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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Gas Gun Model and Comparison to Experimental Performance of Pipe Guns Operating with Light Propellant Gases and Large Cryogenic Pellets

Injection of multiple large (~10 to 30 mm diameter) shattered pellets into ITER plasmas is presently part of the scheme planned to mitigate the deleterious effects of disruptions on the vessel components. To help in the design and optimize performance of the pellet injectors for this application, a model referred to as “the gas gun simulator” has been developed and benchmarked against experimental data. The computer code simulator is a Java program that models the gas-dynamics characteristics of a single-stage gas gun. Following a stepwise approach, the code utilizes a variety of input parameters to incrementally simulate and analyze the dynamics of the gun as the projectile is launched down the barrel. Using input data, the model can calculate gun performance based on physical characteristics, such as propellant-gas and fast-valve properties, barrel geometry, and pellet mass. Although the model is fundamentally generic, the present version is configured to accommodate cryogenic pellets composed of H2, D2, Ne, Ar, and mixtures of them and light propellant gases (H2, D2, and He). The pellets are solidified in situ in pipe guns that consist of stainless steel tubes and fast-acting valves that provide the propellant gas for pellet acceleration (to speeds ~200 to 700 m/s). The pellet speed is the key parameter in determining the response time of a shattered pellet system to a plasma disruption event. The calculated speeds from the code simulations of experiments were typically in excellent agreement with the measured values. With the gas gun simulator validated for many test shots and over a wide range of physical and operating parameters, it is a valuable tool for optimization of the injector design, including the fast valve design (orifice size and volume) for any operating pressure (~40 bar expected for the ITER application) and barrel length for any pellet size (mass, diameter, and length). Key design parameters and proposed values for the pellet injectors for the ITER disruption mitigation systems are discussed.

Combs, S. K.↗
DOE OSTI2017

Stan : A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

active distribution network↗
DOE OSTI2011

Economic analysis and assessment of syngas production using a modeling approach

Economic analysis and modeling are essential and important issues for the development of current feedstock and process technology for bio-gasification. The objective of this study was to develop an economic model and apply to predict the unit cost of syngas production from a micro-scale bio-gasification facility. An economic model was programmed in C++ computer programming language and developed using a parametric cost approach, which included processes to calculate the total capital costs and the total operating costs. The model used measured economic data from the bio-gasification facility at Mississippi State University. The modeling results showed that the unit cost of syngas production was $1.217 for a 60 Nm-3 h-1 capacity bio-gasifier. The operating cost was the major part of the total production cost. The equipment purchase cost and the labor cost were the largest part of the total capital cost and the total operating cost, respectively. Sensitivity analysis indicated that labor costs rank the top as followed by equipment cost, loan life, feedstock cost, interest rate, utility cost, and waste treatment cost. The unit cost of syngas production increased with the increase of all parameters with exception of loan life. The annual cost regarding equipment, labor, feedstock, waste treatment, and utility cost showed a linear relationship with percent changes, while loan life and annual interest rate showed a non-linear relationship. This study provides the useful information for economic analysis and assessment of the syngas production using a modeling approach.

