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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2020

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

42 ENGINEERING↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Investigation of two concrete thermal energy storage system configurations for continuous power production

Two Modelica concrete thermal energy storage (CTES) models are built to analyze potential CTES system designs. One design is the single pipe network design wherein a heat transfer fluid (HTF) flows in one direction during heat deposition and the opposite direction during heat removal. All pipes in the network are used for either deposition (charging) or removal (discharging). The second design is a dual pipe network design in which a network of HTF pipes carries charging fluid and a separate piping network carries discharging HTF. Operation of these two configurations to produce constant steam is evaluated. Here, results indicate that the designs are likely appropriate for different applications with the single pipe network appearing appropriate for batch energy applications and the dual pipe network for continuous energy applications.

25 ENERGY STORAGE↗
DOE OSTI2020

A Robust Hierarchical Dispatch Scheme for Active Distribution Networks Considering Home Thermal Flexibility

Distribution networks are changing from passive absorbers of electric energy to active distribution networks (ADNs) capable of operating and participating in electricity markets. In the context of residential microgrids, which is a type of ADNs, aggregated home heating, ventilation and air-conditioning (HVAC) loads present a key opportunity to drive operational and economic objectives, facilitate high renewable energy penetration, and enhance both system resiliency and flexibility. A robust, hierarchical dispatch scheme is developed and presented in this paper, which connects an upper level multi-phase distribution optimal power flow (DOPF) to a lower level model predictive control (MPC)-based HVAC fleet controller. The approach is tested and verified on a modified IEEE 13 bus system in an intraday market application. The results demonstrate that the proposed hierarchical dispatch scheme is able to drive both economic and operational objectives for the ADN operator.

Rooks, Cody D.↗
DOE OSTI2020

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

Beard, Daniel A.↗
DOE OSTI2025

QUANT-NET Control Plane Framework (QNCP) v1.0.0

The QUANT-NET Control Plane (QNCP) provides a software framework for expressing and managing quantum network resources. It may be used to orchestrate a physical quantum testbed with real device driver implementations, or it may be used as a proving ground when developing new protocols and management functions. In practice, both approaches may be useful when undertaking research and development in emerging quantum testbeds. While a number of control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. QNCP is designed to fill this gap. The framework has been designed to provide extensible, modular capabilities that include scheduling, routing, monitoring, and pluggable protocols. A number of reference implementations in each module category have been included in the installable packages; however, the intent is that each of these modules may be extended or re-implemented to meet the needs of the particular deployment or research need. The software is currently being used in the QUANT-NET testbed project, which spans resources between LBNL and UC Berkeley Physics.

Zhang, Liang [Lawrence Berkeley National Laborator↗
DOE OSTI2022

New Industry Partnerships: Solar Energy Technologies Office Support for Early Projects in the Energy Systems Integration Facility: An Agreement Closeout Summary Report

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office (SETO)-funded New Industry Partnerships (NIPs) agreement established multiple new cooperative research projects with industry partners. Each project demonstrated the use of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF) as a national asset for research and development, testing, and validation of new technologies to support high penetrations of solar energy on the electric grid. The agreement was launched in 2013 and represented the SETO portion of the DOE EERE-wide Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) program. The focus of INTEGRATE was on multi-energy system testing and on testing the interactions of energy systems with information technology, communications, and telecommunications (ICT) systems. The NIPs agreement was a bit different from traditional research projects because it was specifically structured to enable partnerships for ESIF testing and as such required at least a 1:1 funds-in cost share from industry partners. In the end, the project engaged six different industry partnerships and resulted in testing a large number of innovative technologies. These included tiny inverters; power-to-gas technologies; and advanced simulation, analysis, and power-hardware-in-the-loop testing techniques. The agreement also provided significant impact for both research and industrial communities. This summary report provides a brief, high-level overview of these projects and their highlights along with lists of citations and other impacts. Readers are referred to the corresponding project reports for more in-depth information.

