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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

First-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Development of a Griffin model of the advanced test reactor

In the pursuit of a higher fidelity deterministic simulation capability of the Advanced Test Reactor, it is important to have a fast yet accurate deterministic neutronics model. Here, to achieve this, we employed an advanced two-step method. The first step involves generating homogenized cross sections using OpenMC, a cutting-edge Monte Carlo neutron transport code. OpenMC offers excellent modular capabilities, allowing for easy component integration and flexibility in incorporating new designs into the model. The second step involves deterministic transport calculations, which are performed using Griffin, a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). To ensure the accurate spatial resolution and assignment of material cross sections, a Cubit-generated mesh for the Advanced Test Reactor is utilized as an intermediate step between the OpenMC and Griffin models; Griffin utilizes the mesh for its finite element solution, while OpenMC material identifications are written to the mesh file to be used in Griffin material assignments. Additionally, a Python-based script converts the cross sections generated by OpenMC into the ISOXML format required by Griffin. Initial comparisons using the Griffin diffusion solver indicated good agreement between the neutron multiplication factors obtained from the standalone OpenMC model and the Griffin model, with differences of less than 10 pcm in the 2D geometry configuration; it was later determined that this agreement was likely due to compensating effect and was more likely on the order of –700 pcm relative to the OpenMC solution. However, in three-dimensional calculations, an unacceptably large error (almost 8,000 pcm) was found in the Griffin solution with the diffusion solver. Subsequent calculations using Griffin’s discrete ordinates solver demonstrated substantially improved agreement, within 116 pcm of the OpenMC solution used to generate the cross sections for Griffin. Building on this capability, future work will seek to perform more detailed validation calculations. The ultimate goal is to evaluate both transient and multiphysics simulations of the reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2023

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗
DOE OSTI2018

Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty

We introduce the problem of scheduling observations on a constellation of remote sensors, to maximize the aggregate quality of the collections obtained. While automated tools exist to schedule remote sensors, they are often based on heuristic scheduling techniques, which typically fail to provide bounds on the quality of the resultant schedules. To address this issue, we first introduce a novel deterministic mixed-integer programming (MIP) model for scheduling a constellation of one to n satellites, which relies on extensive pre-computations associated with orbital propagators and sensor collection simulators to mitigate model size and complexity. Our MIP model captures realistic and complex constellation-target geometries, with solutions providing optimality guarantees. We then extend our base deterministic MIP model to obtain two-stage and three-stage stochastic MIP models that proactively schedule to maximize expected collection quality across a set of scenarios representing cloud cover uncertainty. Our experimental conclusions on instances of one and two satellites demonstrate that our stochastic MIP models yield significantly improved collection quality relative to our base deterministic MIP model. We further demonstrate that commercial off-the-shelf MIP solvers can produce provably optimal or near-optimal schedules from these models in time frames suitable for sensor operations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs

Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions. The number of gPC terms increases dramatically as the dimension of the random input variables increases. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs when the corresponding deterministic solver is computationally expensive, evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solutions, in both spatial and random domains, by coupling Bayesian model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spatial points, via (1) the Bayesian model average (BMA) or (2) the median probability model, and their construction as spatial functions on the spatial domain via spline interpolation. The former accounts for the model uncertainty and provides Bayes-optimal predictions; while the latter provides a sparse representation of the stochastic solutions by evaluating the expansion on a subset of dominating gPC bases. Moreover, the proposed methods quantify the importance of the gPC bases in the probabilistic sense through inclusion probabilities. We design a Markov chain Monte Carlo (MCMC) sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed methods are suitable for, but not restricted to, problems whose stochastic solutions are sparse in the stochastic space with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the accuracy and performance of the proposed methods and make comparisons with other approaches on solving elliptic SPDEs with 1-, 14- and 40-random dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Coupling sample paths to the thermodynamic limit in Monte Carlo estimators with applications to gene expression

Many biochemical systems appearing in applications have a multiscale structure so that they converge to piecewise deterministic Markov processes in a thermodynamic limit. The statistics of the piecewise deterministic process can be obtained much more efficiently than those of the exact process. We explore the possibility of coupling sample paths of the exact model to the piecewise deterministic process in order to reduce the variance of their difference. We then apply this coupling to reduce the computational complexity of a Monte Carlo estimator. Motivated by the rigorous results in , we show how this method can be applied to realistic biological models with nontrivial scalings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Harnessing structural stochasticity in the computational discovery and design of microstructures

