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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A comparison of two Stokes ice sheet models applied to the Marine Ice Sheet Model Intercomparison Project for plan view models (MISMIP3d)

Here, we present a comparison of the numerics and simulation results for two "full" Stokes ice sheet models, FELIX-S (Leng et al. 2012) and Elmer/Ice. The models are applied to the Marine Ice Sheet Model Intercomparison Project for plan view models (MISMIP3d). For the diagnostic experiment (P75D) the two models give similar results (< 2 % difference with respect to along-flow velocities) when using identical geometries and computational meshes, which we interpret as an indication of inherent consistencies and similarities between the two models. For the standard (Stnd), P75S, and P75R prognostic experiments, we find that FELIX-S (Elmer/Ice) grounding lines are relatively more retreated (advanced), results that are consistent with minor differences observed in the diagnostic experiment results and that we show to be due to different choices in the implementation of basal boundary conditions in the two models. While we are not able to argue for the relative favorability of either implementation, we do show that these differences decrease with increasing horizontal (i.e., both along- and across-flow) grid resolution and that grounding-line positions for FELIX-S and Elmer/Ice converge to within the estimated truncation error for Elmer/Ice. Stokes model solutions are often treated as an accuracy metric in model intercomparison experiments, but computational cost may not always allow for the use of model resolution within the regime of asymptotic convergence. In this case, we propose that an alternative estimate for the uncertainty in the grounding-line position is the span of grounding-line positions predicted by multiple Stokes models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Adaptive Computing and Multi-Fidelity Strategies for Control, Design and Scale-Up of Renewable Energy Applications

We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

adaptive computing↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2020

NCSP Analytical Methods Subtask 3, AMPX Development and Maintenance, and NCSP Nuclear Data Subtask 6, SAMMY Modernization

The modernization of SAMMY continued with the removal of all container array access for the main program. The container array was used due to the computer limitations at the time of the initial writing of SAMMY, but it prevented the use of some modern code testing options. Additionally, the authors continued to make the code more modular by shifting the storage of the energy grid and the covariance information to C++ storage, thus allowing for better unit testing and the elimination of scratch files. For AMPX, the authors continued to implement the new Evaluated Nuclear Data File format to allow AMPX to read new evaluations as they become available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Voltage regulation in distribution grids: A survey

Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making voltage regulation more relevant than ever. Owing to this and rapid advancements in sensing, communication, and computation technologies, the literature on voltage control techniques is growing at a rapid pace in distribution networks. In particular, there is a paradigm shift from traditional offline centralized approaches to distributed ones leveraging increased and varied types of actuators, real-time sensing, fast and efficient computations, and an overall distributed situational awareness. This paper reviews state-of-the-art voltage control algorithms, summarizes the underlying methods, and classifies their coordination mechanisms into local, centralized, distributed, and decentralized. The underlying solution methodologies are further classified into two categories, open-loop and feedback-based. Two specific example workflows are provided to illustrate these solutions for voltage regulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Implementation of the full viscoresistive magnetohydrodynamic equations in a nonlinear finite element code

Numerical simulations form an indispensable tool to understand the behavior of a hot plasma that is created inside a tokamak for providing nuclear fusion energy. Various aspects of tokamak plasmas have been successfully studied through the reduced magnetohydrodynamic (MHD) model. The need for more complete modeling through the full MHD equations is addressed here. Our computational method is presented along with measures against possible problems regarding pollution, stability, and regularity. The problem of ensuring continuity of solutions in the center of a polar grid is addressed in the context of a finite element discretization of the full MHD equations. A rigorous and generally applicable solution is proposed here. Useful analytical test cases are devised to verify the correct implementation of the momentum and induction equation, the hyperdiffusive terms, and the accuracy with which highly anisotropic diffusion can be simulated. A striking observation is that highly anisotropic diffusion can be treated with the same order of accuracy as isotropic diffusion, even on non-aligned grids, as long as these grids are generated with sufficient care. This property is shown to be associated with our use of a magnetic vector potential to describe the magnetic field. Several well-known instabilities are simulated to demonstrate the capabilities of the new method. The linear growth rate of an internal kink mode and a tearing mode are benchmarked against the results of a linear MHD code. The evolution of a tearing mode and the resulting magnetic islands are simulated well into the nonlinear regime. The results are compared with predictions from the reduced MHD model. Finally, a simulation of a ballooning mode illustrates the possibility to use our method as an ideal MHD method without the need to add any physical dissipation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗
DOE OSTI2024

A Novel Framework to Quantify Power Grid Resilience

The quantification of an operating power grid’s resilience is highly significant today, given its criticality as an enabler of other infrastructures, complexity, and the threat it faces due to a wide range of detrimental events, from extreme climate to cyber attacks. Currently, there exist no standardized definitions and metrics for measuring the resilience of an operating grid. In this paper, we introduce a novel resilience quantification framework and demonstrate a method to measure the flexibility towards topological/structural changes due to potential failures in the power grid to assess operational resilience. We start with the state estimation data from a large utility and use the graph analysis methods and power flow simulation tools to compute the identified resilience parameters.

