Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Simulation model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Validating Simulated Models of Energy Consumption by a Battery Electric Motorcoach: A real-world deployment in a harsh climate.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2022

Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81°N. Excellent agreement (difference less than 1%) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45% and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73°N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER

Global Storm-Resolving Models (GSRMs) are becoming increasingly vital for advancing climate modeling and improving the prediction of extreme weather events. Houston, a coastal region frequently affected by deep convective storms, offers an ideal setting to evaluate the ability of GSRMs to simulate deep convection. This study assesses the performance of the Doubly Periodic Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere Model (DP-SCREAM) using observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model. The DP-SCREAM is demonstrated to be applicable to coastal regions, partially due to the forcing data sets already capturing the influence of breezes. DP-SCREAM also replicates biases persistent in the global version of SCREAM: the underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and the popcorn convection, characterized by small and disorganized convective cells generating the strongest precipitation. To investigate these issues, two sensitivity experiments were conducted: increasing the mixing length and scaling up the buoyancy flux within the Simplified Higher Order Closure scheme. Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence. Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells. In conclusion, an additional resolution sensitivity test at 0.5 km grid spacing demonstrated that a refined horizontal resolution alone is insufficient to resolve these biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗
DOE OSTI2022

Evaluation of coupled wind-wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys.

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long term measurements of hub-height wind speed in the marine environment. In this study, we performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) model, to see if simulated hub height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a standalone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 08 May 2015 and the cold frontal case of 25 Mar 2016, but not the nor-easter of 18 Jan 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 Mar 2016 but worsening them for 08 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the VA buoy, largely due to greater self-consistency between wind and wave fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. In conclusion, the utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Advancing research on compound weather and climate events via large ensemble model simulations

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

Introduction: Forest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA. Methods: We used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period. Results: We found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface. Discussion: Our results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing Neutrino Simulation Modeling with MARLEY: Insights from the NNSA-MSIIP Internship

Core-collapse supernovae are intense sources of tens-of-MeV neutrinos. However, there is no experimental data to validate the current event generator MARLEY (Model of Argon Reaction Low Energy Yields) that can model supernova neutrinos. To validate the model, I developed a new muon capture feature within the MARLEY simulation framework by coding key functions in C++, Python, and ROOT. I generated and analyzed one million simulated muon capture events and comparing the results to experimental data. To improve the model, I worked on optimizing the model’s parameters to improve its precision using statistical methods.

Wong, Baker [Fermilab]↗
DOE OSTI2022

Development of a simplified calibrated building simulation model of a supermarket for proposed ECMs and control strategies impact evaluation

Calibrated building energy simulation is an important pathway to more energy-efficient buildings, but the information requirements of some approaches to this problem are significant. This is particularly true for supermarkets and other so-called “big-box” retail stores. Another characteristic of supermarkets is the significant interaction between Heating Ventilating and Air Conditioning (HVAC) and refrigeration systems in these buildings. These buildings could contain a wide variety of systems and a degree of load diversity that makes calibrated modeling a challenge. This paper describes a simplified approach that uses OpenStudio and EnergyPlus to combine known building parameters with “typical” parameters, resulting in a simplified building that is amenable to calibration. This approach was applied to a big-box store located in Nashville, Tennessee, and a calibrated model was obtained that was used to study potential energy conservation measures. Further, the paper also explores the capabilities of whole-building energy modeling tools, such as EnergyPlus, for modeling the HVAC controls and sequences and their impact evaluation. Although some measures are precluded by the model simplicity, several measures were found to improve the efficiency of the model and demonstrate that the simplified modeling approach is effective. Practical Application: This paper introduces a hybrid approach of building energy model calibration using limited information available from the actual building in combination with characteristics of a “typical” building of the same type. This hybrid approach would also be applicable for other building types than discussed in this paper to calibrate the building energy model using limited information from the actual building.

42 ENGINEERING↗
DOE OSTI2022

Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations

Flash droughts (FD), rapidly emerging in a warming future, disrupt ecosystems, agriculture, and water security. Ecosystem water use efficiency (WUE), the ratio of gross primary production (GPP) to actual evapotranspiration (AET), balances carbon assimilation and water loss. FD rapidly disrupts this balance, making WUE critical for assessing plant stress and recovery. Here, this study investigates the dynamics of landscape-scale WUE, and the components of GPP and AET under FD utilizing both observed data from the Missouri Ozark AmeriFlux site (US-MOz) and version 2 of the U.S. Department of Energy’s Earth, Energy, Exascale System Model (E3SM) Land Model (ELMv2). Observations and simulations reveal GPP as dominant for WUE during earlier FD events (2005, 2007, 2012), shifting to AET in recent events (2014, 2018). This agreement indicates that the ELM can capture the shifting dynamics of GPP and AET in regulating WUE under FD conditions. However, the ELM systematically underestimates both GPP and AET and does so in a manner that does not preserve their ratio. As a result, WUE is also underestimated, suggesting that GPP is more strongly underestimated than AET. Furthermore, the ELM also underestimates the speed of GPP recovery, producing an artificially prolonged GPP recovery time following FD events. Observed environmental drivers such as vapor pressure deficit (VPD), soil moisture (SM), and predawn leaf water potential (PLWP) effectively predict WUE, but ELM primarily highlights SM, underestimating VPD’s role. This study demonstrates that relying solely on soil moisture fails to capture the rapid hydraulic recovery observed in PLWP, underscoring the necessity of integrating plant hydraulics into land surface models to improve flash drought predictability.

