PDE‐ and bound‐constrained optimization problems, that the spectrum of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix is asymptotically independent of discretization and is not impacted by the ill‐conditioning that notoriously plagues interior‐point methods. We exploit symmetry of the IP‐Gauss–Newton linear systems and propose a regularization and log‐barrier Hessian preconditioner for the preconditioned conjugate gradient (PCG)‐based solution of the equivalent IP‐Gauss–Newton–Schur complement linear systems. The eigenvalues of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix, that are not equal to one, are identical to the eigenvalues of the regularization and log‐barrier Hessian preconditioned Schur complement matrix. The scalability of the approach is demonstrated on two example problems. The numerical solution of these optimization problems is shown to require a discretization independent number of IP‐Gauss–Newton linear solves. Furthermore, the linear systems are solved in a discretization and IP ill‐conditioning independent number of preconditioned Krylov subspace iterations. The parallel scalability of the preconditioner, achieved via algebraic multigrid component solvers when applicable, and the aforementioned algorithmic scalability permits a parallel scalable means to compute solutions of a large class of PDE‐ and bound‐constrained problems.

PDE-constrained optimization↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2025

X-ray spectroscopy of multi-temperature plasmas using the differential emission measure formalism

We present a theoretical construct that nominally underlies spectroscopic data analysis of multi-temperature plasmas, known as the differential emission measure (DEM). From a data analytic perspective, the DEM formalism is used to derive temperature distributions from line spectra that are formed in the presence of temperature gradients and by time integrations of evolving plasmas. From a modeling perspective, DEMs are convenient intermediaries between radiation hydrodynamics simulations and spectroscopic measurements acquired in the laboratory. The DEM concept and its associated methodologies were originally developed by spectroscopists working with astrophysical data. We borrow from these earlier investigations. In this manuscript, intended primarily as a tutorial, we discuss the basic concepts, but also augment various aspects of the theory by the way of extension and example, including a detailed treatment of various weighting and averaging schemes, intended to mitigate ambiguities that often arise when reporting temperature information. We focus on high-temperature plasmas that are not in local thermodynamic equilibrium and the x-ray spectra that they produce, although the core ideas presented here are applicable to spectroscopy in other energy bands. A few examples involving the derivation and manipulation of model DEMs in simple geometries are provided.

Liedahl, Duane A. [Lawrence Livermore National Lab↗
NASA NTRS1981

NASA satellite helps airliners avoid ozone concentrations

Results from a test to determine the effectiveness of satellite data for helping airlines avoid heavy concentrations of ozone are reported. Information from the Total Ozone Mapping Spectrometer, aboard the Nimbus-7 was transmitted, for use in meteorological forecast activities. The results show: (1) Total Ozone Mapping Spectrometer profile of total ozone in the atmosphere accurately represents upper air patterns and can be used to locate meteorological activity; (2) route forecasting of highly concentrated ozone is feasible; (3) five research aircraft flights were flown in jet stream regions located by the Total Ozone Mapping Spectrometer to determine winds, temperatures, and air composition. It is shown that the jet stream is coincides with the area of highest total ozone gradient, and low total ozone amounts are found where tropospheric air has been carried along above the tropopause on the anticyclonic side of the subtropical jet stream.

Source record↗
NASA NTRS1993

A technique for deriving column-integrated water content using VAS split-window data

An algorithm is examined that uses VISSR Atmospheric Sounder (VAS) 11- and 12-micron (split-window) data to derive column-integrated water content (IWC) at mesoscale resolution. The algorithm is physically based and derives its first-guess information from radiosonde data. The procedure is applied first to a test case data set and then to the 19 June 1986 study day from the Cooperative Huntsville Meteorological Experiment (COHMEX). Ground truth data for verifying results from the technique include IWC from National Weather Service and COHMEX radiosondes, the Multispectral Atmospheric Mapping Sensor (MAMS), and a special set of VAS soundings (12 channel) using an independent retrieval method. Results from the test case show reasonable accuracy with the rms errors as low as +/- 3.8 mm. On the 19 June case study day IWC analyses depict reasonable gradients and exhibit good spatial and temporal continuity. Furthermore, they provide insight into preferred regions for cumulus cloud and thunderstorm formation. On the average, a mean absolute retrieval error of 2.4 mm (an 8.1 percent error) and a rms error of +/- 2.9 mm are obtained on the case study day. These results compare favorably with those from existing VAS IWC techniques. Overall, the findings indicate that the technique has excellent potential to depict mesoscale moisture variations.

