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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 OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2021

Bringing discrete-time Langevin splitting methods into agreement with thermodynamics

In light of the recently published complete set of statistically correct Grønbech–Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Finally, numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.

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

Differentiable Predictions for Large Scale Structure with SHAMNet

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions of large-scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one- and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Enabling scientific machine learning in MOOSE using Libtorch

A neural-network-based machine learning interface has been developed for the Multiphysics Object-Oriented Simulation Environment (MOOSE). The interface relies on Libtorch, the C++ front-end of PyTorch, and enables an online interaction between modern machine learning algorithms and all the existing simulation, modeling, and analysis processes available in MOOSE. New capabilities in MOOSE include the native generation and training of artificial neural networks together with options to load pretrained neural networks in TorchScript format. Furthermore, the MOOSE stochastic tools module (MOOSE-STM) has been enhanced with neural network-based surrogate and reduced-order model generation options for efficient stochastic analyses. Lastly, a reinforcement learning capability has been added to MOOSE-STM for the interactive control and optimization of complex multiphysics problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

On-line methods for rotorcraft aeroelastic mode identification

The requirements for the on-line identification of rotorcraft aeroelastic blade modes from random response test data are presented. A recursive maximum likelihood (RML) technique is used in conjunction with a bandpass filter to identify isolated blade mode damping and frequency. The RML technique is demonstrated to have excellent convergence characteristics in random measurement noise and random process noise excitation. The RML identification technique uses an ARMA representation for the aeroelastic stochastic system and requires virtually no user interaction while providing accurate confidence bands on the parameter estimates. Comparisons are made with an off-line Newton type maximum likelihood algorithm which uses a state variable model representation. Results are presented from simulation random response data which quantify the identifed parameter convergence behavior for various levels of random excitation which is typical of wind tunnel turbulence levels. The RML technique is applied to hingless rotor test data from the NASA Langley Research Center Helicopter Hover Facility.

Molusis, J. A.↗
NASA NTRS2013

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Ono, Masahiro↗
DOE OSTI2023

Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu–Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials .

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

Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

The augmentation algorithm and molecular phylogenetic trees

Moore's (1977) augmentation procedure is discussed, and it is concluded that the procedure is valid for obtaining estimates of the total number of fixed nucleotide substitutions both theoretically and in practice, for both simulated and real data, and in agreement, for experimentally dense data sets, with stochastic estimates of the divergence, provided the restrictions on codon mutability resulting from natural selection are explicitly allowed for. Tateno and Nei's (1978) critique that the augmentation procedure has a systematic bias toward overestimation of the total number of nucleotide replacements is disputed, and a data analysis suggests that ancestral sequences inferred by the method of parsimony contain a large number of incorrectly assigned nucleotides.

Holmquist, R.↗
DOE OSTI2017

A single-column particle-resolved model for simulating the vertical distribution of aerosol mixing state: WRF-PartMC-MOSAIC-SCM v1.0

Abstract. The PartMC-MOSAIC particle-resolved aerosol model was previously developed to predict the aerosol mixing state as it evolves in the atmosphere. However, the modeling framework was limited to a zero-dimensional box model approach without resolving spatial gradients in aerosol concentrations. This paper presents the development of stochastic particle methods to simulate turbulent diffusion and dry deposition of aerosol particles in a vertical column within the planetary boundary layer. The new model, WRF-PartMC-MOSAIC-SCM, resolves the vertical distribution of aerosol mixing state. We verified the new algorithms with analytical solutions for idealized test cases and illustrate the capabilities with results from a 2-day urban scenario that shows the evolution of black carbon mixing state in a vertical column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗
DOE OSTI2011

The Network Completion Problem: Inferring Missing Nodes and Edges in Networks

Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

List-Mode source injection algorithm for detectors with arbitrary pose and trajectory

environments, introducing actual sources is usually neither feasible nor economical. It is more practical to move the detector (whether airborne, vehicle-borne, or human-portable) through the test environment and then later artificially superimpose either measured or simulated source signatures on the recorded background data. We present a source injection (SI) algorithm that inserts source events into list-mode background data. It is designed to accommodate cases where “injection data” are available only at a limited set of locations. Stochastic methods are employed both to select source events from a measured dataset containing both source and background events, and to scale the number of selected events to match the field conditions of detector-source distance, air attenuation, acquisition duration, and the relative strength of the measured and injected sources. The algorithm is deployed as part of the Airborne Radiation Enhanced-sensor System (ARES) Advanced Technology Demonstration, and is applied to field data obtained using the ARES detector system. For this helicopter-borne detection system, we describe a practical ground-based measurement scheme for acquiring data for later injection. These data are then injected into background data acquired during flight, where arbitrary detector poses and trajectories are possible. Similar flight paths are traversed with and without actual 137Cs and 60Co sources present. SI is applied to the source-absent datasets, and the results are compared to the source-present datasets. SI output is found to be consistent with measured activity within the estimated uncertainties in GPS position and source strength.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Incorporating Wind Energy in Power System Restoration Planning

