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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. Here, a peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

25 ENERGY STORAGE↗
DOE OSTI2019

Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios. Here, the results indicate variable energy savings depending on the drive route and initial battery SOC with potential ranging between 2 to 12% and less than 7 seconds initial optimization for a 24-mile drive cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Concepts and Meta-Programming Library

CAMP provides a portable optimized implementation of template meta-programming primitives. All capabilities found within are available in other open source projects, including metal and mpl among others, but no other library we know of supports all compilers required by LLNL codes. CAMP serves as an efficient facility for writing portable compile-time operations in C++ codes. It was developed as part of the RAJA project to facilitate more complex compile-time introspection and better error message delivery to users, but has since proven useful in Umpire, CHAI and other pre-release LLNL software projects and is thus being re-factored to stand alone. The CAMP library uses portable C++ template mechanisms almost exclusively, with the exception of intrinsics provided for performance by some compilers such as clang and gee. There are no private or protected interfaces and no proprietary information included in the source. Any and all future versions will limit themselves to implementing commonly available and well known primitives for compile-time manipulation of types. A new public github repository will be created to host the code independent of its current location in the raja repository, likely at github.com/llnl/camp.

Kunen, AdamJ.↗
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 OSTI2013

Optimal guidance law in quantum mechanics

Following de Broglie’s idea of a pilot wave, this paper treats quantum mechanics as a problem of stochastic optimal guidance law design. The guidance scenario considered in the quantum world is that an electron is the flight vehicle to be guided and its accompanying pilot wave is the guidance law to be designed so as to guide the electron to a random target driven by the Wiener process, while minimizing a cost-to-go function. After solving the stochastic optimal guidance problem by differential dynamic programming, we point out that the optimal pilot wave guiding the particle’s motion is just the wavefunction Ψ(t,x), a solution to the Schrödinger equation; meanwhile, the closed-loop guidance system forms a complex state–space dynamics for Ψ(t,x), from which quantum operators emerge naturally. Quantum trajectories under the action of the optimal guidance law are solved and their statistical distribution is shown to coincide with the prediction of the probability density function Ψ{sup ∗}Ψ. -- Highlights: •Treating quantum mechanics as a pursuit-evasion game. •Reveal an interesting analogy between guided flight motion and guided quantum motion. •Solve optimal quantum guidance problem by dynamic programming. •Gives a formal proof of de Broglie–Bohm’s idea of a pilot wave. •The optimal pilot wave is shown to be a wavefunction solved from Schrödinger equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Evaluating Performance Portability of OpenACC

Accelerator-based heterogeneous computing is gaining momentum in High Performance Computing arena. However, the increased complexity of the accelerator architectures demands more generic, high-level programming models. OpenACC is one such attempt to tackle the problem. While the abstraction endowed by OpenACC offers productivity, it raises questions on its portability. This paper evaluates the performance portability obtained by OpenACC on twelve OpenACC programs on NVIDIA CUDA, AMD GCN, and Intel MIC architectures. We study the effects of various compiler optimizations and OpenACC program settings on these architectures to provide insights into the achieved performance portability.

Sabne, Amit J↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

1st Computational Physics School for Fusion Research (2019 CPS-FR)

The rising number of applications of machine learning and computational statistics in fusion energy research requires flexibility in adopting a growing variety of tools. The Computational Physics School for Fusion Research (CPS-FR) aims at providing young researchers with critical skill sets to deal with modern fusion energy research challenges. The School aims at covering essentials of: Computational Statistics, Machine Learning, Deep Learning and optimization methods, Parallel Programming and HPC. As the first edition of the CPS-FR just concluded, this report highlights its main results and summarizes its contents.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Understanding Portability of a High-Level Programming Model on Contemporary Heterogeneous Architectures

