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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Mississippi State University Center for Air Sea Technology FY95 Research Program

The Mississippi State University (MSU) Center for Air Sea Technology (CAST) evolved from the Institute for Naval Oceanography's (INO) Experimental Center for Mesoscale Ocean Prediction (ECMOP) which was started in 1989. MSU CAST subsequently began operation on 1 October 1992 under an Office of Naval Research (ONR) two-year grant which ended on 30 September 1994. In FY95 MSU CAST was successful in obtaining five additional research grants from ONR, as well as several other research contracts from the Naval Oceanographic Office via NASA, the Naval Research Laboratory, the Army Corps of Engineers, and private industry. In the past, MSU CAST technical research and development has produced tools, systems, techniques, and procedures that improve efficiency and overcome deficiency for both the operational and research communities residing with the Department of Defense, private industry, and university ocean modeling community. We continued this effort with the following thrust areas: to develop advanced methodologies and tools for model evaluation, validation and visualization, both oceanographic and atmospheric; to develop a system-level capability for conducting temporally and ; spatially scaled ocean simulations driven by or are responsive to ocean models, and take into consideration coupling to atmospheric models; to continue the existing oceanographic/atmospheric data management task with emphasis on distributed databases in a network environment, with database optimization and standardization, including use of Mosaic and World Wide Web (WWW) access; and to implement a high performance parallel computing technology for CAST ocean models

Yeske, Lanny↗
NASA NTRS2000

Microreactor System Design for a NASA In Situ Propellant Production Plant on Mars

The NASA In Situ Resource Utilization (ISRU) program is planning near-term missions to Mars that will include chemical processes for converting the carbon dioxide (CO2) and possibly water from the Martian environment to propellants, oxygen, and other useful chemicals. The use of indigenous resources reduces the size and weight of the payloads from Earth significantly, representing enormous cost savings that make human exploration of Mars affordable. Extraterrestrial chemical processing plants will need to be compact, lightweight, highly efficient under reduced gravity, and extraordinarily reliable for long periods. Microchemical and thermal systems represent capability for dramatic reduction in size and weight, while offering high reliability through massive parallelization. In situ propellant production (ISPP), one aspect of the ISRU program, involves collecting and pressurizing atmospheric CO2, conversion reactions, chemical separations, heat exchangers, and cryogenic storage. A preliminary system design of an ISPP plant based on microtechnology has demonstrated significant size, weight, and energy efficiency gains over the current NASA baseline. Energy management is a strong driver for Mars-based processes, not only because energy is a scarce resource, but because heat rejection is problematic; the low pressure environment makes convective heat transfer ineffective. Energy efficiency gains are largely achieved in the microchemical plant through extensive heat recuperation and energy cascading, which has a small size and weight penalty because the added micro heat exchangers are small. This leads to additional size and weight gains by reducing the required area of waste heat radiators. The microtechnology-based ISPP plant is described in detail, including aspects of pinch analysis for optimizing the heat exchanger network. Three options for thermochemical compression Of CO2 from the Martian atmosphere, adsorption, absorption, and cryogenic freezing, are presented, as well as three options for water decomposition, low temperature electrolysis, high temperature electrolysis, and thermochemical decomposition. Other elements of the plant include Sabatier and reverse water gas shift reactors, water recovery, chemical separations, and cryogenic storage. Data are presented supporting preliminary sizing of components, and results of the system design are compared to the existing NASA baseline that is based on conventional technologies.

TeGrotenhuis, W. E.↗
NASA NTRS2011

Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

Jones, Kennie H.↗
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Deep Space Network↗
NASA NTRS2013

SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community.We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures.In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction.

