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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Balancing Charging Station Utilization and Throughput in Electric Vehicle Charging Stations with Queuing Theory

The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges by integrating queuing theory with modular EVSE architectures, offering a dual strategy to optimize the operation of EV charging stations. A simulation model was developed to assess various configurations of charger capacities and outlet numbers. This model aimed to identify the optimal setup that maximizes station utilization while minimizing charging times and maximizing throughput. The model focused on charger capacities ranging from 50 to 250 kW and analyzed the different capacities’ effects on charging times and the number of vehicles served. The results indicate that a charger capacity of 125 kW is optimal, striking a balance between the charging time and the number of EVs served per hour, thus achieving the highest station utilization rate. This capacity allows for servicing a significant number of EVs with moderate increases in charging times. Lower capacities, although capable of serving more vehicles, lead to longer charging times and decreased throughput efficiency. The study underscores the effectiveness of combining queuing theory with flexible, modular charging systems that can dynamically adjust to EV charging demands.

Kumar, Praveen↗
DOE OSTI2020

Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications

The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.

Bolme, David↗
DOE OSTI2022

Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor’s algorithm, weighted sum optimization problems in data processing and machine learning and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM’s superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands’ superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. Here, we analyze performance trends in our results and speculate on possible future development within this computational paradigm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Control of Grid-Connected Multiport MV Power Electronics Energy Hub: Preprint

In this paper a new concept of multiport modular medium-voltage power electronics hub (M3PE-HUB) is introduced for future power grid. The goal for this project is to design, develop, and demonstrate foundational technologies and capabilities for multiport power electronics energy hubs that can serve as intelligent devices to coordinate and control several different sources and loads. In this paper, the architecture of the controller, the central controls, and its verification is presented.

controls↗
DOE OSTI2017

An Efficient Modulation Strategy for Cascaded Photovoltaic Systems Suffering From Module Mismatch

Modular multilevel cascaded converter (MMCC) is a promising technique for medium/high-voltage high-power photovoltaic systems due to its modularity, scalability, and capability of distributed maximum power point tracking (MPPT) etc. However, distributed MPPT under module-mismatch might polarize the distribution of ac output voltages as well as the dc-link voltages among the modules, distort grid currents, and even cause system instability. For the better acceptance in practical applications, such issues need to be well addressed. Based on mismatch degree that is defined to consider both active power distribution and maximum modulation index, this paper presents an efficient modulation strategy for a cascaded-H-bridge-based MMCC under module mismatch. It can operate in loss-reducing mode or range-extending mode. By properly switching between the two modes, performance indices such as system efficiency, grid current quality, and balance of dc voltages, can be well coordinated. In this way, the MMCC system can maintain high-performance over a wide range of operating conditions. As a result, effectiveness of the proposed modulation strategy is proved with experiments.

14 SOLAR ENERGY↗
DOE OSTI2022

Scaling Superconducting Quantum Computers with Chiplet Architectures

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today’s transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Lastly, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.

quantum architecture↗
DOE OSTI2022

A Blockchain Based Co-Simulation Framework for Integrating DERs to Wholesale Electricity Markets

As the number of distributed energy resources (DERs) continue to increase across energy-delivery systems, there remains a need for integrating their capabilities into traditional grid operations. In this paper, a blockchain-based solution is proposed to facilitate FERC's Order No. 2222 implementations. The presented use-case enables small-scale DERs to participate in wholesale market operations through DER aggregators, while also enabling local distribution system operators to enforce distribution system constraints in a secure and traceable manner. The presented use case is built around the Unified Testing Platform (UTP) being developed as a part of the Blockchain for Optimized Security and Energy Management (BLOSEM) project. This is a multi-lab effort intended to simplify the deployment of blockchain-powered grid solutions by enabling the integration of simulation tools, and blockchain technologies through the use of system-agnostic interfaces that provide a modular, interoperable, and reusable connectivity layer.

