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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-Voltage DC MULTI-terminal SIMulation (HVDC MULTISIM): Technical Program Summary

Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

High Performance Computing Systems Tools, Visualization, and Management

High Performance Computing (HPC) systems are complex setups of servers, storage devices, network switches, and cables that are specifically designed to accommodate hundreds of users running highly computationally intensive applications at a time. These applications require numerous softwares, licenses, and various levels of storage as well. All of these resources must be monitored and managed by HPC administrators, which presents a daunting task. In this project, I created numerous software tools as part of an HPC Visualization and Management system, which is now used by HPC administrators on a daily basis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗
DOE OSTI2024

Wind turbine power phase control with DC collection bus for onshore/offshore windfarms

A DC bus collection system for a wind farm reduces the overall required number of converters and minimizes the energy storage system requirements. The DC bus collection system implements a power phasing control method between wind turbines that filters the variations and improves power quality. The phasing control method takes advantage of a novel power packet network concept with nonlinear power flow control design techniques that guarantees both stable and enhanced dynamic performance.

Weaver, Wayne W.↗
DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-in-one: construction of hydroxyl and imidazolium-bifunctionalized ionic networks in one-pot toward synergistic catalytic CO 2 fixation

Here, a succinct strategy was demonstrated for constructing a hydroxyl group and imidazolium-bifunctionalized ionic network via a onepot quaternization. Key to success lies in the rational design of multi-imidazole precursor and hydroxyl-containing counterpart. Unique properties of the resultant ionic network render its high catalytic efficiency toward CO 2 fixation under ambient conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Feasibility of Hybrid Electro- and Acousto-Dynamical Systems for Quantum Optical Networks

We present results on an exploratory research program that aims to study the feasibility of novel, sustained, dense, and efficient quantum information generation, storage, retrieval, relay and distribution devices and systems towards future advanced quantum networking architectures. The pathfinder program is anchored on original ideas and device conceptual designs that turn the phononic-sourced challenges into an opportunity towards optimized transduction chains over a broad range of length and energy/temperature scales. The proposal involves conceptual design and feasibility towards integration, commissioning and benchmarking of challenging transduction devices in a scaled quantum network setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Fast b -tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Scalable Compact Additively Manufactured Molten Salt to Supercritical Carbon Dioxide Heat Exchanger for Solar Thermal Application

Design of an additively manufactured molten salt (MS) to supercritical carbon dioxide (sCO 2 ) primary heat exchanger (PHE) for solar thermal power generation is presented. The PHE is designed to handle temperatures up to 720 °C on the MS side and an internal pressure of 200 bar on the sCO 2 side. In the core, MS flows through a three-dimensional periodic lattice network, while sCO 2 flows within pin arrays. The design includes integrated sCO 2 headers located within the MS flow, allowing for a counterflow design of the PHE. The sCO 2 headers are configured to enable uniform flow distribution into each sCO 2 plate while withstanding an internal pressure of 200 bar and minimizing obstruction to the flow of MS around it. The structural integrity of the design is verified on additively manufactured (AM) 316 stainless steel sub-scale specimens. An experimentally validated, correlation-based sectional PHE core thermofluidic model is developed to study the impact of flow and geometrical parameters on the PHE performance, with varied parameters including the mass flowrate, surface roughness, and PHE dimensions. A process-based cost model is used to determine the impact of parameter variation on build cost. The model results show that a heat exchanger with a power density of 18.6 MW/m 3 (including sCO 2 header volume) and effectiveness of 0.88 can be achieved at a heat capacity rate ratio of 0.8. As a result, the impact of design and AM machine parameters on the cost of the PHE are assessed.

14 SOLAR ENERGY↗
DOE OSTI2023

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Simulation study of the effects of polymer network dynamics and mesh confinement on the diffusion and structural relaxation of penetrants

The diffusion of small molecular penetrants through polymeric materials represents an important fundamental problem, relevant to the design of materials for applications such as coatings and membranes. Polymer networks hold promise in these applications because dramatic differences in molecular diffusion can result from subtle changes in the network structure. In this paper, we use molecular simulation to understand the role that cross-linked network polymers have in governing the molecular motion of penetrants. By considering the local, activated alpha relaxation time of the penetrant and its long-time diffusive dynamics, we can determine the relative importance of activated glassy dynamics on penetrants at the segmental scale vs entropic mesh confinement on penetrant diffusion. We vary several parameters, such as the cross-linking density, temperature, and penetrant size, to show that cross-links primarily affect molecular diffusion through the modification of the matrix glass transition, with local penetrant hopping at least partially coupled to the segmental relaxation of the polymer network. This coupling is very sensitive to the local activated segmental dynamics of the surrounding matrix, and we also show that penetrant transport is affected by dynamic heterogeneity at low temperatures. To contrast, only at high temperatures and for large penetrants or when the dynamic heterogeneity effect is weak, does the effect of mesh confinement become significant, even though penetrant diffusion more broadly empirically follows similar trends as established models of mesh confinement-based transport.

Chemistry↗
DOE OSTI2021

Agent-Supervisor Coordination for Decentralized Event-Triggered Optimization

This letter proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions that combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local cost and coordinates with the network supervisor for information about the coupling term of the cost. The proposed approach is feedback-based and asynchronous by design, guarantees anytime feasibility, and ensures the asymptotic convergence of the network state to the desired optimizer. Numerical simulations on a power system example illustrate our results.

decentralized algorithms↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one- year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

distribution system state estimation↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is developed to generate the measurement and voltage state data. The IEEE 123 node system is selected as the test network to benchmark the proposed algorithm against the classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.

distribution systems state estimation↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