Sandia National Laboratories and the Future of Engineering and Simulation.
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Nuclear power plant (NPP) utilities operate contingent on receiving a favorable safety assessment from their national regulatory and safety authority. Among the many facets of NPP safety addressed is verification of integral design-basis performance - a demonstration of a plant's response to a suite of challenging transient and accident scenarios. Thermal-hydraulic system computer codes like RELAP5, TRAC, CATHARE, and ATHLET are used as the simulation engine as part of a broader evaluation model that accounts for a variety of uncertainties. It is common for these evaluation models to apply either a super-bounding/ deterministic or best-estimate plus uncertainty (BEPU) approach. The trend over the past two decades has been away from the classical deterministic methods towards methods explicitly incorporating parametric uncertainty models. These methods center upon the evaluation of parametric and system response uncertainties, where these uncertainties are derived from the convolution of one or more parametric uncertainty models. Acceptance of BEPU methods has provided the nuclear industry benefits regarding operational flexibility and, in some cases, power output. The advantage of BEPU for new NPPs is less obvious. Generally, these new designs include engineered safety features that practically eliminate fuel damage resulting from design-basis events. To substantiate such claims, applying super-bounding assumptions on uncertainties may be a more expedient demonstration of a plant's safety case than those based on BEPU. In addition, the requirements on design inputs for a deterministic evaluation model are substantially less than that for a BEPU evaluation model. As such, applying a deterministic evaluation model is expected to have the advantage of reducing the duration of the licensing review process. For the current operating fleet of light-water reactors (LWRs), the nuclear safety authorities in most countries have followed the U.S. Nuclear Regulatory Commission's rules for the preparation of safety analysis. These rules are specified in the U.S. Code of Federal Regulations, most notably in, Part 10, Section 50.46, entitled 'acceptance criteria for emergency core cooling systems for light-water nuclear power reactors' and its Appendix K. Notably, Appendix K specifies deterministic requirements for analysis of the loss-of-coolant accident (LOCA), which is generally regarded as the maximum credible accident for LWRs. Projecting 10 CFR 50, Appendix K, to new plants is not obviously applicable as it addresses light water fluid flow and heat transfer phenomena associated with core uncovery. Preventing core uncovery is a requirement for several designs in the class of small modular reactors (SMR). In doing so, the majority of phenomenological uncertainties prescribed in Appendix K are eliminated. Similarly, the impact of other phenomena to effect a change in various safety measures, i.e., figures-of-merit, is expected to be different. The implication is that development of a deterministic evaluation model must be derived with an original technical basis rather than solely rely on Appendix K. While a logical conclusion, the expectation of original bases, regardless of the treatment of uncertainties, is embodied in the U.S. NRC's Regulatory Guide (RG) 1.203, Transient and Accident Analysis. Within the evaluation model development and application process framework of RG 1.203, this paper describes a BEPU-informed deterministic evaluation model for NPPs with large inherent safety margins. (authors)
Predictive engine simulations are key for rapidly exploring and optimizing the design of cleaner burning and more fuel efficient engines. Injection strategies in advanced engine concepts are resulting in the injection and atomization of fuel under a wide range of operating conditions in order to meet stringent emission regulations. However, the physics governing the breakup of an injected liquid fuel jet into droplets under these conditions have not been well studied or experimentally characterized to date. In the sprays literature, three agents have been proposed as the likely mechanisms contributing to primary atomization in diesel sprays, namely the aerodynamic growth of waves on the fuel jet surface, turbulence generated in the injector nozzle, and cavitation. If computational design tools are to be used to guide the use of direct injection strategies for cleaner and more fuel efficient engines, the physics underpinning the role of these primary atomization mechanisms must be better understood to ensure the development of predictive simulations of fuel-air mixing and vaporization within the engine. Thus, the central aim of this thesis is to improve the physical representation of spray breakup physics within today’s engine simulation packages. The work presented in this thesis investigates the role of the proposed physical mechanisms on the primary atomization process in diesel sprays. In order to advance current understanding of spray breakup, the dynamic and geometric factors contributing to cavitation were suppressed so