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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

MultiSector Dynamics: 2023 Inaugural Workshop Report

Preface The MultiSector Dynamics (MSD) Community of Practice (CoP) hosted an inaugural workshop on October 3-5, 2023 at the University of California, Davis, to bring together members of the MSD community of practice to advance understanding of the co-evolution of human and natural systems, and to build the next generation of tools that bridge sectors, scales, and systems to realize a more resilient and equitable future. The theme of the workshop was "Advancing Complex Adaptive Human-Earth Systems Science in a World of Interconnected Risks". This document outlines the motivation for the workshop, its goals and objectives, the application process, the agenda, overviews of the training sessions offered to the workshop participants and a summary of each breakout session. The MSD workshop report further discusses the feedback from workshop participants and presents some reflections and next steps. The MSD Workshop organizers thank the DOE Office of Science, Earth and Environmental System Modeling, MultiSector Dynamics program area for financial support of its activities through the Integrated Multisector Multiscale Modeling (IM3) project. For more information related to the broader DOE MultiSector Dynamics Program please see https://climatemodeling.science.energy.gov/program-area/multisector-dynamics. D.L.M. and C.M.B. acknowledge support from the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the US Department of Energy (DOE). Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither theUnited States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, complete- ness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial products, process, or service by trade name,trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Pacific Northwest National Laboratory operated by Battelle for the United States Department of Energy Available from:Office of Scientific and Technical Information http://www.OSTI.gov multisectordynamics.org This work is made available under the terms of the Creative Commons Attribution- NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4 Suggested citation: Monier, E., Reed, P.M., Vernon, C.R., Hadjimichael, A., Brelsford, C.M., Burleyson, C.B., Dyreson, A.R., Fletcher, S.M., Giang, A., Gupta, R.S., Jackson, N.D., Jones, A.D., Lamontagne, J.R., McCollum, D.L., Morris, J.F., Moss, R.H., Peng, W., Saari, R.K., Srikrishnan, V., Szinai, J.K., Yoon, J. (2024) MultiSector Dynamics: 2023 Inaugural Workshop Report. MSD-LIVE Data Repository. doi:10.57931/2371710.

Monier, Erwan↗
DOE OSTI2018

Multi‐dimensional lock‐free arrays for multithreaded mode‐directed tabling in Prolog

Summary This work proposes a new design for the supporting data structures used to implement multithreaded tabling in Prolog systems. Tabling is an implementation technique that improves the expressiveness of traditional Prolog systems in dealing with recursion and redundant computations. Mode‐directed tabling is an extension to the tabling technique that supports the definition of alternative criteria for specifying how answers are aggregated, thus being very suitable for problems where the goal is to dynamically calculate optimal or selective answers. In this work, we leverage the intrinsic potential that mode‐directed tabling has to express dynamic programming problems by creating a new design that improves the representation of multi‐dimensional arrays in the context of multithreaded tabling. To do so, we introduce a new mode for indexing arguments in mode‐directed tabled evaluations, named dim , where each dim argument features a uni‐dimensional lock‐free array. Experimental results using well‐known dynamic programming problems on a 32‐core machine show that the new design introduces less overheads and clearly improves the execution time for sequential and multithreaded tabled evaluations.

Areias, Miguel↗
DOE OSTI2022

Deciphering the Dynamic Molecular Program of Radiation-Induced Endothelial Senescence

Radiation-induced cellular senescence is a double-edged sword, acting as both a tumor suppression process limiting tumor proliferation, and a crucial process contributing to normal tissue injury. Endothelial cells play a role in normal tissue injury after radiation therapy. Recently, a study observed an accumulation of senescent endothelial cells (ECs) around radiation-induced lung focal lesions following stereotactic radiation injury in mice. However, the effect of radiation on EC senescence remains unclear because it depends on dose and fractionation, and because the senescent phenotype is heterogeneous and dynamic.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

A Vision for Co-optimized T&D System Interaction with Renewables and Demand Response

The evolution of the power system to the reliable, effi- cient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. The use of demand response to counterbalance the intermittency of re- newable generation brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distri- bution system, these resources are typically modeled as variables at the transmission network level. In this paper, a vision for co- optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system is described. In this frame- work, microgrids encompass consumers, distributed renewables and storage. The energy management system of the microgrid can also sell (buy) excess (necessary) energy from the transmission system. Preliminary work explores price mechanisms to manage the microgrid and its interactions with the transmission system. Wholesale market operations are addressed through the devel- opment of scalable stochastic optimization methods that provide the ability to co-optimize interactions between the transmission and distribution systems. Modeling challenges of the co-optimization are addressed via solution methods for large-scale stochastic op- timization, including decomposition and stochastic dual dynamic programming.

