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DOE OSTI2016

Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations

Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realistic biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Stochastic Adaptive Droop Control in Frequency Regulation of Power Systems With Intermittent Generators

Modern power systems (MPSs), including microgrids (MGs), are increasingly incorporating multiple renewable energy sources (RESs) such as wind and solar power, as well as battery storage and controllable loads. While environmentally beneficial, these sources pose challenges for control and management due to their intermittent and stochastic nature, especially in maintaining frequency stability with multiple interconnected generators of varying capacities. Traditional droop control methods are effective in systems with generators that are dispatchable and have fixed generation capacities, but they fall short when applied to systems with RESs, where generation capacities are dynamic and affected by unpredictable environmental conditions. To address these challenges, this paper introduces a novel stochastic adaptive droop control (SADC) method for load frequency control (LFC). The proposed method adapts droop coefficients in real time, based on the measured stochastic data of power generation capacities, enabling more effective frequency regulation in systems with variable and intermittent power generation. Unlike traditional adaptive control methods, which assume constant or slowly-varying system parameters, this approach accounts for stochastic processes by modeling them as Markov chains, enabling robust performance under highly dynamic and unpredictable conditions. The key contributions of this work include the development of real-time droop coefficient adaptation algorithms, derivation of their stability and convergence properties, and the demonstration of the advantages of the method through simulations. Case studies highlight the improved performance of frequency regulation, particularly in addressing the impact of stochastic weather conditions and the benefits of reducing dependence on battery reserves in dealing with intermittency of RESs. Finally, this paper provides a comprehensive analysis of the theoretical foundations of the method, as well as practical implementation insights for future power systems with high penetration of RESs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗

NASA NTRS1989

Forward flight effects on broadband shock associated noise of supersonic jets

The stochastic model theory of TAM (1987, 1989) for broadband shock associated noise was extended to include the effects of forward flight. The theory was applied to the forward flight simulation experiments of Norum and Shearin (1984, 1986, and 1988). Good agreement is found between calculated and measured far-field noise spectra over the flight Mach number range of 0.0 to 0.4.

Tam, Christopher K. W.↗

NASA NTRS2012

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

Bole, Brian↗

NASA NTRS2017

Predicting Cell Death and Mutation Frequency for a Wide Spectrum of LET by Assuming DNA Break Clustering Inside Repair Domains

The high relative biological effectiveness (RBE) of high charged and energy (HZE) particles for cell death, DNA mutations and cancer remain based on experimental data. In this work, we propose that the existence of DNA repair domains is sufficient to predict both cell death and mutation frequencies for any LET by only taking into account experimental data from low-LET, offering one mechanism for RBE across LET. We hypothesize that whenever multiple DNA double-strand breaks (DSBs) are generated within the same DNA repair domain, DSBs are actively regrouped for more efficient repair [1]. This hypothesis has been supported by the low-LET sublinear dose response observed at doses greater than ~1Gy for 53BP1 radiation-induced foci (RIF) reflecting increasing DSB/RIF with dose [2]. Previously, we modeled radiation-induced cell death of human breast cells by first inferring the size of these domains from the dose dependence of low-LET RIF, and by associating a lethality factor to the number of pairs of DSBs in each RIF [1]. In this work, we first integrate the new NASA computer models RITCARD (Relativistic Ion Tracks, Chromosome Aberrations, Repair, and Damage) [3] and BDSTracks (Biological Damage by Stochastic Tracks) for a more accurate microdosimetry and a better model of the nuclear organization to predict the location of DSBs. A large array of particles and energy are simulated, covering more than three orders of magnitude for LET (~1-1000 keV/Âµm). Next, we extend our previous model to predict mutation frequencies by assuming that clustered DSBs increase mutation probability, which is formalized by the mutation frequency being linearly dependent on both the number of DSBs and the number of pairs of DSBs inside individual RIF. Linear coefficients are estimated so that simulations predict accurately mutation frequencies observed in Chinese hamster cells exposed to low-LET. Keeping these coefficients unchanged, we then predict mutation frequencies induced by HZE by simulating DSBs and obtain RBEs for mutations and cell death following the expected experimental bell shape for LET dependence. We also observe an orientation effect that needs to be confirmed, showing different RBE depending on the angle of the HZE beam hitting the main axis of the cell.

