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At least 1,549 records · Page 86

TEAMER - AquaHarmonics High Fidelity WEC Sim PTO and Control Model Validation, Test Logs and Results

Collaborative effort between AquaHarmonics, Sandia National Laboratories (SNL), and the National Renewable Energy Laboratory (NREL) to revise and validate Aquaharmonics' full wave to wire model, allowing for reduced uncertainty and increased understanding of design requirements of a utility scale wave energy converter (WEC). SNL and NREL in collaboration with AquaHarmonics, will set up and run WEC Simulator (WEC-Sim) models of the AquaHarmonics WEC, building off past model developments for inclusion of custom PTO (power take-off) dynamics. The intent is to review, update, and verify or validate a new WEC-Sim model against wave tank experimental data. Furthermore, the WEC-Sim model will be coupled to an energy storage system model to better understand the wave-to-wire functionality. This data set is described in the "Test Log" excel file. Please refer to that document for details on each specific test date/time, constraint parameters and model hardware setup details. Sim model can be found in the associated MHKDR link below.

16 TIDAL AND WAVE POWER↗

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation

Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.

Energy - Storage↗

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Curation of Ground-Truth Validated Benchmarking Datasets for Fault Detection & Diagnostics Tools

Fault detection and diagnostics (FDD) analytical tools for heating, ventilation and air conditioning (HVAC) systems represent one of the most active areas of smart building technology development. A diversity of techniques is used for FDD analytics, spanning physical models, black box, and rule-based approaches, and researchers continuously strive to develop improved algorithms. With FDD algorithm numbers now in the hundreds, there is a need for performance evaluation of these algorithms in order to assess improvements, improve costeffectiveness, and to prioritize investment in the further development of these technologies. A persistent challenge of FDD advance has been the lack of common datasets to benchmark the performance accuracy of FDD algorithms. This paper summarizes the successful curation of HVAC operational data, paired with validated ground-truth information regarding the presence and absence of faults. The current dataset, consisting of both simulation and experimental data, will evolve to include a larger set of HVAC systems with the objective of creating the largest publicly available dataset to be used by FDD developers, users, and researchers to compare and contrast performance accuracy across FDD algorithms, helping to drive improvements that will spur greater market adoption of FDD tools. Furthermore, in order to avoid previously observed issues with contributed datasets and ensure high quality and consistency of future submissions, the development of data validation and ground-truth assessment protocol is detailed in this study.

Casillas, Armando↗

Method Validation of the Density Procedure

Method Validation is a formal requirement of accreditation standards, including ISO 17025. The tests/methods and instruments must be validated before diagnostic use to ensure reliable data is reported to the customer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

PIV Uncertainty Methodologies for CFD Code Validation at the MIR Facility

Currently, computational fluid dynamics (CFD) is widely used in the nuclear thermal hydraulics field for design and safety analyses. To validate CFD codes, high quality multi dimensional flow field data are essential. The Matched Index of Refraction (MIR) Flow Facility at Idaho National Laboratory has a unique capability to contribute to the development of validated CFD codes through the use of Particle Image Velocimetry (PIV). The significance of the MIR facility is that it permits non intrusive velocity measurement techniques, such as PIV, through complex models without requiring probes and other instrumentation that disturb the flow. At the heart of any PIV calculation is the cross-correlation, which is used to estimate the displacement of particles in some small part of the image over the time span between two images. This image displacement is indicated by the location of the largest peak. In the MIR facility, uncertainty quantification is a challenging task due to the use of optical measurement techniques. Currently, this study is developing a reliable method to analyze uncertainty and sensitivity of the measured data and develop a computer code to automatically analyze the uncertainty/sensitivity of the measured data. The main objective of this study is to develop a well established uncertainty quantification method for the MIR Flow Facility, which consists of many complicated uncertainty factors. In this study, the uncertainty sources are resolved in depth by categorizing them into uncertainties from the MIR flow loop and PIV system (including particle motion, image distortion, and data processing). Then, each uncertainty source is mathematically modeled or adequately defined. Finally, this study will provide a method and procedure to quantify the experimental uncertainty in the MIR Flow Facility with sample test results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Computer Simulation and Experimental Validation on the Oxidation and Sulfate Corrosion Resistance of Novel Chromium Based High Temperature Alloys

This report summarizes our recent works of ab initio molecular dynamics inter-atomic potentials development on dilute rare earth element yttrium (Y) etc. doped chromium (Cr) alloy systems, its applications in oxidation and corrosion resistance simulation, and experiment validation on the candidate systems. The simulation methods, experimental validation techniques, achievements already reached, students training, and future improvement are briefly introduced.

