SEARCH · Engineering Papers

Results for “Distributed optimization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88

All sources NASA NTRS DOE OSTI

NASA NTRS2000

Multidisciplinary High-Fidelity Analysis and Optimization of Aerospace Vehicles: Formulation - Part 1

An objective of the High Performance Computing and Communication Program at the NASA Langley Research Center is to demonstrate multidisciplinary shape and sizing optimization of a complete aerospace vehicle configuration by using high-fidelity, finite element structural analysis and computational fluid dynamics aerodynamic analysis in a distributed, heterogeneous computing environment that includes high performance parallel computing. A software system has been designed and implemented to integrate a set of existing discipline analysis codes, some of them computationally intensive, into a distributed computational environment for the design of a highspeed civil transport configuration. The paper describes the engineering aspects of formulating the optimization by integrating these analysis codes and associated interface codes into the system. The discipline codes are integrated by using the Java programming language and a Common Object Request Broker Architecture (CORBA) compliant software product. A companion paper presents currently available results.

Walsh, J. L.↗

DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1998

An Atmospheric General Circulation Model with Chemistry for the CRAY T3E: Design, Performance Optimization and Coupling to an Ocean Model

The design, implementation and performance optimization on the CRAY T3E of an atmospheric general circulation model (AGCM) which includes the transport of, and chemical reactions among, an arbitrary number of constituents is reviewed. The parallel implementation is based on a two-dimensional (longitude and latitude) data domain decomposition. Initial optimization efforts centered on minimizing the impact of substantial static and weakly-dynamic load imbalances among processors through load redistribution schemes. Recent optimization efforts have centered on single-node optimization. Strategies employed include loop unrolling, both manually and through the compiler, the use of an optimized assembler-code library for special function calls, and restructuring of parts of the code to improve data locality. Data exchanges and synchronizations involved in coupling different data-distributed models can account for a significant fraction of the running time. Therefore, the required scattering and gathering of data must be optimized. In systems such as the T3E, there is much more aggregate bandwidth in the total system than in any particular processor. This suggests a distributed design. The design and implementation of a such distributed 'Data Broker' as a means to efficiently couple the components of our climate system model is described.

Farrara, John D.↗

DOE OSTI2023

Automatic voltage regulation application for PV inverters in low-voltage distribution grids – A digital twin approach

This paper proposes a hierarchical coordinated control strategy for PV inverters to keep voltages in low-voltage (LV) distribution grids within specified limits. The top layer of the proposed architecture consists of the designed automatic voltage regulation (AVR) application, which has access to voltage measurements and grid parameters from the LV distribution grid, both current and historical. The AVR application solves a constrained optimization problem, which provides a set of local control set-points that bring the voltage across the grid within bounds. The middle layer consists of a local Volt/VAR controller, which is adjusted by the AVR app, while the bottom layer is the inner-loop controller of the PV inverter. The proposed method not only improves the voltage quality in the grid but also manages the reactive power outputs of PV inverters efficiently. Further, a digital twin of the cyber-physical system has also been employed that interacts with the control system to ensure its appropriate operation. The effectiveness of the proposed methodology is demonstrated on a representative low-voltage feeder located in Denmark.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Shape Optimization of Header Pipes in Power Plants for Enhanced Efficiency and Environmental Sustainability

In a power plant, the header pipe plays a pivotal role in optimizing the performance of diverse systems by serving as a central conduit for the collection and distribution of steam within the plant. This paper investigates the significance of header pipes within power plant setups, highlighting their critical influence on reliability, efficiency, and the performance of the power plant as a whole. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint of power plant installations. In this paper, we initially developed a methodology designed for optimizing header shapes with the primary goal of reducing the usage of costly new alloy materials and lowering the overall maintenance operation expenses. Secondly, we conducted a case study based on an authentic header sourced from an operational power plant.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS1993

GGOT total pressure loss control concept evaluation

Total pressure loss is one of the most important parameters in the design of a turbine. This parameter effects not only the turbine performance, but consequently the engine power balance and engine performance. Computational Fluid Dynamics (CFD) can be an effective tool in predicting turbine total pressure loss, and also for performing sensitivity studies to achieve an optimal design with respect to pressure loss. In the present study, the AEROVISC code was used to predict the total pressure loss in the Turbine Technology Team Gas Generator Oxidizer Turbine (GGOT). The objectives in this study are two-fold. It is first necessary to determine an optimal methodology in predicting total pressure loss. The type of grid, grid density and distribution are parameters which may affect the loss prediction. Also, the effect of using a standard K-epsilon turbulence model with wall functions versus a two-layer turbulence model needs to be investigated. The use of grid embedding to resolve areas with high flow gradients needs to be explored. The second objective of the study is to apply the optimal methodology toward evaluating different tip leakage control concepts.

