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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗
DOE OSTI2011

Augmented lagrange hopfield network for economic dispatch with multiple fuel options

This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with multiple fuel options. The proposed ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrangian function. The advantages of ALHN over the conventional Hopfield neural network are easier use, more general applications, faster convergence, better optimal solution, and larger scale of problem implementation. The method solves the problem by directly searching the most suitable fuel among the available fuels of each unit and finding the optimal solution for the problem based on minimization of the energy function of the continuous Hopfield neural network. The proposed method is tested on systems up to 100 units and the obtained results are compared to those from other methods in the literature. The results have shown that the proposed method is efficient for solving the ED problem with multiple fuel options and favorable for implementation in large scale problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Foundation Models for the Electric Power Grid

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

AI-based power flow simulation↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Retina-inspired narrowband perovskite sensor array for panchromatic imaging

The retina is the essential part of the human visual system that receives light, converts it to neural signal, and transmits to brain for visual recognition. The red, green, and blue (R/G/B) cone retina cells are natural narrowband photodetectors (PDs) sensitive to R/G/B lights. Connecting with these cone cells, a multilayer neuro-network in the retina provides neuromorphic preprocessing before transmitting to brain. Inspired by this sophistication, we develop the narrowband (NB) imaging sensor combining R/G/B perovskite NB sensor array (mimicking the R/G/B photoreceptors) with a neuromorphic algorithm (mimicking the intermediate neural network) for high-fidelity panchromatic imaging. Compared to commercial sensors, we use perovskite “intrinsic” NB PD to exempt the complex optical filter array. In addition, we use an asymmetric device configuration to collect photocurrent without external bias, enabling a power-free photodetection feature. These results display a promising design for efficient and intelligent panchromatic imaging.

42 ENGINEERING↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
NASA NTRS2012

Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data

This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.

Duong, Tuan A.↗
DOE OSTI2021

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Abstract Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Preprocessing for Unintended Conducted Emissions Classification with ResNet

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Uncertainty Quantification of Classifying Mechanical Breach in the Crash and Burn Problem

The prediction of mechanical breach in the Crash and Burn problem is important for designing the full system to minimize the probability of loss of containment. Due to uncertainties in the impact angle, material properties, and material models, it is not possible to define an exact critical impact speed at which the system breaches. Furthermore, the cost of running a large scale sampling study to determine the empirical probability of breach is prohibitive. In this work, surrogate models from machine learning, namely logistic regression and artificial neural networks, are introduced to predict binary classification of pass versus breach from a limited set of samples. The structure and calibration of these classifiers is discussed, and a set of metrics for describing the performance of the classifiers is introduced. The classifiers are used on the UUR version of the Crash model to attempt prediction of failure probability and perform variable sensitivity analysis. Where a single sample of the computational model can take hundreds of CPU-hours, training and evaluating a classifier can take seconds or less, thus giving high predictive power in a relatively short time.

42 ENGINEERING↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
NASA NTRSDate not supplied

On the Probabilistic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

neural nets↗
DOE OSTI2020

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗
DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