36 MATERIALS SCIENCE↗

Verification and Validation of High-Fidelity Multi-Physics Simulation Codes for Advanced Nuclear Reactors (Year 2)

The project objective is to verify and validate a suite of high-fidelity multi-physics simulation methods and codes developed during the previous I-NERI projects, against Monte Carlo solutions and experimental measurements for LWR and VHTR cores. Using benchmark problems developed during the previous year, ANL and KAERI continued to work on verifying the neutronics code DeCART and associated cross section libraries. Initial progress was also made on validating the code against experimental data. KAERI focused on verifying the DeCART code against numerical benchmark problems such as a PMR200 2-D pin cell/core and a HTTR 2-D block/core as well as improving DeCART capabilities such as the cross-section library group structure optimization, the fundamental and generalized adjoint equation solver, cylindrical boundary treatment, improved ray tracing, and implementing the direct resonance integral table method for resonance treatment. Verification test results for the updated capabilities showed substantial improvements in the estimation of eigenvalue and reaction rates.

Lee, C. H.↗

Validating agent based models through virtual worlds.

As the US continues its vigilance against distributed, embedded threats, understanding the political and social structure of these groups becomes paramount for predicting and dis- rupting their attacks. Agent-based models (ABMs) serve as a powerful tool to study these groups. While the popularity of social network tools (e.g., Facebook, Twitter) has provided extensive communication data, there is a lack of ne-grained behavioral data with which to inform and validate existing ABMs. Virtual worlds, in particular massively multiplayer online games (MMOG), where large numbers of people interact within a complex environ- ment for long periods of time provide an alternative source of data. These environments provide a rich social environment where players engage in a variety of activities observed between real-world groups: collaborating and/or competing with other groups, conducting battles for scarce resources, and trading in a market economy. Strategies employed by player groups surprisingly re ect those seen in present-day con icts, where players use diplomacy or espionage as their means for accomplishing their goals. In this project, we propose to address the need for ne-grained behavioral data by acquiring and analyzing game data a commercial MMOG, referred to within this report as Game X. The goals of this research were: (1) devising toolsets for analyzing virtual world data to better inform the rules that govern a social ABM and (2) exploring how virtual worlds could serve as a source of data to validate ABMs established for analogous real-world phenomena. During this research, we studied certain patterns of group behavior to compliment social modeling e orts where a signi cant lack of detailed examples of observed phenomena exists. This report outlines our work examining group behaviors that underly what we have termed the Expression-To-Action (E2A) problem: determining the changes in social contact that lead individuals/groups to engage in a particular behavior. Results from our work indicate that virtual worlds have the potential for serving as a proxy in allocating and populating behaviors that would be used within further agent-based modeling studies.

Lakkaraju, Kiran↗

Report on THMC Modeling of the Near Field Evolution of a Generic Clay Repository: Model Validation and Demonstration

Shale and clay-rich rock formations have been considered as potential host rocks for geological disposal of high-level radioactive waste throughout the world. Coupled thermal, hydrological, mechanical, and chemical (THMC) processes have a significant impact on the long-term safety of a repository in this type of rocks. For example, the excavation damaged zone (EDZ) near repository tunnels can modify local permeability (resulting from induced fractures), potentially leading to less confinement capability. Maximum allowable temperature is one of the most important design variables for a geological repository, because it determines waste package spacing, distance between disposal galleries, and therefore the overall size (and cost) of the repository for a given amount of waste. This report documents results from four R&D activities to address the above concerns: (1) development and validation of constitutive relationships (Chapter 2); (2) development of a discrete fracture network (DFN) model for investigating coupled processes in the EDZ (Chapter 3); (3) development and validation of a THM model for the Full-Scale Emplacement Experiment (FE) tests at Mont Terri, Switzerland (Chapter 4); and (4) investigation of high-temperature impacts, including both laboratory and modeling studies (Chapter 5). The overall objectives of these activities are to provide an improved understanding of and advanced modeling capabilities for EDZ evolution in clay repositories and the associated coupled processes, and to develop a technical basis for the maximum allowable temperature for a clay repository.

