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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
DOE OSTI2018

Derivative-free robust optimization by outer approximations

We develop an algorithm for minimax problems that arise in robust optimization in the absence of objective function derivatives. The algorithm utilizes an extension of methods for inexact outer approximation in sampling a potentially infinite-cardinality uncertainty set. Clarke stationarity of the algorithm output is established alongside desirable features of the model-based trust-region subproblems encountered. We demonstrate the practical benefits of the algorithm on a new class of test problems.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1970

Economic gas chromatograph system for subambient pressure gas sampling

Gas chromatograph sampling system consists of a manifold with a gas-sample valve, a minimum-volume pressure transducer with a portable monitor, a vacuum-source valve, and a sample inlet valve. Increased accuracy of analysis is obtained by better control of sample size.

Mitchell, S. M.↗
DOE OSTI2018

Antibody Fucosylation Lowers the FcγRIIIa/CD16a Affinity by Limiting the Conformations Sampled by the N162-Glycan

Therapeutic monoclonal antibodies (mAbs) are largely based on the immunoglobulin G1 (IgG1) scaffold and many elicit a cytotoxic cell-mediated response by binding Fc γ receptors. Core fucosylation, a prevalent modification to the asparagine(N)-linked carbohydrate on the IgG1 crystallizable fragment (Fc), reduces Fc γ receptor IIIa (CD16a) binding affinity and mAb efficacy. We determined IgG1 Fc fucosylation reduced CD16a affinity by 1.7 ± 0.1 kcal/mol when compared to afucosylated IgG1 Fc, however, CD16a N-glycan truncation decreased this penalty by 1.2 ± 0.1 kcal/mol or 70%. Fc fucosylation restricted the manifold of conformations sampled by displacing the CD16a Asn162-glycan which impinges upon the linkage between the αmannose(1- 6)βmannose residues and promoted contacts with the IgG Tyr296 residue. Fucosylation also impacted IgG1 Fc structure as indicated by changes in resonance frequencies and nuclear spin relaxation observed by solution NMR spectroscopy. The effects of fucosylation on IgG1 Fc may account for the remaining 0.5 ± 0.1 kcal/mol penalty of fucosylated IgG1 Fc binding CD16a when compared to afucosylated IgG1 Fc. Our results indicated the CD16a Asn162-glycan modulates antibody affinity indirectly through reducing the volume sampled, as opposed to a direct mechanism with intermolecular glycan-glycan contacts previously proposed to stabilize this system. Thus, antibody engineering to enhance intermolecular glycan-glycan contacts will likely provide limited improvement and future designs should maximize affinity by maintaining CD16a Asn162-glycan conformational heterogeneity

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Electroless Plated Nanodiamond Coating for Stainless Steel Passivation

Tritium gas sample bottles and manifold components require passivation surface treatments to minimize the interaction of the hydrogen isotopes with surface contamination on the stainless steel containment materials. This document summarizes the effort to evaluate electroless plated nanodiamond coatings as a passivation layer for stainless steel. In this work, we developed an electroless nanodiamond (ND)-copper (Cu) coating process to deposit ND on stainless steel parts with the diamond loadings of 0%, 25% and 50% v/v in a Cu matrix. The coated Conflat Flanged Vessel Assemblies (CFVAs) were evaluated on surface morphology, composition, ND distribution, residual hydrogen release, and surface reactivity with deuterium. For as-received Cu and ND-Cu coated CFVAs, hydrogen off-gassing is rapid, and the off-gas rates of H 2 was one to two orders of magnitude higher than that for both untreated and electropolished stainless steel CFVAs, and hydrogen and deuterium reacted to form HD as well. These results indicated that residual H 2 was entrapped in the Cu and ND-Cu coated CFVAs during the coating process, and moisture was adsorbed on the surface, and ND and/or Cu might facilitate catalytic isotope exchange reaction for HD formation. However, hydrocarbons (i.e., CH 3 ) did not form, and did not appear to be an issue for the Cu and ND-Cu coated CFVAs. After vacuum heating, residual H 2 and adsorbed H 2 O in the Cu and ND-Cu coated CFVAs were dramatically reduced. The H 2 off-gassing rate after the vacuum treatment of Cu and 50% ND-Cu coated CFVAs was on the level of 10 -14 l mbar/s cm 2 , while H 2 O off-gas rate was on the level of 10 -15 l mbar/s cm 2 , consistent with the untreated or electropolished stainless steel CFVA, but the HD formation remained. The Restek EP bottle was used as a reference for this work. The Restek Electro-Polished (EP) bottle and their SilTek coated bottles tested under a different research project exhibited very little hydrogen off-gassing and unmeasurable HD formation. ND and Cu were initially chosen to develop improved passivation technology, because Cu has a lower permeability of hydrogen, and diamond is more inert than other materials under a hydrogen atmosphere. However, our tests demonstrated that even after an 8-18 day vacuum extraction heat treatment, the electroless plated Cu and ND-Cu coated stainless steel CFVAs exhibited H 2 off-gassing rates that were just comparable to those for the untreated or electropolished stainless steel CFVA, and the HD formation was still observed. Thus, the Restek Electro-Polished (EP) bottle outperformed the electroless plated Cu and ND-Cu coated stainless steel CFVAs, and the electroless plated nanodiamond coating is not promising as a surface passivation technology. However, the ND-Cu coating may be beneficial to another application in which catalyzing the H 2 -D 2 exchange reaction is desired.