Plante, Ianik↗

DOE OSTI2014

ANALYSIS OF SAFETY IMPACTS FROM EXTERNAL FLOODING USING THE RISK-INFORMED SAFETY MARGIN CHARACTERIZATION (RISMC) TOOLKIT

The existing fleet of U.S. nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This paper demonstrates how Idaho National Laboratory (INL) researchers use the RISMC Toolkit to investigate complex nuclear plant phenomena using RAVEN and RELAP-7. The analysis focused on a highly relevant topic currently facing some nuclear power plants – specifically flooding issues. This research and development looked at challenges to a hypothetical pressurized water reactor, including: (1) a potential loss of off-site power followed by the possible loss of all diesel generators (i.e., a station black-out event), (2) earthquake induced station-blackout, and (3) a potential earthquake induced tsunami flood. The analysis is performed by using a set of codes: a thermal-hydraulic code (RELAP-7), a flooding simulation tool (NEUTRINO) and a stochastic analysis tool (RAVEN) – these are currently under development at INL. Using RAVEN, we were able to perform multiple RELAP-7 simulation runs by changing specific parts of the model in order to reflect specific aspects of different scenarios, including both the failure and recovery of critical components. The simulation employed traditional statistical tools (such as Monte-Carlo sampling) and more advanced machine-learning based algorithms to perform uncertainty quantification in order to understand changes in system performance and limitations as a consequence of power uprate. Qualitative and quantitative results obtained gave a detailed picture of the issues associated with potential accident scenarios. These types of insights can provide useful material for decision makers to perform risk-informed margins management.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Resimulation-based self-supervised learning for pretraining physics foundation models

Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Fracture Network Influence on Rock Damage and Gas Transport following an Underground Explosion

Simulations of rock damage and gas transport following underground explosions that omit preexisting fracture networks in the subsurface cannot fully characterize the influence of geo-structural variability on gas transport. Previous studies do not consider the impact that fracture network structure and variability have on gas seepage. In this study, we develop a sequentially coupled, axi-symmetric model to look at the damage pattern and resulting gas breakthrough curves following an underground explosion given different fracture network realizations. We simulate 0.327 and 0.164 kT chemical explosives with burial depths of 100 m for 90 stochastically generated fracture networks. Gases quickly reach the surface in 30% of the higher yield simulations and 5% of the lower yield simulations. The fast breakthrough can be attributed to the formation of connected pathways between fractures to the surface. The formation of a connected damage pathway to the surface is not clearly correlated with the fracture intensity (P32) in our simulations. Breakthrough curves with slower transport are highly variable depending on the fracture network sample. The variability in the breakthrough behavior indicates that ignoring the influence of fracture networks on rock damage, which strongly influences the hydraulic properties following an underground explosion, will likely lead to a large underestimation of the uncertainty in the gas transport to the surface. This work highlights the need for incorporation of fracture networks into models for accurately predicting gas seepage following underground explosions.

58 GEOSCIENCES↗

NASA NTRS1980

Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed data

Previously established results demonstrate that LANDSAT data are autocorrelated and can be described by a univariate linear stochastic process known as auto-regressive-integrated-moving-average model of degree 1, 0, 1 or ARIMA (1, 0, 1). This model has two coefficients of interest for interpretation phi(1) and theta(1). In a comparison of LANDSAT thematic mapper simulator (TMS) data and LANDSAT MSS data several results were established: (1) The form of the relatedness as described by this model is not dependent upon system look angle or pixel size. (2) The phi(1) coefficient increases with decreasing pixel size and increasing topographic complexity. (3) Changes in topography have a greater influence upon phi(1) than changes in land cover class. (4) The theta(1) seems to vary with the amount of atmospheric haze. These patterns of variation in phi(1) and theta(1) are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, characterize topography, and reduce the number of bytes needed to store remotely sensed data.