14 SOLAR ENERGY↗
DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗
DOE OSTI2022

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗
DOE OSTI2020

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of $\sqrt{s}=13\,\text {TeV} $ at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $\,\text {fb}^{-1}$. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to $\hbox {b}\bar{\hbox {b}}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Correlated particle transport enables biological free energy transduction

Studies of biological transport frequently neglect the explicit statistical correlations among particle site occupancies (i.e., they use a mean-field approximation). Neglecting correlations sometimes captures biological function, even for out-of-equilibrium and interacting systems. Here, we show that neglecting correlations fails to describe free energy transduction, mistakenly predicting an abundance of slippage and energy dissipation, even for networks that are near reversible and lack interactions among particle sites. Interestingly, linear charge transport chains are well described without including correlations, even for networks that are driven and include site-site interactions typical of biological electron transfer chains. We examine three specific bioenergetic networks: a linear electron transfer chain (as found in bacterial nanowires), a near-reversible electron bifurcation network (as in complex III of respiration and other recently discovered structures), and a redox-coupled proton pump (as in complex IV of respiration).

bacterial nanowires↗
DOE OSTI2023

Community College Energy Code Training Program

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) funded a series of research and training programs under the Advanced Building Construction with Energy Efficient Technologies and Practices (ABC) initiative. The aim is to develop new training programs to support the development of a skilled workforce in the building energy efficiency industry. In response, the University of Illinois Smart Energy Design Assistance Center (SEDAC), in collaboration with State Energy Offices in Illinois, Hawaii, and Nevada, developed the Community College Energy Code Training Program. The program is designed to prepare a new generation of energy-literate professionals that understand energy efficiency and its relation to energy codes for new construction and existing buildings through: a) the development of a curriculum and innovative pedagogical materials, b) dissemination through a national network of state energy offices: c) outreach targeting Community College students and instructors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The reservoir network: A new network topology for district heating and cooling

Thermal district networks are effective solutions to substitute fossil fuels with renewable energy sources for heating and cooling. Moreover, thermal networking of buildings allows energy efficiency to be further increased. The waste heat from cooling can be reused for heating in thermal district systems. Because of bidirectional energy flows between prosumers, thermal networks require new hydraulic concepts. In this work, we present a novel network topology for simultaneous heating and cooling: the reservoir network. The reservoir network is robust in operation due to hydraulic decoupling of transfer stations, integrates heat sources and heat sinks at various temperature levels and is flexible in terms of network expansion. Furthermore, we used Modelica simulations to compare the new single-pipe reservoir network to a basecase double-pipe network, taking yearly demand profiles of different building types for heating and cooling. The electric energy consumed by the heat pumps and circulations pumps differs between the reservoir and base case networks by less than 1%. However, if the reservoir network is operated with constant instead of variable mass flow rate, the total electrical consumption can increase by 48% compared to the base case. As with any other network topology, the design and control of such networks is crucial to achieving energy efficient operation. Investment costs for piping and trenching depend on the district layout and dimensioning of the network. If a ring layout is applied in a district, the reservoir network with its single-pipe configuration is more economical than other topologies. For a linear layout, the piping costs are slightly higher for the reservoir network than for the base case because of larger pipe diameters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Dilated causal convolutional neural networks for forecasting zone airflow to estimate short-term energy consumption

Here this paper investigates the use of dilated causal convolutional neural networks for fine- grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-hour prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type, month, season, and combinations thereof. When evaluated for a commercial office building in Eastern Washington with 16 zones served by variable air volume air handling units, we find that the highest performance comes from a zone-specific, season-agnostic approach; with it, we obtain an R 2 of 0.704 (averaged over zones) and an average normalized root mean square error (nRMSE) of 0.111. In contrast, the most general model (trained across all zones and seasons) yields an R 2 of only 0.416 and a nRMSE of 0.168, while a baseline zone-specific reduced order model obtains 0.443 R 2 and 0.159 nRMSE. We also report on factors affecting airflow forecasting performance, on the ability of models trained on a specific zone to generalize to other zones, and on the capability of those models trained on a specific month to generalize to other months.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