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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. Further, the reformulation allows variational inference with a small number (e.g., $\mathscr{O}$ (100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2019

Comparison of Pt-BaO/Al 2 O 3 and Pt-CeO 2 /Al 2 O 3 for NOx storage and reduction: Impact of cycling frequency

NOx reduction under net lean and near-stoichiometric conditions was carried out on Pt/Al 2 O 3 , Pt/CeO 2 /Al 2 O 3 and Pt/BaO/Al 2 O 3 washcoated monoliths to compare performance features and identify reaction pathways. The impact of the storage components (BaO, CeO 2 ) on the NOx conversion and byproduct (NH 3 and N 2 O) yields was quantified for a range of feed temperatures, reductant types (H 2 , and C 3 H 6 ), O 2 feed concentrations, and cycle times. The NOx storage functionality is essential for NOx reduction under net lean conditions while the oxygen storage functionality promotes NOx reduction for near-stoichiometric conditions. NOx conversion by H 2 under lean conditions is dependent on the NOx storage capacity of the catalyst, with Pt/CeO 2 /Al 2 O 3 and Pt/BaO/Al 2 O 3 exhibiting the highest NOx conversion below and above 300 °C, respectively. High NOx conversion is achieved over Pt/CeO 2 /Al 2 O 3 for anaerobic rich feeds at temperatures above 400 °C. Increasing the O 2 feed concentration enhances NOx conversion over Pt/CeO 2 /Al 2 O 3 below 400 °C but inhibits NOx conversion above 400 °C. The former is attributed to promotion of NO oxidation leading to NOx storage while the latter is attributed to O 2 inhibition of NO decomposition/reduction. Shorter cycle times increase the NOx conversion with C 3 H 6 as reductant over Pt/BaO/Al 2 O 3 under lean conditions and over PCA for the near-stoichiometric feed. The findings confirm that improved NOx storage utilization is mainly responsible for NOx conversion enhancement. A ceria redox pathway has only a secondary effect on NOx conversion under excess O 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Fenofibrate down-regulates the expressions of androgen receptor (AR) and AR target genes and induces oxidative stress in the prostate cancer cell line LNCaP

Highlights: ► Fenofibrate induces cell cycle arrest in G1 phase and apoptosis in LNCaP cells. ► Fenofibrate reduces the expressions of androgen receptor in LNCaP cells. ► Fenofibrate induces oxidative stress in the prostate cancer cell line LNCaP. -- Abstract: Fenofibrate, a peroxisome proliferator-androgen receptor-alpha agonist, is widely used in treating different forms of hyperlipidemia and hypercholesterolemia. Recent reports have indicated that fenofibrate exerts anti-proliferative and pro-apoptotic properties. This study aims to investigate the effects of fenofibrate on the prostate cancer (PCa) cell line LNCaP. The effects of fenofibrate on LNCaP cells were evaluated by flow cytometry, reverse transcription-polymerase chain reaction, enzyme-linked immunosorbent assays, Western blot analysis, and dual-luciferase reporter assay. Fenofibrate induces cell cycle arrest in G1 phase and apoptosis in LNCaP cells, reduces the expressions of androgen receptor (AR) and AR target genes (prostate-specific antigen and TMPRSS2), and inhibits Akt phosphorylation. Fenofibrate can induce the accumulation of intracellular reactive oxygen species and malondialdehyde, and decrease the activities of total anti-oxidant and superoxide dismutase in LNCaP cells. Fenofibrate exerts an anti-proliferative property by inhibiting the expression of AR and induces apoptosis by causing oxidative stress. Therefore, our data suggest fenofibrate may have beneficial effects in fenofibrate users by preventing prostate cancer growth through inhibition of androgen activation and expression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Androgen-responsive circular RNA circSMARCA5 is up-regulated and promotes cell proliferation in prostate cancer

Prostate cancer (PCa) is one of the most commonly diagnosed cancers in males worldwide. Circular RNA (circRNA) is a unique class of RNA transcribed by RNA polymerase II characterized by jointing 3′ and 5′ ends together via exon or intron circularization. However, the molecular functions of circRNAs in prostate cancer have rarely been explored. In present study, we found circ-SMARCA5 was up-regulated in prostate cancer samples compared to match normal tissues. We also observed circ-SMARCA5 expression was significantly induced after DHT treatment. Functional experiments showed circ-SMARCA5 acted as an oncogene in prostate cancer by promoting cell cycle and inhibiting cell apoptosis. We thought this study provided useful information for exploring circRNAs as potential therapeutic and prognostic targets for prostate cancer. - Highlights: • To our knowledge, this is the first study to explore the molecular functions of circRNA in prostate cancer. • Our study showed that circ-SMARCA5 promoted cell proliferation and inhibited cell apoptosis in prostate cancer. • circ-SMARCA5 was for the first time reported as an androgen-responsive circRNA. • circ-SMARCA5 was for the first time reported to be up-regulated in prostate cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

