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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Sorption Modeling and Verification for Off-Gas Treatment

This project was successfully executed to provide valuable adsorption data and improve a comprehensive model developed in previous work by the authors. Data obtained were used in an integrated computer program to predict the behavior of adsorption columns. The model is supported by experimental data and has been shown to predict capture of off gas similar to that evolving during the reprocessing of nuclear waste. The computer program structure contains (a) equilibrium models of off-gases with the adsorbate; (b) mass-transfer models to describe off-gas mass transfer to a particle, diffusion through the pores of the particle, and adsorption on the active sites of the particle; and (c) incorporation of these models into fixed bed adsorption modeling, which includes advection through the bed. These models are being connected with the MOOSE (Multiphysics Object-Oriented Simulation Environment) software developed at the Idaho National Laboratory through DGOSPREY (Discontinuous Galerkin Off-gas SeParation and REcoverY) computer codes developed in this project. Experiments for iodine and water adsorption have been conducted on reduced silver mordenite (Ag0Z) for single layered particles. Adsorption apparatuses have been constructed to execute these experiments over a useful range of conditions for temperatures ranging from ambient to 250°C and water dew points ranging from -69 to 19°C. Experimental results were analyzed to determine mass transfer and diffusion of these gases into the particles and to determine which models best describe the single and binary component mass transfer and diffusion processes. The experimental results were also used to demonstrate the capabilities of the comprehensive models developed to predict single-particle adsorption and transients of the adsorption-desorption processes in fixed beds. Models for adsorption and mass transfer have been developed to mathematically describe adsorption kinetics and transport via diffusion and advection processes. These models were built on a numerical framework for solving conservation law problems in one-dimensional geometries such as spheres, cylinders, and lines. Coupled with the framework are specific models for adsorption in commercial adsorbents, such as zeolites and mordenites. Utilizing this modeling approach, the authors were able to accurately describe and predict adsorption kinetic data obtained from experiments at a variety of different temperatures and gas phase concentrations. A demonstration of how these models, and framework, can be used to simulate adsorption in fixed- bed columns is provided. The CO 2 absorption work involved modeling with supportive experimental information. A dynamic model was developed to simulate CO 2 absorption using high alkaline content water solutions. The model is based upon transient mass and energy balances for chemical species commonly present in CO 2 absorption. A computer code was developed to implement CO 2 absorption with a chemical reaction model. Experiments were conducted in a laboratory scale column to determine the model parameters. The influence of geometric parameters and operating variables on CO 2 absorption was studied over a wide range of conditions. Continuing work could employ the model to control column operation and predict the absorption behavior under various input conditions and other prescribed experimental perturbations. The value of the validated models and numerical frameworks developed in this project is that they can be used to predict the sorption behavior of off-gas evolved during the reprocessing of nuclear waste and thus reduce the cost of the experiments. They can also be used to design sorption processes based on concentration limits and flow-rates determined at the plant level.

42 ENGINEERING↗
DOE OSTI2015

Virtual machine-based simulation platform for mobile ad-hoc network-based cyber infrastructure

In modeling and simulating complex systems such as mobile ad-hoc networks (MANETs) in de-fense communications, it is a major challenge to reconcile multiple important considerations: the rapidity of unavoidable changes to the software (network layers and applications), the difficulty of modeling the critical, implementation-dependent behavioral effects, the need to sustain larger scale scenarios, and the desire for faster simulations. Here we present our approach in success-fully reconciling them using a virtual time-synchronized virtual machine(VM)-based parallel ex-ecution framework that accurately lifts both the devices as well as the network communications to a virtual time plane while retaining full fidelity. At the core of our framework is a scheduling engine that operates at the level of a hypervisor scheduler, offering a unique ability to execute multi-core guest nodes over multi-core host nodes in an accurate, virtual time-synchronized manner. In contrast to other related approaches that suffer from either speed or accuracy issues, our framework provides MANET node-wise scalability, high fidelity of software behaviors, and time-ordering accuracy. The design and development of this framework is presented, and an ac-tual implementation based on the widely used Xen hypervisor system is described. Benchmarks with synthetic and actual applications are used to identify the benefits of our approach. The time inaccuracy of traditional emulation methods is demonstrated, in comparison with the accurate execution of our framework verified by theoretically correct results expected from analytical models of the same scenarios. In the largest high fidelity tests, we are able to perform virtual time-synchronized simulation of 64-node VM-based full-stack, actual software behaviors of MANETs containing a mix of static and mobile (unmanned airborne vehicle) nodes, hosted on a 32-core host, with full fidelity of unmodified ad-hoc routing protocols, unmodified application executables, and user-controllable physical layer effects including inter-device wireless signal strength, reachability, and connectivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Modeling liquid organic thin films on substrates

