Search NASA⌕ Search

SEARCH · Search NASA

Results for “Network 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.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Synthetic Electricity Market Data Generation and HERON Use Case Setup of Advanced Nuclear Reactors Coupled with Thermal Energy Storage Systems

This study evaluates and optimizes advanced nuclear reactors coupled with thermal energy storage (TES) systems in an Integrated Energy System (IES) architecture to enable advanced nuclear power plants (A NPP) to participate in multi-commodity markets, thus enhancing their economic competitiveness. Nuclear-TES coupling scenarios studied herein are designed attenuate the nuclear heat dynamics and defer energy delivery to a later time, enabling the nuclear reactor to continue operating at or near steady-state design conditions as usual while also enabling flexible generation. Three A-NPPs, namely, an advanced light-water reactor (A LWR), a high temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating the technoeconomic of thermally balanced energy storage coupling design for thermal power extraction. Each of the reactor technologies were evaluated in two different electricity markets. Stochastic optimization approach was adopted which included the evaluation of price signals from the Pennsylvania-New Jersey-Maryland (PJM) market, and Electric Reliability Council of Texas (ERCOT), using an autoregressive moving average (ARMA) model. Risk Analysis Virtual Environment (RAVEN) tool and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON), were used to perform dispatch and capacity optimization, using the price data provided by the ARMA models. The results from the Nuclear-TES use cases will be used to design and characterize dynamic integrated system behavior and feedback.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multilevel implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1982

An optimization technique for lumped-distributed two ports

A frequency domain direct efficient analysis and an optimization technique of a large class of lumped-distributed networks containing active elements are presented. Sensitivity and Hessian matrix calculations are performed using truncated Taylor series expansion of two-port parameters of subnetworks. An interactive computer program was developed to demonstrate the application of the method. Examples of network optimizations are included to illustrate the powerfulness of the technique.

Iobst, K. W.↗
DOE OSTI2026

Relaxations of the steady optimal gas flow problem for a non-Ideal gas

Natural gas ranks second in U.S. primary energy consumption. Because most production sites are remote, gas must be transported through pipeline networks equipped with compressors, valves, and other components. For both economic efficiency and system reliability, it is desirable to operate these networks optimally. The governing physics across pipeline components entails nonlinear, non-convex equality and inequality constraints, and the most general steady-flow operations problem is a Mixed-Integer Nonlinear Program (MINLP).This work focuses on one such steady-flow problem-the Optimal Gas Flow (OGF) for a natural gas pipeline network-which minimizes production cost subject to the steady-flow physics. For day-to-day operations, the ability to quickly compute a globally optimal solution and a strong lower bound for varying demand profiles is crucial. A promising strategy is to build tight relaxations of the OGF’s nonlinear constraints. However, many nonlinearities arising from non-ideal equations of state either lack relaxations or have relaxations that do not scale to realistic network sizes. We address this gap by combining recent advances in polyhedral relaxations for univariate functions to construct tight, computationally efficient relaxations of the OGF with a non-ideal equation of state. These relaxations solve within seconds on a standard laptop. In conclusion, we demonstrate their quality through extensive numerical experiments on very large-scale test networks from the literature and find that the proposed approach proves optimality in 92% of tested instances.

03 NATURAL GAS↗
NASA NTRS1995

The NUONCE engine for LEO networks

Typical LEO networks use constellations which provide a uniform coverage. However, the demand for telecom service is dynamic and unevenly distributed around the world. We examine a more efficient and cost effective design by matching the satellite coverage with the cyclical demand for service around the world. Our approach is to use a non-uniform satellite distribution for the network. We have named this constellation design NUONCE for Non Uniform Optimal Network Communications Engine.

Lo, Martin W.↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗
NASA NTRS1986

An optimization model for the US Air-Traffic System

A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.