08 HYDROGEN↗
DOE OSTI2016

Feasibility and Testing of Additive Manufactured Components

This project focused on demonstrating the ability to fabricate two parts with different geometry: an arc flash interrupter and a hydraulic manifold. Eaton Corporation provided ORNL solid models, information related to tolerances and sensitive parameters of the parts and provided testing and evaluation. ORNL successfully manufactured both components, provided cost models of the manufacturing (materials, labor, time and post processing) and delivered test components for Eaton evaluation. The arc flash suppressor was fabricated using the Renishaw laser powder bed technology in CoCrMo while the manifold was produced from Ti-6Al-4V using the Arcam electron beam melting technology. These manufacturing techniques were selected based on the design and geometrical tolerances required. A full-scale manifold was produced on the Arcam A2 system (nearly 12 inches tall). A portion of the manifold was also produced in the Arcam Q10 system. Although a full scale manifold could not be produced in the system, a full scale manifold is expected to have similar material properties, geometric accuracy, and surface finish as could be fabricated on an Arcam Q20 system that is capable of producing four full scale manifolds in a production environment. In addition to the manifold, mechanical test specimens, geometric tolerance artifacts, and microstructure samples were produced alongside the manifold. The development and demonstration of these two key components helped Eaton understand the impact additive manufacturing can have on many of their existing products. By working within the MDF and leveraging ORNL’s manufacturing and characterization capabilities, the work will ensure the rapid insertion and commercialization of this technology.

42 ENGINEERING↗
DOE OSTI2016

Soil Gas Sample Handling: Evaluation of Water Removal and Sample Ganging

Soil gas sampling is currently conducted in support of Nuclear Test Ban treaty verification. Soil gas samples are collected and analyzed for isotopes of interest. Some issues that can impact sampling and analysis of these samples are excess moisture and sample processing time. Here we discuss three potential improvements to the current sampling protocol; a desiccant for water removal, use of molecular sieve to remove CO 2 from the sample during collection, and a ganging manifold to allow composite analysis of multiple samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
DOE OSTI2019

Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds

Here, we demonstrate, on a scramjet combustion problem, a constrained probabilistic learning approach that augments physics-based datasets with realizations that adhere to underlying constraints and scatter. The constraints are captured and delineated through diffusion maps, while the scatter is captured and sampled through a projected stochastic differential equation. The objective function and constraints of the optimization problem are then efficiently framed as non-parametric conditional expectations. Different spatial resolutions of a large-eddy simulation filter are used to explore the robustness of the model to the training dataset and to gain insight into the significance of spatial resolution on optimal design.