We present the rationale, methods, and results of modeling of thin film organic liquids on various substrates. These liquids may coat surfaces (substrates) either as a result of their production, dispersal via aerosols or spills. Identification of unknown coated surfaces using either reflectance or emittance spectroscopy cannot be accomplished simply through reference to reflectance signature libraries since neither the thickness of the liquid layer nor the substrate type is known beforehand and both contribute to the signature. Liquid spectral libraries offer the complex index of refraction (n,k) as a function of wavelength which by itself is useful only for thick (bulk) liquid layers via computation of reflectance and transmittance coefficients using the Fresnel equations. Thin liquid layers both reflect and refract incident light in combination with reflectance from the substrate. We show modeling of various organic liquids on substrates using commercial thin film design and modeling software, as well as Monte Carlo ray tracing software to demonstrate the variety of potential signatures encountered that depend on the thickness of the liquid layer as well as the characteristics of the substrate (metal or dielectric). These substrates give rise to transflectance behavior, while many dielectric substrates have rich absorption features that provide complex signatures that combine attributes of both the liquid and the substrate. Knowledge of the complex index of refraction of both target liquids and substrates is essential in order to synthesize spectra necessary in the application of target identification algorithms.

complex index of refraction↗
DOE OSTI2020

Ibis Networks/WattIQ (IN2 Final Report)

Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Fixing Amdahl's Law within the Limits of Accelerated Systems: FALLACY

Closeout report for FALLACY project. The performance of Data Model Convergence Initiative (DMC) applications on parallel machines is far below the limit set by Amdahl’s law. Whether the machine is based on many-core, GPUs, FPGAs, or a heterogeneous combination, usually the most significant bottleneck is accessing data from the memory system. Aligning with DMC’s HW/architecture thrust, this project developed a set of memory-centric tools called ‘MemGaze’ that inform the HW/SW stack about an application’s memory behavior, including data access latency and diagnosing poor data layout and data composition. Our approach uses architectural modeling and analysis of workload data accesses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Analyzing the Energy and Power Consumption of Remote Memory Accesses in the OpenSHMEM Model

PGAS models like OpenSHMEM provide interfaces to explicitly initiate one-sided remote memory accesses among processes. In addition, the model also provides synchronizing barriers to ensure a consistent view of the distributed memory at different phases of an application. The incorrect use of such interfaces affects the scalability achievable while using a parallel programming model. This study aims at understanding the effects of these constructs on the energy and power consumption behavior of OpenSHMEM applications. Our experiments show that cost incurred in terms of the total energy and power consumed depends on multiple factors across the software and hardware stack. We conclude that there is a significant impact on the power consumed by the CPU and DRAM due to multiple factors including the design of the data transfer patterns within an application, the design of the communication protocols within a middleware, the architectural constraints laid by the interconnect solutions, and also the levels of memory hierarchy within a compute node. This work motivates treating energy and power consumption as important factors while designing compute solutions for current and future distributed systems.

Jana, Siddhartha↗
DOE OSTI2026

Prototype Modeling for a Light-Trapping Planar-Cavity Enclosed Particle Solar Receiver