Bio-gasification↗
DOE OSTI2025

Benders Decomposition Using Graph Modeling and Multi-Parametric Programming

Benders decomposition is a widely used method for solving large and structured optimization problems, but its performance is affected by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition. Specifically, we express the problem structure by using a graph-theoretic modeling abstraction in which nodes represent optimization subproblems and edges represent connectivity between subproblems. A key innovation of our approach is that we embed multiparametric programming (mp) surrogates for node subproblems, which maps the exact analytical map of the subproblem solution space. The use of mp surrogates allows us to replace subproblem solves with fast look-ups and function evaluations for primal and dual variables during the iterative Benders process. We formally show the equivalence between classical Benders cuts and those derived from the mp solution. We implement our framework in the open-source PlasmoBenders.jl software package. To demonstrate the capabilities of the proposed framework, we apply it to a two-stage stochastic programming problem, which aims to make optimal capacity expansion decisions under market uncertainty. We evaluate both single-cut and multicut variants of Benders decomposition and show that the use of mp surrogates achieves substantial speedups in subproblem solve time, while preserving the convergence guarantees of Benders decomposition. We highlight advantages in solution analysis and interpretability that is enabled by mp critical region tracking; specifically, we show that these reveal how decisions evolve geometrically across the Benders search. Our results aim to demonstrate that combining surrogate modeling with graph modeling offers a promising and extensible foundation for structure-exploiting decomposition. In addition, by decomposing the problem into more tractable subproblems, the proposed approach also aims to overcome scalability issues of mp. Finally, the use of mp surrogates provides a unifying and modular optimization framework that enables the representation of heterogeneous node subproblems as modeling objects with a homogeneous structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Operations research modeling in support of the Hanford direct feed low activity waste program - 15538

Operations Research (OR) modeling is a supplemental system planning tool for the Hanford Tank Farm Contractor. OR models are designed to quantify the impact of equipment failures and other constraints on mission duration; to determine overall system performance; and to identify any potential bottleneck areas and identify any potential improvement areas. OR models are being developed to provide critical insights into the expected operational performance of current and planned facilities and to help identify any operational risks which impact performance of plant and equipment. This information is used to aid decision making on facility design and operations. A new OR model is being developed in support of the Direct Feed Low Activity Waste (DFLAW) Program and will be a valuable tool in the development of overall operational and management strategies as well as providing input to facility design. The OR model will identify predicted system availability, process bottlenecks, and key equipment influencing system performance. The OR model will also test proposed solutions to improve system availability by resolving process bottlenecks and provide this information to the design agent for incorporation into the DFLAW process design. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Monte Carlo Hauser-Feshbach computer code system to model nuclear reactions: YAHFC

A computer program framework, YAHFC, to model low-energy nuclear reactions is presented. The framework allows for reactions with incident particles ranging from protons/neutrons to alphas and is designed to address reactions that ultimately lead to the formation of compound nuclear systems that then decay statistically as outlined in concepts of Hauser and Feshbach. Additionally, instead of a reaction, it is also possible to model the decay of a nuclear system with an initial excitation and population. The code models nuclear decays with a Monte Carlo process that tracks the decay of each state. This allows for an exact representation of the spectra for all emitted particles in each of the final exit channels and the possibility of generating reaction data for simulation purposes. The program is interfaced with the optical model code system FRESCOX to calculate transmission coefficients as well as the effects of coupled channels and other direct excitations via the distorted wave Born approximation (DWBA). Modules are included to account for nuclear processes such as width corrections, pre-equilibrium emission, and fission. The program is controlled by a series of input commands and while a set of input parameters exists for each projectile and target, the input commands allow for complete control over each input parameter. Extensive data files are produced and a program is provided that converts YAHFC data files into nuclear data library entries in the generalized nuclear data structure (GNDS).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Routing Problem for Unmanned Aerial Vehicle Patrolling Missions - A Progressive Hedging Algorithm

This paper presents a two-stage stochastic program to model a routing problem involving an Unmanned Aerial Vehicle (UAV) in the context of patrolling missions. In particular, given a set of targets and a set of supplemental targets corresponding to each target, the first stage decisions involve finding the sequence in which the vehicle has to visit the set of targets. Upon reaching each target, the UAV collects information and if the operator of the UAV deems that the information collected is not of sufficient fidelity, then the UAV has to visit all the supplemental targets corresponding to that target to collect additional information before proceeding to visit the next target. The problem is solved using a progressive hedging algorithm and extensive computational results corroborating the effectiveness of the proposed model and the solution methodology is presented.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Exposing hidden alternative backbone conformations in X-ray crystallography using qFit

