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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2022

Power and Communications Hardware-in-the-Loop CPS Architecture and Platform for DER Monitoring and Control Applications: Preprint

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. The deployment of communications and computation has transformed the traditional physical power grid into a smart cyber-physical system (CPS). To fully understand the interdependency between physical grid and cyber netowrks, this study designed a power and communications hardware-in-the-loop (PCommHIL) CPS architecture, which enables the flexible verification of DER monitoring and control with hybrid communications architectures and Internet protocols. Design, development and case study of a PCommHIL testbed for the DER coordination are discussed in detail, and the proposed platform integrates DER devices, Advanced Metering Infrastructures (AMIs), and a suite of hybrid communications networks for distribution automation applications. Case study on DER situational awareness and Volt-Var control validates the efficacy of this proposed PCommHIL platform with hybrid communications designs. Results show that the HAN communication technologies play a critical role in hybrid designs and it is the bottleneck for DER applications. High performance communication technologies are highly recommended to be applied in the HAN for enhanced monitoring and real-time control of DERs.

AMIs↗
DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2023

A chloroplast diacylglycerol lipase modulates glycerolipid pathway balance in Arabidopsis

SUMMARY Two parallel pathways compartmentalized in the chloroplast and the endoplasmic reticulum contribute to thylakoid lipid synthesis in plants, but how these two pathways are coordinated during thylakoid biogenesis and remodeling remains unknown. We report here the molecular characterization of a homologous ADIPOSE TRIGLYCERIDE LIPASE‐LIKE gene, previously referred to as ATGLL . The ATGLL gene is ubiquitously expressed throughout development and rapidly upregulated in response to a wide range of environmental cues. We show that ATGLL is a chloroplast non‐regioselective lipase with a hydrolytic activity preferentially towards 16:0 of diacylglycerol (DAG). Comprehensive lipid profiling and radiotracer labeling studies revealed a negative correlation of ATGLL expression and the relative contribution of the chloroplast lipid pathway to thylakoid lipid biosynthesis. Additionally, we show that genetic manipulation of ATGLL expression resulted in changes in triacylglycerol levels in leaves. We propose that ATGLL, through affecting the level of prokaryotic DAG in the chloroplast, plays important roles in balancing the two glycerolipid pathways and in maintaining lipid homeostasis in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Residual Stress Modeling and Advanced Diffraction Measurements of 347H Steel Weldments

The 347H stainless steel is a primary high-temperature material for many energy and power generation industries. Stress relief cracking (SRC) has been a particular concern in welding of this material. The residual stress induced by welding and its evolution during post-welding heat treatment (PWHT) and subsequent operating and service conditions is one of the primary factors contributing to SRC. The lifetime of welded structure components is also controlled by the precipitation kinetics that accompanies PWHT, stress relaxation process, and long-term aging and complex synergistic factors. Various theories have been proposed in the past to explain SRC. However, a widely accepted approach to predicting the entire damage evolution and the resulting performance reduction is still lacking. This study is to demonstrate a reliable solution for two critical issues that affect the predictions. First, the residual stress distribution obtained from both simulation and neutron diffraction is compared, which increases the accuracy of mechanical analysis simulation model and therefore builds a solid basis for the lifetime prediction model. Second, the generation, evolution, and annihilation of precipitates are monitored by the synchrotron diffraction experiment. Preliminary results demonstrate the critical importance of precipitation kinetics on the residual stress distribution/redistribution during heat treatments.

Yang, Yi↗
DOE OSTI2020

Necessary and sufficient conditions for resonant mixing of plane waves in elastic solids with quadratic nonlinearity

This paper studies the interactions of two plane waves in elastic solids with quadratic nonlinearity. In particular, the necessary and sufficient conditions for resonant mixing of two plane waves are derived. It is shown that the conventional resonance condition for resonant mixing of plane waves is only a necessary condition, not sufficient. Based on the newly derived necessary and sufficient conditions, resonant mixing of various types of plane waves are investigated and specific conditions for generating a resonant mixed wave are obtained for each case. These results are useful for developing nonlinear ultrasonic nondestructive evaluation techniques using the wave mixing method.

Gao, Xiang↗
DOE OSTI2020

Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Alkali metal halide–coated perovskite redox catalysts for anaerobic oxidative dehydrogenation of n -butane

Oxidative dehydrogenation (ODH) of n-butane has the potential to efficiently produce butadiene without equilibrium limitation or coke formation. Despite extensive research efforts, single-pass butadiene yields are limited to <23% in conventional catalytic ODH with gaseous O 2 . This article reports molten LiBr as an effective promoter to modify a redox-active perovskite oxide, i.e., La 0.8 Sr 0.2 FeO 3 (LSF), for chemical looping–oxidative dehydrogenation of n-butane (CL-ODHB). Under the working state, the redox catalyst is composed of a molten LiBr layer covering the solid LSF substrate. Characterizations and ab initio molecular dynamics (AIMD) simulations indicate that peroxide species formed on LSF react with molten LiBr to form active atomic Br, which act as reaction intermediates for C–H bond activation. Meanwhile, molten LiBr layer inhibits unselective CO 2 formation, leading to 42.5% butadiene yield. The redox catalyst design strategy can be extended to CL-ODH of other light alkanes such as iso-butane conversion to iso-butylene, providing a generalized approach for olefin production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A 3D extra-large-pore zeolite enabled by 1D-to-3D topotactic condensation of a chain silicate

Zeolites are microporous silicates with a large variety of applications as catalysts, adsorbents, and cation exchangers. Stable silica-based zeolites with increased porosity are in demand to allow adsorption and processing of large molecules but challenge our synthetic ability. We report a new, highly stable pure silica zeolite called ZEO-3, which has a multidimensional, interconnected system of extra-large pores open through windows made by 16 and 14 silicate tetrahedra, the least dense polymorph of silica known so far. This zeolite was formed by an unprecedented one-dimensional to three-dimensional (1D-to-3D) topotactic condensation of a chain silicate. With a specific surface area of more than 1000 square meters per gram, ZEO-3 showed a high performance for volatile organic compound abatement and recovery compared with other zeolites and metal-organic frameworks.

Science & Technology - Other Topics↗
DOE OSTI2023

Quantifying Volume Change in Porous Electrodes via the Multi-Species, Multi-Reaction Model

Automotive manufacturers are working to improve individual cell and overall pack design by increasing their performance, durability, and range, while reducing cost; and active material volume change is one of the more complex aspects that needs to be considered during this process. As the time from initial design to manufacture of electric vehicles is decreased, design work that used to rely solely on testing needs to be supplemented or replaced by virtual methods. As electrochemical engineers drive battery and system design using model-based methods, the need for coupled electrochemical/mechanical models that take into account the active material change utilizing physics based or semi-empirical approaches is necessary. In this study, we illustrated the applicability of a mechano-electrochemical coupled modeling method considering the multi-species, multi-reaction model as popularized by Verbrugge and Baker. To do this, validation tests were conducted using a computer-controlled press apparatus that can control the press displacement and press force with precision. The coupled MSMR volume change model was developed and its applicability to graphite and NMC cells was illustrated. The increased accuracy of the model considering the coupled MSMR volume change approach shows in the importance of accounting for individual gallery volume change behavior on cell level predictions.

25 ENERGY STORAGE↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