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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2023

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Direction-optimizing Label Propagation and its Application for Community Detection

Label Propagation is a machine learning algorithm typically used for classification. It has also been found to be an effective method for detecting communities in networks. It has two attractive features as a community detection method: it has nearly linear runtime and it requires no \textit{a priori} community information. We propose a new Direction Optimizing Label Propagation Algorithm (DOLPA) that relies on the use of {\em frontiers} and alternates between label {\em push} and label {\em pull} operations to enhance the performance of LPA. Specifically, DOLPA has parameters for tuning the processing order of vertices in a graph. This reduces the number of edges visited and improves the quality of solution. We apply DOLPA to community detection and present the design and implementation of the algorithm as well as its shared-memory parallelization using OpenMP. Empirically, we evaluate our algorithm using synthetic graphs as well as real-world networks. Compared with the state-of-art \textit{Parallel Label Propagation} algorithm, we achieve at least two times the F-Score while reducing the runtime by 50\% for synthetic graphs with overlapping communities. We also compare DOLPA against the state-of-art parallel implementation of the Louvain method using the same graphs and show that DOLPA achieves about three times the F-Score at 10\% the runtime. On real-world graphs, we get a speedup of up to $10\times$ using 64 threads.

Liu, X↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal–organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Optimizing Classifiers for Radionuclide Identification

Identifying threat nuclear materials is a critical for the prevention of acts of nuclear terrorism on the homeland. For this purpose, many radionuclide identification devices are deployed in the field. However, these will not necessarily be in the hands of non-experts, therefore these devices need to provide ready-made answers for the personnel in the field. This is where advanced algorithms are employed to both interpret the data and provide the identification of the nuclear material being interrogated. We took a machine learning approach to identification, by using training and validation data sets to create and optimize classifiers which determine which radionuclide is consistent with the data. The classifiers investigated were the Random Forest, Decision Tree, Support Vector Machine, and XG Boost and their performance was judged using the F1 Score for both hyperparameter tuning and comparison. In the end, we found out that the Random Forest Classifier worked the best based off the F1 Score they got which was 0.98.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

A GPU-Accelerated Population Generation, Sorting, and Mutation Kernel for an Optimization-Based Causal Inference Model

We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.

Cho, Wendy K. Tam↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2022

Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers

Membrane polymers are a promising technology for use in many challenging gas separation applications. Here, the techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R 2 scores exceeding 0.82.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Additive manufacturing of metal matrix composites

Although Metal matrix composites (MMCs) are superior to most sought-after metallic alloys, their challenging fabricability has limited their widespread use in bulk-form applications. Among the many advanced fabrication techniques, Additive Manufacturing (AM), owing to its unique capabilities to produce near-net shapes, has drawn significant traction in the past two decades, especially for materials that are difficult to process using traditional methods. However, unlike pure metal/alloy systems, MMCs are highly sensitive to the processing conditions prevailing in AM techniques due to factors such as the high melting point of reinforcement particles and the potential for in-situ reactions. Therefore, it may be a while before metal matrix composites are commercially produced via AM. This review will discuss the current state-of-the-art design, fabricability, and performance of various additively manufactured MMCs. A particular focus will be on microstructural evolution and microstructure-property relationships. The most employed AM techniques, such as directed energy deposition, powder bed fusion, binder jetting, sheet lamination, and solid-state friction stir processing, are fundamentally different in terms of thermo-kinetics, forming the perspective for this review. A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. Finally, a critical evaluation of emerging AM technologies for MMCs is also provided, highlighting their potential advantages and challenges.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structure of chalcogen overlayers on Au(111): Density functional theory and lattice-gas modeling

Ordering of different chalcogens, S, Se, and Te, on Au(111) exhibit broad similarities but also some distinct features, which must reflect subtle differences in relative values of the long-range pair and many-body lateral interactions between adatoms. We develop lattice-gas (LG) models within a cluster expansion framework, which includes about 50 interaction parameters. These LG models are developed based on density functional theory (DFT) analysis of the energetics of key adlayer configurations in combination with the Monte Carlo (MC) simulation of the LG models to identify statistically relevant adlayer motifs, i.e., model development is based entirely on theoretical considerations. The MC simulation guides additional DFT analysis and iterative model refinement. Given their complexity, development of optimal models is also aided by strategies from supervised machine learning. The model for S successfully captures ordering motifs over a broader range of coverage than achieved by previous models, and models for Se and Te capture the features of ordering, which are distinct from those for S. More specifically, the modeling for all three chalcogens successfully explains the linear adatom rows (also subtle differences between them) observed at low coverages of ~0.1 monolayer. The model for S also leads to a new possible explanation for the experimentally observed phase with a (5 × 5)-type low energy electron diffraction (LEED) pattern at 0.28 ML and to predictions for LEED patterns that would be observed with Se and Te at this coverage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2024

Augmented Human Analysis (AHA)

Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.

42 ENGINEERING↗