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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Certifiably Robust Neural ODE With Learning-Based Barrier Function

Neural Ordinary Differential Equations (ODEs) have gained traction in many applications. While recent studies have focused on empirically increasing the robustness of neural ODEs against natural or adversarial attacks, certified robustness is still lacking. In this work, we propose a framework for training a neural ODE using barrier functions and demonstrate improved robustness for classification problems. Finally, we further provide the first generalization guarantee of robustness against adversarial attacks using a wait-and-judge scenario approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems

This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework

Hosting capacity is an indication of the amount of solar photovoltaics (PV) that can be hosted in a distribution system without additional changes to infrastructure or oper-ations. This paper presents a framework for estimating the PV hosting capacity at scale. First, we analyze computational, modeling and other key challenges of performing relevant, large-scale simulations, provided along with the experiences and lessons learned. Then, we develop two open-source Python-based software tools to conduct repeatable distribution analyses: the Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations and the Job Automation and Deployment Engine (JADE) for parallelizing jobs on high-performance computing clusters. A case study of hosting capacity estimation for the SMART-DS San Francisco (SFO) 2000+ synthetic feeders, is used to demonstrate the capability of the developed DISCO+JADE framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, which can help them better plan for the overall upgrade costs to integrate more PV in the future. The experiences are shared to aid the tool users and researchers to conduct relevant studies and research.

distributed energy resources↗
DOE OSTI2024

Autonomous Inverter Controls for Resilient and Secure Grid Operation: Vector Control Design for Grid Forming

The project addresses both fundamental and practical challenges of GFM/GFL inverter control for the power grids with high inverter based resources (IBRs) penetration. A data- driven modeling technique is applied to accurately model dynamics of PWM inverters, including electromagnetic-transient (EMT). Systematic and integrative designs of grid- forming (GFM) and grid-following (GFL) primary controls are developed to guarantee system performance under either normal or abnormal operating conditions without violating constraints. This modeling and control framework provides black-start capability in case of an outage without relying on rotating generators, and its secondary control is also shown to enhance resilience against cyber-physical attacks.

14 SOLAR ENERGY↗
DOE OSTI2023

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

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

An open source analysis framework for large-scale building energy modeling

Full integration of building energy modelling into the design and retrofit process has long been a goal of building scientists and practitioners. However, significant barriers still exist. Among them are the lack of available: (1) configurable technology stacks for performing both small- and large-scale analyses, (2) different classes of algorithms compatible with common design workflows, and (3) analysis tools for effectively visualizing large-scale simulation results. This article discusses the OpenStudio® Analysis Framework: a scalable analysis framework for building energy modelling that was developed to overcome the three barriers listed above. The framework is open-source and scalable to facilitate wider adoption and has a clearly defined application programming interface upon which other applications can be built. It runs on high-performance computing systems, within cloud infrastructure, and on laptops, and uses a common workflow to enable different classes of algorithms. Lessons learned from previous development efforts are also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Data-Driven Distribution System Coordinated PV Inverter Control Using Deep Reinforcement Learning

The deployment of distributed solar photovoltaic (PV) systems has increased consistently over the past decades. High penetrations of PVs could cause a series of adverse grid impacts, such as voltage violations. The recent development of smart inverter technologies rises the incentives of developing PV control solutions that regulate the inverter output power and seeking the optimization on system operational objectives. This paper proposes a data-driven control solution based on deep reinforcement learning (DRL) to optimize PV inverters for voltage regulation. The proposed solution can minimize PV real power curtailment while maintaining network voltage at an acceptable range. Comparison results between the proposed DRL control algorithms with deep deterministic policy gradient (DDPG) and volt-var control on a real feeder in west Colorado highlight the advantage of the proposed framework in controlling the system voltage while minimizing the PV real power curtailment.

deep reinforcement learning↗
DOE OSTI2022

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al 2 (OH) 2 TCPP) [H 2 TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.

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

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2026

Single-cell chromatin accessibility and cis -regulatory element analyses in plants using the scPlantReg platform

Understanding gene regulation is fundamental to plant improvement, but the lack of plant-specific single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) frameworks and cross-species databases has limited insights into cell-type-specific cellular regulation. Here we present ‘scPlantReg’, an integrated framework and database for plant scATAC-seq data. scPlantReg supports end-to-end analyses from raw data processing to biological interpretation and features ‘scATACtor’, a supervised machine-learning approach that outperforms existing tools for cell-type annotation. We applied scPlantReg to pearl millet to characterize cell-type-specific chromatin accessibility and identify validated activating and repressing accessible chromatin regions (ACRs), revealing WRKY transcription factors as potential regulators of xylem development. Furthermore, we reanalysed scATAC-seq datasets from 8 plant species, spanning 11 tissues and multiple developmental stages, enabling cross-species comparisons. Furthermore, these analyses uncovered conserved regulatory programmes, including AP2/EREBP-associated ACRs linked to cell wall development and cell-type-conserved TFs across grasses. Collectively, scPlantReg provides a general framework and resource for comparative regulatory analysis in plants.

Epigenomics↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Application-driven Privacy-preserving Data Publishing with Correlated Attributes

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes - which may be hidden and can be inferred by machine learning algorithms - while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Rational design of heterogeneous single-site catalysts via surface organometallic chemistry

Single-site heterogeneous catalysts offer an attractive route to unite the molecular precision of homogeneous catalysis with the durability and practical advantages of solids. Surface organometallic chemistry (SOMC) provides a particularly powerful strategy for this purpose by grafting molecular precursors onto tailored surfaces and converting support functionalities into ligand environments for isolated metal centers. As a result, SOMC brings the language and logic of coordination chemistry to heterogeneous catalysis, where the support becomes an integral part of the active site coordination sphere. This Review surveys recent progress in the rational design of SOMC-derived single-site catalysts, with emphasis on synthetic routes, post synthetic transformations, and the deliberate tuning of catalytic behavior through metal-support interactions. Discussions are made on how support identity, hydroxyl topology, acidity, and redox activity shape the geometry, electronic structure, and oxidation state of supported metal sites, as well as how these factors determine activity, selectivity, and stability. We also examine a central limitation of these systems: despite their molecularly informed design, supported single sites often exist as structurally distributed ensembles rather than uniform species, particularly on amorphous supports. This site heterogeneity, along with catalyst dynamics under operating conditions, remains a major barrier to definitive structure-activity relationships. Therefore, emerging approaches that combine advanced characterization, first-principles modeling, ensemble kinetics, and machine learning to resolve active-site structure and guide catalyst development are highlighted. Together, these advances position SOMC as a versatile coordination chemistry framework for the predictive design of heterogeneous catalysts with well-defined molecularly tailored active sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