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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 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2014

Analysis of double stub tuner control stability in a phased array antenna with strong cross-coupling

Active stub tuning with a fast ferrite tuner (FFT) has greatly increased the ability of fusion ion cyclotron range of frequency (ICRF) systems (50–100 MHz) by allowing for the antenna system to respond dynamically to changes in the plasma load impedance such as during the L–H transition or edge localized modes (ELMs). A high power waveguide double-stub tuner is under development for use with the Alcator C-Mod lower hybrid current drive (LHCD) system at 4.6 GHz. The amplitude and relative phase shift between adjacent columns of an LHCD antenna are critical for control of the launched n || spectrum. Adding a double-stub tuning network will perturb the phase and amplitude of the forward wave particularly if the unmatched reflection coefficient is high. This effect can be compensated by adjusting the phase of the low power microwave drive for each klystron amplifier. Cross-coupling of the reflected power between columns of the launcher must also be considered. The problem is simulated by cascading a scattering matrix for the plasma provided by a linear coupling model with the measured launcher scattering matrix and that of the FFTs. The solution is advanced in an iterative manner similar to the time-dependent behavior of the real system. System performance is presented under a range of edge density conditions from under-dense to over-dense and a range of launched n || . Simulations suggest power reflection coefficients (Γ 2 ) of less than 1% with no contamination of the n || spectrum. Instability of the FFT tuning network can be difficult for certain plasma conditions and relative phasings, but reducing the control gain of the FFT network stabilizes the system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗

DOE OSTI2019

Scalable PETSc Implementation of the Three-Phase Unbalanced AC Power Flow Solver

A revolutionary transition of energy infrastructure, spanning across supply and demand sectors, is happening all around the world. Integration of renewable energy sources into our ever-complex power distribution networks is central to the infrastructure planning of the future. One step towards achieving this goal is to solve unbalanced transmission and distribution systems. Traditional approaches and software tools are not suitable because they assume a completely balanced 3-phase system, hence a single-phase equivalent can be solved. Renewable energy such as wind and solar power are intermittent and can pose a challenge when integrating into an already unbalanced power grid. Furthermore, these power grids can be quite large at scale consisting of potentially thousands of buses, which can be computationally demanding. This poster presents preliminary work on designing a robust and scalable 3-phase unbalanced AC power flow solver. The proposed power flow solver will eventually be built on top of the PETSc library and utilizes state-of-the-art parallel data structures like the DMNetwork as well as iterative solvers and preconditioners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

National Solar Jobs Accelerator (Final Technical Report (FTR))

The aptitudes and experiences gained through military service—such as dynamic leadership, teamwork and critical thinking skills, technical specialization, and a mission-completion work ethic—make veterans exceptional candidates for a wide range of solar energy careers. The solar industry offers a highly collaborative and purpose-driven work environment that resonates with service members and veterans looking to rise to their next challenge, and solar employers are eager to tap into this valuable talent pool. From October 2019 - February 2023, The National Solar Jobs Accelerator (publicly the Solar Ready Vets Network TM (SRV Network; SRVN)) enhanced and streamlined options for military service members and veterans to pursue solar training, certification, and employment, while advancing solar employers’ efforts and capacity to invest in military talent as part of a long-term workforce development strategy. The SRVN was led by the Interstate Renewable Energy Council (IREC) in partnership with the Solar Energy Industries Association (SEIA), the US Chamber of Commerce Foundation’s Hiring Our Heroes program (HOH) and the North American Board of Certified Energy Practitioners (NABCEP). Through several direct-impact and indirect, high-impact capacity building initiatives aligned with six key objectives, the SRV Network strengthened solar career pathways, and promoted increased representation of military talent across all levels and sectors of the solar workforce. A work-based learning Corporate Fellowship model connected transitioning service members with on-the-job experience in leadership roles with solar employers nationwide. The project advanced broader veteran recruitment and talent development by expanding GI Bill eligibility and streamlining veterans’ pathways for solar training and credentialing, supported direct connections to jobs with top solar employers, and led coordination among key education and industry partners to advance registered apprenticeships aligned with solar career pathways. To ensure that the project best served the needs of all stakeholders, an Advisory Committee of military-connected solar professionals, solar employers, and training providers met biannually to guide project activities and sustainability plans. The project team engaged the broader “SRV Network” (comprised of over 2,000 veterans, employers and training organizations) through regular newsletters and targeted outreach to share resources, hiring fairs, webinars, and other opportunities for engagement. The work done under this award builds on the previous iterations of the Department of Energy’s Solar Ready Vets ® program. As the solar industry continues to grow rapidly over the next decade, the military community will continue to be a highly valuable source of talent. The relationships established and work accomplished through this project will have an enduring positive impact well beyond the funding period.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Impacts and Conclusions of NASA DEVELOP’s Environmental Justice Landscape Analyses and Feasibility Studies

