Second Symposium on Nonlinear Reduced Order Models (NROM 2018) Program.
Abstract not provided.
Brink, Adam Ray↗
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Development of the SPE & DAG Geologic Framework Models - posters for program review meeting
To be presented at the Nuclear Explosion Monitoring Portfolio Review, LLNL, 19-21 June 2018. Materials include poster, poster overview slide, and "pentachart" slide.
58 GEOSCIENCES↗
Heat Pipe Modeling Capabilities in Sockeye
Presentation to be given in a meeting with stakeholders of the Nuclear Energy Advanced Modeling and Simulation program for modeling related to microreactors. This presentation gives an overview of the capabilities and status of the heat pipe code Sockeye.
97 MATHEMATICS AND COMPUTING↗
NEAMS Problem Draft (FY2020 Assessment)
This report documents the physical configuration required to model the assessment problem posed by the Reactor Physics focus area within the Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. This model is based on the Empire model developed by staff at Los Alamos National Laboratory under the MEITNER (Modeling-Enhanced Innovations Trailblazing Nuclear Energy Reinvigoration) program. The reference report does not provide dimensions for development of computational models; those data are provided elsewhere in published. Thus, this report has been prepared to provide a single reference that can be used to create consistent models by assessment participants.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization
Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decom-position strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installation and explore various decomposition techniques to enhance solution efficiency.
Ovalle, Daniel↗
Overview of INL fusion safety program and research efforts in modeling and simulation
The fusion safety program at INL has several distinct modeling efforts currently underway. A few of the main tools and specific efforts are discussed in this presentation intended for an EPRI audience
70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
An L$_0$L$_1$-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming
First-principles based lattice models allow the modeling of ab initio thermodynamics of crystalline mixtures for applications such as the construction of phase diagrams and the identification of ground state atomic orderings. The recent development of compressive sensing approaches for the construction of lattice models has further enabled the systematic construction of sparse physical models without the need for human intuition other than requiring the compactness of effective cluster interactions. However, conventional compressive sensing based on L1-norm regularization is strictly only applicable to certain classes of optimization problems and is otherwise not guaranteed to generate optimally sparse and transferable results, so that the method can only be applied to some materials science applications. In this paper, we illustrate a more robust L0L1-norm compressive-sensing method that removes the limitations of conventional compressive sensing and generally results in sparser lattice models that are at least as predictive as those obtained from L1-norm compressive sensing. Apart from the theory, a practical implementation based on state-of-the-art mixed-integer quadratic programming (MIQP) is proposed. The robustness of our methodology is illustrated for four different transition-metal oxides with relevance as battery cathode materials: Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a practical and robust approach for the construction of sparser and more predictive lattice models, improving on the compressive sensing paradigm and making it applicable to a much broader range of applications.
Huang, W↗
(U) Physics Validation of the RMI-Based Ejecta Source Model Implementation in FLAG: L2 Milestone #6035 Report
The Los Alamos Physics and Engineering Models (PEM) program has developed a model for Richtmyer-Meshkov instability (RMI) based ejecta production from shock-melted surfaces, along with a prescription for a self-similar velocity distribution (SSVD) of the resulting ejecta particles. We have undertaken an effort to validate this source model using data from explosively driven tin coupon experiments. The model’s current formulation lacks a crucial piece of physics: a method for determining the duration of the ejecta production interval. Without a mechanism for terminating ejecta production, the model is not predictive. Furthermore, when the production interval is hand-tuned to match time-integrated mass data, the predicted time-dependent mass accumulation on a downstream sensor rises too sharply at early times and too slowly at late times because the SSVD overestimates the amount of mass stored in the fastest particles and underestimates the mass stored in the slowest particles. The functional form of the resulting m(t) is inconsistent with the available time-dependent data; numerical simulations and analytic studies agree on this point. Simulated mass tallies are highly sensitive to radial expansion of the ejecta cloud. It is not clear if the same effect is present in the experimental data but if so, depending on the degree, this may challenge the model’s compatibility with tin coupon data. The current implementation of the model in FLAG is sensitive to the detailed interaction between kinematics (hydrodynamic methods) and thermodynamics (material models); this sensitivity prohibits certain physics modeling choices. The appendices contain an extensive analytic study of piezoelectric ejecta mass measurements, along with test problems, excerpted from a longer work (LA-UR-17-21218).
