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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.

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At least 73 records · Page 4

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NASA NTRS2007

Large Solar-Rejection Filter

NASA utilized Image Intensified Video Cameras for ATV data acquisition from a jet flying at 12.8 km. Afterwards the video was digitized and then analyzed with a modified commercial software package, Image Systems Trackeye. Astrometric results were limited by saturation, plate scale, and imposed linear plate solution based on field reference stars. Time-dependent fragment angular trajectories, velocities, accelerations, and luminosities were derived in each video segment. It was evident that individual fragments behave differently. Photometric accuracy was insufficient to confidently assess correlations between luminosity and fragment spatial behavior (velocity, deceleration). Use of high resolution digital video cameras in future should remedy this shortcoming. A scenically accurate description of matter interpreted as a substance made up of corpuscular constituents was established during the course of the 19th century. In this description, atoms--the building blocks of the matter--form molecules. The properties of the molecules were described by chemistry or thermodynamics depending on what characteristics of the matter were investigated. In both theories, the molecules can dissociate to atoms when the kinetic energies of the atoms exceed the strength of the chemical bonds. The number of atoms is always preserved in a closed system. This is not true, however, when the matter takes up much higher energies at relativistic scales. New particles can be produced at the expense of the kinetic energy. The number of particles is no longer preserved. There are other conserved quantities, however, these quantities, the charge, baryon number, lepton number, are associated with particles that are considered elementary today. The properties and behavior of these elementary particles is the subject of Particle Physics or High Energy Physics. Practice Page: A crewmember touching a positively charged surface was thought to be galvanically isolated from the vehicle ground analogous to a bird on a high voltage power wire. Recent analysis confirms that positive floating potentials, ionospheric currents to the EVA suit, can be hazardous. The analysis is wrong in that the ionospheric plasma itself can close the circuit. Parametric analysis of very low voltage exposures (2 to 15 volts) could cause pain and/or involuntary muscle tetani or spinal cord shock. NASA worked with the Naval Health Research Center Detachment Directed Energy Bioeffects Laboratory to examine the affects electrical hazards could have on extravehicular activity using two models. The results of the two computational models were combined to predict areas of the body in which neurons of different diameters would be excited. They predicted that physiologically active current could be conducted across the crew member causing catastrophic hazards. Future work to analyze additional current paths was proposed. The FUSE spectrum of BB Dor, observed in a high state, is modeled with an accretion disk with a very low inclination (possibly lower than 10 degrees). Assuming an average WD mass of 0.8 solar mass leads to a distance of the order of approximately 650pc, consistent with the extremely low galactic reddening in its direction, and a mass accretion rate of 10 (exp -9) solar mass a year. The spectrum presents some broad and deep silicon and sulfur absorption lines, indicating that these elements are over-abundant: silicon is 3 times solar, and sulfur is 20 times solar. The FUSE spectrum of BB Dor, observed in a high state, is modeled with an accretion disk with a very low inclination (possibly lower than 10 degrees). Assuming an average WD mass of 0.8 solar mass leads to a distance of the order of approximately 650pc, consistent with the extremely low galactic reddening in its direction, and a mass accretion rate of 10 (exp -9) solar mass a year. The spectrum presents some broad and deep silicon and sulfur absorption lines, indicating that these elements are over-abundant: silicon is times solar, and sulfur is 20 times solar. From its previously observed optical emission lines, P831-57 (WD 0334 6400 or Ret 1 in A Catalog and Atlas of Cataclysmic Variables: Living Edition) has been suspected to contain an accretion disk associated with a companion star in orbit around a subdwarf star with a temperature T is greater than 21,000K. P831-57 has therefore been classified as a nova-like. However, our present observations show it to be a DA + dMe binary. The analysis of its Far Ultraviolet Spectroscopic Explorer (FUSE) spectrum (continuum and lines) reveal an average mass white dwarf (Log(g) approximately equals 7.8 plus or minus 0.1)with a temperature T approximately equals 37,000 plus or minus 500K, an extremely low projected rotational velocity, and a distance of about 115 plus or minus 5pc. The photosphere contains C, N, Si, and S (at about 1% of solar abundances). The dMe star is seen as a flux excess in near-infrared photometry and appears to show occasional flaring of about one magnitude as seen in the Harvard plates. There is no evidence of periodic variability in the spectroscopic or photometric data. We find no evidence of a an accretion disk, instead we find evidence of wind accretion as the stellar carbon abundance (N(C)/N(H) = 2.5 x 10 (exp -6) is about ten times larger than predicted by radiative levitation for such a gravity and temperature. The power needs and solutions for the space exploration and lunar mobility program are discussed. Long term missions in space and on the lunar surface require high energy batteries. Rechargeable batteries for mobility systems and portable utility pallet are needed for successful exploration missions. Nanomaterial usage increases the energy density of the cells apart from increasing the power density. The symptoms and threats from acute mountain sickness (AMS) are discussed. The underlying assumptions concerning spacecraft atmosphere mean there is a potential risk to astronauts. The baseline worst case scenario is about 25% based on direct ascent to 8.0 psia with 32% O2. The practical mitigation approach is a staged depressurization scheme. Due to the uncertainty about potential AMS risk the flight surgeon should prepare, focused research should proceed, and current analytical efforts should continue. This slide presentation reviews the threat astronauts face from acute mountain sickness (AMS). The incidence of AMS is highly variable and the risk to astronauts is unclear. However, the potential risk is higher than current EAAs suggest and precautions should be implemented. Due to the uncertain nature of the risk flight surgeons should prepare, and focused research should proceed. Quasiparticle tunneling can be understood at low temperatures using a non-equilibrium kinetic theory. Low-temperature tunneling can be suppressed using existing techniques: island-lead gap engineering, lead cooling with SIN junctions, and quasiparticle traps. The slide presentation reviews the science goals and mission planning for the proposed Jupiter System Observer (JSO). The spacecraft would accommodate 9 instruments making the instrument payload more capable than any previously flown to Jupiter. The would-be mission includes study programs for Jupiter's satellites, interior, magnetosphere and atmosphere. The power needs and solutions for the space exploration and lunar mobility program are discussed. Long term missions in space and on the lunar surface require high energy batteries. Rechargeable batteries for mobility systems and portable utility pallet are needed for successful exploration missions. Nanomaterial usage increases the energy density of the cells apart from increasing the power density. This slide presentation reviews the power requirements for the space exploration and the lunar surface mobility programs. It includes information about the specifications for high energy batteries and the power requirements for lunar rovers,