that primary atomization due to aerodynamics and nozzle-generated turbulence could be studied in isolation. In the absence of sufficiently resolved images to visualize the primary atomization process under dieselrelevant conditions, droplet sizing spray measurements are needed to characterize the outcomes of the spray breakup process. Therefore, a new experimental methodology was developed and applied in a high-pressure spray chamber to characterize the average size of droplets formed from the spray breakup process. This experimental data, in conjunction with x-ray measurements from the Advanced Photon Source at Argonne National Laboratory, have been used to assess predictions from existing spray breakup models. Comparison between predicted and measured drop size distributions revealed that a spray atomization model, premised on droplet formation from the growth of aerodynamically induced instabilities, could capture experimentally observed sensitivities and features in the measured droplet size distributions under conventional diesel engine conditions. However, for injection into relatively lower ambient density environments, aerodynamic breakup models could not accurately predict the initial rate of droplet size decrease in the near-nozzle region, suggesting that other mechanisms, such as turbulence generated inside the nozzle, likely augment and enhance the primary breakup process. Evaluation of newly available droplet sizing measurements under low ambient density conditions allowed for the turbulence-induced breakup process to be studied, while minimizing the influence of aerodynamic inertial forces on the spray. Although several turbulence-induced breakup models have been proposed in the literature, the scaling of droplet sizes with the integral length scale, assumed in the majority of turbulent breakup models, was found to be inconsistent with the experimentally observed trends in droplet size along the spray centerline. However, empirical correlations describing droplets formed from eddies within the inertial sub-range of the turbulence spectrum were better able to capture the measured sensitivities in droplet size to changes in ambient and injection conditions. These findings informed recommendations for an improved hybrid spray breakup model, capable of representing both aerodynamic and turbulent breakup mechanisms in the atomization of non-cavitating diesel sprays.
An important aspect of successful Nuclear Power Plant (NPP) digital control system upgrade projects is to upgrade the Operator Training Simulator (OTS) or a related Engineering Simulator (ES), prior to installation of the Distributed Control System (DCS) on the plant allowing for early testing of the digital controls/HMI and for early training of the plant operators while maintaining OTS configuration management (i.e. OTS in sync with the plant's current design). Experience has shown that early implementation of digital upgrades on the OTS and/or an ES can reduce plant startup duration (related to digital systems commissioning) and ensure operators are proficient with the new digital systems to ensure safe and reliable plant operation. Including simulators in the design process offers many benefits including early identification of design deficiencies, testing deficiency fixes, developing and validating plant procedures and supporting human factors engineering, to name a few. Implementing digital upgrades on the OTS is also a regulatory requirement driven by the ANSI/ANS-3.5-2009 standard (Nuclear Power Plant Simulators for Use in Operator Training and Examination) if the new DCS affects actions to be taken by the plant operator in the main control room. This paper presents the benefits of implementing digital upgrades on the OTS and/or ES prior to plant implementation. The various simulator implementation strategies (stimulation, emulation, simulation or a hybrid solution) and selection factors are discussed. The paper also covers four (4) case studies demonstrating how digital upgrades can be implemented on simulators. The first case study covers the implementation of a Siemens offgas Programmable Logic Controller (PLC) system on the Enrico Fermi OTS (Michigan). The second case study covers the implementation of a GE main generator excitation system on the Cernavoda OTS (Romania). The third case study covers the implementation of a Siemens main feedwater control system on the Callaway OTS and ES (Missouri). The final case study covers the implementation of the non-safety related and safety related DCS on the Nuclear New Build (NNB) Hongyanhe Phase I dual-purpose OTS/ES (China). (authors)
Internal combustion engine design is increasingly driven by computational models used to predict performance metrics, which previously would have been predicted by limited design intuition or expensive and timeconsuming physical testing. Improved model capabilities shorten design cycles and enable the production of cleaner and more efficient engines. This project focuses on advancing the state of the art in internal combustion engine simulations. The overarching goal is to enable predictive models and reduced time to solution for simulations that have impacts on combustion engine design.