Mulvey, J. M.↗
DOE OSTI2022

Nonconvex regularization for sparse neural networks

Convex ℓ 1 regularization using an infinite dictionary of neurons has been suggested for constructing neural networks with desired approximation guarantees, but can be affected by an arbitrary amount of over-parametrization. This can lead to a loss of sparsity and result in networks with too many active neurons for the given data, in particular if the number of data samples is large. As a remedy, in this paper, a nonconvex regularization method is investigated in the context of shallow ReLU networks: We prove that in contrast to the convex approach, any resulting (locally optimal) network is finite even in the presence of infinite data (i.e., if the data distribution is known and the limiting case of infinite samples is considered). Moreover, here we show that approximation guarantees and existing bounds on the network size for finite data are maintained.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Neural Learning Using Orthogonal Arrays

The paper proposes the use of the orthogonal arrays for neural network learning. Learning can be seen as a search for the neural weights that give an optimal network performance.

orthogonal arrays weight values robust design opti↗
DOE OSTI2024

Nuclear Thermal Energy Storage Configurations for Industrial Combined Heat and Power Supply: Conceptual Study and Engineering Designs

The industries examined in this report primarily rely on moderate-temperature heat provided by gas- or coal-fired boilers and combined heat and power (CHP) plants, delivered through standard process steam systems. High-temperature energy demands are often industry-specific and typically exceed the capabilities of high-temperature gas-cooled reactors (HTGRs). While it is technically feasible to replace process steam from fossil-based heat sources with nuclear energy, certain industries, such as methanol production and pulp and paper, face technoeconomic challenges in integrating nuclear energy without major changes or a technological shift. This is mainly due to the limited external energy demand remaining after the use of internal byproducts, waste heat recovery, and simple efficiency improvements. Achieving full decarbonization of these processes with nuclear energy would require significant technological advancements, involving experimental technology and substantial investments, making widespread adoption in existing industrial plants unlikely in the near term. This study reviews TES options in the context of enabling a flexible CHP supply while maintaining a steady nuclear heat input. Heat storage systems that interface between the reactor primary fluid and the CHP system offer superior performance and flexibility. Specifically, steam extraction downstream of the reheater with a two-tank molten-salt TES appears as the best solution regarding thermodynamic system benefits and system drawbacks. Using selected system configurations, a conceptual design of an industrial energy park was developed for industries with varying energy demands, such as steel production plants utilizing electric arc furnaces (EAFs) and chemical plants, as well as for those with constant energy demands, like petroleum refineries. This design highlights the capabilities of TES and explores its potential business cases. The study also conceptually develops the potential for integrating additional energy sources with nuclear systems through the implementation of TES. The potential of the HTGR-TES-CHP system was also evaluated considering key uncertainties such as industrial demand profiles, external grid access availability, and eligible tax credit levels, using the Holistic Energy Resource Optimization Network. Sensitivity of net present value to these uncertainties was analyzed to determine the optimal number of nuclear reactors (and CHP systems) and the suitable TES capacity. The results were interpreted from a decision-maker’s perspective, focusing on three key areas: deployment strategy (oversized units vs. undersized units with TES support), industrial process characteristics (thermal-intensive single profiles vs. electricity-intensive combined profiles), and operational goals (maximizing profits vs. minimizing natural gas (NG) consumption or external grid dependence). The optimization results indicate that the HTGR-TES-CHP system significantly reduces reliance on NG boilers for individual industrial processes by 9-60% (in NG capacity factor), with an average reduction of 38%, compared to standalone NG boiler operation case (Business As Usual [BAU]). For combined industrial processes, the reduction ranges from 37-77%, with an average of 60%. Additionally, the system greatly reduces dependence on external grids. In meeting industrial electrical demands, a 33-100% self-sufficient internal electricity supply is achieved for single industrial process, with an average of 74%, compared to the BAU scenario, where 100% of electricity is imported. For combined processes, 35-100% of internal electricity demands are met by the reactor, with an average of 73%. At last, the relative NG price levels at which the proposed HTGR-TES-CHP system can cost-effectively enter the market currently dominated by existing NG boilers were estimated. For a moderate HTGR CAPEX level ($\$$2500/kWth, $\$$6329/kWe), the analysis suggests that NG prices must be 2.5 to 7 times higher than HTGR variable operating and maintenance costs for single industrial process, and 5.5 to 9.5 times higher for a combined process scenario. Tax credit modeling shows that the Investment Tax Credit significantly reduces the price threshold needed to break even, making the system competitive with NG boilers in certain cases.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗
NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗
NASA NTRS2004

A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators

The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.

Patnaik, Surya N.↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Neural Network Prediction of New Aircraft Design Coefficients

This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules.

Norgaard, Magnus↗
DOE OSTI2023

Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing.

24 POWER TRANSMISSION AND DISTRIBUTION↗