Concentrating solar thermal (CST) systems present a promising avenue for affordable and reliable energy production. Solar receivers are key components that determine the efficiency and longevity of these systems. Particle-based solar receivers have emerged as a compelling alternative to traditional technologies, offering several advantages that address limitations in current CST systems. This is especially true as next-generation CST technologies target applications including electricity generation, thermochemical processes, and industrial process heat, many of which necessitate higher operating temperatures than current commercial molten salt systems. Molten-salt thermal energy storage (TES) systems, commonly used in CSP, face challenges related to freezing and corrosion. Particle-based TES systems, in contrast, do not experience these issues, as particles are stable at high temperatures, exceeding 1000 degrees Celsius. This capability allows for a wider range of applications, including those requiring higher temperatures for industrial processes and efficient electricity generation. A novel innovation in particle-based solar receiver technology is the light-trapping planar cavity receiver (LTPCR) configuration developed by NREL. The LTPCR design consists of small cavity-like structures using opaque planar surfaces, enabling efficient capture and absorption of solar energy. A high incident flux concentration at the cavity aperture is absorbed on the receiver walls, and subsequently transferred to particles on the inside of cavities. The particles flow through the system, forming a fluidized bed inside of the receiver panels, effectively capturing the absorbed solar heat. Air is used as a fluidizing medium in this process to enhance particle heat transfer and mixing. The effectiveness of this design lies in its ability to manage solar flux conditions and ensure high solar-to-thermal receiver efficiency. A 100-kW prototype is currently being tested at the King Saud University in Saudi Arabia to assess the receiver performance. A range of modeling analyses for the optical, thermal, and mechanical effects were conducted to assess the performance of the receiver under on-sun conditions. The solar flux resulting from the KSU heliostat field was modeled using NREL SolTrace software and produced up to 600 kW/m2 at the receiver aperture. The solar flux absorbed on the receiver walls was then used within a computational fluid dynamics (CFD) model to predict wall temperature distributions along with radiation and convection loss. A two-phase CFD model was developed for the fluidized bed of silica sand inside the receiver panels to predict local wall-to-particle heat transfer coefficients, particle temperature distributions, and outlet temperature of the particles. We have also conducted analyses to understand the thermomechanical behavior of these innovative enclosed light-trapping solar receivers optimized for particle heating. We used finite element analysis (FEA) to predict the receiver's performance using temperature distributions obtained from CFD and based on the resulting stress profiles, evaluated creep-fatigue damage with a goal of achieving a 30-year service life. Analysis showed a significant impact of the particle-to-wall heat transfer coefficients (HTCs) on receiver performance, with higher HTCs resulting in reduced stress and increased lifespan. For instance, when using Inconel 740H, increasing the HTC from 800 W/m2 K to 1400 W/m2 K increased the creep life from 4,000 hours to over 100,000 hours. This highlights the importance of understanding and optimizing heat transfer in the design of high-efficiency receivers.

14 SOLAR ENERGY↗
DOE OSTI2014

Analyzing the Energy and Power Consumption of Remote Memory Accesses in the OpenSHMEM Model

PGAS models like OpenSHMEM provide interfaces to explicitly initiate one-sided remote memory accesses among processes. In addition, the model also provides synchronizing barriers to ensure a consistent view of the distributed memory at different phases of an application. The incorrect use of such interfaces affects the scalability achievable while using a parallel programming model. This study aims at understanding the effects of these constructs on the energy and power consumption behavior of OpenSHMEM applications. Our experiments show that cost incurred in terms of the total energy and power consumed depends on multiple factors across the software and hardware stack. We conclude that there is a significant impact on the power consumed by the CPU and DRAM due to multiple factors including the design of the data transfer patterns within an application, the design of the communication protocols within a middleware, the architectural constraints laid by the interconnect solutions, and also the levels of memory hierarchy within a compute node. This work motivates treating energy and power consumption as important factors while designing compute solutions for current and future distributed systems.

Jana, Siddhartha↗
DOE OSTI2023

Computational studies of impurity migration during induction stirring of molten uranium

Understanding and controlling impurity behavior in actinide metal casting processes are foundational for efficient part production yet remain major challenges for researchers and industry. To help provide insight regarding impurity distribution during actinide metal casting, we have developed computational fluid dynamics (CFD) models for a laboratory-scale system using commercial and open-source code. Multiple simulation frameworks allow for improved confidence in the resulting outputs, while taking advantage of the maturity and convenience offered by commercial platforms and simultaneously maintaining the transparency and flexibility often provided by open-source software. Here, we describe multiple experiment-informed models designed to simulate a specific laboratory system in which uranium melt, containing a known starting concentration of carbon impurity, is electromagnetically stirred in an induction furnace. Here, the goal of the simulations is to predict the motion of uranium carbide microparticles in the velocity field of the melt. Prior to simulating the uranium-carbon system, numerical models were validated using a previously published nonradioactive experimental system. Effects of the size and shape of impurity particles in the models were investigated and agree with experimental findings. Simulation of smaller particles (< 50 µm) shows more homogenous distribution throughout the stirred melt. With increased particle size (100 µm), the body forces, which include the buoyancy force, dominate over the drag force, causing larger particles to move toward the crucible walls and upward in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Modeling of Gas Dynamic Processes in the Interelectrode Gap of a Multielement Thermionic Electricity Generating Channel by Solving the Kinetic Equation