A mixture of n-dodecane and m-xylene is investigated as a diesel fuel surrogate for compression ignition engine applications. Compared to neat n-dodecane, this binary mixture is more representative of diesel fuel because it contains an alkyl-benzene which represents an important chemical class present in diesel fuels. A detailed multi-component mechanism for n-dodecane and m-xylene was developed by combining a previously developed n-dodecane mechanism with a recently developed mechanism for xylenes. The xylene mechanism is shown to reproduce experimental ignition data from a rapid compression machine and shock tube, speciation data from the jet stirred reactor and flame speed data. This combined mechanism was validated by comparing predictions from the model with experimental data for ignition in shock tubes and for reactivity in a flow reactor. The combined mechanism, consisting of 2885 species and 11754 reactions, was reduced to a skeletal mechanism consisting 163 species and 887 reactions for 3D diesel engine simulations. The mechanism reduction was performed using directed relation graph (DRG) with expert knowledge (DRG-X) and DRG-aided sensitivity analysis (DRGASA) at a fixed fuel composition of 77% of n-dodecane and 23% m-xylene by volume. The sample space for the reduction covered pressure of 1 – 80 bar, equivalence ratio of 0.5 – 2.0, and initial temperature of 700 – 1600 K for ignition. The skeletal mechanism was compared with the detailed mechanism for ignition and flow reactor predictions. Finally, the skeletal mechanism was validated against a spray flame dataset under diesel engine conditions documented on the Engine Combustion Network (ECN) website. These multidimensional simulations were performed using a Representative Interactive Flame (RIF) turbulent combustion model. In conclusion, encouraging results were obtained compared to the experiments with regards to the predictions of ignition delay and lift-off length at different ambient temperatures.
Spawn is the latest whole-building energy simulation engine developed by the US Department of Energy, National Labs and industry. Whereas EnergyPlus was designed as a successor to DOE-2, Spawn is not a direct successor of–nor is it intended as an imminent replacement for– EnergyPlus. Instead, Spawn reuses parts of EnergyPlus while supporting new use cases in HVAC and controls. Spawn is intended to provide several capabilities that significantly advance beyond EnergyPlus. It is intended to support the evaluation of novel HVAC and district energy systems in a more physically realistic way. Critically, it can model control in a physically realistic way, using portable specifications that can be compiled for execution on control platforms. Spawn is also intended to support co-simulation in an intrinsic way to enable integration with third-party models. This paper describes the software architecture of Spawn from model authoring to compilation and simulation. It explains how Spawn reuses the envelope and daylighting modules of EnergyPlus and couples them to HVAC and control models from the Modelica Buildings Library using the Functional Mockup Interface (FMI) standard. It presents a number of examples that: i) validate Spawn’s coupled simulation approach by comparing its results to those of EnergyPlus, ii) illustrate the Spawn methodology for modeling and simulating HVAC systems, and iii) evaluate the performance of Spawn’s Quantized State System (QSS) time integration algorithms
Most multidimensional engine simulations spend much time solving for non-equilibrium spray dynamics (atomization, collision, vaporization). However, their accuracy is limited by significant grid dependency, and the need for extensive calibration. This is critical for modeling cold-start diesel fuel post injections, which occur at low temperatures and pressures, far from typical model validation ranges. At the same time, resolving micron-scale spray phenomena would render full Eulerian multiphase calculations prohibitive. In this study, an improved phase equilibrium based approach was implemented and assessed for simulating diesel catalyst heating operation strategies. A phase equilibrium solver based on the model by Yue and Reitz [1] was implemented: a fully multiphase CFD solver is employed with an engineering-size engine grid, and fuel injection is modeled using the standard Lagrangian parcels approach. Mass and energy from the liquid parcels are released to the Eulerian multiphase mixture according to an equilibrium-based liquid jet model. An improved phase equilibrium solver was developed to handle large real-gas mixtures such as those from accurate chemical kinetics mechanisms. The liquid-jet model was improved such that momentum transfer to the Eulerian solver better reproduces the physical spray jet structure. Validation of liquid/vapor penetration predictions showed that the model yields accurate results with very limited tuning and low sensitivity to the few calibration constants. In-cylinder simulations of diesel catalyst heating operation strategies showed that capturing spray structure is paramount when short, transient injection pulses and low temperatures are present. Furthermore, the EP model provides improved predictions of post-injection spray structure and ignitability, while conventional spray modeling does not capture the increase of liquid penetration during the expansion stroke. Finally, the only important EP model calibration constant, Cliq, does not affect momentum transfer, but it changes the local charge cooling distribution through the local energy transfer, which makes it candidate to additional research. The results confirm that non-equilibrium spray processes do not need to be resolved in engineering simulations of high-pressure diesel sprays.