blockchain interoperability↗
DOE OSTI2020

Highly Reliable Multi-Port Smart Inverter Modules for PV-Based Energy Systems

Generally, large electrolytic capacitors (E-caps) are required to decouple the double-line frequency power fluctuation inherent in all single-phase dc-ac or ac-dc converters. These E-caps pose the weak link in the photovoltaic (PV) inverter because they have poor reliability as a result of short life expectancy and higher equivalent series resistance (ESR) which leads to lower system efficiency. In this paper, multiport smart dual-inverter modules are proposed for residential PV inverter systems with balanced outputs to eliminate the requirement of large decoupling capacitors, thus leading to more reliable and highly efficient inverter modules. The balanced dual outputs are controlled in phase quadrature such that the double frequency current is completely eliminated. One phase supports the standalone residential loads, and the other phase interfaces with the grid, which is synchronized to the utility grid. Also, the proposed modular configuration is scalable to accommodate higher power rating by stacking and paralleling multiple modules. Simulation results for the dual-inverter modules rated for 1 kW each are presented to validate the proposed concept. Experimental results from a controller hardware-in-the-loop (CHIL) platform also verify its feasibility.

14 SOLAR ENERGY↗
DOE OSTI2022

Model-based interface design for smart field-device integration

Operational complexity is ever-increasing for electric utilities that face challenges including integration of DERs, customer expectation of energy choices, the proliferation of non-utility-owned resources, new business models with energy service providers, and new technology with IT/OT convergence. To maintain and improve the quality of operations, planning, and decision-making in general, utilities need to manage and navigate the complexity. Managing complexity requires a modular, scalable, and flexible solution. Connecting large amounts of DERs and introducing new services requires increased grid control and evolving applications. In this paper, we show an approach utilizing model-based standardized interfaces that simplifies integration and deployment of new algorithms and smart field devices for interoperability across legacy or new systems. A modular design is presented for a reference implementation of widely used DNP3 and IEEE 2030.5 interfaces within an open-source, standards-based data integration platform for integration of smart field devices with independently developed, best-of-breed applications.

Model-driven development, system integration, smar↗
DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗
DOE OSTI2020

A Distributed Control Architecture for Cascaded H-Bridge Converter with Integrated Battery Energy Storage

Cascaded H-bridge topology has been used in grid-tied converters for battery energy storage system due to its modular structure. To fully utilize the converter’s modularity, this paper proposes a hierarchical distributed control architecture that consists of primary control, secondary control and battery state of charge (SOC) balancing control. Primary control ensures accurate current tracking while a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. Here, a distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.

25 ENERGY STORAGE↗
DOE OSTI2022

7.2 kV Three-Port SiC Single-Stage Current-Source Solid-State Transformer With 90 kV Lightning Protection

This article proposes a multiport modular single-stage current-source solid-state transformer (SST) for applications like photovoltaic, energy storage integration, electric vehicle fast charging, data center, etc. The 7.2 kV 50 kVA current-source SST consists of five input-series output-parallel modules, each based on 3.3 kV SiC reverse-blocking MOSFET-plus-diode modules. The proposed SST has some unique features. First, compared to the voltage-source or matrix converter-based SSTs, the current-source SST has a unique advantage of single-stage isolated AC/DC or AC/AC conversion with an inductive DC link, but no medium-voltage (MV) AC experiments have been reported. This article for the first time demonstrates MV AC current-source SST up to 7.5 kV peak. Second, the multiport SST has a buffer port for active power decoupling (APD) or energy storage integration. The double-line-frequency power ripple from single-phase AC grid normally results in a large capacitor size in MV SSTs. The APD scheme is proposed in MV applications for the first time to enable a reduced DC link and the electrolytic capacitor-less SST with high reliability. Third, as a direct grid-connected converter without line-frequency transformer, insulation and protection are critical. A medium-frequency transformer design passes 55 kV basic-insulation level (BIL) and 60 kV high potential dielectrics withstand test with only 0.09% leakage inductance. Importantly, a lightning protection scheme is presented to protect the SST itself from 90 kV BIL impulse. Fourth, the proposed current-source SST topology is a modular soft-switching solid-state transformer (M-S4T) with full-range zero-voltage switching and controlled dv/dt for low electromagnetic interference. Furthermore, these concepts are verified in a three-port M-S4T prototype with forced oil cooling under single-module, stacked-module, steady-state, and dynamic operations.

