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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.

42 ENGINEERING↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we will instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.

James, Conrad D.↗
DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Dispersed Nickel Phthalocyanine Molecules on Carbon Nanotubes as Cathode Catalysts for Li-CO 2 Batteries

The Li-CO 2 battery has great potential for both CO 2 utilization and energy storage, but its practical application is limited by low energy efficiency and short cycle life. Efficient cathode catalysts are needed to address this issue. Herein, this work reports on molecularly dispersed electrocatalysts (MDEs) of nickel phthalocyanine (NiPc) anchored on carbon nanotubes (CNTs) as the cathode catalyst for Li-CO 2 batteries. The dispersed NiPc molecules efficiently catalyze CO 2 reduction, while the conductive and porous CNTs networks facilitate CO 2 evolution reaction, leading to enhanced discharging and charging performance compared to the NiPc and CNTs mixture. Octa-cyano substitution on NiPc (NiPc-CN) further enhances the interaction between the molecule and CNTs, resulting in better cycling stability. The Li-CO 2 battery with the NiPc-CN MDE cathode shows a high discharge voltage of 2.72 V and a small discharging–charging potential gap of 1.4 V, and can work stably for over 120 cycles. The reversibility of the cathode is confirmed by experimental characterizations. Finally, this work lays a foundation for the development of molecular catalysts for Li-CO 2 battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Effect of spatial distribution of polymer fibers on preventing spalling of UHPC at high temperatures

Polypropylene (PP) fibers are commonly used for the prevention of thermal spalling of ultra-high performance concrete (UHPC). In this work, the effect of fiber distribution on permeability and spalling resistance is investigated and an analytical model for permeability at 150 °C is proposed. This model, considering the parameters like fiber dimensions, dosage, and percolation, is based on Kozeny-Carman equation. It was found that the percolation of the interconnecting fiber network resulted in a significant increase in permeability of UHPC. X-ray tomography data on the three-dimensional spatial distribution of fibers reiterated that fiber aspect ratio and dosage (and fiber number density) were critical in increasing the fiber connectivity (percolation). It was also found that a vapor permeability of larger than 0.6 × 10{sup −16} m{sup 2} at 150 °C could eliminate spalling. Further, based on a semi-empirical approach, aspect ratio between 300 and 600 was recommended for spalling prevention with a fiber dosage of 0.3–0.4 vol%.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning assisted bayesian inference of mix and hot-spot conditions in NIF implosions

Experiments on the National Ignition Facility (NIF) have provided clear evidence of ablator material mixing into the Hot-Spot, leading to degraded performance. However, inferring the amount of mix and Hot-Spot conditions from typical experimental observations (e.g. x-ray spectra and images) is highly challenging. Here, we have developed an analysis method that utilizes machine learning assisted Bayesian inference to find the probability distributions of the Hot-Spot and mix conditions. This approach uses a neural network, trained on an idealized 2-dimensional representation of the Hot-Spot and mix distribution, and Bayesian inference to find the statistical distributions of Hot-Spot conditions that provide a match with observations. We have tested this method with synthetic data from simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The developments in modifying functionality of lignin and its application in biocomposites

With the advancement of sustainable material innovations, renewable natural biopolymers are gradually replacing traditional metal and petroleum-based synthetic materials due to their environmental friendliness, biodegradability, and economic advantages. Lignin, the second most abundant natural aromatic polymer in the plant kingdom, has emerged as a key candidate raw material for the development of green polymer systems because of its unique phenylpropane unit network structure, high carbon content, and rich functional group characteristics. However, challenges such as the inherent structural complexity, chemical inertness, and uneven molecular weight distribution of lignin limit its direct application. By employing modification strategies such as chemical functionalization and physical regulation, researchers can precisely control its reactivity, thermal stability, and interfacial compatibility, enabling the preparation of high-performance lignin-based functional composites. Here, this paper systematically reviews the principles and methodological advancements in lignin's multi-dimensional modification technology. It analyzes the mechanisms by which various chemical and physical modification techniques enhance the mechanical properties, functional responsiveness, and environmental adaptability of materials, and discusses innovative applications in fields such as intelligent packaging, biomedical materials, and energy storage devices. Furthermore, this review addresses the key challenges encountered in the high-value transformation of lignin, with the aim of offering a theoretical framework and technical pathway for the transformative development of lignin from agricultural and forestry by-products to functional material platforms.

