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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Semiparametric modeling: Correcting low-dimensional model error in parametric models

In this paper, a semiparametric modeling approach is introduced as a paradigm for addressing model error arising from unresolved physical phenomena. Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters. Practically, the proposed approach consists of the following steps. Given a physics-based model and a noisy data set of historical observations, a Bayesian filtering algorithm is used to extract a time-series of the parameter values. Subsequently, the diffusion forecast algorithm is applied to the retrieved time-series in order to construct the auxiliary model for the time evolving parameters. The semiparametric forecasting algorithm consists of integrating the existing physics-based model with an ensemble of parameters sampled from the probability density function of the diffusion forecast. To specify initial conditions for the diffusion forecast, a Bayesian semiparametric filtering method that extends the Kalman-based filtering framework is introduced. In difficult test examples, which introduce chaotically and stochastically evolving hidden parameters into the Lorenz-96 model, we show that our approach can effectively compensate for model error, with forecasting skill comparable to that of the perfect model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity

Graphical abstract: -- Highlights: •Problems concerning multi-compartment population balance equations are studied. •A class of fragmentation weight transfer functions is presented. •Three stochastic weighted algorithms are compared against the direct simulation algorithm. •The numerical errors of the stochastic solutions are assessed as a function of fragmentation rate. •The algorithms are applied to a multi-dimensional granulation model. -- Abstract: This paper introduces stochastic weighted particle algorithms for the solution of multi-compartment population balance equations. In particular, it presents a class of fragmentation weight transfer functions which are constructed such that the number of computational particles stays constant during fragmentation events. The weight transfer functions are constructed based on systems of weighted computational particles and each of it leads to a stochastic particle algorithm for the numerical treatment of population balance equations. Besides fragmentation, the algorithms also consider physical processes such as coagulation and the exchange of mass with the surroundings. The numerical properties of the algorithms are compared to the direct simulation algorithm and an existing method for the fragmentation of weighted particles. It is found that the new algorithms show better numerical performance over the two existing methods especially for systems with significant amount of large particles and high fragmentation rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

First principles simulation of ultracold ion crystals in a Penning trap with Doppler cooling and a rotating wall potential

A direct numerical simulation of many interacting ions in a Penning trap with a rotating wall is presented. The ion dynamics is modeled classically. Both axial and planar Doppler laser cooling processes are modeled using stochastic momentum impulses based on two-level atomic scattering rates. The plasmas being modeled are ultracold two-dimensional crystals made up of hundreds of ions. We compare Doppler cooled results directly to a previous linear eigenmodes analysis. Agreement in both frequency and mode structure is obtained. Additionally, when Doppler laser cooling is applied, the laser cooled steady state plasma axial temperature agrees with the Doppler cooling limit. Numerical simulations using the approach described and benchmarked here will provide insights into the dynamics of large trapped-ion crystals, improving their performance as a platform for quantum simulation and sensing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Stochastic Error Cancellation in Analog Quantum Simulation

Analog quantum simulation is a promising path towards solving classically intractable problems in many-body physics on near-term quantum devices. However, the presence of noise limits the size of the system and the length of time that can be simulated. In our work, we consider an error model in which the actual Hamiltonian of the simulator differs from the target Hamiltonian we want to simulate by small local perturbations, which are assumed to be random and unbiased. We analyze the error accumulated in observables in this setting and show that, due to stochastic error cancellation, with high probability the error scales as the square root of the number of qubits instead of linearly. We explore the concentration phenomenon of this error as well as its implications for local observables in the thermodynamic limit. Moreover, we show that stochastic error cancellation also manifests in the fidelity between the target state at the end of time-evolution and the actual state we obtain in the presence of noise. This indicates that, to reach a certain fidelity, more noise can be tolerated than implied by the worst-case bound if the noise comes from many statistically independent sources.