This work is devoted to the application of a numerical method of solving Boltzmann’s equation for modeling the behavior of radionuclides (Kr, Xe, Rb, Sr, Cs, Ba) in the cavity of the interelectrode gap of a multielement electricity generating channel. Modeling methods were developed and software system implemented in the course of this work. Calculations were performed for two structural arrangements: with uni- and bilateral extraction of radionuclides into the vacuum-cesium system. Data on the pressure and flow distributions were obtained. The krypton and xenon pressure near the collector as functions of their flow and the cesium gas pressure at the gap exits were determined computationally.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Piston Bowl Geometry Effects on Gasoline Compression Ignition in a Heavy-Duty Diesel Engine

A design optimization campaign was conducted to search for improved combustion profiles that enhance gasoline compression ignition in a heavy-duty diesel engine with a geometric compression ratio of 17.3. A large-scale design of experiments approach was used for the optimization, employing three-dimensional computational fluid dynamics simulations. The main parameters explored include geometric features, injector specifications, and swirl motion. Both stepped-lip and re-entrant bowls were included in order to assess their respective performance implications. A total of 256 design candidates were prepared using the software package CAESES for automated and simultaneous geometry generation and combustion recipe perturbation. The design optimization was conducted for three engine loads representing light to medium load conditions. The design candidates were evaluated for fuel efficiency, emissions, fuel-air mixing, and global combustion behavior. Simulation results showed that the optimum designs were all stepped-lip bowls, due to improvements in fuel-air mixing, as well as reduced heat loss and emissions formation. Improvements in indicated specific fuel consumption of up to 3.2% were achieved while meeting engine-out NOx emission targets of 1-1.5 g/kW center dot h. Re-entrant bowls performed worse compared to the baseline design, and significant performance variations occurred across the load points. Specifically, the re-entrant bowls were on par with the stepped-lip bowls under light load conditions, but significant deteriorations occurred under higher load conditions. As a final task, selected optimized designs were then evaluated under full-load conditions.

CFD↗
DOE OSTI2023

Coupled Experimental and High-Temperature Discrete-Element Method Modeling Studies of Aluminosilicate Particle Handling in Concentrated Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrated solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrated solar power environments. Coupled experimental and numerical studies of aluminosilicate particles in rotary kilns and dense particle curtains were performed for bulk temperatures up to 1073 K. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Temperature, spatial, and velocity profile data were extracted from experimental runs using embedded K-type thermocouple probes and particle image velocimetry techniques. Experimental and numerical studies were compared using spatial temperature profiles, velocity fields, and shape profiles of the bulk, granular flows. Numerical models were developed using commercially available discrete element method modeling software, Aspherix®. Existing Aspherix® functionality was expanded by introducing coupled radiative exchange modeling tools. The laboratory-scale rotary kiln was developed to investigate the steady-state heat and mass transfer performance of aluminosilicate particles based on particle type, bulk handling temperature, and wall roughness. The rotational speed of the rotary kiln was varied to control the relative impact of collisional and frictional effects upon the granular flow behavior. Heat and mass transfer performance was categorized based on the Froude number and the observed flow regimes of slipping, rolling, cascading, and centrifuging. Coupled discrete element method modeling studies were used to evaluate the effects of temperature-dependent, particulate mechanical properties upon bulk flow behavior and upon the relative effects of radiative, advective, and/or conductive heat transfer. A high-temperature (< 1073 K) falling particle curtain was similarly fabricated to investigate the heat and mass transfer performance of aluminosilicate particles in particle handling situations dominated by inter-particle collisions. The impact of particle type, flow preheat temperatures (< 1073K), and bulk mass flow rates were investigated upon the particle curtain shape, temperature, and velocity profiles. Coupled discrete element method modeling studies were performed to evaluate the varying impact of temperature-dependent, particulate mechanical properties on the bulk flow behavior and the temperature profile of the particle curtain.

