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DOE OSTI2010

Time evolution of the luminosity of colliding heavy-ion beams in BNL Relativistic Heavy Ion Collider and CERN Large Hadron Collider

We have studied the time evolution of the heavy ion luminosity and bunch intensities in the Relativistic Heavy Ion Collider (RHIC), at BNL, and in the Large Hadron Collider (LHC), at CERN. First, we present measurements from a large number of RHIC stores (from Run 7), colliding 100 GeV/nucleon {sup 197}Au{sup 79}+ beams without stochastic cooling. These are compared with two different calculation methods. The first is a simulation based on multi-particle tracking taking into account collisions, intrabeam scattering, radiation damping, and synchrotron and betatron motion. In the second, faster, method, a system of ordinary differential equations with terms describing the corresponding effects on emittances and bunch populations is solved numerically. Results of the tracking method agree very well with the RHIC data. With the faster method, significant discrepancies are found since the losses of particles diffusing out of the RF bucket due to intrabeam scattering are not modeled accurately enough. Finally, we use both methods to make predictions of the time evolution of the future {sup 208}Pb+{sup 82+} beams in the LHC at injection and collision energy. For this machine, the two methods agree well.

43 PARTICLE ACCELERATORS↗

DOE OSTI2014

Fluorescence Correlation Spectroscopy and Nonlinear Stochastic Reaction-Diffusion

The currently existing theory of fluorescence correlation spectroscopy (FCS) is based on the linear fluctuation theory originally developed by Einstein, Onsager, Lax, and others as a phenomenological approach to equilibrium fluctuations in bulk solutions. For mesoscopic reaction-diffusion systems with nonlinear chemical reactions among a small number of molecules, a situation often encountered in single-cell biochemistry, it is expected that FCS time correlation functions of a reaction-diffusion system can deviate from the classic results of Elson and Magde [Biopolymers (1974) 13:1-27]. We first discuss this nonlinear effect for reaction systems without diffusion. For nonlinear stochastic reaction-diffusion systems there are no closed solutions; therefore, stochastic Monte-Carlo simulations are carried out. We show that the deviation is small for a simple bimolecular reaction; the most significant deviations occur when the number of molecules is small and of the same order. Extending Delbrück-Gillespie’s theory for stochastic nonlinear reactions with rapidly stirring to reaction-diffusion systems provides a mesoscopic model for chemical and biochemical reactions at nanometric and mesoscopic level such as a single biological cell.

Del Razo, Mauricio↗

DOE OSTI2014

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Optimized Renewable Energy Integration for EV High-Power Dynamic Wireless Charging Systems

Dynamic wireless charging for electric vehicles is an emerging technology to reduce on-board battery size and extend driving range. Due to its unique characteristic of vehicle-speed-related pulse-like load profile, the high-power dynamic wireless charging system (DWCS) introduces high stress to the utility grid. In this paper, an optimization model for renewable energy integration in the DWCS is proposed to mitigate the grid impact and minimize the operation costs of the whole system. As the load profile of DWCS is related to the traffic volume and various approaching vehicle speeds, the annual average daily traffic data and a stochastic model are used to develop 24-hour load profile of DWCS. To find a tradeoff between grid impact mitigation and operation costs minimization, relationships among power demand from power grid, photovoltaic (PV) capacity, wind energy (WE) capacity and energy storage (ES) capacity are analyzed, and the optimization objective and constraints are developed. Numerical simulation results demonstrate that energy storage integration can greatly mitigate the grid impact of DWCS, and optimal ratio of PV and WE can significantly reduce the operation cost of DWCS.