Guillory, Anthony R.↗
NASA NTRS2024

Hawai’i Climate: Utilizing NASA Earth Observations to Assess Thermal Stress Impacts on Coastal Hawaiian Fishponds

Hawaiian fishponds (loko i’a), ancient aquaculture systems created over 1500 years ago, historically served to harvest prized native fish species. Colonization-induced changes in land management have reduced active loko i’a from over 500 to ~20, destabilizing nearshore ecosystems and diminishing native Hawaiian cultural traditions. In recent decades, efforts to restore loko i’a have intensified as part of a broader emphasis on preserving Hawaiian cultural identity. Anthropogenic-induced climate change threatens these systems, particularly through the increase in frequency and magnitude of marine heatwaves. Though largely unexplored, these events are effectively studied utilizing remote sensing via Earth observations. Leveraging this technique enables autonomous extraction of water quality parameters to inform fishpond management. This study examined seven loko i’a across the main Hawaiian Islands (Hawai’i Island, Moloka’i, Maui, O’ahu, Kaua’i). Sea surface temperature and normalized difference chlorophyll index measurements were derived in fishponds utilizing Landsat 8 Thermal Infrared Sensor Thermal Infrared Sensor and Sentinel-2 MultiSpectral Instrument between 2013 to 2023, while regional patterns in temperature and chlorophyll-a were observed via Aqua’s Moderate Resolution Imaging Spectroradiometer (2002-2021). Employing these sensors, we focused analysis around the 2015 and 2019 marine heatwaves in the Pacific. We observed a heat stress gradient across the archipelago, creating thermally sheltered and unsheltered fishponds, yet identified no notable localized relationships between temperature and chlorophyll-a. Additionally, we applied pre-determined thermal maxima for culturally significant fish species (Striped Mullet: Mugil cephalus; Milkfish: Chanos Chanos) to assess historical habitat suitability within fishponds. While the direct impacts of marine heatwaves on loko i’a ecology are unknown, temperature patterns observed in this study are cause for concern.

Raz Wachtel↗
DOE OSTI2022

Trainability of Dissipative Perceptron-Based Quantum Neural Networks

Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we call dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at the layer’s output. We find that DQNNs can exhibit barren plateaus, i.e., gradients that vanish exponentially in the number of qubits. Moreover, we provide quantitative bounds on the scaling of the gradient for DQNNs under different conditions, such as different cost functions and circuit depths, and show that trainability is not always guaranteed. Here our work represents the first rigorous analysis of the scalability of a perceptron-based QNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
NASA NTRS1981

Measurement of sound absorption in the air

The large temperature gradient in each section of the resonance tube resulting from the liquid nitrogen coolant necessitated a design modification to the cooling system. A timer and four solenoid valves were installed so that the coolant flow can be reversed periodically. The hardware and software for controlling the analog to digital converter and conversion rate were completed, and the system is operational. A duty cycle control circit was implemented so that on the sixteenth conversion a relay shuts off the vibration exciter used to generate the sound wave. Thus the starting point of each decay curve is exactly known. This information is necessary for evaluating the g digital decay information. The data acquisition and digital decay evaluation programs are described.

Meredith, R.↗
NASA NTRS1985

A review and analysis of boundary layer transition data for turbine application

A number of data sets from the open literature that include heat transfer data in apparently transitional boundary layers, with particular application to the turbine environment, were reviewed and analyzed to extract transition information. The data were analyzed by using a version of the STAN5 two-dimensional boundary layer code. The transition starting and ending points were determined by adjusting parameters in STAN5 until the calculations matched the data. The results are presented as a table of the deduced transition location and length as functions of the test parameters. The data sets reviewed cover a wide range of flow conditions, from low-speed, flat-plate tests to full-scale turbine airfoils operating at simulated turbine engine conditions. The results indicate that free-stream turbulence and pressure gradient have strong, and opposite, effects on the location of the start of transition and on the length of the transition zone.

Gaugler, R. E.↗
DOE OSTI2020

The effect of microstructure on the relationship between grain boundary sliding and slip transmission in high purity aluminum