14 SOLAR ENERGY↗
DOE OSTI2016

Optimal Control of a Surge-Mode WEC in Random Waves

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from an array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Adaptive critic design-based reinforcement learning approach in controlling virtual inertia-based grid-connected inverters

In this report, an adaptive critic design (ACD) approach is proposed to control the phase and voltage of a grid-connected virtual synchronous generator (VSG). The penetration of fast responding inertia-less power converters significantly affect the stability of the power system, especially weak systems such as micro grids. The concept of virtual inertia addresses this concern by virtually emulating the behavior of a synchronous generator. However, the conventional VSG is designed based on two conditions: (i) fixed operating point and (ii) inductive grid connections. The performance of VSGs in low-voltage semi-resistive microgrids is far from optimal. To overcome the aforementioned concerns, a heuristic dynamic programing (HDP) approach is proposed to optimally control grid-connected VSGs. The neural-network-based inherence of the HDP enables the proposed technique to adapt to any impedance angle. The HDP controller includes two subnetworks: (i) the action network that controls the system optimally and (ii) the critic network, which evaluates the effectiveness of the action network. The simulation and experimental results are provided to evaluate the effectiveness of the proposed technique. As shown, the HDP-based approach illustrates a better performance in comparison with the conventional PI-based VSG in various operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Achieving designed texture and flows in bulk active nematics using optimal control theory

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Furthermore, our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

Complex systems theory↗
DOE OSTI2021

Overvoltage Prevention and Curtailment Reduction Using Adaptive Droop-Based Supplementary Control in Smart Inverters

Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.

42 ENGINEERING↗
DOE OSTI2018

Exact and heuristic algorithms for minimizing Tardy/Lost penalties on a single-machine scheduling problem

This paper addresses minimizing Tardy/Lost penalties with common due dates on a single machine. According to this penalty criterion, if tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. The problem is formulated as an integer programming model, and a heuristic algorithm is constructed. Then, using the proposed dominance rules and lower bounds, we develop two dynamic programming algorithms as well as a branch and bound. Experimental results show that the heuristic algorithm has an average optimality gap less than 2 % in all problem sizes. Instances up to 250 jobs with low variety of process times are optimally solved and for high process time varieties, the algorithms solved all instances up to 75 jobs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Final Report from The University of Texas at Austin for DEGAS: Dynamic Global Address Space programming environments

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. Our approach is to provide an efficient and scalable programming model that can be adapted to application needs through the use of dynamic runtime features and domain-specific languages for computational kernels. We address the following technical challenges: Programmability: Rich set of programming constructs based on a Hierarchical Partitioned Global Address Space (HPGAS) model, demonstrated in UPC++. Scalability: Hierarchical locality control, lightweight communication (extended GASNet), and ef- ficient synchronization mechanisms (Phasers). Performance Portability: Just-in-time specialization (SEJITS) for generating hardware-specific code and scheduling libraries for domain-specific adaptive runtimes (Habanero). Energy Efficiency: Communication-optimal code generation to optimize energy efficiency by re- ducing data movement. Resilience: Containment Domains for flexible, domain-specific resilience, using state capture mechanisms and lightweight, asynchronous recovery mechanisms. Interoperability: Runtime and language interoperability with MPI and OpenMP to encourage broad adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Nonadiabatic Molecular Dynamics by Multiconfiguration Pair-Density Functional Theory

