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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effectively Using Remote I/O For Work Composition in Distributed Workflows

Distributed scientific workflows are becoming more important with the interest in incorporating AI into their loops. A critical programming and performance question is how to compose workflow tasks when data is produced on one system but must be consumed on another. Since the dominant technique is composition with remote I/O, this paper explores its performance expectations. We describe BigFlowSim, a workflow I/O simulator that captures key implementation choices for remote I/O, including intensity, reuse, locality, access pattern, and data movement.With BigFlowSim, we generate a synthetic benchmark. We quantify the effects of each parameter with a performance sensitivity study. We explain trends in terms of data movement reduction and show that, under certain conditions, it is possible to establish a total order among most parameters. We apply these insights to a high energy physics workflow, Belle II Monte Carlo and simulate several I/O optimizations. Speedups range from 5% to 2×, without changing compute time.

Friese, Ryan D.↗
DOE OSTI2026

Elevating SolTrace's Capabilities for the Next Generation of Concentrating Solar Analysis

SolTrace is an open-source Monte Carlo ray tracing software developed at NREL. SolTrace can characterize concentrating solar thermal (CST) collector optical performance and is CST technology agnostic. Shown in Fig. 1, SolTrace is a foundational tool in NREL's CST system and component modeling suite. SolTrace's generic surface elements can flexibly model novel collector and receiver designs to predict spatial and temporal flux distributions - critical to understand for CST component design, performance prediction, and system integration. Since its initial development, SolTrace has over 1,650 references on Google Scholar, over 9,800 downloads since 2017, and has served the CST research and development community as a benchmark of 3rd party verification. SolTrace provides users with many options for defining surface shape and boundaries. However, SolTrace provides limited documentation which can result in a steep learning curve for new users. Additionally, SolTrace lacks the computational performance required to evaluate optical performance of a CST system over the course of a year and/or iteratively over design parameters in a timely manner. To address this, we are working towards a new release of SolTrace that enables increased computational throughput by implementing ray tracing acceleration structures and enabling GPU parallelization. Additionally, we are working to improve SolTrace's usability, accessibility, and maintainability by (1) automating solar position time-dependent simulation processes, (2) creating general CST collector templates of grouped elements, (3) updating the user interface to better visualize model inputs and outputs, and (4) creating a user support network through forums, "how to" videos, and documentation.

14 SOLAR ENERGY↗
DOE OSTI2020

Gradient-Based Multi-Area Distribution System State Estimation

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset

To satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have, we aimed at developing an updated version of the meteorological WIND Toolkit, named WIND Toolkit Long-term Ensemble Dataset (WTK-LED), which is a meteorological dataset providing time series every 5 min and 2 km, including model uncertainty of wind speed at every modeling grid point so that users are provided with a range of possible wind speeds every 2 km. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: 1) Numerical simulations covering the continental United States, Alaska, and Hawaii, with high-resolution data being available for 3 years (2018-2020). 2) Climate simulations from Argonne National Laboratories covering the North American continent, including Alaska, Canada, and most of Mexico and the Caribbean Islands. These simulations complement the new WTK-LED to offer a 4-km dataset covering 20 years, from 2001-2020. 3) Specific long-term,high-resolution offshore simulations have been conducted separately for the US coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind data set. This report focuses on a description of the land-based WTK-LED for CONUS, Hawaii, and Alaska, for the 3-year 2-km/5-min dataset and the 20-year 4-km/hourly dataset, as well as the uncertainty quantification method. We also provide limited validation results. Based on our results to date, we suggest use cases and applications for each dataset of the WTK-LED.

17 WIND ENERGY↗
DOE OSTI2020

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. Herein we present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Simulations Supporting the Development of Northstar's Indirect Beam Parameters Monitoring System