Labovitz, M. L.↗

DOE OSTI2015

Numerical Uncertainty Estimation for Stochastic Particle-in-Cell Simulations Applied to Verification and Validation.

Systematic veri cation and validation (V&V) is necessary to establish the credibility for high consequence simulations. In this paper, we focus on a radiation-induced plasma experimental validation exercise for simulations which uses both numerical error estimation and input parameter uncertainty quanti cation to provide a direct comparison between Particle-In-Cell (PIC) plasma simulations and experiments. This approach demonstrates how careful validation can uncover missing physics in the simulation. Three di erent validation examples are shown; a vacuum space charge limited cavity, a gas lled space charge limited cavity, and a vacuum non space charge limited cavity. Two of the example are picked to show the importance of error estimation in uncovering inaccuracy/incomplete simulation models. We also report on a newly-developed numerical error estimation approach, StREEQ, which is a notable improvement to past approaches. In the StREEQ approach, a multi- tting scheme based on L 1 , L 2 , and L$\infty$ error norms and alternate weightings is used to propagate uncertainties in the relative importance of outliers and coarse/re ned discretization levels. Bootstrap sampling is used to represent the stochasticity in the response data. The resulting method appears to robustly and conservatively predict the fully-converged response within estimated numerical error bounds for stochastic simulations. The StREEQ approach is demonstrated on two related prototype electron diode problems, and preliminary results are reported for a radiation-induced plasma simulation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

A Comprehensive Scheduling Framework using SP-ADMM for Residential Demand Response with Weather and Consumer Uncertainties

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. Here, the results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Problem-free time-dependent variational principle for open quantum systems

Methods of quantum nuclear wave-function dynamics have become very efficient in simulating large isolated systems using the time-dependent variational principle (TDVP). However, a straightforward extension of the TDVP to the density matrix framework gives rise to methods that do not conserve the energy in the isolated system limit and the total system population for open systems where only energy exchange with environment is allowed. These problems arise when the system density is in a mixed state and is simulated using an incomplete basis. Thus, the basis set incompleteness, which is inevitable in practical calculations, creates artificial channels for energy and population dissipation. To overcome this unphysical behavior, we have introduced a constrained Lagrangian formulation of TDVP applied to a non-stochastic open system Schrödinger equation [L. Joubert-Doriol, I. G. Ryabinkin, and A. F. Izmaylov, J. Chem. Phys. 141, 234112 (2014)]. While our formulation can be applied to any variational ansatz for the system density matrix, derivation of working equations and numerical assessment is done within the variational multiconfiguration Gaussian approach for a two-dimensional linear vibronic coupling model system interacting with a harmonic bath.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Simulation of Osmotic Swelling by the Stochastic Immersed Boundary Method

We develop here computational methods for the simulation of osmotic swelling phenomena relevant to microscopic vesicles containing transformable solute molecules. We introduce stochastic immersed boundary methods (SIBMs) that can capture osmotically driven fluid transport through semipermeable elastic membranes subject to thermal fluctuations. We further develop numerical methods to handle within SIBMs an elastic shell model for a neo-Hookean material. Our extended SIBM allows for capturing osmotic swelling phenomena driven by concentration changes and interactions between a discrete collection of confined particles while accounting for the thermal fluctuations of the semipermeable membrane and the hydrodynamic transport of solvent. We use our computational methods to investigate osmotic phenomena in regimes that go beyond the classical Van't Hoff theory. We develop statistical mechanics theories for osmotic swelling of vesicles when there are significant interactions between particles that can transform over time. We validate our theoretical results against detailed computational simulations. Our methods are expected to be useful for a wide class of applications allowing for the simulation of osmotically driven flows, thermally fluctuating semipermeable elastic structures, and solute interactions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Inter-annual variation of tropical cyclones simulated by GEOS-5 AGCM with modified convection scheme