14 SOLAR ENERGY↗
DOE OSTI2020

HAZARD ANALYSIS OF DIGITAL ENGINEERED SAFETY FEATURES ACTUATION SYSTEM IN ADVANCED NUCLEAR POWER PLANTS USING A REDUNDANCY-GUIDED APPROACH

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. This paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the triggers of potential single points of failure in the design of digital safety systems in advanced plant designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Towards adaptive spiking label propagation

Graph algorithms are a new class of applications for neuromorphic hardware. Rather than adapting deep learning and standard neural network approaches to a low-precision spiking environment, we use spiking neurons to analyze undirected graphs (e.g., the underlying modular structure). While fully connected spin glass implementations of spiking label propagation have shown promising results on graphs with dense communities, identifying sparse communities remains difficult. This work focuses on steps towards an adaptive spike-based implementations of label propagation, utilizing sparse embeddings and synaptic plasticity. Sparser embeddings reduce the number of inhibitory connections, and synaptic plasticity is used to simultaneously amplify spike responses between neurons in the same community, while impeding spike responses across different communities. We present results on identifying communities in sparse graphs with very small communities.

Hamilton, Kathleen↗
DOE OSTI2022

The SODA Approach: Leveraging High-Level Synthesis for Hardware/Software Co-design and Hardware Specialization: Invited

Novel "converged" applications combine phases of scientific simulation with data analysis and machine learning. Each computational phase can benefit from specialized accelerators. However, algorithms evolve so quickly that mapping them on existing accelerators is suboptimal or even impossible. This paper presents the SODA (Software Defined Accelerators) framework, a modular, multi-level, open-source, no-human-in-the-loop, hardware synthesizer that enables end-to-end generation of specialized accelerators. SODA is composed of SODA-Opt, a high-level frontend developed in MLIR that interfaces with domain-specific programming frameworks and allows performing system level design, and Bambu, a state-of-the-art high-level synthesis engine that can target different device technologies. The framework implements design space exploration as compiler optimization passes. We show how the modular, yet tight, integration of the high-level optimizer and lower-level HLS tools enables the generation of accelerators optimized for the computational patterns of converged applications. We then discuss some of the research opportunities that such a framework allows, including system-level design, profile driven optimization, and supporting new optimization metrics.

Bohm Agostini, Nicolas↗
DOE OSTI2022

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint

We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.

MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Implementation and Synthesis of Math Library Functions

Achieving speed and accuracy for math library functions like exp, sin, and log is difficult. This is because low-level implementation languages like C do not help math library developers catch mathematical errors, build implementations incrementally, or separate high-level and low-level decision making. This ultimately puts development of such functions out of reach for all but the most experienced experts. To address this, we introduce MegaLibm, a domain-specific language for implementing, testing, and tuning math library implementations. MegaLibm is safe, modular, and tunable. Implementations in MegaLibm can automatically detect mathematical mistakes like sign flips via semantic wellformedness checks, and components like range reductions can be implemented in a modular, composable way, simplifying implementations. Once the high-level algorithm is done, tuning parameters like working precisions and evaluation schemes can be adjusted through orthogonal tuning parameters to achieve the desired speed and accuracy. MegaLibm also enables math library developers to work interactively, compiling, testing, and tuning their implementations and invoking tools like Sollya and type-directed synthesis to complete components and synthesize entire implementations. MegaLibm can express 8 state-of-the-art math library implementations with comparable speed and accuracy to the original C code, and can synthesize 5 variations and 3 from-scratch implementations with minimal guidance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

PowerSystems.jl and PowerSimulations.jl [SWR-18-23]

Data structures in Julia to enable power systems analysis. Also, PowerSimulations.jl is a Julia package for power system modeling and simulation found at https://github.com/NREL/PowerSimulations.jl. The objectives of the package are: Provide a flexible modeling framework that can accommodate problems of different complexity and at different time-scales. Streamline the construction of large scale optimization problems to avoid repetition of work when adding/modifying model details. Exploit Julia's capabilities to improve computational performance of large scale power system simulations. The flexible modeling framework is enabled through a modular set of capabilities that enable scalable power system analysis and exploration of new analysis methods. The modularity of PowerSimulations results from the structure of the simulations enabled by the package: Simulations define a set of problems that can be solved using numerical techniques. Problems are generated by expressing model formulations against system data For example, an annual production cost modeling simulation can be created by formulating a unit commitment model against system data to assemble a set of 365 daily time-coupled scheduling problems.

Lara, Jose Daniel↗