This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic microstructure design domains has yet to be created. The proposed methodology resolves this issue by learning a unified feature space that embodies diverse structural patterns with continuously varying stochasticity levels. A highly diverse microstructure database is established to incorporate various types of deterministic and stochastic microstructure patterns. A property-aware deep generative model is proposed to learn a unified feature space of the structural characteristics, as well as the relationship between structure features and properties of interest. Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. Microstructural designs with tailorable stochasticity and properties are obtained by searching the unified feature space. Multiple design cases are presented to demonstrate the capability of designing microstructures for structural stochasticity and properties. Furthermore, the proposed method is employed to create stochastically graded structures, which manipulate the mechanical behaviors by varying the local stochasticity of the structure.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows

Here, we develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number of stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2011

End-of-life flows of multiple cycle consumer products

Explicit expressions for the end-of-life flows (EOL) of single and multiple cycle products (MCPs) are presented, including deterministic and stochastic EOL exit. The expressions are given in terms of the physical parameters (maximum lifetime, T, annual cycling frequency, f, number of cycles, N, and early discard or usage loss). EOL flows are also obtained for hi-tech products, which are rapidly renewed and thus may not attain steady state (e.g. electronic products, passenger cars). A ten-step recursive procedure for obtaining the dynamic EOL flow evolution is proposed. Applications of the EOL expressions and the ten-step procedure are given for electric household appliances, industrial machinery, tyres, vehicles and buildings, both for deterministic and stochastic EOL exit, (normal, Weibull and uniform exit distributions). The effect of the physical parameters and the stochastic characteristics on the EOL flow is investigated in the examples: it is shown that the EOL flow profile is determined primarily by the early discard dynamics; it also depends strongly on longevity and cycling frequency: higher lifetime or early discard/loss imply lower dynamic and steady state EOL flows. The stochastic exit shapes the overall EOL dynamic profile: Under symmetric EOL exit distribution, as the variance of the distribution increases (uniform to normal to deterministic) the initial EOL flow rise becomes steeper but the steady state or maximum EOL flow level is lower. The steepest EOL flow profile, featuring the highest steady state or maximum level, as well, corresponds to skew, earlier shifted EOL exit (e.g. Weibull). Since the EOL flow of returned products consists the sink of the reuse/remanufacturing cycle (sink to recycle) the results may be used in closed loop product lifecycle management operations for scheduling and sizing reverse manufacturing and for planning recycle logistics. Decoupling and quantification of both the full age EOL and of the early discard flows is useful, the latter being the target of enacted legislation aiming at increasing reuse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Abstract Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Practical Probabilistic Benchmark for AI Weather Models

Since the weather is chaotic, it is necessary to forecast an ensemble of future states. Recently, multiple AI weather models have emerged claiming breakthroughs in deterministic skill. Unfortunately, it is hard to fairly compare ensembles of AI forecasts because variations in ensembling methodology become confounding and the baseline data volume is immense. We address this by scoring lagged initial condition ensembles—whereby an ensemble can be constructed from a library of deterministic hindcasts. This allows the first parameter‐free intercomparison of leading AI weather models' probabilistic skill against an operational baseline. Lagged ensembles of the two leading AI weather models, GraphCast and Pangu, perform similarly even though the former outperforms the latter in deterministic scoring. These results are elaborated upon by sensitivity tests showing that commonly used multiple time‐step loss functions damage ensemble calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mesoscale atomic engineering in a crystal lattice