54 ENVIRONMENTAL SCIENCES↗

Verifying and Validating Simulation Models

This presentation is a high-level discussion of the Verification and Validation (V&V) of computational models. Definitions of V&V are given to emphasize that “validation” is never performed in a vacuum; it accounts, instead, for the current state-of-knowledge in the discipline considered. In particular comparisons between physical measurements and numerical predictions should account for their respective sources of uncertainty. The differences between error (bias), aleatoric uncertainty (randomness) and epistemic uncertainty (ignorance, lack-of- knowledge) are briefly discussed. Four types of uncertainty in physics and engineering are discussed: 1) experimental variability, 2) variability and randomness, 3) numerical uncertainty and 4) model-form uncertainty. Statistical sampling methods are available to propagate, and analyze, variability and randomness. Numerical uncertainty originates from the truncation error introduced by the discretization of partial differential equations in time and space. Model-form uncertainty is introduced by assumptions often formulated to render a complex problem more tractable and amenable to modeling and simulation. The discussion concludes with high-level guidance to assess the “credibility” of numerical simulations, which stems from the level of rigor with which these various sources of uncertainty are assessed and quantified.

Mathematics & Computing(97)↗

Casting Process Modeling and Validation

Computational simulations of gravity casting for metallic U-Pu-Zr nuclear fuel rods were performed with the free surface flow solver Flow3d. Two different casting apparatus designs were explored for studying the effects on the final cast rod profiles. Experimental data for the casting problem obtained from Idaho National Laboratory (INL) was validated with numerical simulations on one of the designs considered. The volume of the rods cast, length of the rods, and temperature profiles were predicted through the transient casting process and were found to match closely with experimental data. A sensitivity analysis with vent locations, pressure, mesh refinement, and effects of surface tension at the interface were explored. This work serves as a validation benchmark for the casting problem and provides guidelines when performing simulation exercises for modeling casting. This paper also provides the mathematical framework to enabling modeling of the casting process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Multi-component testing using HZ-PAN and AgZ-PAN Sorbents for OSPREY Model validation

In efforts to further develop the capability of the Off-gas SeParation and RecoverY (OSPREY) model, multi-component tests were completed using both HZ-PAN and AgZ-PAN sorbents. The primary purpose of this effort was to obtain multi-component xenon and krypton capacities for comparison to future OSPREY predicted multi-component capacities using previously acquired Langmuir equilibrium parameters determined from single component isotherms. Experimental capacities were determined for each sorbent using two feed gas compositions of 1000 ppmv xenon and 150 ppmv krypton in either a helium or air balance. Test temperatures were consistently held at 220 K and the gas flowrate was 50 sccm. Capacities were calculated from breakthrough curves using TableCurve® 2D software by Jandel Scientific. The HZ-PAN sorbent was tested in the custom designed cryostat while the AgZ-PAN was tested in a newly installed cooling apparatus. Previous modeling validation efforts indicated the OSPREY model can be used to effectively predict single component xenon and krypton capacities for both engineered form sorbents. Results indicated good agreement with the experimental and predicted capacity values for both krypton and xenon on the sorbents. Overall, the model predicted slightly elevated capacities for both gases which can be partially attributed to the estimation of the parameters and the uncertainty associated with the experimental measurements. Currently, OSPREY is configured such that one species adsorbs and one does not (i.e. krypton in helium). Modification of OSPREY code is currently being performed to incorporate multiple adsorbing species and non-ideal interactions of gas phase species with the sorbent and adsorbed phases. Once these modifications are complete, the sorbent capacities determined in the present work will be used to validate OSPREY multicomponent adsorption predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Development of a Two-fluid Drag Law for Clustered Particles using Direct Numerical Simulation and Validation through Experiments

This study presents a new drag model, based on the cohesive inter-particle forces, implemented in the MFIX code. This new drag model combines an existing standard model in MFIX with a particle-based drag model based on a switching principle. Switches between the models in the computational domain occur where strong particle-to-particle cohesion potential is detected. Three versions of the new model were obtained by using one standard drag model in each version. Later, performance of each version was compared against available experimental data for a fluidized bed, published in the literature and used extensively by other researchers for validation purposes. In our analysis of the results, we first observed that standard models used in this research were incapable of producing closely matching results. Then, we showed for a simple case that a threshold is needed to be set on the solid volume fraction. This modification was applied to avoid non-physical results for the clustering predictions, when governing equation of the solid granular temperate was solved. Later, we used our hybrid technique and observed the capability of our approach in improving the numerical results significantly; however, improvement of the results depended on the threshold of the cohesive index, which was used in the switching procedure. Our results showed that small values of the threshold for the cohesive index could result in significant reduction of the computational error for all the versions of the proposed drag model. In addition, we redesigned an existing circulating fluidized bed (CFB) test facility in order to create validation cases for clustering regime of Geldart A type particles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Computational Design and Experimental Validation of New Thermal Barrier Systems