NMR-based serum metabolomics study reveals a innovative diagnostic model for missed abortion

A missed abortion (MA) is an in-utero death of the embryo or fetus before the 20th week of gestation with retained products of conception. In order to discover novel biomarkers for MA, a {sup 1}H NMR spectroscopy-based metabolomics approach was applied to detect human MA serum metabolic profiles. Serum samples were obtained from patients with MA (n = 15) and healthy controls (n = 9) for study. The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing MA patients from healthy subjects. The results revealed that 24 metabolites altered in MA patients compared with the control population. Metabolomic pathway analysis demonstrated that alanine, aspartate and glutamate metabolism, citrate cycle (TCA cycle), taurine and hypotaurine metabolism were significantly altered in MA. The results indicated that serum NMR-based metabolomic profiling method is sensitive and specific enough to distinguish MA and from healthy controls, this method could be developed as a clinically useful diagnostic tool for MA. The finding from the MA serum metabolic profiling shed a new light on further understanding of MA disease mechanisms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

HIF-1α induces immune escape of prostate cancer by regulating NCR1/NKp46 signaling through miR-224

Highlights: • This study clarified the mechanisms underlying prostate tumor cells escaping from NK cells mediated by HIF-1α. • NCR1 was identified as a target of miR-224, and the interaction between HIF-1α and miR-224 was validated in prostate cancer. • These findings of this study may facilitate the development of effective immunotherapy for metastatic prostate cancer. Metastasis of prostate cancer (PCa) is largely affected by natural killer (NK) cells. This study aimed to clarify the mechanisms underlying tumor cells escaping from NK cells mediated by HIF-1α.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

c-Myc-driven glycolysis via TXNIP suppression is dependent on glutaminase-MondoA axis in prostate cancer

Highlights: • c-Myc suppresses TXNIP transcription in a glutamine-dependent manner. • c-Myc induction of GLS1 is necessary for TXNIP suppression. • TXNIP represses glycolytic target genes broadly in c-Myc-driven cancers. • c-Myc and TXNIP levels are negatively associated in PCA tissues and predict patient outcome. Oncogenic c-Myc-induced metabolic reprogramming triggers cellular dependency on exogenous glucose and glutamine. Understanding how nutrients are used may provide new target for therapeutic intervention. We previously provided an alternate route to c-Myc-driven glucose metabolism via the repression of thioredoxin-interacting protein (TXNIP), which is a potent negative regulator of glucose uptake. Herein, we demonstrate that c-Myc suppression of TXNIP is predominantly through the activation of glutaminolysis via glutaminase (GLS1) in prostate cancer cells. Glutamine depletion blocked c-Myc-dependent reductions of TXNIP and its principal regulator MondoA transcriptional activity. Further, GLS1 inhibition by either siRNA or CB-839 resumed TXNIP expression that was repressed by c-Myc. The TXNIP promoter with mutant E-Box region, which was recognized by MondoA, failed to respond to c-Myc or GLS1, indicating c-Myc repression of TXNIP by GLS1 is predominantly through the blockage of MondoA activity. Especially, ectopic TXNIP expression decreased c-Myc-induce glucose uptake and lead to a broad range of glycolytic target gene suppressions. Thus TXNIP is a key adaptor for c-Myc-driven aerobic glycolysis. Supporting the biological significance of c-Myc and TXNIP, their reciprocal relationship are correlates with patient outcome and contributes to the aggressive phenotype in PCAs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cosolvent enhanced lignocellulosic fractionation tailoring lignin chemistry and enhancing lignin bioconversion