Herein, we present the first implementation of multiconfiguration pair-density functional theory (MC-PDFT) ab initio molecular dynamics. MC-PDFT is a multireference electronic structure method that in many cases has a similar accuracy (or even better accuracy) the complete active space second-order perturbation theory (CASPT2) at a significantly lower computational cost. In this study, we introduced MC-PDFT analytical gradients into the SHARC molecular dynamics program for ab initio, nonadiabatic molecular dynamics simulations. We verify our implementation by examining the intersystem crossing dynamics of thioformaldehyde, and we observe excellent agreement with recent CASPT2 and experimental findings. Moreover, with MC-PDFT, we could perform dynamics simulations with the 12 electron in 10 orbitals active space that was computationally too expensive for direct dynamics with CASPT2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Charging Decision Making for Automated Electric Vehicles

Abstract: Overview/Significance With the emerging of autonomous driving and its application in electric vehicles, the charging decision making should be taken over by the vehicles. Autonomous electric vehicles will require the capability to make charging decisions according to the battery energy state, the travel demand and also the available charging infrastructure. The introduction of autonomous driving technology would remove the challenge of co-locating charging infrastructure with driver destinations and presents a driver-free method for EVs to reach nearby charging stations. This will significantly change the charging behavior of electric vehicles. EV driver will no longer need to be present at charging stations for charging actions. Automated EVs can drive to nearby charging stations to perform charging actions by themselves when necessary. Meanwhile, connected vehicles technology makes electric vehicles have the capability to sense and obtain pertinent information from nearby charging station networks and then calculate the corresponding costs and availability for charging. All these promising capabilities bring new opportunities to construct an automatic charging decision making system and improve energy efficiency of charging actions for automatic electric vehicles. Abstract: Methodology This research introduces optimization tools and case studies on charging decision making for automated electric vehicles under personal usage scenario. The designed optimization tools aim to provide charging strategies, i.e. the optimal choice of charging station outside home and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. Abundant real world EV data is utilized to construct stochastic energy consumption prediction model under realistic driving conditions. Advanced dynamic programming technology is utilized to model and solve the potential multi-stage decision making process by considering realistic uncertainties of EV energy cost and dynamics of itinerary information. Large-scale simulation studies using Chicago itinerary data are demonstrated to illustrate energy benefits from automatic charging decision making for future autonomous EVs. Abstract: Results/Conclusions Optimal automatic charging decision making and its energy benefits have been studied for personal automated electric vehicles. A data-driven method based on large amount of real-world data and the corresponding real-time update algorithm have been proposed to construct multi-channel stochastic energy consumption prediction model. Based on the energy cost prediction, dynamic multi-stage charging decision-making models are established for optimal strategies during a daily itinerary. Derived charging strategies can minimize the monetary and energy cost of charging actions for autonomous vehicles. Simulations and case studies show the potential ability of autonomous electric vehicles to reduce the range anxiety and charging infrastructure dependency. Automatic charging decision making can mitigate the pain from electric vehicle charging necessity outside home. In conclusion, automated electric vehicles equipped with an appropriate automatic charging decision making system will help EV driver to relieve from heavy time cost of recharging, reduce the energy cost of performing charging actions and improve the energy efficiency in future electrified transportation system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

TASK ALLOCATION IN GEO-DISTRIBUTED CYBER-PHYSICAL SYSTEMS

This paper studies the task allocation algorithm for a distributed test facility (DTF), which aims to assemble geo-distributed cyber (software) and physical (hardware in the loop components into a prototype cyber-physical system (CPS). This allows low cost testing on an early conceptual prototype (ECP) of the ultimate CPS (UCPS) to be developed. The DTF provides an instrumentation interface for carrying out reliability experiments remotely such as fault propagation analysis and in-situ testing of hardware and software components in a simulated environment. Unfortunately, the geo-distribution introduces an overhead that is not inherent to the UCPS, i.e. a significant time delay in communication that threatens the stability of the ECP and is not an appropriate representation of the behavior of the UCPS. This can be mitigated by implementing a task allocation algorithm to find a suitable configuration and assign the software components to appropriate computational locations, dynamically. This would allow the ECP to operate more efficiently with less probability of being unstable due to the delays introduced by geo-distribution. The task allocation algorithm proposed in this work uses a Monte Carlo approach along with Dynamic Programming to identify the optimal network configuration to keep the time delays to a minimum.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Connected Vehicle-Based Traffic Signal Coordination

This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.

42 ENGINEERING↗