Analog quantum simulation↗
DOE OSTI2015

Coupling fracture facies with in-situ permeability measurements to generate stochastic simulations of tight carbonate aquifer properties: Example from the Lower Cretaceous aquifer, Northern Provence, SE France

The relationships between fracture facies and permeability distribution have been explored using a well-constrained 3D implicit structural model of hemipelagic low porosity/low permeability carbonate from the Northern Provence region, SE France. Fracture and permeability facies were determined using an extensive dataset of 99 hydrogeological wells. Data processing was undertaken using a step-by-step approach, involving: (i) identification of the fracture facies based on well logs detailing structure and stratigraphy; (ii) determination of permeability facies from the a priori correlation between the dimension of the hydraulic radius of influence (deduced from slug test analyses) and the type of reservoir heterogeneity (fissure, fracture, fault zone, etc.); (iii) three dimensional plot of fracture and permeability facies in the geological model using a variographic analysis of data. Thirty-three sequential indicator simulations (SIS) based on geostatistic analyses were realised on both fracture and permeability facies. Finally, a connectivity algorithm was developed to compute the probability of connection between selected infiltration areas and the major aquifer springs via moderate- to high-permeability geological bodies. Key results are: (i) fault zones have a greater role in controlling permeability facies distribution than on fracture facies repartition; (ii) there is little correlation between permeability and fracture facies distributions; (iii) connectivity results highlight the compartmentalisation of aquifers in the Cadarache area, the extensions of permeable geological bodies being limited by the N130 faults.

58 GEOSCIENCES↗
DOE OSTI2014

Characterizing acid diffusion lengths in chemically amplified resists from measurements of deprotection kinetics

The acid-catalyzed deprotection of glassy poly(4-hydroxystyrene-co-tertbutyl acrylate) films was studied with infrared absorbance spectroscopy and stochastic simulations. Experimental data were interpreted with a simple description of subdiffusive acid transport coupled to second-order acid loss. This model predicts key attributes of observed deprotection rates, such as fast reaction at short times, slow reaction at long times, and a nonlinear dependence on acid loading. Fickian diffusion is approached by increasing the post-exposure bake temperature or adding plasticizing agents to the polymer resin. These findings demonstrate that acid mobility and overall deprotection kinetics are coupled to glassy matrix dynamics. To complement the analysis of bulk kinetics, acid diffusion lengths were calculated from the anomalous transport model and compared with nanopattern line widths. The consistent scaling between experiments and simulations suggests that the anomalous diffusion model could be further developed into a predictive lithography tool.

Patil, Abhijit A.↗
NASA NTRS1972

Two stochastic models useful in petroleum exploration

A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposits

Kaufman, G. M.↗
NASA NTRS1985

An investigation of adaptive controllers for helicopter vibration and the development of a new dual controller

An investigation of the properties important for the design of stochastic adaptive controllers for the higher harmonic control of helicopter vibration is presented. Three different model types are considered for the transfer relationship between the helicopter higher harmonic control input and the vibration output: (1) nonlinear; (2) linear with slow time varying coefficients; and (3) linear with constant coefficients. The stochastic controller formulations and solutions are presented for a dual, cautious, and deterministic controller for both linear and nonlinear transfer models. Extensive simulations are performed with the various models and controllers. It is shown that the cautious adaptive controller can sometimes result in unacceptable vibration control. A new second order dual controller is developed which is shown to modify the cautious adaptive controller by adding numerator and denominator correction terms to the cautious control algorithm. The new dual controller is simulated on a simple single-control vibration example and is found to achieve excellent vibration reduction and significantly improves upon the cautious controller.