The Goddard Earth Observing System version 5 (GEOS-5) global climate model with a 50-km horizontal resolution is forced by observed sea surface temperature (SST) to examine the fidelity of the seasonal-mean and inter-annual variation of tropical cyclones (TCs) in the western North Pacific (WNP) and the North Atlantic (NATL). The standard Relaxed Arakawa Schubert (RAS) deep convection scheme is modified to improve the representation of TCs, where the scheme implements a stochastic limit of the cumulus entrainment rate. The modification drives mid- and upper-tropospheric cooling and low- to mid-tropospheric drying in the background state, which tends to increase atmospheric instability. This enables the model to increase convective variability on an intra-seasonal timescale and improve the simulation of intense storms. Five-member ensemble runs with the modified RAS scheme for 12 years (1998–2009) exhibit realistic spatial distributions in the climatological-mean TC development area and their pathways over WNP and NATL. The GCM is able to reproduce the inter-annual variation of accumulated cyclone energy (ACE) by prescribing yearly varying observed SST even though the individual TC intensity is still underpredicted. A sensitivity of TC activity to El Niño–Southern Oscillation (ENSO) phase is also reproduced realistically over WNP in terms of the spatial pattern changes in the main development region and TC pathways. However, the model exhibits a notable deficiency in NATL in reproducing the observed inter-annual variation of TC activity and the sensitivity to the ENSO.

accumulated cyclone energy↗

DOE OSTI2019

1D PIC-DSMC analysis of a high-pressure nanosecond pulse discharge breakdown in helium

Kinetic simulations of plasma phenomena during and after formation of the conductive plasma channel of a nanosecond pulse discharge are analyzed and compared to existing experimental measurements. Particle-in-cell with direct simulation Monte Carlo collisions (PIC-DSMC) modeling is used to analyze a discharge in helium at 200 Torr and 300 K over a 1 cm gap. The analysis focuses on physics that would not be reproduced by fluid models commonly used at this high number density and collisionality, specifically non-local and stochastic phenomena. Similar analysis could be used to improve the predictive capability of lower fidelity or reduced order models. First, the modeling results compare favorably with experimental measurements of electron number density, temperature, and 1D electron energy distribution function at the same conditions. Second, it is shown that the ionization wave propagates in a stochastic, stepwise manner, dependent on rare, random ionization events ahead of the ionization wave when the ionization fraction in front of the ionization wave is very low, analagous to the stochastic branching of streamers in 3D. Third, analysis shows high-energy runaway electrons accelerated in the cathode layer produce electron densities in the negative glow region over an order of magnitude above those in the positive column. Finally, future work to develop reduced order models of these two phenomena would improve the accuracy of fluid plasma models without the cost of PIC-DSMC simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Self-contained filtered density function

The filtered density function (FDF) closure is extended to a “self-contained” format to include the subgrid-scale (SGS) statistics of all of the hydro-thermo-chemical variables in turbulent flows. These are the thermodynamic pressure, the specific internal energy, the velocity vector, and the composition field. In this format, the model is comprehensive and facilitates large-eddy simulation (LES) of flows at both low and high compressibility levels. A transport equation is developed for the joint pressure-energy-velocity-composition filtered mass density function (PEVC-FMDF). In this equation, the effect of convection appears in closed form. The coupling of the hydrodynamics and thermochemistry is modeled via a set of stochastic differential equation for each of the transport variables. This yields a self-contained SGS closure. We demonstrated how LES is conducted of a turbulent shear flow with transport of a passive scalar. Finally, the consistency of the PEVC-FMDF formulation is established, and its overall predictive capability is appraised via comparison with direct numerical simulation (DNS) data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Simulation of the Quality Criterion of a Mixture in a Drum-Belt Apparatus

A method is proposed for calculating the inhomogeneity coefficient in the workspace of a drum-belt mixer of bulk components with brush elements in a three-stage batch mixing method with non-uniform proportioning. The method is based on a stochastic model of the motion of rarefied particle flows following interaction with flexible elements fixed along a helical line on the surface of the mixer drums. The principal coefficients affecting the quality of a bulk mixture are analyzed in the form of combined structural and regime complexes of parameters of the mixer and the characteristics of the physical and mechanical state of the mixed media are analyzed. The results obtained can be used to develop an engineering technique for calculating the parameters of a new drum-belt mixer of friable components.

42 ENGINEERING↗