14 SOLAR ENERGY↗
DOE OSTI2012

Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools

Background: MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results: BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. Conclusions: BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fluid modeling of low-temperature plasmas

Fluid models are essential for understanding and predicting low-temperature plasma (LTP) behavior in various scientific and industrial settings. This paper provides an introductory tutorial on fluid modeling of LTPs, covering model formulation, implementation, and computational simulations. The tutorial focuses on five main components of the formulation of LTP fluid models: fluid flow, energy, chemistry, electromagnetism, and material properties, as well as in essential aspects of model implementations, including multiscale phenomena, multiphysics coupling, and numerical convergence. Designed for students and early-career researchers, this work offers a practical foundation for developing and using fluid models, from in-house computational codes to commercial software, bridging fundamental theory with real-world applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Numerical modeling of hydrogen mixing in a direct-injection engine fueled with gaseous hydrogen

Hydrogen is considered as one of the most promising options to achieve effective decarbonization of the energy and transportation sectors. As such, it has recently been receiving increasing attention because of its promising potential as an energy carrier for advanced energy and propulsion systems. With a focus on internal combustion engines, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential for high engine efficiency and specific power while reducing the risk of backfiring and pre-ignition issues. Therefore, many experimental and numerical efforts have recently been dedicated to understanding the physical and chemical behaviors of hydrogen in engine during mixing and combustion. This study focuses on computational fluid dynamics (CFD) modeling of the hydrogen DI process in a hydrogen optical research engine. Under the conditions studied, gaseous hydrogen is injected into the combustion chamber via a centrally located single-hole injector at a pressure of 100 bar. Two configurations, namely low-and high-tumble, are investigated to understand the impact of different in-cylinder flow patterns on the fuel-air mixture preparation. Simulations are carried out using the commercial CFD software CONVERGE. Here, the in-cylinder turbulence is modeled with an unsteady Reynolds-averaged Navier-Stokes (URANS) formulation closed by the renormalization group (RNG) k-ε model. Several numerical methods and model constants, including but not limited to turbulent Schmidt number, are evaluated. The numerical results are systematically compared against experimental measurements of velocity and hydrogen concentration fields on the vertical center plane to assess the performance of the CFD model, unveil the physics of hydrogen mixing, and establish best practices for modeling hydrogen DI under relatively high injection pressure conditions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

3D Flow and Temperature Analysis of Filling a Plutonium Mold (Revision 1) [Poster]

The plutonium foundry at Los Alamos National Laboratory casts products for various special nuclear applications. However, plutonium’s radioactivity, material properties, and security constraints complicate the ability to perform experimental analysis of mold behavior. The Manufacturing Engineering and Technologies (MET-2) group previously developed a graphite mold to vacuum cast small plutonium disks to be used by the Department of Homeland Security as point sources for radiation sensor testing. A two-stage pouring basin consisting of a funnel and an angled cavity directs the liquid into a vertical runner. A stack of ten disk castings connect to the runner by horizontal gates. Volumetric flow rates were implemented to limit overflow into the funnel and minimize foundry returns. Models using Flow-3D computational fluid dynamics software are employed here to determine liquid Pu flow paths, optimal pour regimes, temperature changes, and pressure variations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2016

Comparison of Surrogate Models with Physical Models for Dynamic Probabilistic Risk Analysis Using the RAVEN Code

Dynamic probabilistic risk analysis (DPRA) has emerged in the nuclear industry as an effective tool to identify points of weakness in nuclear reactors and create a comprehensive approach to quantifying risk. While DPRA allows for detailed analysis, it usually requires thousands of runs due to the necessity of exploring low probability, high consequence events, which can be computationally prohibitively difficult to assess. Risk Analysis Virtual Environment (RAVEN) is a software package being developed under the Nuclear Energy Advanced Modeling and Simulation program at the Idaho National Laboratory. It is able to manage complex control logic to drive a RELAP-7 simulation and to handle sampling strategies including Monte Carlo, Latin Hypercube, Grid and Adaptive samplers. By performing a thorough investigation of the possibility space of an accident scenario with RELAP-7, RAVEN is able to identify consequences of accident scenarios that have not been encountered. The most computational resource intensive step of a RAVEN analysis is running the RELAP-7 code modeling the physical system behavior. Using surrogate models to represent the outcomes of more complex simulations rather than the full model can greatly reduce the computation effort required. In situations where the uncertainty distributions of a model are unknown, or when there are multiple sets of uncertainty distributions, DPRA generally requires rerunning the physical model for each set of uncertainty distributions used. By taking an unbiased sample of the physical model and creating a surrogate model from the results, the surrogate model can be sampled under a different set of uncertainty distributions to provide equivalent results. This paper presents an example of this approach. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