Cosolvent Enhanced Lignocellulosic Fractionation (CELF) is an emerging solvolysis pretreatment to fractionate lignocellulosic biomass. Herein, the bioconversion performance of CELF lignin was fully evaluated for the first time. Results showed that CELF lignin possessed higher content of carboxylic acid OH, lower molecular weight, and disappeared β-O-4 and β-5 linkages compared to other two technical lignins including a conventional ethanol organosolv lignin (EOL) and a kraft lignin (KL). Rhodococcus opacus PD630 cell count from CELF lignin fermentation reached the highest value of 3.9 10 7 CFU/mL, representing a 62.5% and 77.3% improvement over EOL and KL, respectively. Correspondingly, lipid yield reached 143 mg/L from CELF lignin, which was 36.2% and 26.5% higher than from EOL and KL, respectively. Principal component analysis (PCA) revealed that more carboxylic acid groups and lower molecular weight contributed to the enhanced bioconversion performance of CELF lignin. This study demonstrates that CELF lignin is a promising candidate for bioconversion.

09 BIOMASS FUELS↗
DOE OSTI2023

Data-driven analysis and prediction of wastewater treatment plant performance: Insights and forecasting for sustainable operations

Here this study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant's performance is conducted. The analysis involves independently assessing historical operational conditions, plant production, and their statistical correlations using various statistical techniques. The second objective focuses on developing a data-driven forecasting approach to predict the plant's production one month in advance, using multiple machine learning models. The results highlight the effectiveness of principal component analysis (PCA) in simplifying operational data, revealing distinct operational clusters, and identifying seasonal production patterns while showing correlations between operational conditions and overall power production. The support vector machine (SVM) forecasting model emerged as the top performer, showcasing the potential of a hybrid forecasting approach. The findings offer valuable perspectives for enhancing operational efficiency, refining production planning, and ultimately improving the environmental impact of the plant.

42 ENGINEERING↗
DOE OSTI2014

Purine derivatives as potent Bruton’s tyrosine kinase (BTK) inhibitors for autoimmune diseases

Investigation of various heterocyclic core isosteres of imidazopyrazines 1 & 2 yielded purine derivatives 3 & 8 as potent and selective BTK inhibitors. Subsequent SAR studies of the purine series led to the discovery of 20 as a leading compound. Compound 20 is very selective when screened against a panel of 400 kinases and is a potent inhibitor in cellular assays of human B cell function including B-Cell proliferation and CD86 cell surface expression and exhibited in vivo efficacy in a mouse PCA model. Its X-ray co-crystal structure with BTK shows that the high selectivity is gained from filling a BTK specific lipophilic pocket. However, physical and ADME properties leading to low oral exposure hindered further development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning

Here, we show that unsupervised machine learning (ML) using principal component analysis (PCA) provides a straightforward pathway for developing accurate and interpretable electronic-structure descriptors of the chemical and catalytic properties of materials. We demonstrate the approach by finding chemisorption descriptors for metal alloys and surface oxygens on metals and metal oxides. In both cases, the principal component (PC) descriptors yield ML models that predict the material’s chemical properties with competitive accuracy compared to ML models built using established descriptors. Importantly, interpreting the electronic-structure patterns captured by each PC descriptor via signal reconstruction suggests potential design motifs for future electronic-structure descriptor design and allows us to identify links between a material’s geometric and catalytic properties. Ultimately, we show that the unsupervised ML approach provides a route to find electronic-structure descriptors of the catalytic properties of materials that readily connect to geometric structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition

Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Surface evolution of synthetic bilgewater emulsion

Bilgewater is oil-in-water emulsion emitted from shipboard and it is a source of pollution to the ocean. Wastewater treatment needs fundamental knowledge of the bilgewater surface changes; however, limited information is available. We have reported the first surface characterization of synthetic bilgewater emulsion using time-of-flight secondary ion mass spectrometry (ToF-SIMS) coupled with optical microscopy. A NAVY synthetic bilgewater emulsion consisting of three oils and a detergent mixture is used as the synthetic bilgewater model. Both fresh and aged emulsion samples are analyzed to determine their droplet size distributions (DSDs) and surface chemical compositions. Our results show that fresh emulsions are largely mono-modal with hydrocarbon fragments as the main surface composition. Aged emulsions are also monomodal with slightly larger size. Both SIMS spectral comparison and Principal Component Analysis (PCA) show that some surfactant components appear on the fresh emulsion surface immediately while larger molecular weight components appear at the aged bilge droplet surface. Our results indicate that the oil-water interface evolves after emulsion droplet forms. More importantly, surface evolution not only changes the bilgewater DSD but also surface chemical composition and reactivity.

bilgewater, droplet size distribution, ToF-SIMS, s↗