Zeng, Rong↗

DOE OSTI2015

WE-AB-303-06: Combining DAO with MV + KV Optimization to Improve Skin Dose Sparing with Real-Time Fluoroscopy

Purpose: Real-time fluoroscopy may allow for improved patient positioning and tumor tracking, particularly in the treatment of lung tumors. In order to mitigate the effects of the imaging dose, previous studies have demonstrated the effect of including both imaging dose and imaging constraints into the inverse treatment planning object function. That method of combined MV+kV optimization may Result in plans with treatment beams chosen to allow for more gentle imaging beam-on times. Direct-aperture optimization (DAO) is also known to produce treatment plans with fluence maps more conducive to lower beam-on times. Therefore, in this work we demonstrate the feasibility of a combination of DAO and MV+kV optimization for further optimized real-time kV imaging. Methods: Therapeutic and imaging beams were modeled in the EGSnrc Monte Carlo environment, and applied to a patient model for a previously treated lung patient to provide dose influence matrices from DOSXYZnrc. An MV + kV IMRT DAO treatment planning system was developed to compare DAO treatment plans with and without MV+kV optimization. The objective function was optimized using simulated annealing. In order to allow for comparisons between different cases of the stochastically optimized plans, the optimization was repeated twenty times. Results: Across twenty optimizations, combined MV+kV IMRT resulted in an average of 12.8% reduction in peak skin dose. Both non-optimized and MV+kV optimized imaging beams delivered, on average, mean dose of approximately 1 cGy per fraction to the target, with peak doses to target of approximately 6 cGy per fraction. Conclusion: When using DAO, MV+kV optimization is shown to Result in improvements to plan quality in terms of skin dose, when compared to the case of MV optimization with non-optimized kV imaging. The combination of DAO and MV+kV optimization may allow for real-time imaging without excessive imaging dose. Financial support for the work has been provided in part by NIH Grant T32 EB002103, ACS RSG-13-313-01-CCE, and NIH S10 RR021039 and P30 CA14599 grants. The contents of this submission do not necessarily represent the official views of any of the supporting organizations.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1988

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

This work focuses on the characterization of natural, spatially variable, semivegetated landscapes using a linear, stochastic, canopy-soil reflectance model. A first application of the model was the investigation of the effects of subpixel and regional variability of scenes on the shape and structure of red-infrared scattergrams. Additionally, the model was used to investigate the inverse problem, the estimation of subpixel vegetation cover, given only the scattergrams of simulated satellite scale multispectral scenes. The major aspects of that work, including recent field investigations, are summarized.

Eagleson, Peter S.↗

NASA NTRS1989

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined.

Jasinski, Michael F.↗

DOE OSTI2022

Development of a three-dimensional APOLLO3 neutrons deterministic scheme for the CABRI reactor

CABRI is an experimental reactor to study the fuel behavior during reactivity injection transients. These transients being highly multiphysics, the development of suitable modeling and simulation tools to simulate them is important for the optimization of the tests and the control of the experimental conditions. This paper focuses on the development of an APOLLO3 deterministic core calculation tool dedicated to the CABRI transient analysis. It represents the first stage of the incremental process for the implementation of a multiphysics time-dependent modeling of the CABRI transient. The neutron calculation scheme is based on a classical two-step approach. The first step consists of a 281-energy group calculation flux with the TDT-MOC (Method Of Characteristics) solver for cross-section space and energy (23 groups) collapsing for the CABRI different assembly clusters. The bias on a 2D core neutron calculation due to the self-shielding calculation and collapsing on a restricted pattern are investigated thanks to a comparison with a direct full 2D calculation on a quarter of core. The second step relies on a pin-resolved transport 3D transport core calculation with the SN solver MINARET. A progressive numerical validation process is followed to quantify the calculation biases on reactivity and reaction rates at each step using reference calculations with the stochastic code TRIPOLI4. The next development stage toward a multiphysics scheme will be the implementation of the 3D-kinetics equation resolution and the coupling with a core thermal-hydraulics model. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Hybrid methods for muon accelerator simulations with ionization cooling