NorthStar Medical Radioisotopes, LLC is planning to produce the important medical radioisotope molybdenum-99 (Mo-99), the parent of technetium-99m (Tc-99m), through photonuclear reactions in molybdenum-100 (Mo-100). In this approach, a target comprising multiple thin disks of enriched molybdenum metal is bombarded with a 40-MeV electron beam. Electrons impinged on the molybdenum target produce bremsstrahlung X-rays that cause the nuclear reaction. Because enriched Mo-100 is expensive, there is a desire to utilize as much beam power as possible to achieve maximum production yield and minimize the size of the target. This requirement leads to very high beam power density (and heat deposition in the target), which creates challenging requirements for the cooling of the target. The critical part of the target is the target window. It separates the high-pressure helium cooled target from the vacuum beamline and the subject of structural and thermal stress. The temperature of the target window is proportional to the energy density deposited by the beam, so it is critical to maintain the desired beam profile on the target window. The feasibility of indirectly monitoring the maximum energy density of the beam on the beam window through beam losses at the main collimator (Collimator) before the production target was verified. A model of the NorthStar beam transport line was constructed for this purpose using MAD-X and Tao/Bmad codes. Beam optics were computed for the standard operational scenario, followed by an investigation involving approximately 400 cases with parameter variations in the last tuning quadrupoles. This was done to assess the correlation between losses in the collimator and the peak energy density on the target. We developed a model to explore the potential application of Optical transition radiation (OTR) for controlling beam parameters in the NorthStar beam delivery system. This model was based on a generic formula derived from the fundamental solution of the inhomogeneous wave equation of the vector potential, and allowed us to consider various surfaces, even those with irregular or random features, using numerical integration. We applied the model to OTR generated by relativistic electrons impacting an Inconel® 718 beam window. We examined cases with different levels of the window’s surface roughness, ranging from 0.5 to 3.0 microns of root square mean (RMS) deviation. The results of the OTR simulations provided distributions of OTR photons that can be used to study the limitations of optical systems for controlling beam parameters.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A Comprehensive Comparison of Methods for Evaluating Dispatch of Long-Duration Energy Storage in Power Systems Models

Long-duration energy storage (LDES) could play a pivotal role in the transformation of electricity grids with high shares of variable renewable energy (VRE) such as solar and wind. However, the weather-dependent nature of VRE introduces challenges for grid balancing and stability, which LDES - along with short-duration energy storage (SDES) - can help address. However, modeling LDES in production cost models (PCMs) is particularly challenging due to the need for high temporal resolution over extended optimization windows while preserving chronology, which ensures the alignment of energy storage operations with VRE generation over multi-day periods. This report compares traditional dispatch methods with advanced LDES dispatch strategies, such as the extended horizon approach, across different PCM platforms and examines tradeoffs and scalability. The comparison reveals that the traditional 1-day optimization horizon within the PCM leads to inefficient utilization of LDES. In contrast, extending the optimization horizon as much as possible significantly reduces curtailment and improves storage dispatch, especially in renewable-dense systems. There is also promise in using state-of-charge or end volume targets set by an external model, however this requires an additional modeling set and generally increases computational burden. This paper presents a comparison of these various methods in a number of power systems, showing algorithms initially in small test systems and scaling up to large, country-wide simulations. Overall, the research presents the trade-offs of various computational methods and illustrates how LDES may play an essential role in power systems of the future.

14 SOLAR ENERGY↗
DOE OSTI2024

Investigation of CAD-based Geometry Workflows for Multiphysics Fusion Problems Using OpenMC and MOOSE

Fusion system designs are complex and require intricate and accurate meshes to be properly modeled. In this study, we investigate the use of CAD-based geometry workflows in fusion systems multiphysics problems. A simplified tokamak was introduced and modeled in CAD using a multiphysics coupling of OpenMC Monte Carlo transport and MOOSE heat conduction. The meshed geometry was prepared using direct accelerated geometry Monte Carlo (DAGMC) for particle transport, and a volumetric mesh was also prepared to be used in MOOSE and to tally OpenMC results. Cardinal was used to run OpenMC Monte Carlo particle transport within MOOSE framework. The heat source distribution and tritium production were calculated in OpenMC. The data transfer system was used to transfer heat source and temperature distribution between OpenMC and MOOSE. Two computational studies related to mesh refinement were performed: (1) refining the DAGMC and volumetric meshes used for tallying results and solving heat conduction and (2) only refining the DAGMC particle transport mesh. The refinement of the tally mesh has a much larger effect on the runtime compared to the refinement of the DAGMC particle transport surface mesh.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Variational quantum eigensolver with reduced circuit complexity