Computer methods were developed and demonstrated that make it possible to predict the combustion and pyrolysis chemistry of proposed biofuels, even before the biofuels are ever manufactured or experimentally tested. The methods are based on first-principles calculations of reaction rates and equilibria. A large number of complicated calculations are required, so we developed a computer workflow that automates many of the steps. With the chemical structure of the biofuel and the system conditions (temperature, pressure, compositions, etc.) as the input, the computer methods can construct a detailed kinetic model with refined parameters without intense human intervention. The automation of model construction makes it practical for a single engineer to make the predictions in a reasonable time period. The accuracy of the computer predictions was tested by measuring the time-profiles of various reaction intermediates (carbon monoxide, carbon dioxide, water, formaldehyde, methane, and ethylene) formed as the biofuels pyrolyze or ignite using sophisticated laser shock tube methods. Several biofuels, including methyl propyl ether, cyclopentanone, butyl acetate isomers, etc., were carefully studied to demonstrate the validity of the proposed computational and experimental methodology. This research project opens up the possibility for assessing some proposed biofuels on the computer without expensive experimental campaigns. The new computer methods are open source and well-documented, allowing a knowledgeable modeler to easily access the developed methods and implement them in their work routine. Moreover, they are also convenient and powerful, promising for developing the fuel chemistry models needed for use in engine simulators, facilitating co-optimization of new engine designs with new fuels.
Systems and methods are provided for generating faults and analyzing fault propagation and its effects. Starting from the ontologies of components, functions, flows, and faults, systems and methods are provided that describe, generate and track faults in a computer system across multiple domains throughout design and/or development. In order to construct the system and fault models, a series of concepts is introduced in the form of ontologies and their dependencies. An investigation is performed into the faults, including their type, cause, life-cycle aspects, and effect. Principles and rules are created to generate various faults based on system configurations. After the modeling process, a simulation engine is described to execute actions and simulate the process of fault generation and propagation. As a result, fault paths that impact components and functions can be obtained.
Heat pumps play an instrumental role in buildings decarbonization strategies. Recent advances in heat pump systems employ variable-speed compressor technology and electronically commutated fan motors. Inherently, the heating capacity and efficiency of heat pumps decrease with falling outdoor temperatures. Compared to single-speed heat pumps, variable speed systems can maintain higher heating and cooling efficiencies over a wider range of outdoor temperatures. The goal of this multi-phase project was to determine the energy savings of a high efficiency, variable-speed, air-source, split system heat pump designed for cold climate applications. The first project phase was to evaluate the performance of the heat pump in the laboratory under varying outdoor conditions in heating and cooling modes. The second phase was to translate the laboratory-measured performance into lookup tables for EnergyPlus hourly building simulation engine. Then, two sets of annual building simulations were performed using typical meteorological year weather from the Chicago-O'Hare airport for three different building types (a single-family residence, a strip mall, and a low-rise office building). The first set simulated a standard efficiency heat pump while the second set utilized the phase two performance tables to model a high efficiency heat pump. The high efficiency heat pump produced significant annual heating energy savings in all three buildings. The variable speed compressor and fan control also contributed to cooling energy savings. The simulated annual energy savings ranged from 22% to 35% over their respective baseline. The project's findings helped a Midwest electric utility, Commonwealth Edison (ComEd), design new incentives around high efficiency heat pumps.