Muon ionization cooling involves passing particles through solid or liquid absorbers. Careful simulations are required to design muon cooling channels. New features have been developed for inclusion in the transfer map code COSY Infinity to follow the distribution of charged particles through matter. To study the passage of muons through material, the transfer map approach alone is not sufficient. The interplay of beam optics and atomic processes must be studied by a hybrid transfer map-Monte-Carlo approach in which transfer map methods describe the deterministic behavior of the particles, and Monte-Carlo methods are used to provide corrections accounting for the stochastic nature of scattering and straggling of particles. The advantage of the new approach is that the vast majority of the dynamics are represented by fast application of the high-order transfer map of an entire element and accumulated stochastic effects. The gains in speed are expected to simplify the optimization of cooling channels which is usually computationally demanding. In conclusion, progress on the development of the required algorithms and their application to modeling muon ionization cooling channels is reported.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Probing the transition from shallow to deep convection using ASR data and large-eddy simulations

Over the duration of this project, we have made the following advances. 1) We have developed a novel approach to obtain a Lagrangian view of convection from high-resolution numerical model through Lagrangian tracking. This approach nicely complements the more traditionally used Eulerian statistics. We have applied this approach to a range of problem. 2) We have looked into improving and extending our parameterizations based on stochastically entraining parcels, developed previously for shallow convection. 3) This grant also supported our effort on a paper where we compared cumulus parameterizations and cloud resolving models in terms of their linear response functions. This work will help the community to better evaluate and develop cumulus parameterization. 4) We have applied Lagrangian tracking to shallow convection, deep convection with and without convective organization to better characterize their dynamics and the transition between them. 5) We have devised a novel way of using Lagrangian to identify cold pools, an area identified as of great interest by the ASR community. Our algorithm has a number of advantages and in particular can handle merging cold pools more gracefully than existing techniques. 6) We demonstrated that we can, for the first time, correctly reproduce both the diurnal and seasonal cycle of the hydrologic cycle in the Amazon using a strategy that explicitly represents convection but parameterizes large-scale circulation. In addition we showed that the main cause of the wet season is the presence of an early morning fog, which insulate the surface from top of the atmosphere shortwave radiation. In essence this fog makes the day shorter because radiation cannot penetrate to the surface in the early morning. This is why all fluxes are reduced in the wet season compared to the dry season. 7) We have investigated the life cycle of cold pools and the role of surface diabatic heating. We show that surface heating can kill cold pols and reduce the number of large cold pools and the organization of convection. The effect is quite dramatic over land where the entire distribution of cold pools is modified, and the cold pools are much warmer and more humid with surface diabatic heating below the cold pools. The PI and the co-PI continue to work together on parameterization of cold pools.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Mesh-Based Entry Vehicle and Explosive Debris Re-Contact Probability Modeling

The risk to a crewed vehicle arising from potential re-contact with fragments from an explosive breakup of any jettisoned spacecraft segments during entry has long sought to be quantified. However, great difficulty lies in efficiently capturing the potential locations of each fragment and their collective threat to the vehicle. The method presented in this paper addresses this problem by using a stochastic approach that discretizes simulated debris pieces into volumetric cells, and then assesses strike probabilities accordingly. Combining spatial debris density and relative velocity between the debris and the entry vehicle, the strike probability can be calculated from the integral of the debris flux inside each cell over time. Using this technique it is possible to assess the risk to an entry vehicle along an entire trajectory as it separates from the jettisoned segment. By decoupling the fragment trajectories from that of the entry vehicle, multiple potential separation maneuvers can then be evaluated rapidly to provide an assessment of the best strategy to mitigate the re-contact risk.

McPherson, Mark A.↗

DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗

DOE OSTI2018

Analysis of Solids Flow Rate through Nonmechanical L-Valve in an Industrial-Scale Circulating Fluidized Bed Using Group B Particles

A series of statistically designed experiments were conducted using two different Geldart Group B particles in a 0.3 m diameter circulating fluidized bed (CFB) cold model to evaluate the nonmechanical L-valve for controlling the solids flow rate. The objective of this study was to investigate the effects of standpipe aeration, L-valve aeration, solids inventory, and superficial gas velocity through the riser on the solids flow rate. A stochastic correlation was developed for calculating the solid flow rate as a function of these variables. It was found that the solid flow rate increased directly proportional to each of these variables. Dimensional analysis was used to generate an expression for the solids turnover ratio. The model developed in the present study is intended to simulate flow in the present CFB loop. In conclusion, it is not intended for scale-up and will probably not apply for alternative CFB loops with different geometries/pressure balance conditions.