Proteins must move between different conformations of their native ensemble to perform their functions. Crystal structures obtained from high-resolution X-ray diffraction data reflect this heterogeneity as a spatial and temporal conformational average. Although movement between natively populated alternative conformations can be critical for characterizing molecular mechanisms, it is challenging to identify these conformations within electron density maps. Alternative side chain conformations are generally well separated into distinct rotameric conformations, but alternative backbone conformations can overlap at several atomic positions. Our model building program qFit uses mixed integer quadratic programming (MIQP) to evaluate an extremely large number of combinations of sidechain conformers and backbone fragments to locally explain the electron density. Here, we describe two major modeling enhancements to qFit: peptide flips and alternative glycine conformations. We find that peptide flips fall into four stereotypical clusters and are enriched in glycine residues at the n+1 position. The potential for insights uncovered by new peptide flips and glycine conformations is exemplified by HIV protease, where different inhibitors are associated with peptide flips in the “flap” regions adjacent to the inhibitor binding site. Our results paint a picture of peptide flips as conformational switches, often enabled by glycine flexibility, that result in dramatic local rearrangements. Our results furthermore demonstrate the power of large-scale computational analysis to provide new insights into conformational heterogeneity. Furthermore, improved modeling of backbone heterogeneity with high-resolution X-ray data will connect dynamics to the structure-function relationship and help drive new design strategies for inhibitors of biomedically important systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Stochastic Models for an Optimal Blending of Biomass under Cost, Quality and Uncertainty Considerations

Blending biomass materials of different physical or chemical properties provides an opportunity to passively adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the Thermochemical conversion process at the minimum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass availability and quality. The proposed CCP model takes as inputs the physical and chemical properties, and biomass availability. We use the sample average approximation (SAA) method to solve the problem. We develop a case study for South Carolina by using data provided by the Billion Ton Study. We conduct extensive numerical analysis to evaluate the quality of the solutions obtained.

09 BIOMASS FUELS↗
DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗
DOE OSTI2014

Final Report on Reservoir Modeling (SECARB Phase III Work Product 4.2.4.c)

The SECARB Partnership Anthropogenic Test, located in the Southeast Unit of the Citronelle oilfield will inject CO 2 into the saline waters of the Cretaceous Paluxy sandstone formation for three years. In order to accurately model the injection, migration, and long-term fate of CO 2 in the subsurface, GEM, a Computer Modeling Group software program for transport and geochemical modeling, was selected for the simulation. GEM is a very robust compositional reservoir simulator, with all the necessary options available for modeling CO 2 trapping mechanisms (including solubility, residual gas and mineralization). This simulation tool is widely known and used in the petroleum and sequestration industries. A reservoir model was built, based on a previously developed comprehensive picture of the subsurface geology at the site (Work Product 4.2.1.d - Report on Subsurface Characterization for the Anthropogenic Test Project Site). This report describes in details the different inputs that were used to create the model as well as the results of the injection scenario in terms of achievable injection rates, plume extent and pressure response.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Initial Fracture Propagation Modeling of Graphite Components with Grizzly

Graphite has historically been extensively used in power reactor cores and will be used in multiple types of advanced reactors currently under development. These graphite structural components can experience significant stresses due to nonuniform volumetric strains induced by irradiation and thermal expansion, which can lead to fracture. Robust tools for predicting fracture initiation and propagation in graphite structural components in nuclear reactors are important for evaluating component integrity, developing design standards, and interpreting experimental results to characterize graphite performance. The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program has been developing degradation models for other structural components in nuclear reactors within the Grizzly and BlackBear codes. This report documents an effort to develop initial capabilities for modeling graphite fracture within these codes, building on prior efforts to model fracture in other materials. Major elements of this effort include developing a new system for modeling fracture nucleation and growth in two dimensions using the extended finite element method and incorporating a damage and plasticity model. These capabilities are applied here to model a representative graphite component and a splitting disc experiment used to obtain tensile strength.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of mechanistic, microstructure-informed BISON models for fission product-induced failure mechanisms in advanced nuclear fuels