42 ENGINEERING↗
NASA NTRS1985

An intercomparison of nitric oxide measurement techniques

Results from an intercomparison of techniques to measure tropospheric levels of nitric oxide (NO) are discussed. The intercomparison was part of the National Aeronautics and Space Administration's Global Tropospheric Experiment and was conducted at Wallops Island, VA, in July 1983. Instruments intercompared included a laser-induced fluorescence system and two chemiluminescence instruments. The intercomparisons were performed with ambient air at NO mixing ratios ranging from 10 to 60 pptv and NO-enriched ambient air at mixing ratios from 20 to 170 pptv. All instruments sampled from a common manifold. The techniques exhibited a high degree of correlation among themselves and with changes in the NO mixing ratio. Agreement among the three techniques was placed at approximately + or - 30 percent. Within this level of agreement, no artifacts or species interferences were identified.

Hoell, J. M., Jr.↗
DOE OSTI2020

Device for residue handling minimization with vacuum-assisted separations

A device for collection of residue from separation columns. A manifold provides for multiple sample ports that are fluidly connected to a vacuum port by at least one channel. Residue from the separation columns is pulled through the sample ports, into the channel, and then to the vacuum port. From the vacuum port the residue can be moved, under the force of the vacuum, directly to a drain or to a collection vessel connected with a drain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Generative models on phase space

Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introduce generative models which are, by construction, confined at every step of their sampling trajectory to the manifold of massless N-particle Lorentz-invariant phase space in the center-of-momentum frame. In the case of diffusion models, the "pure noise" forward process endpoint corresponds to the uniform distribution on phase space, which provides a clear starting point from which to identify how correlations among the particles emerge during the reverse (de-noising) process. We demonstrate that our models are able to learn both few-particle and many-particle distributions with various singularity structures, paving the way for future interpretability studies using generative models trained on simulated jet data.

Bogorad, Zachary [Fermilab]↗
DOE OSTI2019

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation) [SWR-26-095]

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation): Multifidelity aerodynamic polar data generation for hydrofoil/tidal-turbine airfoil sections. Foilpolars ties together three pieces: *AeroSandbox supplies the baseline airfoil coordinates (UIUC database). *G2Aero parameterizes those shapes on a Grassmannian manifold (Karcher mean + PGA basis) and samples new perturbed shapes around that basis. *XFoil (panel method) and NeuralFoil (neural-network surrogate, shipped with AeroSandbox) each solve the resulting shapes for lift, drag, moment, and pressure at the swept angles of attack, Reynolds numbers, and n_crit values. Design optimization of foil shapes in a computationally efficient way requires polars data across many candidate shapes, not just a handful of baseline foils. However, high-fidelity CFD at that scale is too costly, and naive shape perturbation strays from realistic geometries. FOILPOLARS addresses this by loading baseline airfoils (via AeroSandbox) and mapping them onto a Grassmannian manifold (via G2Aero), computing a Karcher mean and principal geodesic analysis (PGA) basis. New shapes are sampled by perturbing PGA coefficients, keeping them close to the manifold of realistic foils. Each sampled shape is evaluated across a configurable sweep of angle of attack, Reynolds number, and critical amplification factor using two solvers: XFoil (panel method) and NeuralFoil (neural-network surrogate), producing a paired dataset of lift, drag, moment, pressure, convergence, and confidence, indexed alongside each shape's PGA coefficients and shared Grassmannian basis in a single xarray dataset. From this, FOILPOLARS produces convergence summaries and comparison plots per shape, Reynolds number, and n_crit. A command-line interface exposes each pipeline stage independently, supporting data-driven design, optimization, and machine-learning workflows for foils.

Sandhu, Rimple [National Laboratory of the Rockies↗
NASA NTRS1972

Sampling functions for geophysics

A set of spherical sampling functions is defined such that they are related to spherical-harmonic functions in the same way that the sampling functions of information theory are related to sine and cosine functions. An orderly distribution of (N + 1) squared sampling points on a sphere is given, for which the (N + 1) squared spherical sampling functions span the same linear manifold as do the spherical-harmonic functions through degree N. The transformations between the spherical sampling functions and the spherical-harmonic functions are given by recurrence relations. The spherical sampling functions of two arguments are extended to three arguments and to nonspherical reference surfaces. Typical applications of this formalism to geophysical topics are sketched.