Evapotranspiration↗
DOE OSTI2022

Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Enabling kilometer-scale E3SM land model simulation over North America: A new integrated framework solution

This study introduces a novel framework designed to enhance the performance, scalability, and portability of the kilometer-scale E3SM Land Model (km-ELM) within the E3SM modeling infrastructure. By seamlessly integrating cutting-edge data tools, we address existing challenges such as slow performance, limited scalability, and difficulties in software integration in current data-driven ELM simulation over large geographic areas. Our innovative approach leverages the KiloCraft data toolkit to generate unified inputs for simulations ranging from a single-cite case, to a 72,083-cell regional case to a continental configuration encompassing 21.6 million land grid cells at a 1 km × 1 km resolution. We conduct extensive strong- and weak-scaling experiments on three state-of-the-art supercomputers, utilizing up to 100,800 CPU cores across 2400 compute nodes to evaluate end-to-end metrics including wall-clock time, simulation-years-per-day (SYPD), initialization costs, and I/O throughput. Our results reveal the land (LND) component’s efficient scaling, demonstrating near-ideal weak scaling and strong-scaling parallel efficiencies reaching up to 87% at 50,400 cores. We confirm portability and reproducibility through bitwise-equivalent outputs across different machines using identical inputs over supported machines. Notably, at extreme scales, we identify I/O as a critical bottleneck and that leads to effective solution with the SCORPIO/ADIOS stack. Collectively, these findings validate the deployment of km-ELM at a continental scale with high parallel efficiency and provide essential guidance on configuration, decomposition, and I/O settings for optimized kilometer-scale land simulations in E3SM. This work emphasizes the innovative design and practical solutions that enhance the operational capabilities of km-ELM, focusing on software performance and scalability while leaving detailed scientific evaluations of simulated land processes for future investigations.

E3SM land model (ELM), km-ELM, scalability, perfor↗
DOE OSTI2022

Kiwi-TNT Simulation Model [Slides]

The Kiwi-TNT Test (Toonerville Trolley) was performed on January 12, 1965. LANL has the objective: "LANL will develop a defensible methodology for establishing the maximum number of fissions for postulated inadvertent criticality events. The methodology may include several methods, such as hand calculations, sophisticated modeling, and the use of historical data.” The historic test, Kiwi TNT, posed a starting point for predictive simulations to determine the number of fissions and excursion understanding. This presentation gives results of that effort.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Assessing the Impact of Variable Air Volume Box Damper Stuck Faults Using a Building Automation System and Building Energy Simulation Model

The study examines the impact of variable air volume (VAV) damper stuck faults on the system operation, building indoor conditions, and reheating energy consumption. This study includes both experimental and simulation studies for five test scenarios, including a fault-free scenario. We implemented VAV damper stuck fault through the building automation system (BAS). Results show that a damper stuck in a high opening position (60% damper opening) results in supplying an excessive amount of cold air from the rooftop unit (RTU) to the conditioned zone, increasing reheating energy consumption. The results of this research can serve as a foundational resource for developing fault detection algorithms.

Jung, Sungkyun↗
DOE OSTI2025

Quench process modeling & simulation in the heat-treatment of critical aerospace components

To manufacture light-weight, advanced metal alloy components for gas turbine engines, quench heat-treatment processes are typically used. By quenching the component from elevated temperatures, the alloy sometimes undergoes a solid-state phase transformation which produces special microstructures with the required, enhanced mechanical properties. However, the quenching can also lead to cracks forming in the component. Addressing the quench cracking problems adds a significant burden to the cost, schedule, and energy demand of manufacture. Currently, optimizing the quench process to mitigate or avoid the cracking is performed largely by trial-and-error, relying heavily on costly experimental (thermocouple)trials to understand the local thermal gradients which cause the cracks to form. In this first part (Phase 1) of the work, high-performance computing is employed to establish the ability of modern CFD (computational fluid dynamics) to alleviate or wholly replace the experimental quenching trials by virtual testing. A Baseline CFD model is defined and its accuracy established to be comparable to(and which usually exceeds) the accuracy of existing HTC (heat-transfer coefficient) based simulation methods of quenching. As a first-principles based approach, “calibration” of the Baseline CFD model is independent of the quench process itself, but instead relies on the accuracy of the underlying (modeled),generic two-phase fluid processes which cannot be currently resolved by CFD for large, industrial-scale cases. A novel, high-fidelity DNS capability has been developed and verified to examine and further improve upon the mean-field closure submodels on which the Baseline CFD approach is based.1

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Quench process modeling & simulation in the heat-treatment of critical aerospace components

To manufacture light-weight, advanced metal alloy components for gas turbine engines, quench heat-treatment processes are typically used. By quenching the component from elevated tempera-tures, the alloy sometimes undergoes a solid-state phase transformation which produces special microstructures with the required, enhanced mechanical properties. However, the quenching can also lead to cracks forming in the component. Addressing the quench cracking problems adds a significant burden to the cost, schedule, and energy demand of manufacture. Currently, optimizing the quench process to mitigate or avoid the cracking is performed largely by trial-and-error, relying heavily on costly experimental (thermocouple) trials to understand the local thermal gradients which cause the cracks to form. In this first part (Phase 1) of the work, high-performance computing is employed to establish the ability of modern CFD (computational fluid dynamics) to alleviate or wholly replace the experimental quenching trials by virtual testing. A baseline CFD model is defined and its accuracy established to be comparable to (and which usually exceeds) the accuracy of existing HTC (heat-transfer correlation) based simulation methods of quenching. As a first-principles based approach, “calibration” of the Baseline CFD model is independent of the quench process itself, but instead relies on the accuracy of the underlying (modeled), generic two-phase fluid processes which cannot be currently resolved by CFD for large, industrial-scale cases. A novel, high-fidelity DNS capability has been developed and verified to examine and further improve upon the mean-field closure submodels on which the Baseline CFD approach is based.

36 MATERIALS SCIENCE↗