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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Refining fast calorimeter simulations with a Schrödinger Bridge

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time.

Calorimeter methods↗
DOE OSTI2022

Structure of the Human BK Ion Channel in Lipid Environment

Voltage-gated and ligand-modulated ion channels play critical roles in excitable cells. To understand the interplay among voltage sensing, ligand binding, and channel opening, the structures of ion channels in various functional states and in lipid membrane environments need to be determined. Here, the random spherically constrained (RSC) single-particle cryo-EM method was employed to study human large conductance voltage- and calcium-activated potassium (hBK or hSlo1) channels reconstituted into liposomes. The hBK structure was determined at 3.5 Å resolution in the absence of Ca 2+ . Instead of the common fourfold symmetry observed in ligand-modulated ion channels, a twofold symmetry was observed in hBK in liposomes. Compared with the structure of isolated hSlo1 Ca 2+ sensing gating rings, two opposing subunits in hBK unfurled, resulting in a wider opening towards the transmembrane region of hBK. In the pore gate domain, two opposing subunits also moved downwards relative to the two other subunits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗
DOE OSTI2022

Combined Imaging and RNA-Seq on a Microfluidic Platform for Viral Infection Studies

The goal of this work was to pioneer a novel, low-overhead protocol for simultaneously assaying cell-surface markers and intracellular gene expression in a single mammalian cell. The purpose of developing such a method is to be able to understand the mechanisms by which pathogens engage with individual mammalian cells, depending on their cell surface proteins, and how both host and pathogen gene expression changes are reflective of these mechanisms. The knowledge gained from such analyses of single cells will ultimately lead to more robust pathogen detection and countermeasures. Our method was aimed at streamlining both the upstream cell sample preparation using microfluidic methods, as well as the actual library making protocol. Specifically, we wanted to implement a random hexamer-based reverse transcription of all RNA within a single cell (as opposed to oligo dT-based which would only capture polyadenylated transcripts), and then use a CRISPR-based method called scDash to deplete ribosomal DNAs (since ribosomal RNAs make up the majority of the RNA in a mammalian cell). After significant troubleshooting, we demonstrate that we are able to prepare cDNA from RNA using the random hexamer primer, and perform the rDNA depletion. We also show that we can visualize individually stained cells, setting up the pipeline for connecting surface markers to RNA-sequencing profiles. Finally, we test a number of devices for various parts of the pipeline, including bead generation, optical barcoding and cell dispensing, and demonstrate that while some of these have potential, more work is needed to optimize this part of the pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

On the Approximability of Random-Hypergraph MAX-3-XORSAT Problems with Quantum Algorithms

Constraint satisfaction problems are an important area of computer science. Many of these problems are in the complexity class NP which is exponentially hard for all known methods, both for worst cases and often typical. Fundamentally, the lack of any guided local minimum escape method ensures the hardness of both exact and approximate optimization classically, but the intuitive mechanism for approximation hardness in quantum algorithms based on Hamiltonian time evolution is poorly understood. We explore this question using the prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms for quantum exact and approximation hardness are fundamentally distinct. We qualitatively identify why traditional methods such as quantum adiabatic optimization are not good approximation algorithms. We propose a new spectral folding optimization method that does not suffer from these issues and study it analytically and numerically. We consider random rank-3 hypergraphs including extremal planted solution instances, where the ground state satisfies an anomalously high fraction of constraints compared to truly random problems. We show that, if we define the energy to be $E = N_{unsat}-N_{sat}$, then spectrally folded quantum optimization will return states with energy $E \leq A E_{GS}$ (where $E_{GS}$ is the ground state energy) in polynomial time, where conservatively, $A \simeq 0.6$. We thoroughly benchmark variations of spectrally folded quantum optimization for random classically approximation-hard (planted solution) instances in simulation, and find performance consistent with this prediction. We do not claim that this approximation guarantee holds for all possible hypergraphs, though our algorithm's mechanism can likely generalize widely. These results suggest that quantum computers are more powerful for approximate optimization than had been previously assumed.

