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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗
DOE OSTI2024

Land Resources for Wind Energy Development Requires Regionalized Characterizations

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. Here, we developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m 2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m 2 /MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m 2 and 0.05 ± 0.01 m 2 /MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

17 WIND ENERGY↗
DOE OSTI2025

Deep-learning-based domain adaptation for cavity fault prediction at Jefferson Laboratory

Superconducting radio-frequency (SRF) cavities are the core components of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab, providing high-power electron beams for nuclear physics experiments. The facility comprises 418 SRF cavities, and any fault in these cavities can lead to interruptions in the electron beam supply. Cavity faults are the leading cause of beam trips in CEBAF. Predicting and mitigating those faults before onset can help maintain normal operation. Existing models face challenges in distinguishing between normal and fault signals when changes occur in the underlying time-series data, from changes in control software, operational parameters, or the environment. This work proposes a deep learning domain adaptation model that leverages transfer learning to address fault prediction challenges by improving accuracy. The model is trained and fine-tuned using a dataset collected for faulty and normal operation using a data acquisition system in CEBAF. Our deep learning-based domain adaptation model achieves a prediction accuracy of 89.61% of the fault and normal signals. The developed model effectively predicts normal running signals compared to the baseline approach without domain adaptation. This capacity is essential for the fault prediction task in the CEBAF because of heavily imbalanced data containing vast amounts of normal signals. The model performs well for predicting faults several hundred milliseconds before the fault onset compared to other models where no adaptation is applied. Incorporating deep learning-based domain adaptation techniques will significantly improve the fault prediction performance.

Rahman, Md Monibor [Old Dominion Univ., Norfolk, V↗
DOE OSTI2018

Online Diagnosis of Performance Variation in HPC Systems Using Machine Learning

As the size and complexity of HPC systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variation due to shared resource contention as well as software- and hardware-related problems. Such performance variations can lead to failures and inefficiencies, and are among the main challenges in system resiliency. To minimize the impact of performance variation, one must quickly and accurately detect and diagnose the anomalies that cause the variation and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert the collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. Here, we evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98% of injected anomalies and consistently outperforms existing anomaly diagnosis techniques.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Analytical learning and term-rewriting systems

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques.

Laird, Philip↗
DOE OSTI2023

Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers

Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants—such as the Lyapunov exponent spectrum and the fractal dimension—in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using reservoir computing, a specific kind of recurrent neural network. Finally, results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model of the atmosphere, both typical test cases for numerical weather prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements

Event classification is one of the central components of automated disturbance analysis based on PMU measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. Furthermore, it is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Artificial intelligence (AI)/machine learning, and big data are transforming the global business environment. They have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an existing database of production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source artificial AI library developed by Google) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for aircraft engine design-space exploration during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.

Michael T Tong↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗
DOE OSTI2022

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition

Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film’s microstructural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Also phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation—DeepThermo. By using deep learning–based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e 10,000 for a real material. We also demonstrate DeepThermo’s performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

Yin, Junqi↗
NASA NTRSDate not supplied

SatCORPS Global Cloud Composite (GCC): the Design and Delivery of A High Quality, High Resolution, Global Cloud Product Available in Near-Real Time

The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) supports the development of an analysis ready and cloud-optimized data transformation pipeline and geospatial service enablement of a global cloud composite (GCC) product derived from global geostationary satellite imagery. This geospatial service will be available at high temporal and spatial resolution via the SatCORPS web mapping application for visualization and analysis as well as direct ingestion to common geospatial software and custom programming. The resulting global cloud composite products from the processing pipeline can then be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. Near real time global observations are created through the composition of five geostationary satellites that provides modelling and forecasting communities with the capability to provide high quality and timely information to start the projection process. The Global Cloud Composite product combines information from geostationary satellites, GOES-16, GOES-17, Himawari-8, Meteosat-11 and Meteosat-9 to create a single global composite netcdf file and images using the different products within the netcdf file. The SatCORPS team, though our Global Cloud Composite (GCC) product and web-based visualization tools including Geographical Information System (GIS) services provide near real time global cloud product information to both automated processes and traditional web users that is timely and high quality derived from geostationary satellites. The Global Cloud Composite product takes advantage of the scalable processing resources provided by the AWS batch service to provide new composites every thirty minutes. Because information from each of the low earth orbiting satellites is available on schedules tuned to the specific satellite, the processing algorithm temporally composites the final dataset as each satellite’s information becomes available. The SatCORPS team has leveraged our experience using Amazon Web Services (AWS) to build a low latency high availability tool that allows end users both human and automated to acquire high quality and high-resolution Geostationary Earth Orbiting (GEO) information at zero cost to the end user. This presentation will describe how we architected and implemented the service as well as lessons learned based on our experiences both developing and operating the system. The lessons learned include how we integrated multiple services including Amazon Batch, Amazon S3 and Amazon Lambda service to create a low cost but high-performance processing system that is capable of identifying and processing the most appropriate satellite overpass information into global cloud composites. We will also describe our web-based tools including our Geographic Information System that can be used for visualization and analysis. The products from the processing can be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. The SatCORPS Global Composite Cloud product provides sophisticated global composited cloud research products with very low latency that we see that as filling a rapidly growing need in the research and modelling community with no up-front nor ongoing costs associated with downloading or using the information.

AWS AMCE SMCE GCC SATCORPS GLOBAL CLOUD COMPOSITE ↗
DOE OSTI2024

Controlling optical-cavity locking using reinforcement learning

Abstract This study applies an effective methodology based on Reinforcement Learning to a control system. Using the Pound–Drever–Hall locking scheme, we match the wavelength of a controlled laser to the length of a Fabry-Pérot cavity such that the cavity length is an exact integer multiple of the laser wavelength. Typically, long-term drift of the cavity length and laser wavelength exceeds the dynamic range of this control if only the laser’s piezoelectric transducer is actuated, so the same error signal also controls the temperature of the laser crystal. In this work, we instead implement this feedback control grounded on Q-Learning. Our system learns in real-time, eschewing reliance on historical data, and exhibits adaptability to system variations post-training. This adaptive quality ensures continuous updates to the learning agent. This innovative approach maintains lock for eight days on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning Hamiltonians from disordered image data in quantum materials

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of V⁢O 2 . We then apply our trained convolutional neural network architecture to new optical microscope images of a different V⁢O 2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. Finally, we also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Exploring AI/ML-based Real-time Anomaly Detection in DUNE for Supernova Burst Neutrinos

The Deep Underground Neutrino Experiment (DUNE) is currently under construction with far detectors consisting of 4 liquid argon time projection chamber (LArTPC) modules at SURF (South Dakota Underground Research Facility) and a near detector complex with neutrino beam production at Fermilab to unambiguously determine neutrino mass ordering, to discover and precisely measure Charge-Parity (CP) violation phase in leptonic sector, to search for Beyond Stand Model (BSM) physics, and to study solar and supernova burst neutrinos. Anomalies in this project are classified in three categories: new physics signals, supernova burst neutrinos, and detector malfunction. We report here on promising early studies toward an Artificial Intelligence/Machine Learning-based real-time anomaly detection system, using a prototype autoencoder model currently under development. Additionally, the current status of an improved model and its performance will be presented. The model will be evaluated not only for its sensitivity to supernova neutrinos, but also to BSM physics signals and detector malfunctions. We will also consider how such a real-time algorithm might be used in DUNE.

de Jonge, Anselm [Kirchhoff Inst. Phys.] (ORCID:00↗