Controlling individual atoms using lasers, ion traps and scanning probe tips has transformed our understanding of matter and enabled breakthroughs in quantum science. Extending this control into three-dimensional (3D) solids and across mesoscopic scales, however, remains a foundational challenge. Electron irradiation in electron microscopes is known to induce atomic displacements, and atomic manipulation has been proposed and demonstrated. Yet repeated and deterministic control has remained elusive. Here, in this study, we demonstrate deterministic atomic engineering in a 3D crystal, creating ordered arrangements of more than 40,000 user-defined defects within minutes across a 150 nm × 100 nm × 13 nm volume. By steering individual Cr atoms in the magnetic semiconductor CrSBr into selected interstitial sites using an electron beam directed with sub-20-pm-scale accuracy, we create vacancy–interstitial complexes. The resulting impurity array forms a mesoscale crystal embedded within the host lattice, a new form of engineered artificial matter that remains stable at room temperature and outside the microscope. By tracking Cr atom displacements, we identify conditions under which the defect structures are predictable. Our calculations suggest that these defects form correlated impurity states with intra-defect optical transitions and inter-defect kinetic and Coulomb interactions. This establishes a generalizable platform for atomic defect engineering at mesoscopic, and potentially macroscopic, scales, opening opportunities for scalable quantum technologies, including deterministic colour-centre placement, quantum simulation of many-body lattice models and atomic-scale manufacturing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2010

Distribution and regulation of stochasticity and plasticity in Saccharomyces cerevisiae

Stochasticity is an inherent feature of complex systems with nanoscale structure. In such systems information is represented by small collections of elements (e.g. a few electrons on a quantum dot), and small variations in the populations of these elements may lead to big uncertainties in the information. Unfortunately, little is known about how to work within this inherently noisy environment to design robust functionality into complex nanoscale systems. Here, we look to the biological cell as an intriguing model system where evolution has mediated the trade-offs between fluctuations and function, and in particular we look at the relationships and trade-offs between stochastic and deterministic responses in the gene expression of budding yeast (Saccharomyces cerevisiae). We find gene regulatory arrangements that control the stochastic and deterministic components of expression, and show that genes that have evolved to respond to stimuli (stress) in the most strongly deterministic way exhibit the most noise in the absence of the stimuli. We show that this relationship is consistent with a bursty 2-state model of gene expression, and demonstrate that this regulatory motif generates the most uncertainty in gene expression when there is the greatest uncertainty in the optimal level of gene expression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

A one-dimensional ion beam figuring system for x-ray mirror fabrication

We report on the development of a one-dimensional Ion Beam Figuring (IBF) system for x-ray mirror polishing. Ion beam figuring provides a highly deterministic method for the final precision figuring of optical components with advantages over conventional methods. The system is based on a state of the art sputtering deposition system outfitted with a gridded radio frequency inductive coupled plasma ion beam source equipped with ion optics and dedicated slit developed specifically for this application. Here, the production of an IBF system able to produce an elongated removal function rather than circular is presented in this paper, where we describe in detail the technical aspect and present the first obtained results.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Daniell method for power spectral density estimation in atomic force microscopy

An alternative method for power spectral density (PSD) estimation—the Daniell method—is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion—the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to a more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

On the Usage of Ultrasound Computational Models for Decision Making Under Ambiguity

Computer modeling and simulation is becoming pervasive within the non-destructive evaluation (NDE) industry as a convenient tool for designing and assessing inspection techniques. This raises a pressing need for developing quantitative techniques for demonstrating the validity and applicability of the computational models. Computational models provide deterministic results based on deterministic and well-defined input, or stochastic results based on inputs defined by probability distributions. However, computational models cannot account for the effects of personnel, procedures, and equipment, resulting in ambiguity about the efficacy of inspections based on guidance from computational models only. In addition ambiguity arises when model inputs, such as the representation of realistic cracks, cannot be defined deterministically, probabilistically, or by intervals. In this work, Pacific Northwest National Laboratory demonstrates the ability of computational models to represent field measurements under known variabilities, and quantify the differences using maximum amplitude and power spectrum density metrics. Sensitivity studies are also conducted to quantify the effects of different input parameters on the simulation results.

Dib, Gerges↗
DOE OSTI2021

A semi-analytical model for laser induced densification in fused silica at elevated temperatures

A semi-analytical model for deterministic surface smoothing of fused silica below the evaporation temperature threshold is presented. Using this model, a method for calculating deterministic track depths for laser polishing fused silica is derived and validated with experiments. In this work, model predictions were in good agreement with experiment across varying laser preheating temperature and power. With the semi-analytical method presented here, deterministic laser smoothing can be achieved in the densification regime to close the process loop of laser polishing.

36 MATERIALS SCIENCE↗