The NASA DEVELOP National Program builds dual capacity in their partner organizations and participants (students and early or transitioning career professionals) to apply Earth science and remote sensing for decision making about environmental and policy issues. The first iteration of projects at DEVELOP’s Environmental Justice (EJ) node conducted landscape analyses to identify geospatial understandings and needs of organizations working at the intersection of EJ and disasters and air quality. Their findings about collaboration, community engagement, networking, and holistic approaches to justice informed the planning and structures of the EJ node’s successive feasibility projects, which leverage NASA Earth observations and sociodemographic and economic data to provide geospatial analyses tailored to partner concerns. This poster provides an overview of DEVELOP’s approach to conducting EJ feasibility projects, use of Earth observations, end products, project outcomes, challenges, and impacts.

Julianne Bo-Heen Liu↗

NASA NTRS1989

Learning to read aloud: A neural network approach using sparse distributed memory

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Joglekar, Umesh Dwarkanath↗

NASA NTRS2005

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗

NASA NTRS2007

Hybrid Neural Network and Support Vector Machine Method for Optimization

Rai, Man Mohan↗

DOE OSTI2019

Large Scale Unsupervised Domain Adaptation of Segmentation Networks with Adversarial Learning

Most current state-of-the-art methods for semantic segmentation on remote sensing imagery require large labeled data, which is scarcely available. Due to the distribution shifting phenomenon inherent in remote sensing imagery, the reuse of pre-trained models on new areas of interest rarely yield satisfactory results. In this paper, we approach this problem from an adversarial learning perspective toward unsupervised domain adaptation. The core concept is to infuse fully convolutional neural networks and adversarial networks for semantic segmentation assuming the structures in the scene and objects of interest are similar in two set of images. Models are trained on a source dataset where ground truth is available and adapted to new target dataset iteratively via a adversarial loss on unlabeled samples. We use two real large scale datasets to validate the framework: 1) cross city road extraction and 2) cross country building extraction. The preliminary results show the usefulness of considering adversarial learning for indirect re-use of the pre-trained models. Experimental validation suggests significant benefits over models without adaptation.

Deng, Xueqing↗

DOE OSTI2022

Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. Here, we also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Method of up-front load balancing for local memory parallel processors

In a parallel processing computer system with multiple processing units and shared memory, a method is disclosed for uniformly balancing the aggregate computational load in, and utilizing minimal memory by, a network having identical computations to be executed at each connection therein. Read-only and read-write memory are subdivided into a plurality of process sets, which function like artificial processing units. Said plurality of process sets is iteratively merged and reduced to the number of processing units without exceeding the balance load. Said merger is based upon the value of a partition threshold, which is a measure of the memory utilization. The turnaround time and memory savings of the instant method are functions of the number of processing units available and the number of partitions into which the memory is subdivided. Typical results of the preferred embodiment yielded memory savings of from sixty to seventy five percent.