Abstract The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenstates of a given Hamiltonian on noisy intermediate-scale quantum devices (NISQ). The practical realization is limited by the complexity of quantum circuits. Here we present an approach to reduce quantum circuit complexity in VQE for electronic structure calculations. Our ClusterVQE algorithm splits the initial qubit space into clusters which are further distributed on individual (shallower) quantum circuits. The clusters are obtained based on mutual information reflecting maximal entanglement between qubits, whereas inter-cluster correlation is taken into account via a new “dressed” Hamiltonian. ClusterVQE therefore allows exact simulation of the problem by using fewer qubits and shallower circuit depth at the cost of additional classical resources, making it a potential leader for quantum chemistry simulations on NISQ devices. Proof-of-principle demonstrations are presented for several molecular systems based on quantum simulators as well as IBM quantum devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Demystifying Cyberattacks: Potential for Securing Energy Systems With Explainable AI : Preprint

Modernization of energy systems has led to in- creased interactions among multiple critical infrastructures and diverse stakeholders making the challenge of operational decision making more complex and at times beyond cognitive capabilities of human operators. The state-of-the-art machine learning and deep learning approaches show promise of supporting users with complex decision-making challenges, such as those occurring in our rapidly transforming cyber-physical energy systems. However, successful adoption of data-driven decision support technology for critical infrastructure will be dependent on the ability of these technologies to be trustworthy and contextually interpretable. In this paper, we investigate the feasibility of implementing XAI for interpretable detection of cyberattacks in the energy system. Leveraging a proof-of-concept simulation use case of detection of a data falsification attack on a photovoltaic system using XGBoost algorithm, we demonstrate how Local Interpretable Model-Agnostic Explanations (LIME), a flavor XAI approach, can help provide contextual and actionable interpretation of cyberattack detection.

artificial intelligence↗
DOE OSTI2020

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

17 WIND ENERGY↗
DOE OSTI2024

Integrated photonic structures for photon-mediated entanglement of trapped ions

Trapped atomic ions are natural candidates for quantum information processing and have the potential to realize or improve quantum computing, sensing, and networking. These applications often require the collection of individual photons emitted from ions into guided optical modes, in some cases for the production of entanglement between separated ions. Proof-of-principle demonstrations of such photon collection from trapped ions have been performed using high-numerical-aperture lenses or cavities and single-mode fibers, but integrated photonic elements in ion-trap structures offer advantages in scalability and manufacturability over traditional optics. In this paper we analyze structures monolithically fabricated with an ion trap for collecting ion-emitted photons, coupling them into waveguides, and manipulating them via interference. We calculate geometric limitations on collection efficiency for this scheme, simulate a single-layer grating that shows performance comparable to demonstrated free-space optics, and discuss practical fabrication and fidelity considerations. Based on this analysis, we conclude that integrated photonics can support scalable systems of trapped ions that can distribute quantum information via photon-mediated entanglement.

Knollmann, Felix W.↗
DOE OSTI2022

Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers

Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To illustrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Co-Simulation Meets AI: MCP-Driven Power System Analysis

GridGPT, a fine-tuned Generative AI model is designed for on-premise use in grid control rooms. This presentation will demonstrate how eGridGPT can seamlessly integrate with control room solutions to offer operators, engineers, and corporate users enhanced guidance and decision support. It is to show how this innovative AI solution can improve state estimation, boost variable energy forecasting, and optimize grid operations. By leveraging eGridGPT's unique features, audience will learn to unlock new levels of automation, predictive analytics, and reliability within their power systems, ultimately leading to reduced downtime and improved operational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Distributed Conditions for Small-signal Stability of Power Grids and Local Control Design

Operating modern power grids with stability guarantees is markedly important. Typical methods for analyzing and certifying power grid stability are largely centralized relying on the ability of the system operator to gather network-wide information and accurately compute the system's eigenvalues. These methods are oftentimes not privacy-preserving and computationally burdensome. They are therefore, not well-suited to modern power grids where small-signal stability has to be evaluated timely, efficiently and in a privacy-preserving fashion. Herein, we introduce a distributed methodology for certifying small-signal stability of power grids and designing the local controllers. First, we analytically derive distributed conditions for network-wide stability that bus agents can inspect using local information. By leveraging these conditions, we then introduce a distributed control design algorithm (DCDA) that can guide the local control design so that stability of the interconnected system is guaranteed. The agents that adopt the proposed distributed algorithm are responsible for tuning their local controllers, producing their local control commands and ensuring that their local stability condition is met. The system operator is only responsible for verifying network-wide stability upon receiving affirmative responses from all agents and, announcing, that the overall system is stable. The proposed DCDA algorithm is numerically validated via simulations using the IEEE 39-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