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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗
DOE OSTI2022

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

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

Land-Based Wind Reference Architecture

This report describes a summary and the final deliverables for the Wind Reference Architecture project funded by the Department of Energy's (DOE) Wind Energy Technology Office (WETO). The project objective was to further refine the reference architecture of the existing wind power plant reference architecture developed by Idaho National Laboratory (INL) and Sandia National Laboratories (SNL) by expanding the wind turbine generator (WTG) into a separate individual wind turbine reference architecture. Additional details regarding the wind turbine system and how it integrates with a wind power plant were researched and reflected in the reference architecture. This addition is important because it allows researchers to understand the components and devices in a wind turbine, how they function and how a wind turbine integrates into a wind power plant to be able to perform cybersecurity evaluations on the system. The communication and control systems were the focus while developing this reference architecture to understand the cybersecurity posture of a wind turbine and power plant. The information technology (IT) and operational technology (OT) systems perspective are integral in helping improve the cybersecurity posture by creating a starting point to study how wind energy can be safely designed and deployed in our power grid from an integrated systems standpoint. A holistic wind power plant and turbine reference architecture and simulation was developed and made open-sourced for further research efforts.

17 WIND ENERGY↗
DOE OSTI2024

Human readiness levels and Human Views as tools for user-centered design

The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. Here, this paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.

42 ENGINEERING↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multichannel meta-imagers for accelerating machine vision

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. Further, in this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

pnnl/UUDEX

UUDEX describes a communications architecture and protocol suite that allows organizations to exchange data and information. It does this by defining relations between a set of client nodes that share data via a set of server nodes. The primary focus of UUDEX is to facilitate communications between control centers, operations centers, and other trusted organization.

Welsh, Jeff↗
DOE OSTI2023

Development of an Energy Services Interface for the EGoT

The Energy Services Interface (ESI) is a set of rules that ensure private, secure, and trustworthy information exchange between Grid Service Providers (GSPs) and utility customers. Large-scale adoption of Distributed Energy Resources (DERs) will be necessary for GSPs to dispatch effective grid services, and to stimulate technological innovations in DER and DERMS (DER Management Systems) technologies, as well as novel grid service programs. The ESI promotes these objectives by advancing a set of rules and interoperability requirements that define bi-directional, service-oriented, logical interfaces between GSPs and customers’ DERs, with expectations for privacy, security, and trust. The ESI rules and interoperability requirements establish boundaries between customers and GSPs that delineate the functions and responsibilities that must be implemented by the developers of Energy Grid of Things (EGoT) ecosystem products. These rules impose constraints on the implementation of a DERMS. The ESI interoperability requirements are based on the Interoperability Maturity Model (IMM), developed by the Grid Modernization Laboratory Consortium. By emphasizing private, secure, and trustworthy information exchange, and by mandating a service-oriented and interoperable architecture, the ESI promotes the development of an EGoT ecosystem that motivates customer participation and technological innovation. Interoperability will encourage innovation by reducing barriers to entry and increasing confidence of stakeholders. Customers will be willing to participate in DER service programs that establish trust and emphasize customer choice. Large-scale customer participation ensures GSPs have ample DER resources to provide grid services that have significant impact on grid reliability and reduce electricity cost for consumers. This in turn signals economic opportunities that encourage innovation, resulting in the development of a robust EGoT ecosystem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Topological and Dynamical Representations for Radio Frequency Signal Classification

Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.

Myers, Audun D.↗
DOE OSTI2025

DNN-based Signal Processing for Liquid Argon Time Projection Chambers

We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground Neutrino Experiment.

Bhat, Avinay [Chicago U.]↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2021

Managing Cyber Supply Chain Risk for Renewable Energy Technologies

On July 1, 2021, the U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) hosted a virtual workshop facilitated by the National Renewable Energy Laboratory (NREL). Cybersecurity supply chain experts, researchers, and leaders in government and industry came together to share information on current and future challenges in securing emerging technologies and technical architecture. From a cybersecurity perspective, we need to move from a cybersecurity approach that focuses principally on legacy asset owners to one that incorporates more emphasis on end-point device manufacturers and third-party integrators. Cybersecurity for the global digital supply chain for manufacturers of consumer end-point devices—such as smart solar inverters and smart electric vehicle (EV) chargers—will be critical to the future cyber health of the grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner

This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

SWAP Gate between a Majorana Qubit and a Parity-Protected Superconducting Qubit

High fidelity quantum information processing requires a combination of fast gates and long-lived quantum memories. Here, in this Letter, we propose a hybrid architecture, where a parity-protected superconducting qubit is directly coupled to a Majorana qubit, which plays the role of a quantum memory. The superconducting qubit is based upon a π-periodic Josephson junction realized with gate-tunable semiconducting wires, where the tunneling of individual Cooper pairs is suppressed. One of the wires additionally contains four Majorana zero modes that define a qubit. We demonstrate that this enables the implementation of a SWAP gate, allowing for the transduction of quantum information between the topological and conventional qubit. This architecture combines fast gates, which can be realized with the superconducting qubit, with a topologically protected Majorana memory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO Practice

As awareness around building energy consumption increases, practitioners are encouraged to consider performance aspects regarding the built environment more closely and find ways to improve its efficiency. Improvements in building information modeling (BIM) and building performance simulation (BPS) tools present opportunities to facilitate information communication with a wider range of stakeholders. The building sector can benefit from the integration of performance informatics; however, there has been limited success in utilizing available technologies that promote data integration and management in favor of enriching our knowledge and understanding of buildings as artifacts of information. This phenomenon was investigated by conducting a survey, together with a review of relevant literature, to depict the relevant challenges and opportunities for the architecture, engineering, construction, and owner-operated (AECO) industry, as it undergoes digital transformation, as well as the working practices that have formed around them. It is argued that the current tools available to practitioners do not support effective data serialization between design and analytics processes, affecting the collaboration between team members. Lastly, a series of functional goals are proposed to support a higher level of reliability in the ways information is mobilized, by rethinking the technologies and methods for organizing information systems.

42 ENGINEERING↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploration of superconducting multi-mode cavity architectures for quantum computing

Superconducting radio-frequency (SRF) cavities coupled to transmon circuits have proven to be a promising platform for building high-coherence quantum information processors. An essential aspect of this realization involves designing high quality factor three-dimensional superconducting cavities to extend the lifetime of quantum systems. To increase the computational capability of this architecture, we are exploring a multimode approach. This paper presents the design optimization process of a multi-cell SRF cavity to perform quantum computation based on an existing design developed in the scope of particle accelerator technology. We perform parametric electromagnetic simulations to evaluate and optimize the design. In particular, we focus on the analysis of the interaction between a nonlinear superconducting circuit known as the transmon and the cavity. This parametric design optimization is structured to serve as a blueprint for future studies on similar systems.

Reineri, Alessandro↗