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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density.

42 ENGINEERING↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2020

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. Here, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none have investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

The Cascade Effectiveness of 3-Terminal Tandem Photocathode Architectures as Applied to CO 2 Reduction

Cascade catalysis for photoelectrochemical CO 2 reduction (CO 2 R) decouples the overall reaction into sequential steps occurring on separately optimized catalysts (for example, Ag and Cu) between which an intermediate species such as CO is transferred. A 3-terminal tandem (3TT) photovoltaic architecture advantageously holds two different catalytic regions at different potentials under a single illumination source, but its overall efficiency is low. Using a stochastic reaction-diffusion model, we have examined 3TT photocathode design principles, focusing on the coupling of surface chemistry and transport of CO both inside the boundary layer and outward, into the bulk electrolyte. We find that ensuring that the lateral diffusion distance within the boundary layer is short compared to the boundary layer thickness and controlling bulk flow are key, with interdigitated designs showing an overall conversion efficiency improvement by 2 orders of magnitude compared to the side-by-side case without flow. These findings can be adapted for other cascaded architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Generalized master equation for particle transport in binary random media with renewal statistics

Particle transport in binary stochastic mixtures is classically modeled assuming Markovian or exponential mixing statistics but in many applications material memory invalidates the Markov assumption. For non-Markovian mixing characterized by alternating renewal processes, a transport-theoretic framework is presented that provides an exact description of transport in nonscattering random binary media with general non-exponential statistics. Our approach is to Markovianize the problem by augmenting the {material type, particle flux} state space with the age or distance from the last interface. A Chapman-Kolmogorov equation is formulated for the joint probability density of the material type, particle flux, and age, and subsequently reduced to a generalized Master equation (GME) in differential form. This constitutes the primary result of this work. A state-updating Monte Carlo algorithm consistent with the GME is developed and benchmarked against analytical solutions for multiple chord-length laws. For purely absorbing renewal statistical media, the GME reproduces analytical benchmarks for the equilibrium age distribution, interior mean/variance of material-conditioned fluxes, and boundary transmittance. Simulations further demonstrate that a Markov (exponential) approximation of non-exponential statistics can introduce large errors in transmittance and interior flux profiles. Lastly, the reintroduction of memory due to scattering is briefly addressed through heuristic considerations.

Fluctuations & noise↗
DOE OSTI2025

Voltage Probability Density Function Shaping Control Strategy Considering Grid Operational Uncertainties

It is well-known that power systems operation always affected by various uncertainties which make the bus voltage a random process that can be characterized by its probability density function (PDF) at any time instant. In this context, this paper presents a novel PDF-based voltage control framework for power systems. By modeling voltage as a stochastic process, we formulate a stochastic differential equationthat captures grid uncertainties. The associated Fokker-Planck-Kolmogorov equation is derived to describe the evolution of the voltage PDF, which enables the formulation of a PDF-shaping control strategy. To simplify the PDF control formulation, a B-spline neural network is introduced for real-time estimation and regulation of the voltage distribution. The proposed PDF control law updates voltage references for energy storage systems and synchronous generators using real-time PDF measurements and feedback signals. The proposed method is validated on a modified Kundur’s two-area system. Simulation results demonstrate that the controller can significantly improve the voltage stability under stochastic conditions, highlighting its effectiveness in modern inverter-rich grids.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2023

Lagrangian and Eulerian Supersaturation Statistics in Turbulent Cloudy Rayleigh–Bénard Convection: Applications for LES Subgrid Modeling

Abstract Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysis

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. Here, the validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A stochastic biomass blending problem in decentralized supply chains

Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the mini-mum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.

09 BIOMASS FUELS↗
DOE OSTI2020

Stochastic Fault Detection

This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.

Wang, Aiping↗
DOE OSTI2024

Physics-based modeling and data analytics [Slides]

This presentation contains a summary of ongoing work within the physics-based modeling and data analytics work package within the Nuclear Materials Discovery and Qualification initiative (NMDQi). Topics include work on MOOSE-based crystal plasticity, molecular dynamics modeling of recombination in metals and alloys, the MOOSE Stochastic Tools Module, and machine learning and atomistic modeling to predict thermo-kinetic properties of nuclear structural materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS

Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remain on the device. However, since all data are collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as federated stochastic neural networks. In this work we will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling nonindependent and identically distributed data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhancing Convection Parameterization for Next Generation E3SM (Final Technical Report)

This technical report summarizes the achievements during the project period. The general goal of the project is to enhance the representation of atmospheric deep convection in the DOE global climate model E3SM. By implementing a stochastic convection scheme into E3SM, we improved the simulation of precipitation intensity and frequency. As a result, it has a profound impact on the simulation of other fields in the model, including aerosol scavenging by precipitation and aerosol radiative effects. We also implemented a convective cloud microphysics scheme into E3SMv2 to represent more accurately the interaction between convection and grid-scale clouds. An extensive analysis of model simulations as part of the Next Generation Development (NGD) convection scheme assessment project found that including the convective microphysics scheme improved the simulation of Madden-Julian oscillations (MJO) in the model. To make convective parameterization schemes more scale-aware as the GCM resolution increases, we analyzed the output from cloud-resolving model (CRM) simulations of both organized and unorganized convection by coarse-graining the CRM data to different GCM resolutions. Using a novel approach to modify the convective closure, we demonstrated that the convection scheme used in E3SM (ZM scheme) can be made scale-aware.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Using metacommunity ecology to understand environmental metabolomes

Abstract Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dynamics of non-Gaussian fluctuations in model A

Motivated by the experimental search for the QCD critical point, we perform simulations of a stochastic field theory with purely relaxational dynamics (model A). We verify the expected dynamic scaling of correlation functions. Using a finite size scaling analysis, we obtain the dynamic critical exponent z = 2.026(56). We investigate time dependent correlation functions of higher moments M n (t) of the order parameter M(t) for n = 1, 2, 3, 4. We obtain dynamic scaling with the same critical exponent z for all n, but the relaxation constant depends on n. We also study the relaxation of M n (t) after a quench, where the simulation is initialized in the high temperature phase, and the dynamics is studied at the critical temperature T c . Finally, we find that the evolution does not follow simple scaling with the dynamic exponent z, and that it involves an early time rise followed by late stage relaxation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Toward data assimilation of ship-induced aerosol–cloud interactions

Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol–cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation.

54 ENVIRONMENTAL SCIENCES↗