Kapit, Eliot↗
DOE OSTI2020

Dynamic Response of a Semiactive Suspension System with Hysteretic Nonlinear Energy Sink Based on Random Excitation by means of Computer Simulation

This paper aims to investigate the property and behavior of the hysteretic nonlinear energy sink (HNES) coupled to a half vehicle system which is a nine-degree-of-freedom, nonlinear, and semiactive suspension system in order to improve the ride comfort and increase the stability in shock mitigation by using the computer simulation method. The HNES model is a semiactive suspension device, which comprises the famous Bouc–Wen (B-W) model employed to describe the force produced by both the purely hysteretic spring and linear elastic spring of potentially negative stiffness connected in parallel, for the half vehicle system. Nine nonlinear motion equations of the half vehicle system are derived in terms of the seven displacements and the two dimensionless hysteretic variables, which are integrated numerically by employing the direct time integration method for studying both the variables of vertical displacements, velocities, accelerations, chassis pitch angle, and the ride comfort and driver safety, respectively, based on the bump and random road inputs of the pseudoexcitation method as excitation signal. Simulation results show that, compared with the HNES model and the magnetorheological (MR) model coupled to the half vehicle system, the ride comfort and stability have been evidently improved. A successful validation process has been performed, which indicated that both the ride comfort and driver safety properties of the HNES model coupled to half vehicle significantly improved.

Chen, Hui↗
DOE OSTI2024

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Abstract We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

UQ Toolkit v 2.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterizaton and propagation of uncertainties in computational models. For the characterization of uncertainties, Bayesian inference tools are provided to infer uncertain model parameters, as well as Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, and also Karhunen-Loève expansions for representing stochastic processes. Uncertain parameters are treated as random variables and represented with Polynomial Chaos expansions (PCEs). The library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Safta, Cosmin↗
DOE OSTI2021

Space-Time Reduced-Order Modeling for Uncertainty Quantification

This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2021

A predictive discrete-continuum multiscale model of plasticity with quantified uncertainty

Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of uncertainty in the computational predictions delivered by them. In this work, a sequential multiscale model has been developed, incorporating discrete dislocation dynamics (DDD) simulations and a strain gradient plasticity (SGP) model to predict the size effect in plastic deformations of metallic micro-pillars. The DDD simulations include uniaxial compression of micro-pillars with different sizes and over a wide range of initial dislocation densities and spatial distributions of dislocations. An SGP model is employed at the continuum level that accounts for the size-dependency of flow stress and hardening rate. Sequences of uncertainty analyses have been performed to assess the predictive capability of the multiscale model. The variance-based global sensitivity analysis determines the effect of parameter uncertainty on the SGP model prediction. The multiscale model is then constructed by calibrating the continuum model using the data furnished by the DDD simulations. A Bayesian calibration method is implemented to quantify the uncertainty due to microstructural randomness in discrete dislocation simulations (density and spatial distribution of dislocations) on the macroscopic continuum model prediction (size effect in plastic deformation). Here, the outcomes of this study indicate that the discrete-continuum multiscale model can accurately simulate the plastic deformation of micro-pillars, despite the significant uncertainty in the DDD results. Additionally, depending on the macroscopic features represented by the DDD simulations, the SGP model can reliably predict the size effect in plasticity responses of the micropillars with below 10% of error.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hooks versus Pedicle Screws at the Upper Instrumented Level: An In Vitro Biomechanical Comparison