The US continues to prioritize commercial deployment of advanced reactors—particularly those utilizing U-Zr metallic and TRISO particle fuels. Fission product-induced failure mechanisms for these systems include fuel–cladding chemical interaction in metallic fuel rods, which threatens cladding integrity, and Pd penetration in TRISO particles, which degrades SiC layer properties. Empirical models can be applied within the bounds of existing irradiation databases, but their utility is limited when considering new designs or investigating fundamental material behaviors. The Nuclear Energy Advanced Modeling and Simulation Program is therefore developing mechanistic models for these behaviors, which are expected to aid in fuel design, assist in development of failure mitigation strategies, and provide support for qualification and licensing. These efforts leverage multiscale capabilities to capture the microstructural features and processes that govern these behaviors. This talk summarizes past and ongoing efforts to develop and validate these models for the BISON fuel performance code.

FCCI↗
DOE OSTI2013

Compiling MPI for Many-Core Systems

Processors with multiple (or many) cores and shared memory are becoming ubiquitous across the com puting spectrum. MPI, the current de facto program ming model for scalable parallel applications, enforces copies between source and target processes and thus can not fully utilize shared memory and cache architectures of modern machines. To enable MPI-based programs to more fully exploit features of multi- and many-core architectures, we present a compiler-based transformation that transforms MPI processes into threads and fuses message serialization and deserialization loops such that send and receive calls can be replaced by direct memory accesses. Our compiler replaces most of the MPI communication functions with direct load/store accesses and our runtime provides a threaded MPI implementation to handle the remaining functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Runtime Tracing of the Community Earth System Model: Feasibility Study and Benefits

The Community Earth System Model (CESM) is one of US's leading earth system modeling frameworks, which has decades of development history and was embraced by a large, active user community. In this paper, we first review the software development history of CESM and we explain the general objectives of performance analysis. Then we present an offline global community land model simulation within the CESM framework to demonstrate the procedure of runtime tracing of CESM using the Vampir toolset. Finally, we explain the benefits of runtime tracing to the general earth system modeling community. We hope those considerations can be beneficial to many other modeling research programs involving legacy high-performance computing applications.

42 ENGINEERING↗
DOE OSTI2019

Development of a Fleet-Level Human Factors Engineering Program to Support the Digital Modernization of Multiple Turbine Control Systems

A number of years ago, Duke Energy and the United States (U.S.) Department of Energy (DOE) entered into a collaboration to support Duke’s digital upgrade activities for the main control rooms at three of their commercial nuclear power stations. The collaboration specifically focused on the human factors engineering (HFE) aspects of Duke’s plans to upgrade the legacy turbine control systems (TCS) at their Brunswick, Robinson, and Harris plants. This TSC upgrade involved installing a common digital control system (DCS), or distributed instrumentation and control (I&C) system platform, which multiple digital plant control systems can be integrated onto as the control rooms are modernized over time. Given the breadth and depth of the scope of this upgrade, DOE researchers at Idaho National Laboratory (INL) with expertise in human factors collaborated with Duke throughout the entire HFE life cycle to help ensure the upgrade was performed in a manner consistent with regulatory human factors review criteria, such as the U.S. Nuclear Regulatory Commission’s (NRC) Human Factors Engineering Program Review Model (NUREG 0711, 2012). This paper describes the HFE researchers performed through the early and middle phases of these TCS upgrades. Specific HFE activities include: 1) the development of a human factors program management plan, 2) performing an operational experience review, 3) evaluating the design human system interface while it was still in the early stages of development, and 4) developing a framework to help map these HFE activities to NUREG-0711 to help understand and catalog the value of different types and phases of human performance data collection. These four HFE activities are described in more detail below.

99 GENERAL AND MISCELLANEOUS↗