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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.
The overall objective of this project was to develop an updated model Energy Conservation training program for stationary engineers. This revision to the IUOE National Training Fund’s existing Energy Conservation training curriculum is designed to enable stationary engineers to incorporate essential energy management into routine building operation and maintenance tasks. The curriculum uses a blended learning approach that includes classroom, hands-on, computer simulation and web-based training in addition to a portfolio requirement for a workplace-based learning application. The Energy Conservation training program goal is development of a workforce that can maintain new and existing commercial buildings at optimum energy performance levels. The grant start date was July 6, 2010 and the project continued through September 30, 2012, including a three month non-funded extension.
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.
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.
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
Benchmark models were developed to evaluate 11 critical core configurations of the HTR-PROTEUS pebble bed experimental program. Various additional reactor physics measurements were performed as part of this program; currently only a total of 37 absorber rod worth measurements have been evaluated as acceptable benchmark experiments for Cores 4, 9, and 10. Dominant uncertainties in the experimental keff for all core configurations come from uncertainties in the ²³⁵U enrichment of the fuel, impurities in the moderator pebbles, and the density and impurity content of the radial reflector. Calculations of k eff with MCNP5 and ENDF/B-VII.0 neutron nuclear data are greater than the benchmark values but within 1% and also within the 3σ uncertainty, except for Core 4, which is the only randomly packed pebble configuration. Repeated calculations of k eff with MCNP6.1 and ENDF/B-VII.1 are lower than the benchmark values and within 1% (~3σ) except for Cores 5 and 9, which calculate lower than the benchmark eigenvalues within 4σ. The primary difference between the two nuclear data libraries is the adjustment of the absorption cross section of graphite. Simulations of the absorber rod worth measurements are within 3σ of the benchmark experiment values. The complete benchmark evaluation details are available in the 2014 edition of the International Handbook of Evaluated Reactor Physics Benchmark Experiments.
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.
In this study, we examined the responses of East Asian Summer Monsoon (EASM) to natural (solar variability and volcanic aerosols) and anthropogenic (greenhouse gasses and aerosols) forcings simulated in the 17 latest Coupled Model Intercomparison Program phase 5 (CMIP5) models with 105 realizations. The observed weakening trend of low-level EASM circulation during 1958-2001 is partly reproduced under all-forcing runs. A comparison of separate forcing experiments reveals that the aerosol-forcing plays a primary role in driving the weakened low-level monsoon circulation. The preferential cooling over continental East Asia caused by aerosol affects the monsoon circulation through reducing the land-sea thermal contrast and results in higher sea level pressure over northern China. In the upper-level, both natural-forcing and aerosol-forcing contribute to the observed southward shift of East Asian subtropical jet through changing the meridional temperature gradient.
The development of viable nuclear energy source depends on ensuring structural materials integrity. Structural materials in nuclear reactors will operate in harsh radiation conditions coupled with high level hydrogen and helium production, as well as formation of high density of point defects and defect clusters, and thus will experience severe degradation of mechanical properties. Therefore, the main objective of this work is to develop a capability that predicts aging behavior and in-service lifetime of nuclear reactor components and, thus provide an instrumental tool for tailoring materials design and development for application in future nuclear reactor technologies. Towards this end goal, the long term effort is to develop a physically based multiscale modeling hierarchy, validated and verified, to address outstanding questions regarding the effects of irradiation on materials microstructure and mechanical properties during extended service in the fission and fusion environments. The focus of the current investigation is on modern steels for use in nuclear reactors including high strength ferritic-martensitic steels (Fe-Cr-Ni alloys). The effort is to develop a predicative capability for the influence of irradiation on mechanical behavior. Irradiation hardening is related to structural information crossing different length scales, such as composition, dislocation, and crystal orientation distribution. To predict effective hardening, the influence factors along different length scales should be considered. Therefore, a hierarchical upscaling methodology is implemented in this work in which relevant information is passed between models at three scales, namely, from molecular dynamics to dislocation dynamics to dislocation-based crystal plasticity. The molecular dynamics (MD) was used to predict the dislocation mobility in body centered cubic (bcc) Fe and its Ni and Cr alloys. The results are then passed on to dislocation dynamics to predict the critical resolved shear stress (CRSS) from the evolution of local dislocation and defects. In this report the focus is on the results obtained from large scale dislocation dynamics simulations. The effect of defect density, materials structure was investigated, and evolution laws are obtained. These results will form the bases for the development of evolution and hardening laws for a dislocation-based crystal plasticity framework. The hierarchical upscaling method being developed in this project can provide a guidance tool to evaluate performance of structural materials for next-generation nuclear reactors. Combined with other tools developed in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the models developed will have more impact in improving the reliability of current reactors and affordability of new reactors.
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).
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.
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.
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.
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
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.
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".
Purpose: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called “biophysical” map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT + MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using volumetric modulated arc therapy. In the three cases, the required target prescription doses and constraints on organs at risk were fulfilled in a short enough time to allow routine clinical implementation. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements. Conclusions: A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation time.
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.