The relationship between grain boundary sliding (GBS) and slip transmission is investigated experimentally at grain boundaries in 99.99% aluminum with a through-thickness, coarse grained microstructure deformed in tension at 190 °C. Using scanning electron microscope enabled digital image correlation (SEM-DIC) and electron backscatter diffraction (EBSD), high resolution strain fields and microstructural information were measured to examine the influence of microstructural neighborhoods on interactions between GBS and slip transmission and strain localization. Several distinct cases are presented that highlight important microstructural factors that govern deformation near grain boundaries. The findings include (1) direct transmission and GBS were anti-compatible and facilitated by opposing boundary types (low misorientation and high energy grain boundaries respectively); (2) increased GBS activity was correlated with decreased indirect transmission behavior; (3) GBS accommodation at triple junctions was enabled by intragranular plasticity; and (4) the local intragranular plastic strain discontinuity between grains determined the magnitude of GBS gradients. Finally, this work provides insight into the nature of these mechanisms and can be used to identify strain transfer criteria that can lead to improved GBS-sensitive crystal plasticity models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Abstract Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow‐slip beneath the seismogenic zone within the more ductile portion of the crust. Monitoring efforts over 15 years detected >1 million LFEs. We train a gradient boosted tree model using statistical features describing the seismic waveforms to estimate the hourly LFE event count. The burst‐like LFE behavior is reproduced, while lower amplitudes are predicted during the most active periods. The hourly event counts are up to 18% greater than the catalog. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational‐intensive template‐matching catalog. Similar waveform statistical features are found between detecting LFEs and tremors, which provides additional evidence tremors are composed of LFEs. The approach extracts information contained in continuous seismic waveforms that might benefit detecting precursory signals.

58 GEOSCIENCES↗
NASA NTRS2001

The Science of Detached Bridgman Growth and Solutocapillary Convection in Solid Solution Crystals

Bridgman and Float-zone crystal growth experiments are planned for NASA's First Materials Science Research Rack using the European Space Agency's Materials Science Laboratory with the Low Gradient Furnace (LGF) and Float Zone Furnace with Rotating Magnetic Field (FMF) inserts, respectively. Samples will include germanium and germanium-silicon alloys with up to 10 atomic percent silicon. The Bridgman part of the investigation includes detached growth samples and so there will be a solid-liquid-gas tri-junction in those experiments just as there will be in all float-zone experiments. There are other similarities as well as significant differences between the types of growth that will be discussed. The presentation will call attention to the reasons that experiments in microgravity will provide information unattainable from Earth-based experiments.

Szofran, F. R.↗
NASA NTRS2002

Turbomachinery Course

In this viewgraph presentation, information and diagrams are provided on rocket engine turbopumps. These turbomachines are highly complex and have several unique features: (1) They are generally very high power density machines; (2) They experience high fluid dynamic loads; (3) They are exposed to severe thermal shocks in terms of rapid starts and stops and extremely high heat transfer coefficients; (4) They have stringent suction performance requirements to minimize tank weight; (5) Their working fluids significantly impact the design: oxidizers are generally explosive, they afford almost no lubrication for bearings and seals, some fuels can degrade material properties, cryogenics result in severe thermal gradients; (6) Their life requirements are short relative to other turbomachines in that there are hundreds of cycles and a few hours of operation for reusable systems. The design of rocket engine turbomachines is a systems engineering challenge because multiple engineering disciplines must be integrated to deal with issues pertaining to stress, structural dynamics, hydrodynamics, aerodynamics, thermodynamics, and materials and process selection.

Stinson, Henry↗
DOE OSTI2025

Genomic insights into local adaptation and migration success in reintroduced Coho Salmon of the Wenatchee River basin

ABSTRACT Objective Reintroduction of salmonids into regions where they have been extirpated is a common conservation strategy that is often implemented through natural recolonization, translocation of natural populations, or hatchery-based programs. Locally adapting to specific environmental conditions is critical for long-term population viability, particularly for species like Coho Salmon Oncorhynchus kisutch, which face diverse selective pressures during their migration. This study focused on the mid-Columbia River Coho Salmon reintroduction program managed by Yakama Nation Fisheries, which has successfully reintroduced Coho Salmon into the Wenatchee and Methow River basins, Washington. Notably, these populations have adapted to the longer migration route than those in the founding stock, with selection favoring individuals with an earlier arrival time and that can navigate a 15-km, high-gradient canyon to reach optimal spawning grounds. The objectives of this study were to investigate whether specific genomic regions are under selection for traits associated with return location and timing in Coho Salmon. Methods Low-coverage whole-genome resequencing data were used to screen for genomic regions associated with the phenotypes of interest. Results A weak polygenic signal in female Coho Salmon was found to be associated with return group, with a subset of candidate adaptive regions occurring across eight chromosomes. Conclusions These findings provide insights into the genomic mechanisms underlying local adaptation in reintroduced salmon populations and inform broodstock selection strategies aimed at promoting natural production and long-term population sustainability.

Horn, Rebekah L.↗
NASA NTRSDate not supplied

Design Under Uncertainty with Design-Dependent Uncertain Variables

Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.

Joanna N Schmidt↗