Baffes, Paul Thomas↗

NASA NTRS1994

Program Aids Analysis And Optimization Of Design

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Rogers, James L., Jr.↗

DOE OSTI2016

Quantum cluster equilibrium model of N-methylformamide–water binary mixtures

The established quantum cluster equilibrium (QCE) approach is refined and applied to N-methylformamide (NMF) and its aqueous solution. The QCE method is split into two iterative cycles: one which converges to the liquid phase solution of the QCE equations and another which yields the gas phase. By comparing Gibbs energies, the thermodynamically stable phase at a given temperature and pressure is then chosen. The new methodology avoids metastable solutions and allows a different treatment of the mean-field interactions within the gas and liquid phases. These changes are of crucial importance for the treatment of binary mixtures. For the first time in a QCE study, the cis-trans-isomerism of a species (NMF) is explicitly considered. Cluster geometries and frequencies are calculated using density functional theory (DFT) and complementary coupled cluster single point energies are used to benchmark the DFT results. Independent of the selected quantum-chemical method, a large set of clusters is required for an accurate thermodynamic description of the binary mixture. The liquid phase of neat NMF is found to be dominated by the cyclic trans-NMF pentamer, which can be interpreted as a linear trimer that is stabilized by explicit solvation of two further NMF molecules. This cluster reflects the known hydrogen bond network preferences of neat NMF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

A Scalable, Distribution Network-Aware, Customer Privacy-Preserving Framework for Operation of Virtual Power Plants

This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗

DOE OSTI2016

Applications of Wireless Sensor Networks for Area Coverage in Microgrids

Area coverage is a critical issue which will have a major impact on the sensing quality over targeted regions in wireless sensor networks. This paper studies the area coverage problem (ACP) with non-penetrable obstacles in microgrids, where a sensor cannot be deployed or sensing signals cannot pass through. The target coverage region is discretized in a deployment map representation of non-penetrable obstacles. A probability detection model is utilized to represent sensors with a variable distance coverage. The ACP is formulated as a mixed integer nonlinear programming problem and an iterative process is developed to solve the problem. The microgrid at Illinois Institute of Technology (IL Tech) is used as a practical example. Both the normal and the N-1 contingency deployments of sensors are simulated for optimizing the ACP at the IL Tech microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Walking the ‘design–build–test–learn’ cycle: flux analysis and genetic engineering reveal the pliability of plant central metabolism

Oilseeds are of great economic importance for food and animal feed and their contribution to renewable energy production. Soybean seeds (Glycine max (L.) Merr.) contain c. 40% protein, 20% oil, and 30% carbohydrate (Song et al., 2023). Due to the massive scale of soybean production worldwide, even small improvements in seed protein and oil content make economic sense (Song et al., 2023). Successful manipulation of seed composition largely depends on a thorough understanding of the processes and pathways involved in the biosynthesis of fatty acids and amino acids, which are the building blocks of lipids and proteins. Rational engineering of the synthesis of storage reserves, that is, the rerouting of metabolic flux in central metabolism, is difficult to accomplish due to the complexity of the central metabolic network, the intricate regulation of its enzymes at multiple levels, and the often-unpredictable effects of genetic manipulation (Sweetlove et al., 2017). Therefore, the advancement of our understanding of central metabolism and its control of carbon partitioning requires following an iterative ‘design–build–test–learn’ (DBTL) cycle (Lin & Eudes, 2020) where metabolic flux analysis and hypothesis testing by transgenic approaches are important components. Previous metabolic studies on soybeans using isotopic tracers and metabolic flux analysis have provided insight into how lipid and protein biosynthesis occurs simultaneously during seed development (Allen et al., 2009; Allen & Young, 2013; Kambhampati et al., 2021). In an article published in this issue of New Phytologist, Morley et al. (2023; 1834–1851) put the insights they have gained into the delivery of metabolic precursors and energy cofactors to oil synthesis to the test and arrive at a successful metabolic engineering design. They show that an increase in seed oil content in soybeans can be achieved by overexpression of malic enzyme (ME) during seed development. Malic enzyme refers to a class of decarboxylating malate dehydrogenase enzymes that oxidize malate with NAD + or NADP + as redox cofactor while generating pyruvate and CO 2 . Like higher plants in general, soybean has distinct NADH- or NADPH-producing ME isoforms localized to the cytosol, plastid, or mitochondria (Gerrard Wheeler et al., 2016). As Morley et al. show, an increase in seed oil can be achieved in particular when a NADP+-dependent enzyme isoform (EC 1.1.1.40) is overexpressed in the plastid. Given the complex compartmentalization of pyruvate, malate, and redox metabolism (Fig. 1), increased oil production appears to depend on additional pyruvate and reducing equivalents being produced in the same compartment where de novo fatty acid biosynthesis occurs: the plastid.

59 BASIC BIOLOGICAL SCIENCES↗