Wind energy is rapidly growing. While wind brings us clean and inexpensive energy, its inherent variability and uncertainty present challenges for the power grid. In particular, employing wind energy for power system restoration is very challenging. A fast and reliable restoration plays a vital role to achieve the self-healing power grid. This paper develops a novel offline restoration planning tool for harnessing wind energy to enhance grid resilience. The Wind-for-Restoration problem is formulated as a stochastic mixed-integer linear programming problem with generated wind energy scenarios. The problem is then decomposed into two stages and solved with the integer Lshaped algorithm. Numerical experiments have been conducted through different case studies using the modified IEEE 57-bus system. The developed tool can provide the scheduled wind power at each restoration time. The impact of wind energy is investigated from the aspects of location and inertia capability, as well as wind penetration, fluctuation, and uncertainty. Moreover, a dynamic response validation tool is developed to validate the results of optimization problem in a dynamic simulation software. Finally, simulation results demonstrate that the optimal wind harnessing strategy can help improve system restoration process and enhance system resilience.

17 WIND ENERGY↗
DOE OSTI2013

A hybrid approach to the neutron transport K-eigenvalue problem using NDA-based algorithms

In order to provide more physically accurate solutions to the neutron transport equation it has become increasingly popular to use Monte Carlo simulation to model nuclear reactor dynamics. These Monte Carlo methods can be extremely expensive, so we turn to a class of methods known as hybrid methods, which combine known deterministic and stochastic techniques to solve the transport equation. In our work, we show that we can simulate the action of a transport sweep using a Monte Carlo simulation in order to solve the k-eigenvalue problem. We'll accelerate the solution using nonlinear diffusion acceleration (NDA) as in [1,2]. Our work extends the results in [1] to use Monte Carlo simulation as the high-order solver. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomly switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Testing of Strategies for the Acceleration of the Cost Optimization

The general problem addressed in the Nuclear-Renewable Hybrid Energy System (N-R HES) project is finding the optimum economical dispatch (ED) and capacity planning solutions for the hybrid energy systems. In the present test-problem configuration, the N-R HES unit is composed of three electrical power-generating components, i.e. the Balance of Plant (BOP), the Secondary Energy Source (SES), and the Energy Storage (ES). In addition, there is an Industrial Process (IP), which is devoted to hydrogen generation. At this preliminary stage, the goal is to find the power outputs of each one of the N-R HES unit components (BOP, SES, ES) and the IP hydrogen production level that maximizes the unit profit by simultaneously satisfying individual component operational constraints. The optimization problem is meant to be solved in the Risk Analysis Virtual Environment (RAVEN) framework. The dynamic response of the N-R HES unit components is simulated by using dedicated object-oriented models written in the Modelica modeling language. Though this code coupling provides for very accurate predictions, the ensuing optimization problem is characterized by a very large number of solution variables. To ease the computational burden and to improve the path to a converged solution, a method to better estimate the initial guess for the optimization problem solution was developed. The proposed approach led to the definition of a suitable Monte Carlo-based optimization algorithm (called the preconditioner), which provides an initial guess for the optimal N-R HES power dispatch and the optimal installed capacity for each one of the unit components. The preconditioner samples a set of stochastic power scenarios for each one of the N-R HES unit components, and then for each of them the corresponding value of a suitably defined cost function is evaluated. After having simulated a sufficient number of power histories, the configuration which ensures the highest profit is selected as the optimal one. The component physical dynamics are represented through suitable ramp constraints, which considerably simplify the numerical solving. In order to test the capabilities of the proposed approach, in the present report, the dispatch problem only is tackled, i.e. a reference unit configuration is assumed, and each one of the N-R HES unit components is assumed to have a fixed installed capacity. As for the next steps, the main improvement will concern the operation strategy of the ES facility. In particular, in order to describe a more realistic battery commitment strategy, the ES operation will be regulated according to the electricity price forecasts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Enhanced mechanical property evaluation using innovative data analytics capability

This report focuses on efforts to improve Multiphysics Object-Oriented Simulation Environ-ment (MOOSE) for mechanical property evaluation using data analytics. These efforts involveimprovements to the stochastic tools module (STM) for stochastic simulations of MOOSE mul-tiphysics model and the development of inverse optimization capabilities. The report gives anoverview of the STM and describes recent updates to its core capabilities and theory on itsreduced-order model (ROM) schemes. Examples are also provided showing the impact of theseupdates and exhibits the usefulness of ROMs. An overview of the gradient based inverse opti-mziation algorithms are given along with examples of their application to source identification.Inverse optimization will provide a new methodology in STM for fitting model parameters toexperimental data.

97 MATHEMATICS AND COMPUTING↗