Yoginath, Srikanth↗
DOE OSTI2019

Sculpt: Automatic Parallel Hexahedral Mesh Generation

Sculpt is a companion application to Cubit designed to run in parallel for generating all-hex meshes of complex geometry. It uses a unique overlay-grid procedure that extracts surfaces from a volume-fraction representation of the geometry. This allows for fast, automatic, fault-tolerant meshing in a high-performance computing (HPC) environment. Although Sculpt can be driven from Cubit as a GUI front-end, Sculpt was developed as a separate application so that it can be run independently from Cubit on HPC computing platforms. It was also designed as a separable software library so it can be easily integrated as an in-situ meshing solution within other codes. This work provides a brief technical discussion of the algorithms used in Sculpt as well as a complete user's manual. It includes details of the Cubit interface to Sculpt and the complete manual for the stand-alone application, including examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Neural Algorithms for Low Power Implementation of Partial Differential Equations

The rise of low-power neuromorphic hardware has the potential to change high-performance computing; however much of the focus on brain-inspired hardware has been on machine learning applications. A low-power solution for solving partial differential equations could radically change how we approach large-scale computing in the future. The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by grid cells in the brain. The second method tracks the densities of random walkers at each spatial location directly. We present the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions. Finally, we present implementations of these algorithms on neuromorphic hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Chapter 5: Integration of Renewable Energy Technologies in Buildings and Cities

Sustainable Energy Technologies, such as the solar, wind, and waterpower systems described in other chapters of this book, need to be integrated into our energy systems to expand access to energy while avoiding harmful effects on the environment. The existing utility system or 'grid' is a network that consists of components for generation, transmission, distribution, and control of electric power from producers (also called 'resources') to consumers (also called 'loads' or 'demand'). Public safety necessitates that the grid be reliable, and public policy requires that grid power be affordable and broadly accessible. Much of the existing power grid was constructed before the advent of modern information technology for controls and automation. The existing grid is facing new challenges related to increases in power demand, complexity in managing more diverse and distributed resources, power quality issues introduced by 'non-linear' loads (that have parasitic inductance and capacitance like LED lights or computers), physical and cybersecurity threats, and challenges to the existing economic model of costs and revenues. Increases in power demand include not only growth in population and per-capita electricity use, but also call for electricity to meet the need for heating and transportation as well. Electric vehicles are a rapidly growing example of this electrification of previously fossil-fueled uses. [Your author's Chevrolet Volt vehicle uses about the same amount of electricity as his house, around 300 kWh/month. Fortunately, both are served with solar photovoltaics (PVs)]. Therefore, there is an immediate need for improvement toward a reliable, self-regulating, efficient, and economical grid system that will allow the integration of renewable distributed power generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Situational Awareness of Grid Anomalies (SAGA)

The modern power industry becomes more vulnerable to cyber events due to the growing interconnectivity, interdependence, and complexity of the electric power grid. High-fidelity modeling and simulation tools that support the preventative risk analysis on potential cyber-relevant events is essential for ensuring the situational awareness of the system operator as it provides an inexpensive and risk-free environment to test the system responses under various cyber-relevant events and hereby can support research on cyber anomaly detection, optimal protective resource allocation, and mitigation measures. In this webinar, we will share NREL's cybersecurity research capabilities by highlighting the development of a scalable cyber-physical event test bed and demonstration with real hardware in the loop. The developed cyber-physical event test bed is backboned by an integrated transmission, distribution, and communication dynamic co-simulation framework and a plug-and-play cyber event generation module. It is designed to be modular and compatible with parallel computing, and thereby supports large-scale system simulations at an affordable computation cost. The test bed can capture millisecond-to-minutes dynamic frequency and voltage responses under cyber events from the bulk transmission system to the active distribution systems and distributed energy resources at the grid edge.

co-simulation↗
DOE OSTI2024

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation

Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Wind Turbine Modeling for Computational Fluid Dynamics

With the shortage of fossil fuel and the increasing environmental awareness, wind energy is becoming more and more important. As the market for wind energy grows, wind turbines and wind farms are becoming larger. Current utility-scale turbines extend a significant distance into the atmospheric boundary layer. Therefore, the interaction between the atmospheric boundary layer and the turbines and their wakes needs to be better understood. The turbulent wakes of upstream turbines affect the flow field of the turbines behind them, decreasing power production and increasing mechanical loading. With a better understanding of this type of flow, wind farm developers could plan better-performing, less maintenance-intensive wind farms. Simulating this flow using computational fluid dynamics is one important way to gain a better understanding of wind farm flows. In this study, we compare the performance of actuator disc and actuator line models in producing wind turbine wakes and the wake-turbine interaction between multiple turbines. We also examine parameters that affect the performance of these models, such as grid resolution, the use of a tip-loss correction, and the way in which the turbine force is projected onto the flow field.

17 WIND ENERGY↗
DOE OSTI2015

Computational imaging using a mode-mixing cavity at microwave frequencies

We present a 3D computational imaging system based on a mode-mixing cavity at microwave frequencies. The core component of this system is an electrically large rectangular cavity with one corner re-shaped to catalyze mode mixing, often called a Sinai Billiard. The front side of the cavity is perforated with a grid of periodic apertures that sample the cavity modes and project them into the imaging scene. The radiated fields are scattered by the scene and are measured by low gain probe antennas. The complex radiation patterns generated by the cavity thus encode the scene information onto a set of frequency modes. Assuming the first Born approximation for scattering dynamics, the received signal is processed using computational methods to reconstruct a 3D image of the scene with resolution determined by the diffraction limit. The proposed mode-mixing cavity is simple to fabricate, exhibits low losses, and can generate highly diverse measurement modes. The imaging system demonstrated in this letter can find application in security screening and medical diagnostic imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