Blumenthal, R. F.↗

NASA NTRS2019

"Sensor Web Evolution - Webs of Webs for NASA Science - Focus on small Uninhabited Aerial Systems (sUAS)"

This paper will describe the evolution of information collection, derivation and delivery mechanisms in webs of NASA sensor webs, with a focus on recent advancements in small Uninhabited Aerial Systems (sUAS). I will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, sUASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary sUASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. I will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing sUASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to sUAS development. This paper will focus on application, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

Sensor Web↗

NASA NTRSDate not supplied

UASs in the VOG/Edge/FOG Sensor Web Environment

This paper will describe the evolution of information collection, derivation and delivery mechanisms in sensor webs utilizing Uninhabited Aerial Systems (UAS).We will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, UASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary UASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. The Author will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing UASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to other UAS development This paper will focus on applications, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

UAS↗

NASA NTRS1991

Inverse problems and optimal experiment design in unsteady heat transfer processes identification

Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.

Artyukhin, Eugene A.↗

DOE OSTI2020

Towards Robustness Guarantees for Feedback-Based Optimization

Feedback-based online optimization algorithms have gained traction in recent years because of their simple implementation, their ability to reject disturbances in real time, and their increased robustness to model mismatch. While the robustness properties have been observed both in simulation and experimental results, the theoretical analysis in the literature is mostly limited to nominal conditions. In this work, we propose a framework to systematically assess the robust stability of feedback-based online optimization algorithms. We leverage tools from monotone operator theory, variational inequalities and classical robust control to obtain tractable numerical tests that guarantee robust convergence properties of online algorithms in feedback with a physical system, even in the presence of disturbances and model uncertainty. The results are illustrated via an academic example and a case study of a power distribution system.

approximation algorithms↗

DOE OSTI2021

Spatial coupling of gyrokinetic simulations, a generalized scheme based on first-principles

In this paper, we present a scheme that spatially couples two gyrokinetic codes using first-principles. Coupled equations are presented and a necessary and sufficient condition for ensuring accuracy is derived. This new scheme couples both the field and the particle distribution function. The coupling of the distribution function is only performed once every few time-steps, using a five-dimensional (5D) grid to communicate the distribution function between the two codes. This 5D grid interface enables the coupling of different types of codes and models, such as particle and continuum codes, or delta-f and total-f models. Transferring information from the 5D grid to the marker particle weights is achieved using a new resampling technique. Demonstration of the coupling scheme is shown using two XGC gyrokinetic simulations for both the core and edge. We also apply the coupling scheme to two continuum simulations for a one-dimensional advection–diffusion problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Optimal Managed Fast-Charging Model for Electric Vehicle Fleets with High Utilization and Multiple Charge-Acceptance Curves

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet - under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow: Preprint

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS2017

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments. In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE-SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE-SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agents and moving obstacles. Simulation results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.

Bandyopadhyay, Saptarshi↗

DOE OSTI2023

Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations

Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional (> $2D$) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. Furthermore, we demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

FORCE-DISPATCHES Integration - Initial Demonstration

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to improve economic viability under uncertain market and weather conditions. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Continuous efforts and investments from the IES programs have been made to expand and improve the versatility of the FORCE toolset in fiscal year 2022. Code-coupling and cross-tool communication have been important methods for improving this versatility. This report focuses on an additional workflow in the HERON tool for capacity and dispatch stochastic optimization through integration with the external tool Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES). DISPATCHES was primarily developed by the National Energy Technology Laboratory, in collaboration with other national laboratories, which included INL, universities, and industry partners. It is coupled to a library of algebraic models for specific plant components, and to a framework for stochastic optimization different from that provided in the current Risk Analysis Virtual Environment (RAVEN)-running-RAVEN algorithm in HERON. HERON currently conducts stochastic optimization via an outer-inner loop: it optimizes over variable capacity on the outer loop, and at each step within the capacity parameter space, conducts an inner optimization over scenarios (of market signals, demand, and/or weather patterns) and hourly dispatch throughout a user-specified number of years. On the other hand, DISPATCHES conducts stochastic optimization via an “all-at-once” strategy in which capacity variables are optimized at the same level as dispatch variables, as all scenarios are considered at once. The latter method works especially well for projects of limited size and project length, as the necessary computational power and memory increases with the number of variables and scenarios. The new capability to use the DISPATCHES workflow in HERON enhances standalone simulations by leveraging FORCE tools—namely, the economic metrics from the Tool for Economic Analysis (TEAL) and reduced-order model (ROM) sampling from RAVEN. The initial demonstration of the DISPATCHES workflow simulates an existing nuclear-case flowsheet within the DISPATCHES repository—this models a NPP with a secondary revenue stream for hydrogen production. Electrical output from the plant is converted to hydrogen via a proton-exchange membrane (PEM) electrolyzer, hydrogen tanks are used for storage, and an additional turbine is added for hydrogen combustion. Continued work regarding this FORCE-DISPATCHES integration will include automatic generation of DISPATCHES models from HERON inputs, offering analysts the option of using either the RAVEN-runsRAVEN or DISPATCHES workflow to solve technoeconomic optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