Accelerator-based heterogeneous computing is gaining momentum in the high-performance computing arena. However, the increased complexity of heterogeneous architectures demands more generic, high-level programming models. OpenACC is one such attempt to tackle this problem. Although the abstraction provided by OpenACC offers productivity, it raises questions concerning both functional and performance portability. In this article, the authors propose HeteroIR, a high-level, architecture-independent intermediate representation, to map high-level programming models, such as OpenACC, to heterogeneous architectures. They present a compiler approach that translates OpenACC programs into HeteroIR and accelerator kernels to obtain OpenACC functional portability. They then evaluate the performance portability obtained by OpenACC with their approach on 12 OpenACC programs on Nvidia CUDA, AMD GCN, and Intel Xeon Phi architectures. They study the effects of various compiler optimizations and OpenACC program settings on these architectures to provide insights into the achieved performance portability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Advanced Energy Storage Management in Distribution Network

With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.

linearization↗
DOE OSTI2018

High-Performance Molecular Dynamics Simulation for Biological and Materials Sciences: Challenges of Performance Portability, Dallas, TX, November 2018.

Highly-optimized parallel molecular dynamics programs have allowed researchers to achieve ground-breaking results in biological and materials sciences. This type of performance has come at the expense of portability: a significant effort is required for performance optimization on each new architecture. Using a metric that emphasizes speedup, we assess key accelerating programming components of four different best-performing molecular dynamics programs - GROMACS, NAMD, LAMMPS and CP2K-- each having a particular scope of application, for contribution to performance and for portability. We use builds with and without these components, tested on HPC systems. We also analyze the code-bases to determine compliance with portability recommendations. We find that for all four programs, the contributions of the non-portable components to speed are essential to the programs' performances; without them we see a reduction in time-to-solution of a magnitude that is insufferable to domain scientists. This characterizes the performance efficiency that must be approached for good performance portability on a programmatic level, suggesting solutions to this difficult problem, which should come from developers, industry and funding institutions, and possibly new research in programming languages.

Computer Science↗
DOE OSTI2022

A combined experimental and numerical approach that eliminates the non-uniqueness associated with the Johnson-Cook parameters obtained using inverse methods

Abstract Johnson-Cook constitutive model is a commonly used material model for machining simulations. The model includes five parameters that capture the initial yield stress, strain-hardening, strain-rate hardening, and thermal softening behavior of the material. These parameters are difficult to determine using experiments since the conditions observed during machining (such as high strain-rates of the order of $$10^5$$ 10 5 /sec - $$10^6$$ 10 6 /sec) are challenging to recreate in the laboratory. To address this problem, several researchers have recently proposed inverse approaches where a combination of experiments and analytical models are used to predict the Johnson-Cook parameters. The errors between the measured cutting forces, chip thicknesses and temperatures and those predicted by analytical models are minimized and the parameters are determined. In this work, it is shown that only two of the five Johnson-Cook parameters can be determined uniquely using inverse approaches. Two different algorithms, namely, Adaptive Memory Programming for Global Optimization (AMPGO) and Particle Swarm Optimization (PSO), are used for this purpose. The extended Oxley’s model is used as the analytical tool for optimization. For determining a parameter’s value, a large range for each parameter is provided as an input to the algorithms. The algorithms converge to several different sets of values for the five Johnson-Cook parameters when all the five parameters are considered as unknown in the optimization algorithm. All of these sets, however, yield the same chip shape and cutting forces in FEM simulations. Further analyses show that only the strain-rate and thermal softening parameters can be determined uniquely and the three parameters present in the strain-hardening term of the Johnson-Cook model cannot be determined uniquely using the inverse method. A combined experimental and numerical approach is proposed to eliminate this determine all parameters uniquely.

42 ENGINEERING↗
DOE OSTI2019

Optimally placing photovoltaic arrays to maximize value of energy production based on peak power production, local solar radiation, weather, electricity market prices and rate structures

A method, system and computer program product for optimally placing photovoltaic arrays to maximize a value of energy production. Incident solar radiation for various placements of the photovoltaic arrays accommodating different azimuths and tilts is calculated. Alternating current solar photovoltaic electricity energy and power production is then estimated from the calculated solar radiation on a plane and weather data. Furthermore, a value of solar photovoltaic electricity energy and power produced by the photovoltaic arrays for the various placements is calculating using the estimated alternating current solar photovoltaic electricity production. A placement out of the various placements for the photovoltaic arrays is then selected corresponding to a highest value of the solar photovoltaic electricity produced by the photovoltaic arrays. In this manner, the appropriate placement for the photovoltaic arrays is determined that maximizes the value of energy production (where “value” may correspond to an economic value or a non-economic value).