Functional composites↗
DOE OSTI2025

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence rate of residuals at different training points, where the slowest convergence rate dominates the overall solution convergence. Based on these observations, we propose a pointwise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. In conclusion, through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

Balanced convergence rate↗
DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗
DOE OSTI2016

Systematic network assessment of the carcinogenic activities of cadmium

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Informative and non-informative decomposition of turbulent flow fields

Not all the information in a turbulent field is relevant for understanding particular regions or variables in the flow. Here, we present a method for decomposing a source field into its informative Φ I (x, t) and residual Φ R (x, t) components relative to another target field. The method is referred to as informative and non-informative decomposition (IND). All the necessary information for physical understanding, reduced-order modelling and control of the target variable is contained in Φ I (x, t), whereas Φ R (x, t) offers no substantial utility in these contexts. The decomposition is formulated as an optimisation problem that seeks to maximise the time-lagged mutual information of the informative component with the target variable while minimising the mutual information with the residual component. The method is applied to extract the informative and residual components of the velocity field in a turbulent channel flow, using the wall shear stress as the target variable. We demonstrate the utility of IND in three scenarios: (i) physical insight into the effect of the velocity fluctuations on the wall shear stress; (ii) prediction of the wall shear stress using velocities far from the wall; and (iii) development of control strategies for drag reduction in a turbulent channel flow using opposition control. In case (i), IND reveals that the informative velocity related to wall shear stress consists of wall-attached high- and low-velocity streaks, collocated with regions of vertical motions and weak spanwise velocity. This informative structure is embedded within a larger-scale streak–roll structure of residual velocity, which bears no information about the wall shear stress. In case (ii), the best-performing model for predicting wall shear stress is a convolutional neural network that uses the informative component of the velocity as input, while the residual velocity component provides no predictive capabilities. Finally, in case (iii), we demonstrate that the informative component of the wall-normal velocity is closely linked to the observability of the target variable and holds the essential information needed to develop successful control strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Promising Proton Conductor for Intermediate-Temperature Fuel Cells: Li 13.9 Sr 0.1 Zn(GeO 4 ) 4

Commercialization of fuel cell technologies hinges on the development of solid electrolytes of sufficient ionic conductivity at intermediate temperatures (200–600 °C). Here we report a novel proton conductor derived from Li 13.9 Sr 0.1 Zn(GeO 4 ) 4 (LSZG), demonstrating the highest protonic conductivity (0.034 S cm –1 at 600 °C) among all known proton conducting ceramics, which is much higher than those of several well-known oxygen ion conducting electrolytes (e.g., ~0.009 and 0.018 S cm –1 , respectively, for zirconia- and ceria-based oxide electrolyte at 600 °C). Interestingly, after fully replacing the mobile Li + ions by H + through proper ion exchange, the H+ conductivity increases from 0.034 to 0.048 S cm –1 at 600 °C. A simple but effective ab initio molecular dynamics simulation study suggests a unique H + /Li + transport mechanism: the proton in LSZG moves freely in the Li+ interstitial space within the 3D Li+ transport network (i.e., 4c and 4a sites, as the occupancies of the Li1 and Li 2 sites are 55% and 16%, respectively). In particular, a solid oxide fuel cell (SOFC) based on an LSZG electrolyte (~40 μm thick) demonstrates high open circuit voltage (~1.1 V) and good peak power density (377 mW cm –2 ) at 600 °C. The cell performance may be further improved if the electrode–electrolyte interface can be optimized. The new transport mechanism and excellent proton conductivity suggest that the LSZG represents an important family of electrolyte materials, which may be used as a proton-conducting membrane for intermediate-temperature SOFCs and hydrogen production or separation.