Mookerjee, P.↗
NASA NTRS1999

Dynamical Signatures of Living Systems

One of the main challenges in modeling living systems is to distinguish a random walk of physical origin (for instance, Brownian motions) from those of biological origin and that will constitute the starting point of the proposed approach. As conjectured, the biological random walk must be nonlinear. Indeed, any stochastic Markov process can be described by linear Fokker-Planck equation (or its discretized version), only that type of process has been observed in the inanimate world. However, all such processes always converge to a stable (ergodic or periodic) state, i.e., to the states of a lower complexity and high entropy. At the same time, the evolution of living systems directed toward a higher level of complexity if complexity is associated with a number of structural variations. The simplest way to mimic such a tendency is to incorporate a nonlinearity into the random walk; then the probability evolution will attain the features of diffusion equation: the formation and dissipation of shock waves initiated by small shallow wave disturbances. As a result, the evolution never "dies:" it produces new different configurations which are accompanied by an increase or decrease of entropy (the decrease takes place during formation of shock waves, the increase-during their dissipation). In other words, the evolution can be directed "against the second law of thermodynamics" by forming patterns outside of equilibrium in the probability space. Due to that, a specie is not locked up in a certain pattern of behavior: it still can perform a variety of motions, and only the statistics of these motions is constrained by this pattern. It should be emphasized that such a "twist" is based upon the concept of reflection, i.e., the existence of the self-image (adopted from psychology). The model consists of a generator of stochastic processes which represents the motor dynamics in the form of nonlinear random walks, and a simulator of the nonlinear version of the diffusion equation which represents the mental dynamics. It has been demonstrated that coupled mental-motor dynamics can simulate emerging self-organization, prey-predator games, collaboration and competition, "collective brain," etc.

Zak, M.↗
DOE OSTI2023

BISON Fuel Fragmentation Relocation and Dispersal (FFRD) Assessment Database for Eventual Use in Bayesian Calibration

Existing light-water reactor (LWR) fuel vendors have been interested in seeking increased discharge burnups of nuclear fuel rods for improved economics for quite a few years. With increased burnups come additional challenges that must be addressed. It has been experimentally observed that average burnups higher than the current regulatory limit of 62 MWd/kgU may undergo fuel fragmentation, relocation, and dispersal (FFRD) during a loss-of-coolant accident (LOCA). Industry must demonstrate approaches to mitigate FFRD. In an effort to support industry, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program within the U.S. Department of Energy (DOE) has for several years invested in developing multiscale models and creating a validation/assessment database for these models to study the mechanisms driving FFRD. This report provides an update on changes made to the assessment database and new models added to BISON to support the study of fuel rod behavior during FFRD. An effort has been initiated this year to begin adding dedicated inputs to the publicly available Virtual Test Bed (VTB) repository for industry use. A section of this report details the efforts made in this area. NEAMS has recently developed new capabilities in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework’s stochastic tools module for calibration using Bayesian inference. The goal in the future is to use these capabilities to calibrate and identify weaknesses in the existing BISON models for FFRD. The report concludes with a discussion on the models most likely to benefit the most from such calibration.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1975

Stochastic simulation of vertically nonhomogeneous gusts

The small-scale horizontal gust structure of detailed wind profiles along the vertical in the first 20 km of the atmosphere is a vertically nonhomogeneous process. A linear stochastic model was developed based on the process covariance function. This model was formulated through the use of a scaling hypothesis which transforms the nonhomogeneous gust process into a nondimensional gust process which is homogeneous in a nondimensional gust height coordinate. The velocity scaling parameter for the gust process is the gust standard deviation, and the length scale used to nondimensionalize the altitude is the vertical space lag associated with the first zero of the gust covariance function. State space theory was used to derive a digital filter from the model, which can be readily used to simulate gusts for space vehicle design applications.

Fichtl, G. H.↗
DOE OSTI2020

On the Parameterization of Convective Downdrafts for Marine Stratocumulus Clouds

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

Wu, Elynn↗
DOE OSTI2025

High fidelity multiphysics tightly coupled model for a lead cooled fast reactor concept and application to statistical calculation of hot channel factors

A tightly coupled multiphysics code system is established using the MOOSE framework for hot channel factor (HCF) evaluation on a Lead Fast Reactor (LFR) concept. The coupled system is driven by the Griffin multiphysics coupling capability under which the MOOSE Heat Transfer module and NekRS computational fluid dynamics solver are coupled for conjugate heat transfer using the Cardinal application. The coupled capability is demonstrated on an LFR assembly model based on materials and geometry of a prototypical lead-cooled fast reactor design by Westinghouse Electric Company, LLC. Moreover, the work integrates the Multiphysics Object Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM) to perform calculations for statistical analysis of HCF. Furthermore, the coupling strategy and workflow demonstrated in this paper is not only useful for predicting accurate hot channel factors for different kinds of advanced reactors but also for other engineering applications such as control rod worth assessment, generation of high-fidelity database for Artificial intelligence (AI)/machine learning (ML) training, design optimization and multi-resolution modeling.