The focus of this project is on the development of a reliable and efficient ab initio based computational high temperature material design method which can be used to assist the Thermal Barrier Coating (TBC) bond-coat and top-coat design. Experimental evaluations on the new TBCs are conducted to confirm the new TBCs’ properties. Southern University is the subcontractor on this project with a focus on the computational simulation method development. We have performed ab initio density functional theory (DFT) method and molecular dynamics simulation on screening the top coats and bond coats for gas turbine thermal barrier coating design and validation applications. For experimental validations, our focus is on the hot corrosion performance of different TBC systems. For example, for one of the top coatings studied, we examined the thermal stability of TaZr 2.75 O 8 and confirmed it’s hot corrosion performance.

36 MATERIALS SCIENCE↗

Validation of cleaning method for various parts fabricated at a Beryllium facility

This study evaluated and documented a cleaning process that is used to clean parts that are fabricated at a beryllium facility at Los Alamos National Laboratory. The purpose of evaluating this cleaning process was to validate and approve it for future use to assure beryllium surface levels are below the Department of Energy’s release limits without the need to sample all parts leaving the facility. Inhaling or coming in contact with beryllium can cause an immune response that can result in an individual becoming sensitized to beryllium, which can then lead to a disease of the lungs called chronic beryllium disease, and possibly lung cancer. Thirty aluminum and thirty stainless steel parts were fabricated on a lathe in the beryllium facility, as well as thirty-two beryllium parts, for the purpose of testing a parts cleaning method that involved the use of ultrasonic cleaners. A cleaning method was created, documented, validated, and approved, to reduce beryllium contamination.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Validation Testing for Automated Solubility Measurement Equipment Final Report

Laboratory tests have been completed to test the validity of automated solubility measurement equipment using sodium nitrate and sodium chloride solutions (see test plan WRPS-1404441, “Validation Testing for Automated Solubility Measurement Equipment”). The sodium nitrate solution results were within 2-3% of the reference values, so the experiment is considered successful using the turbidity meter. The sodium chloride test was done by sight, as the turbidity meter did not work well using sodium chloride. For example, the “clear” turbidity reading was 53 FNU at 80 °C, 107 FNU at 55 °C, and 151 FNU at 20 °C. The sodium chloride did not work because it is granular and large; as the solution was stirred, the granules stayed to the outside of the reactor and just above the stir bar level, having little impact on the turbidity meter readings as the meter was aimed at the center of the solution. Also, the turbidity meter depth has an impact. The salt tends to remain near the stir bar level. If the meter is deeper in the slurry, it will read higher turbidity, and if the meter is raised higher in the slurry, it will read lower turbidity (possibly near zero) because it reads the “clear” part of the slurry. The sodium chloride solution results, as measured by sight rather than by turbidity instrument readings, were within 5-6% of the reference values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SPR Hydrostatic Column Model Verification and Validation.

A Hydrostatic Column Model (HCM) was developed to help differentiate between normal "tight" well behavior and small-leak behavior under nitrogen for testing the pressure integrity of crude oil storage wells at the U.S. Strategic Petroleum Reserve. This effort was motivated by steady, yet distinct, pressure behavior of a series of Big Hill caverns that have been placed under nitrogen for extended period of time. This report describes the HCM model, its functional requirements, the model structure and the verification and validation process. Different modes of operation are also described, which illustrate how the software can be used to model extended nitrogen monitoring and Mechanical Integrity Tests by predicting wellhead pressures along with nitrogen interface movements. Model verification has shown that the program runs correctly and it is implemented as intended. The cavern BH101 long term nitrogen test was used to validate the model which showed very good agreement with measured data. This supports the claim that the model is, in fact, capturing the relevant physical phenomena and can be used to make accurate predictions of both wellhead pressure and interface movements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