25 ENERGY STORAGE↗
DOE OSTI2019

110th Anniversary: Microkinetic Modeling of the Vapor Phase Upgrading of Biomass-Derived Oxygenates

Bio-oil produced from fast pyrolysis of biomass is a complex mixture of more than 200 compounds, including oxygenates and acids. As these species are highly undesirable in fuels, catalytic upgrading of biomass pyrolysis product vapors, also known as catalytic fast pyrolysis, is performed to upgrade the vapors to valuable fuels and chemicals. This work presents a detailed microkinetic model, composed of elementary steps, of the catalytic upgrading of acetic acid and acetone, two common oxygenates present in bio-oil. An automated network generator was utilized to construct a reaction network composed of 580 unique species and 2160 unique reactions. The kinetic parameters for each reaction in the network were estimated using transition state theory, the Evans-Polanyi relationship, and thermodynamic data. The resulting mechanistic model is able to describe experimental data presented in the literature for the transformation of acetic acid and acetone on HZSM-5 in a fixed-bed reactor, which is modeled as a plug-flow reactor. Additionally, the model solutions reveal vital information regarding the mechanism by which acetic acid and acetone are upgraded to valuable fuels and chemicals. In the first phase of the mechanism, acetic acid is converted to acetone via acylium ion addition to acetic acid; this is followed by decarboxylation of acetoacetic acid. The second phase is dominated by the self-aldol condensation of acetone, which is shown to occur predominantly through the keto form of acetone rather than the enol form, and subsequent deoxygenation reactions leading to olefins and aromatics. Finally, net rate analysis shows that aromatics are primarily formed via a pathway including aldol condensation of mesityl oxide, whereas olefins are produced from the addition of isobutene and subsequent cracking.

09 BIOMASS FUELS↗
DOE OSTI2017

Intermetallic Pd3Pb Nanowire Networks Boost Ethanol Oxidation and Oxygen Reduction Reaction with Significantly Improved Methanol Tolerance

Intermetallic nanocrystals are currently receiving extensive attentions due to their well- defined crystal structures, highly ordered atomic distribution and superior structural stability that endow them with optimized catalytic activities, stabilities and high selectivity as electrocatalysts for fuel cells. Here, for the first time, we reported the facile synthesis of intermetallic Pd3Pb nanowire networks (IM-Pd3Pb NNs) with a one-step wet-chemical strategy at a relatively low temperature (i.e. 170 °C) in 1 h. The as-prepared IM-Pd3Pb NNs exhibited superior bifunctional catalytic performances toward oxygen reduction reaction (ORR) and ethanol oxidation reaction (EtOR) compared to commercial Pt/C and Pd black, respectively. Significantly, IM-Pd3Pb NNs also showed excellent methanol- and CO-tolerant ability as ORR cathode and EtOR anode electrocatalysts, respectively. The electrochemical active surface area and mass activity of IM-Pd3Pb NNs are about 3.4 times and 2 times higher than Pd black toward EtOR, respectively. As the Pt-free bifunctional electrocatalysts, 3D IM-Pd3Pb architectures with exceptional catalytic performances hold great promise in various applica-tions of energy conversion and storage devices, sensors, electronics, optics and so on.

Shi, Qiurong↗
DOE OSTI2020

Crosslinked metallo-polyelectrolytes with enhanced flexibility and dimensional stability for anion-exchange membranes

High-performing anion-exchange membranes (AEMs) have attracted tremendous interest for applications in emerging energy storage and conversion devices. In this work, we present a strategy for the synthesis of crosslinked metallo-polyelectrolytes as mechanically flexible, dimensionally stable and ionically conductive AEMs. The water uptake and swelling ratio are suppressed remarkably by introducing a crosslinked polymeric network. The as-prepared membranes also exhibit excellent thermal stability. The phase-separated morphology allows rapid ion-transport with low water uptake at various temperatures. Specifically, a conductivity of 53.3 mS cm -1 at 80 °C was achieved with an ion exchange capacity of 1.07 mmol g -1 and a low swelling ratio of 13%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