Cardinal↗
NASA NTRS1976

Stochastic simulation of vertically nonhomogeneous gusts

The small-scale horizontal gust structure of detailed wind profiles along the vertical in the first 20 km of the atmosphere is a vertically nonhomogeneous process. A linear stochastic model is developed for the process based on the process covariance function. This model is formulated through the use of a scaling hypothesis that transforms the nonhomogeneous gust process into a nondimensional gust process which is homogeneous in a nondimensional height coordinate. The velocity scaling parameter for the gust process is the gust standard deviation, and the length scale used to nondimensionalize the altitude is the vertical space lag associated with the first zero of the gust covariance function. State space theory is used to derive a digital filter from the model, which can be readily used to simulate gusts for space vehicle design applications.

Fichtl, G. H.↗
DOE OSTI2016

Stochastic behavior of nanoscale dielectric wall buckling

The random buckling patterns of nanoscale dielectric walls are analyzed using a nonlinear multi-scale stochastic method that combines experimental measurements with simulations. The dielectric walls, approximately 200 nm tall and 20 nm wide, consist of compliant, low dielectric constant (low-k) fins capped with stiff, compressively stressed TiN lines that provide the driving force for buckling. The deflections of the buckled lines exhibit sinusoidal pseudoperiodicity with amplitude fluctuation and phase decorrelation arising from stochastic variations in wall geometry, properties, and stress state at length scales shorter than the characteristic deflection wavelength of about 1000 nm. The buckling patterns are analyzed and modeled at two length scales: a longer scale (up to 5000 nm) that treats randomness as a longer-scale measurable quantity, and a shorter-scale (down to 20 nm) that treats buckling as a deterministic phenomenon. Statistical simulation is used to join the two length scales. Through this approach, the buckling model is validated and material properties and stress states are inferred. In particular, the stress state of TiN lines in three different systems is determined, along with the elastic moduli of low-k fins and the amplitudes of the small-scale random fluctuations in wall properties—all in the as-processed state. The important case of stochastic effects giving rise to buckling in a deterministically sub-critical buckling state is demonstrated. The nonlinear multiscale stochastic analysis provides guidance for design of low-k structures with acceptable buckling behavior and serves as a template for how randomness that is common to nanoscale phenomena might be measured and analyzed in other contexts.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2004

Computation Molecular Kinetics Model of HZE Induced Cell Cycle Arrest

Cell culture models play an important role in understanding the biological effectiveness of space radiation. High energy and charge (HZE) ions produce prolonged cell cycle arrests at the G1/S and G2/M transition points in the cell cycle. A detailed description of these phenomena is needed to integrate knowledge of the expression of DNA damage in surviving cells, including the determination of relative effectiveness factors between different types of radiation that produce differential types of DNA damage and arrest durations. We have developed a hierarchical kinetics model that tracks the distribution of cells in various cell phase compartments (early G1, late G1, S, G2, and M), however with transition rates that are controlled by rate-limiting steps in the kinetics of cyclin-cdk's interactions with their families of transcription factors and inhibitor molecules. The coupling of damaged DNA molecules to the downstream cyclin-cdk inhibitors is achieved through a description of the DNA-PK and ATM signaling pathways. For HZE irradiations we describe preliminary results, which introduce simulation of the stochastic nature of the number of direct particle traversals per cell in the modulation of cyclin-cdk and cell cycle population kinetics. Comparison of the model to data for fibroblast cells irradiated photons or HZE ions are described.

Cucinotta, Francis A.↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