42 ENGINEERING↗

DOE OSTI2013

A general moment expansion method for stochastic kinetic models

Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. In this paper we derive a general moment expansion method for any type of propensities and which allows expansion up to any number of moments. For some chemical reaction systems, more than two moments are necessary to describe the dynamic properties of the system, which the linear noise approximation is unable to provide. Moreover, also for systems for which the mean does not have a strong dependence on higher order moments, moment approximation methods give information about higher order moments of the underlying probability distribution. We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. We show that for the dimerisation reaction and Michaelis-Menten enzyme kinetics system higher order moments have limited influence on the estimation of the mean, while for the p53 system, the solution for the mean can require several moments to converge to the average obtained from many stochastic simulations. We also find that agreement between lower order moments does not guarantee that higher moments will agree. Compared to stochastic simulations, our approach is numerically highly efficient at capturing the behaviour of stochastic systems in terms of the average and higher moments, and we provide expressions for the computational cost for different system sizes and orders of approximation. We show how the moment expansion method can be employed to efficiently quantify parameter sensitivity. Finally we investigate the effects of using too few moments on parameter estimation, and provide guidance on how to estimate if the distribution can be accurately approximated using only a few moments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Comparing thermal wave function methods for multi-configuration time-dependent Hartree simulations

We compare two methods for creating stochastic temperature wave functions that can be used for Multi-Configuration Time-Dependent Hartree (MCTDH) simulations. In the first method, the MCTDH coefficients are chosen randomly, while the other method uses a single Hartree product of random single-particle functions (SPFs). We find that using random SPFs dramatically improves convergence for a model system for surface sticking.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Quintessential isocurvature in separate universe simulations

In a universe with quintessence isocurvature, or perturbations in dark energy that are independent from the usual curvature perturbations, structure formation is changed qualitatively. The existence of two independent fields, curvature and isocurvature, causes the growth rate of matter perturbations to depend on their initial conditions. The quintessence perturbations cause their growth to depend on scale. We perform the first separate universe simulations for this cosmology. We demonstrate that the power spectrum response and the halo bias depend on scale and initial conditions and that the presence of the isocurvature mode changes the mapping from these quantities to the halo auto- and cross-power spectra, and the squeezed-limit bispectrum. We compare the bias to several models, finding reasonable agreement with both a power-spectrum-response model with one free parameter and a model that fits two independent bias parameters for curvature and isocurvature sourced fluctuations. We also verify that simulation responses to pure isocurvature and pure curvature modes can be linearly combined to reproduce responses with different ratios of isocurvature and curvature. Furthermore, this allows our results to be used to predict the halo power spectrum and stochasticity with arbitrary large-scale curvature and isocurvature power spectra. In an Appendix, we study the generation of quintessence isocurvature during inflation and show that a modified kinetic term is typically required to produce observable isocurvature modes in a field with w Q ≈ –1.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design

We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Strong Ground Motion Simulations of the M7.1 Kumamoto, Japan Earthquake Using Characterized Heterogeneous Source Models

The objective of our study is to analyze the performance of characterized heterogeneous rupture models in simulations of near-fault ground motion from the M7 Kumamoto, Japan earthquake, using Graves and Pitarka (2016) broad-band simulation method. We developed a kinematic earthquake rupture generator that combines the Graves and Pitarka (2016) (GP2016) rupture model with the multiple asperity characterized rupture model of Irikura and Miyake (IM2011), also known as Irikura recipe (Irikura and Miyake, 2011). The proposed kinematic rupture model incorporates distinct features of both original rupture models such as small-scale stochastic rupture variability, and shallow areas with large slip separated from areas with high sliprate, inherited from the GP2016, and desired near-fault large-scale rupture directivity effects produced by the multiple asperity model IM2011.

58 GEOSCIENCES↗