Study Design. Controlled laboratory study Objective. To compare motions at the upper instrumented vertebra (UIV) and supra-adjacent level (UIV+1) between two fixation techniques in thoracic posterior spinal fusion (PSF) constructs. We hypothesized there would be greater motion at UIV+1 after cyclic loading across all constructs and bilateral pedicle screws (BPS) with posterior ligamentous compromise would demonstrate the greatest UIV+1 range of motion (ROM). Summary of Background Data. Proximal junctional kyphosis (PJK) is a well-recognized complication following long thoraco-lumbar PSF, however its mechanism is poorly understood. Methods. Twenty-seven thoracic functional spine units (FSU) were randomly divided into three UIV fixation groups (n=9): (1) BPS, (2) bilateral transverse process hooks (TPH), and (3) BPS with compromise of the posterior elements between UIV and UIV+1 (BPS-C). Specimens were tested on a servohydraulic materials testing system in native state, following instrumentation, and after cyclic loading. FSUs were loaded in flexion-extension (FE), lateral bending (LB), and axial rotation (AR). Results. After cyclic testing, the TPH group had a mean 29.4% increase in FE ROM at UIV+1 versus 76.6% in the BPS group ( P <0.05). The BPS-C group showed an increased FE of 49.9% and 62.19% with sectioning of the facet joints and interspinous ligament respectively prior to cyclic testing. Conclusion. Bilateral pedicle screws at the UIV led to greater motion at UIV+1 compared to bilateral TPH after cyclic loading. This is likely due to the increased rigidity of BPS compared to TPH leading to a “softer” transition between the TPH construct and native anatomy at the supra-adjacent level. Facet capsule compromise led to a 49.9% increase in UIV+1 motion, underscoring the importance of preserving the posterior ligamentous complex. Clinical studies that account for fusion rates are warranted to determine if constructs with a “soft transition” result in less PJK in vivo.

Neurosciences & Neurology↗
DOE OSTI2022

NF-κB perturbation reveals unique immunomodulatory functions in Prx1 + fibroblasts that promote development of atopic dermatitis

Skin is composed of diverse cell populations that cooperatively maintain homeostasis. Up-regulation of the nuclear factor κB (NF-κB) pathway may lead to the development of chronic inflammatory disorders of the skin, but its role during the early events remains unclear. Here, through analysis of single-cell RNA sequencing data via iterative random forest leave one out prediction, an explainable artificial intelligence method, we identified an immunoregulatory role for a unique paired related homeobox-1 (Prx1) + fibroblast subpopulation. Disruption of Ikkb–NF-κB under homeostatic conditions in these fibroblasts paradoxically induced skin inflammation due to the overexpression of C-C motif chemokine ligand 11 (CCL11; or eotaxin-1) characterized by eosinophil infiltration and a subsequent T H 2 immune response. Because the inflammatory phenotype resembled that seen in human atopic dermatitis (AD), we examined human AD skin samples and found that human AD fibroblasts also overexpressed CCL11 and that perturbation of Ikkb–NF-κB in primary human dermal fibroblasts up-regulated CCL11. Monoclonal antibody treatment against CCL11 was effective in reducing the eosinophilia and T H 2 inflammation in a mouse model. Together, the murine model and human AD specimens point to dysregulated Prx1 + fibroblasts as a previously unrecognized etiologic factor that may contribute to the pathogenesis of AD and suggest that targeting CCL11 may be a way to treat AD-like skin lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Implementation of stacked ensemble machine learning for the detection of surrogate plutonium contamination in soil via LIBS

Supervised machine learning methods have demonstrated increased utility for the quantification of lanthanide and actinide elements in atomic spectroscopy applications. This study implements laser-induced breakdown spectroscopy (LIBS) for the identification of plutonium surrogate material (CeO 2 ) in soil matrices by training supervised machine learning methods on the recorded spectral data. A bagged ensemble using Random Forest yields the highest sensitivity predictions with a detection limit of 0.015 wt.% CeO 2 . However, high precision in Ce content prediction required the use of a stacked ensemble regression, which provided the superlative Ce quantification model with an error of 0.107% and a detection limit of 0.022 wt.%. Furthermore, the high performance of the stacked ensemble demonstrates its potential to enhance the accuracy and sensitivity of nuclear contaminant detection using field-deployable spectroscopic analyzers in real-world scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Performance and automatic calibration scheme of the waveform sampler in the ETROC2 ASIC chip

The waveform sampler in the CMS ETROC2 chip for LGAD gain aging monitoring is a 2.56-GS/s 12-bit 8x-Interleaved ADC that consists of a coarse SAR stage, and a fine stage. This architecture delivers high performance on a relatively modest 65 nm process, while requires finding up to 24 calibration constants through calibration. We developed an automatic calibration method using charge injection test data. After calibration, the baseline random error is reduced by a factor of 2.5–3 compared to the default calibration, and a 5% charge measurement precision is achieved in 15 fC charge injection tests.

Fu, Tao [Unlisted, US]↗