Webber, Michael E.↗
DOE OSTI2025

Managing Workplace Charging: Argonne National Laboratory’s Reservation-Based Smart EV Charging Platform

The Smart Electric Power Alliance (SEPA) partnered with Argonne National Laboratory (Argonne) to produce a case study on Argonne’s workplace electric vehicle (EV) charging program, designed to optimize employees’ ability to reserve EV chargers and allow Argonne to implement a workplace managed charging solution. Formally known as EVrez, the program offers Argonne’s employees access to more than 50 Level 2 chargers and 4 DC fast chargers (DCFC). Employees must reserve and manage their EV sessions through the EVrez mobile app platform. This report outlines the EVrez program, from inception to maturity, highlighting key learnings and best practices from the Argonne team. As other workplaces seek to offer their own workplace charging offerings, this report highlights foundational steps and considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Applying Social Science Methods to Assess and Improve Program Outcomes: A Case Study on the Gap Region Outreach Project

This paper outlines a replicable framework for assessing gaps, reach, and the impact of technical assistance (TA) programs, using the Energy to Communities (E2C) Gap Region Outreach Project as a case study. By applying qualitative social science methods, this framework provides tools for effectively engaging with underrepresented regions, identifying barriers to participation, and tailoring solutions to local needs. The study highlights the importance of leveraging stakeholder insights to address disparities in program engagement and participation, offering actionable recommendations to enhance program responsiveness and optimize resource allocation. This approach serves as a guide for practitioners seeking to expand the reach and effectiveness of federally funded TA programs while ensuring alignment with community priorities and capacity needs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

SNoGloDe: A Structured Nonlinear Global Decomposition Solver

Large-scale optimization problems often require decomposition strategies and customized algorithms to achieve optimal solutions within a reasonable time. Building on the work of Cao and Zavala (2019) for solving nonlinear two-stage stochastic programs to global optimality, we implement and extend their approach. We generalize to optimization problems reformulated with a block-angular constraint structure (e.g., temporal decomposition). Our framework, written in Python using Pyomo, is highly customizable and enables parallel execution of the decomposition. SNoGloDe allows tailored branching strategies, lower bounding problems, and candidate generators to leverage problem-specific knowledge. To demonstrate effectiveness, we compare SNoGloDe’s performance with Gurobi on a temporally decomposed produced water case study.

algorithms↗
DOE OSTI2017

A framework for modeling and optimizing dynamic systems under uncertainty

Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A mixed-integer PDE-constrained optimization formulation for constructing electromagnetic cloaks with multiple materials

We study the design of an electromagnetic cloak from multiple materials with an additional constraint on the mass of the cloak. Our problem is an example of a topology optimization problem, and we formulate this problem as a mixed-integer partial-differential equation constrained optimization (MIPDECO) problem, where Maxwell’s equation models the propagation of the wave through the cloak and surrounding medium. We use binary variables to model the assignment of the different materials, and their relevant properties (permittivity and density). The mass constraint adds a nontrivial constraint to this problem. We propose a two-phase strategy to solve this problem. In the first phase, we solve a continuous relaxation, and then propose a new variant of the feasibility pump that exploits the structure of the PDE to obtain an initial integral solution candidate. In the second phase, we use a trust-region approach to improve this incumbent. We also consider a continuation or mesh-sequencing approach to find better solutions faster on consecutively finer meshes. We present detailed numerical results to illustrate the effectiveness of our approaches for constructing multi-material cloaks with a mass constraint.

Calculus of Variations and Optimization↗