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NEAMS Advanced Reactor Model Contributions to the NRIC Virtual Test Bed
The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. Several NEAMS advanced reactor models (including input files, documentation, and discussion of results) have recently been contributed to the NRIC VTB. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. This report summarizes and provides links to these new models, which include modeling phenomena important to liquid metal cooled fast reactors, molten salt reactors, high temperature gas-cooled reactors, and microreactors.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NEAMS Model Contributions in 2023 to the National Reactor Innovation Center Virtual Test Bed for Use by Industry and Other Stakeholders
The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. This work documents NEAMS efforts to support industry adoption of advanced modeling tools through contribution of 10 NEAMS models to the NRIC Virtual Test Bed including models for the High Temperature Test Facility (HTTF), TRISO fuel failure in a microreactor, and multiphysics models of a molten chloride fast reactor, among others. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. In particular, the HTTF model is being used for code validation and benchmarking activities. The microreactor and molten chloride fast reactor models are representative of analysis that may be useful for current candidates of DOME and LOTUS, NRIC’s physical testbeds. This report summarizes and provides links to these new models, among others.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Depot Charging Schedule Optimization for Medium- and Heavy-Duty Battery-Electric Trucks
Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.
Song, Shuhan↗
Equation-based languages – A new paradigm for building energy modeling, simulation and optimization
Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE AGM and NAERM FY23 Annual Accomplishments
This document summarizes FY2023 work performed by Lawrence Livermore National Laboratory (LLNL) in support of the DOE Office of Electricity’s (OE) Advanced Grid Modeling (AGM) program, North American Energy Resilience Model (NAERM), and Protective Relaying (PR) program.
24 POWER TRANSMISSION AND DISTRIBUTION↗
DoE Early Career Research Program: Final Report: Model-Independent Dark-Matter Searches at the ATLAS Experiment and Applications of Many-core Computing to High Energy Physics
This is the final report of for DoE Early Career Research Program Grant Titled "Model-Independent Dark-Matter Searches at the ATLAS Experiment and Applications of Many-core Computing to High Energy Physics".
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A stochastic biomass blending problem in decentralized supply chains
Blending biomass materials of different physical or chemical properties provides an opportunity to 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 mini-mum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.
09 BIOMASS FUELS↗
From graphical to model-based distillation column design: A McCabe-Thiele-inspired mathematical programming approach
We propose a mixed-integer nonlinear programming (MINLP) model for simple and complex distillation column design and optimization. The model is based upon the concepts and equations underpinning the McCabe-Thiele method. Generalizing this method, we introduce material balances at various locations of the column and employ binary variables to determine the optimal number of trays and optimal feed locations. We model the vapor–liquid equilibrium using continuous piecewise linear approximating functions. The model is extended to account for multicomponent mixtures and non-constant-molar overflow. We also discuss how to estimate the minimum number of trays and the minimum reflux ratio.
42 ENGINEERING↗
Sizing Energy Storage System for Energy Arbitrage in Extreme Fast Charging Station
This paper proposes a non-linear programming (NLP) model to optimally size the energy storage system (ESS) and obtain an optimal energy management for energy arbitrage of an extreme fast charging station (XFCS) for electric vehicles (EVs), with minimized total cost of XFCS operation and ESS investment. Different from most reported work on sizing the ESS for EV charging stations, this paper proposes a pragmatic approach to model the ESS life degradation and accurately count the ESS cycles. Moreover, this work incorporates the peak demand charges in the operational cost of the charging station which are often overlooked in the literature. The proposed model is formulated and solved using AIMMS. Finally, a thorough sensitivity analysis is performed to offer insights into how different input parameters impact the ESS sizing and savings from the energy arbitrage perspective.
25 ENERGY STORAGE↗
The overview in safety review of human factors engineering and control room design in chinese AP1000 nuclear power plant - 174
The first AP1000 nuclear power plant is constructed in Sanmen county in Zhejiang Province in China, which has creative and distinctive design characteristics. Human factors engineering disciplines are applied to the design of the AP1000. In addition to the elements of the program review model(including human factors engineering program management, operating experience review, functional requirements analysis and allocation, task analysis, staffing, integration of human reliability analysis with human factors engineering, human system interface design, procedure development, training program development, human system interface verification and validation, design implementation, human performance monitoring),the minimum inventory of controls, displays, and alarms present in the main control room and at the remote shutdown workstation. These contents mentioned above are reviewed according to standards, rules and regulations. This article introduces review process, and several important issues during the reviewing are described in detail. These issues include many elements, such as operating experience review, task analysis, human system interface design, verification and validation,and so on. This paper emphasizes on the main control room design(including environment and layout). Background noise in the actual main control room may exceed too much to design value. Another important issue is about main control room habitability systems(VES) changes to satisfy post-actuation performance requirements in AP1000 design change proposal. All the wall panel displays will be closed in this design change proposal. Review process and proposal are described in the paper. Since the AP1000 unit in China is the first constructed all over the world, verification and validation are especially important and necessary. The plan of verification and validation, the results summary report of verification and validation, the report of human engineering discrepancies are focus attention. Verification and validation in human factors engineering include HSI task support verification, HFE design verification and integrated system validation. This paper introduces the review process of verification and validation, the issues found in verification and validation. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