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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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49 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. In conclusion, the proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.

42 ENGINEERING↗
DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗
DOE OSTI2024

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗
DOE OSTI2023

Artificial Intelligence-Enhanced, Multi-Level, Modular System Design

As Moore’s Law and Dennard Scaling come to an end, it is becoming increasingly important to develop non-von Neumann computing architectures that can perform low-power computing in the domains of scientific computing, artificial intelligence, embedded systems, and edge computing. Next-generation computing technologies, such as neuromorphic computing and quantum computing, have the potential to revolutionize computing. However, in order to make progress in these fields, it is necessary to fundamentally change the current computing paradigm by codesigning systems across all system level, from materials to software. Because skilled labor is limited in the field of next-generation computing, we are developing artificial intelligence-enhanced tools to automate the codesign and co-discovery of next-generation computers. Here, we develop a method called Modular and Multi-level MAchine Learning (MAMMAL) which is able to perform analog codesign and co-discovery across multiple system levels, spanning devices to circuits. We prototype MAMMAL by using it to design simple passive analog low-pass filters. We also explore methods to incorporate uncertainty quantification into MAMMAL and to accelerate MAMMAL by using emerging technologies, such as crossbar arrays. Ultimately, we believe that MAMMAL will enable rapid progress in developing next-generation computers by automating the codesign and co-discovery of electronic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2022

Real-Time Hardware-in-the-Loop Testbed to Evaluate FLISR Implemented with OpenFMB

With the increasing complexity of the distribution smart grid architecture, algorithms such as the fault location, isolation, and service restoration (FLISR) scheme rely on robust communications that are resilient to natural and man-made adverse conditions and exhibit robustness. Existing communications infrastructure for information exchange are centralized at the distribution management system, with very little autonomy or intelligence at the grid-edge. As a first step towards achieving grid-edge self-healing, this paper aims to bridge this shortcoming by implementing a centrally coordinated rules-based FLISR scheme and integrating it with Open Field Message Bus (OpenFMB), which is a flexible publish-subscribe architecture with the potential to enable point-to-multipoint communications and is more robust and resilient to natural and man-made adverse conditions. A proof of concept is developed to validate the centrally coordinated FLISR and OpenFMB mounted on an SEL-3360 computer that interacts with a simple feeder network of five SEL-651R relays, an SEL-3530 RTAC, and a hardware-in-the-loop testbed. Results demonstrate the efficacy of this approach in enabling direct, low-latency information exchange. OpenFMB's publish-subscribe data model also opens new ways to enable grid-edge interoperability among devices of different vendors interacting with different protocols.

Sundararajan, Aditya↗
DOE OSTI2022

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗
DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Distilling particle knowledge for fast reconstruction at high-energy physics experiments

Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources, in particular on edge devices. In this article, we consider proton-proton collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is a DNN that is trained to learn about the provenance of particles, as provided by the soft labels that are the GNN outputs, to predict whether or not a particle originates from the primary interaction vertex. The results indicate that for this problem, which is one of the main challenges at the HL-LHC, there is minimal loss during the transfer of knowledge to the small student network, while improving significantly the computational resource needs compared to the teacher. This is demonstrated for the distilled student network on a CPU, as well as for a quantized and pruned student network deployed on a field programmable gate array. Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. Such an approach can become essential at the HL-LHC experiments, e.g. to comply with the resource budget of their trigger stages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation (Workshop Report)

The Artificial Intelligence Enhanced Co-Design for Next Generation Microelectronics virtual workshop was held April 4-5, 2023, and attended by subject matter experts from universities, industry, and national laboratories. This was the third in a series of workshops to motivate the research community to identify and address major challenges facing microelectronics research and production. The 2023 workshop focused on a set of topics from materials to computing algorithms, and included discussions on relevant federal legislation and such as the Creating Helpful Incentives to Produce Semiconductors and Science Act (CHIPS Act) which was signed into law in the summer of 2022. Talks at the workshop included edge computing in radiation environments, new materials for neuromorphic computing, advanced packaging for microelectronics, and new AI techniques. We also received project updates from several of the Department of Energy (DOE) microelectronics co-design projects funded in the fall of 2021, and from three of the Energy Frontier Research Centers (EFRCs) that had been funded in the fall of 2022. The workshop also conducted a set of breakout discussions around the five principal research directions (PRDs) from the 2018 Department of Energy workshop report: 1) define innovative material, device, and architecture requirements driven by applications, algorithms, and software; 2) revolutionize memory and data storage; 3) re-imagine information flow unconstrained by interconnects; 4) redefine computing by leveraging unexploited physical phenomena; 5) reinvent the electricity grid through new materials, devices, and architectures. We tasked each breakout group to consider one primary PRD (and other PRDs as relevant topics arose during discussions) and to address questions such as whether the research community has embraced co-design as a methodology and whether new developments at any level of innovation from materials to programming models requires the research community to reevaluate the PRDs developed back in 2018.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

NSTX-U National Research Program: White Paper in Response to Call from FESAC Sub-Committee

Both scientific and technical innovation is needed for the realization of an attractive engineering solution for a timely and cost-effective Pilot Plant, the design and construction of which is the overarching recommendation of the FESAC Long Range Plan, and the 2021 NASEM Pilot Plant reports, which underpin the Bold Decadal Vision. The two most significant plasma physics gaps to close for a Compact Pilot Plant (CPP) are core confinement improvement and heat flux mitigation, neither of which have been closed in an integrated fashion for any planned fusion power production device. High core confinement and stability are essential for producing majority self-driven plasmas in CPPs with reduced size and auxiliary heating power requirements, with an improvement in confinement being the major driver for cost reduction of a CPP. The National Spherical Tokamak Experiment - Upgrade (NSTX-U) is a unique low aspect ratio research facility that will address the fundamental challenge of developing the science and technology basis for a CPP design that integrates high core and edge confinement with the ability to mitigate very high incident heat fluxes. NSTX-U capabilities will enable the high performance, already achieved on NSTX, to extend into physics regimes much closer to those anticipated in Spherical Tokamak (ST)-based CPPs. These confinement and stability properties will be assessed by a full complement of diagnostics and analysis tools, which will also aid in the development of the underlying theory and predictive models needed for further optimization. Both conventional and transformative heat flux mitigation methods, such as liquid lithium plasma-facing components, will be developed and tested in-situ in NSTX-U at incident heat fluxes of ~100 MW/m 2 , and will inform plans and reduce risk for a subsequent major upgrade to the device to fully heated, high-Z wall and full liquid lithium divertor capability, a technology that potentially could then be implemented on any magnetic confinement device at any aspect ratio. NSTX-U research is fully complementary to programs performed on other STs, nationally and internationally. Furthermore, NSTX-U research has a direct connection to the private sector by informing design choices for future power production facilities being developed by these companies. The NSTX-U program will operate as a national User Facility, with collaborating researchers, engineers, and graduate students from 19 outside institutions, and open to participation and experiments led by researchers from both public and private entities. The research program will advance workforce development through training of young scientists, engineers, and technicians, and it will also serve for further diagnostic innovation, especially for high heat flux and high-Z wall environments, and implementation of advanced artificial intelligence (AI) for plasma and heat flux control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