Giacaglia, G. E. O.↗
DOE OSTI2023

Deep nonparametric estimation of intrinsic data structures by chart autoencoders: Generalization error and robustness

Autoencoders have demonstrated remarkable success in learning low-dimensional latent features of high-dimensional data across various applications. Assuming that data are sampled near a low-dimensional manifold, we employ chart autoencoders, which encode data into low-dimensional latent features on a collection of charts, preserving the topology and geometry of the data manifold. Our paper establishes statistical guarantees on the generalization error of chart autoencoders, and we demonstrate their denoising capabilities by considering n noisy training samples, along with their noise-free counterparts, on a d-dimensional manifold. By training autoencoders, we show that chart autoencoders can effectively denoise the input data with normal noise. We prove that, under proper network architectures, chart autoencoders achieve a squared generalization error in the order of n–$\frac{2}{d+2}$log 4 n, which depends on the intrinsic dimension of the manifold and only weakly depends on the ambient dimension and noise level. We further extend our theory on data with noise containing both normal and tangential components, where chart autoencoders still exhibit a denoising effect for the normal component. As a special case, our theory also applies to classical autoencoders, as long as the data manifold has a global parametrization. Furthermore, our results provide a solid theoretical foundation for the effectiveness of autoencoders, which is further validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Biological sample collector

A biological sample collector is adapted to a collect several biological samples in a plurality of filter wells. A biological sample collector may comprise a manifold plate for mounting a filter plate thereon, the filter plate having a plurality of filter wells therein; a hollow slider for engaging and positioning a tube that slides therethrough; and a slide case within which the hollow slider travels to allow the tube to be aligned with a selected filter well of the plurality of filter wells, wherein when the tube is aligned with the selected filter well, the tube is pushed through the hollow slider and into the selected filter well to sealingly engage the selected filter well and to allow the tube to deposit a biological sample onto a filter in the bottom of the selected filter well. The biological sample collector may be portable.

47 OTHER INSTRUMENTATION↗
NASA NTRS2008

Deep-Sea Hydrothermal-Vent Sampler

An apparatus is being developed for sampling water for signs of microbial life in an ocean hydrothermal vent at a depth of as much as 6.5 km. Heretofore, evidence of microbial life in deep-sea hydrothermal vents has been elusive and difficult to validate. Because of the extreme conditions in these environments (high pressures and temperatures often in excess of 300 C), deep-sea hydrothermal- vent samplers must be robust. Because of the presumed low density of biomass of these environments, samplers must be capable of collecting water samples of significant volume. It is also essential to prevent contamination of samples by microbes entrained from surrounding waters. Prior to the development of the present apparatus, no sampling device was capable of satisfying these requirements. The apparatus (see figure) includes an intake equipped with a temperature probe, plus several other temperature probes located away from the intake. The readings from the temperature probes are utilized in conjunction with readings from flowmeters to determine the position of the intake relative to the hydrothermal plume and, thereby, to position the intake to sample directly from the plume. Because it is necessary to collect large samples of water in order to obtain sufficient microbial biomass but it is not practical to retain all the water from the samples, four filter arrays are used to concentrate the microbial biomass (which is assumed to consist of particles larger than 0.2 m) into smaller volumes. The apparatus can collect multiple samples per dive and is designed to process a total volume of 10 L of vent fluid, of which most passes through the filters, leaving a total possibly-microbe-containing sample volume of 200 mL remaining in filters. A rigid titanium nose at the intake is used for cooling the sample water before it enters a flexible inlet hose connected to a pump. As the water passes through the titanium nose, it must be cooled to a temperature that is above a mineral-precipitation temperature of 100 C but below the upper working temperature (230 C) of switching valves and tubes in the apparatus. The sample water then passes into a manifold tube, from whence the switching valves can direct the water through either a bypass tube or any one of the filter arrays, without contamination from a previous sample. Each filter array consists of series of filters having pore sizes decreasing in the direction of flow: 90-, 60-, 15-, and 7-micron prefilters and a large-surface-area 0.2-micron collection filter. All the filter taps are located between the intake and the bypass tube so that each time the bypass tube is used, the entire manifold tube is flushed as well.

Behar, Alberto E.↗