The Particle/Ray Interaction Simulation Manager (PRISM) is a software tool developed by Triple Ring Technologies (TRT), http://www.tripleringtech.com/, under a Small Business Innovation Research Award from the Department of Homeland Security (DHS) to investigate the utility of simulations in X-ray-based explosive detection systems. The PRISM tool is intended to provide a standardized and intuitive user interface to control and configure realistic simulations of X-ray imaging systems. It includes a command-line interface that enables the user to configure simulated X-ray radiography and computed tomography (CT) systems. It allows the import of computer-aided design (CAD) models of objects and it supports Monte Carlo simulation engines such as GEANT4. PRISM is designed to simulate realistic X-ray projection images including scatter, noise, and various detector effects such as quantum efficiency, lag, and veiling glare. At the request of DHS, the authors assessed PRISM’s capabilities to simulate data generated by a Micro-CT test-bed system (MCT-TB) housed at the Lawrence Livermore National Laboratory (LLNL). This document describes the results of that assessment.
This project was part of an effort by ComEd's emerging technology program to evaluate the energy saving potential of new energy efficiency technologies. The focus of this technology assessment was to determine energy and peak demand savings potentials of a high efficiency variable speed, air-source, split system heat pump designed for cold climate applications. The results of this technology assessment will be used by CLEAResult to develop a new energy efficiency measure for Commonwealth Edison Company's incentive programs. The project utilized the National Renewable Energy Laboratory's (NREL) Thermal Test Facility to experimentally characterize cooling and heating performance of a high efficiency heat pump split system under varying outdoor climate conditions. The selected climate conditions represented summer and low temperature winter conditions in ComEd's service territory. The laboratory experimentation results were used to develop equipment performance curves required by EnergyPlus hourly simulation engine. Using typical meteorological year 3 weather data for the Chicago O'Hare airport, hourly building simulations (using the EnergyPlus engine) was utilized to estimate the annual energy savings of the high efficiency heat pump in comparison to a standard efficiency unit in the following U.S. Department of Energy (DOE) building codes program energy prototypes: Single-family residence; Strip mall; and Low-rise office.
The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).
Resiliency, reliability, and security of the next-generation smart grid depend upon leveraging advanced communication and computing technologies, integrating them with physical power systems, and developing real-time, fast, data-based applications to help in wide-area monitoring and control of the grid. Using a high sampling data rate from phasor measurement units (PMUs) to develop applications has opened the door to achieving the next-generation grid requirements. The North American Synchrophasor Initiative Network (NASPlnet) was developed in 2007-09 to create a standard and guide for PMU data exchanges. With the advancement in both networking and grid requirements, it is necessary to evaluate the performance of different NASPInet versions and their impact on applications. Therefore, we need a cyber-power cosimulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents a cyber-physical co-simulation testbed using NS3 to model the communication network, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application based on PMU data in an Institute of Electrical and Electronics Engineers 39-bus test system is presented using this co-simulation testbed.
This L2 milestone is a multifaceted review of DevOps practices across Weapon Simulation and Computing/Computational Physics (WSC/CP) Program, which includes Weapons, ICF, and Engineering simulation codes and supporting libraries. It complements the FY22 Spack/MARBL L2: Advanced Simulation and Computing (ASC) L2 Milestone 7904, “Workflow Portability Across Cloud Services”. The overall goal is to develop a path forward for improving DevOps practices in WSC/CP which will increase developer productivity, improve software quality, and speed up our ability to deliver capabilities to end users.