Pseudospectral (PS)↗
NASA NTRS2014

Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-fired Power Plants During 2005-2012

Due to the rapid growth of electricity demand and the absence of regulations, sulfur dioxide (SO2) emissions from coal-fired power plants in India have increased notably in the past decade. In this study, we present the first interannual comparison of SO2 emissions and the satellite SO2 observations from the Ozone Monitoring Instrument (OMI) for Indian coal-fired power plants during the OMI era of 2005-2012. A detailed unit-based inventory is developed for the Indian coal-fired power sector, and results show that its SO2 emissions increased dramatically by 71 percent during 2005-2012. Using the oversampling technique, yearly high-resolution OMI maps for the whole domain of India are created, and they reveal a continuous increase in SO2 columns over India. Power plant regions with annual SO2 emissions greater than 50 Gg year-1 produce statistically significant OMI signals, and a high correlation (R equals 0.93) is found between SO2 emissions and OMI-observed SO2 burdens. Contrary to the decreasing trend of national mean SO2 concentrations reported by the Indian Government, both the total OMI-observed SO2 and average SO2 concentrations in coal-fired power plant regions increased by greater than 60 percent during 2005-2012, implying the air quality monitoring network needs to be optimized to reflect the true SO2 situation in India.

SO2↗
NASA NTRS2014

Using SpF to Achieve Petascale for Legacy Pseudospectral Applications

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community. We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. Highlevel abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely inprocessor. The granularity of domain decomposition provided by SpF is only constrained by the datalocality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures. In this presentation, we will describe our experience in porting legacy pseudospectral models, MoSST and DYNAMO, to use SpF as well as present preliminary performance results provided by the improved scalability.

DYNAMO↗
NASA NTRS2019

Quantum Technologies for UAS (QTech)

Harness the power of quantum technologies to assure the availability of UAS communications against disruptions. Make use of quantum computing (e.g. quantum optimization) and quantum communication (e.g. quantum key distribution) to address the availability cybersecurity challenge. Our approach is three-fold: (1) Utilize quantum optimization algorithms to design robust network with routing redundancy that can respond adaptively to dynamically changing real-time environment and disruptions, (2) Utilize quantum optimization algorithms resource allocation for detection, localization, and tracking of mobile communication disruption agents, (3) Utilize quantum key distribution (QKD) to execute secure key sharing in high data rate optical communication and/or anti-jamming protocols for secure RF communication.

Quantum Computing↗
NASA NTRS2019

A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The network’s parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results.

Balachandran, Swee↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗
DOE OSTI2023

Distributed Energy Resource-Cognizant Upgrade Paths to the Traditional Restoration Strategy of Utilities for Improved Load Restoration

Climate change has resulted in increasingly impactful and more frequent occurrences of extreme weather events. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks under fault scenarios; however, currently, utilities deploying the automated fault location, isolation and restoration (FLISR) function in their advanced distribution management system (ADMS) do not take into account the available generation and load-modification capabilities of distributed energy resources present in the disconnected network due to an upstream isolated fault. This results in the network reconfiguration and restoration to result in sub-optimal load restoration. Therefore, this paper presents two approaches that can upgrade the existing FLISR capabilities of distribution utilities to significantly increase the restoration of critical loads. The performance of the proposed approaches is evaluated on a numerical model of a real distribution feeder in Georgia, USA.

DER↗
DOE OSTI2024

Development of a Distribution Optimal Power Flow Federate for Open-Source OEDI-SI Platform

Increasing numbers of distributed generators in the electric power distribution networks require developing a control strategy to optimize solutions in real time. Linearized optimal distribution flow development has seen growth and acceptance in the distribution systems literature for efficiently modeling the \glspl{opf} for distribution systems. This paper examines the implementation and integration procedure for linearized optimal distribution flow federate to \gls{oedisi} platform. Specifically, we discuss i) the usage of the \gls{oedisi} platform, ii) obtaining a tractable solution using developed \gls{opf} federate, and iii) validation of solutions and bench-marking the \gls{oedisi} platform with developed \gls{opf} federate using OpenDSS. In brief, we demonstrate how a general linearized optimal distribution flow federate can be developed and integrated with a co-simulation environment to mimic real-world examples. The efficacy of the proposed method is demonstrated using the IEEE 123-bus test system under different scenarios to obtain a tractable solution and compare its results.

Sadnan, Rabayet↗
DOE OSTI2020

The Heuristic Dynamic Programming Approach in Boost Converters

In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

Patnaik, Surya N.↗
DOE OSTI2022

Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

—Distribution grids, deep reinforcement learning, ↗
NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗
DOE OSTI2023

Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