Roberts, William↗

DOE OSTI2025

Demand response of loads having thermal reserves

Systems and methods are described herein that improve grid performance by smoothing demand using thermal reserves. The smoothed demand can reduce peak loads as well as the ramp rate of demand that will otherwise require the use of inefficient, expensive generation sources. These improvements are tied to the selective switching on or off electrical loads that are coupled to thermal reserves, effectively using the thermal reserves as an energy storage mechanism. Historical data of past usage can be used to create load model and ensure that effects on customer comfort are minimized while still accomplishing the beneficial effects for the overall grid, which enables grid owners to both reduce their operational cost by avoiding expensive generation and improve system reliability by achieving more predictable power demand.

Ren, Wei↗

DOE OSTI2026

Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage

Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2022

Spectral function for 4 He using the Chebyshev expansion in coupled-cluster theory

Here, we compute spectral function for 4 He by combining coupled-cluster theory with an expansion of integral transforms into Chebyshev polynomials. Our method allows us to estimate the uncertainty of spectral reconstruction. The properties of the Chebyshev polynomials make the procedure numerically stable and considerably lower in memory usage than the typically employed Lanczos algorithm. We benchmark our predictions with other calculations in the literature and with electron-scattering data in the quasi-elastic peak. The spectral function formalism allows one to extend ab initio lepton-nucleus cross sections into the relativistic regime. This makes it a promising tool for modeling this process at higher-energy transfers. The results we present open the door for studies of heavier nuclei, important for the neutrino oscillation programs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗

DOE OSTI2025

Core-edge integrated predictive studies of ST40 and NSTX plasmas with the scrape-off layer box model

The ability to model the interplay between the core and edge of tokamak plasmas is crucial to designing both the plasma operating scenario of a fusion pilot plant and the design of the tokamak itself. Scrape-off-layer (SOL) models that are tailored to integrated scenario modeling need to have fast turn-around time and minimal computational burden to enable wide parameter-space coverage for design scoping. The SOL 0-D Box model is a reduced SOL model based on global power and particle balance that captures the essential physics of SOL transport with little computational cost. The usage of the 0-D Box model in core-edge coupled simulations has been demonstrated in both interpretive and predictive modes on a variety of devices. This paper presents a sensitivity study of the 0-D Box model to the input SOL heat-flux width for an ST40 plasma. This study demonstrates that accurate prediction of this width is crucial to predicting global performance parameters of a plasma scenario, such as energy confinement time and flux consumption. We also present an extension of the Box model to 1-D to allow for parallel variation of plasma parameters along the magnetic field lines. The 1-D Box model is then compared with SOLPS-ITER simulations of an NSTX plasma. Advantages and limitations of the Box model are discussed, and future directions are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Clustering-Based Predictive Analytics to Improve Scientific Data Discovery

Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.

Devarakonda, Ranjeet↗

NASA NTRS2011

Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

Saha, Sankalita↗

DOE OSTI2024

North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo1−x−yO2 cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments “(LCA)” have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry’s shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction.

25 ENERGY STORAGE↗

DOE OSTI2020

Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals

Efficient and high-capacity storage materials are indispensable for a hydrogen-based economy. In silico tools can accelerate the process of discovery of new adsorbent materials with optimal hydrogen adsorption enthalpies. Density functional theory is well-poised to become a very useful tool for enabling high-throughput screening of potential materials. In this work, we have identified density functional approximations that provide good performance for hydrogen binding applications following a two-pronged approach. First, we have compiled a data set (H2Bind275) that comprehensively represents the hydrogen binding problem capturing the chemical and mechanistic diversity in the binding sites encountered in hydrogen storage materials. We have also computed reference interaction energies for this data set using coupled-cluster theory. Second, we have assessed the performance of 55 density functional approximations for predicting H 2 interaction energies and have identified two hybrid density functionals (ωB97X-V and ωB97M-V), two double hybrid density functionals (DSD-PBEPBE-D3(BJ) and PBE0-DH), and one semilocal density functional (B97M-V) as the best performing ones. We have recommended the addition of empirical dispersion corrections to systematically underbinding density functionals such as revPBE, BLYP, and B3LYP for improvements in performance at negligible additional cost. We have also recommended the usage of the def2-TZVPP basis set as it represents a good compromise between accuracy and cost, limiting the finite basis set errors to less than 1 kJ/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

The objective of this study was to develop a repeatable and defensible methodology to analyze the energy savings for Home Energy Management Systems (HEMS) that meets the minimum requirements for certification under ENERGY STAR ® Smart Home Energy Management System (SHEMS) Version 1. Mandatory connected loads include a smart thermostat, two smart lights, and one smart power strip or smart outlet. Control strategies must include feedback to occupants through an in-home display, user programming, occupancy sensor-based controls, and responsiveness to utility signals such as demand response programs. Several occupant behavior patterns were selected to quantify the range of energy savings potential for a HEMS with this basic functionality. A literature review was conducted to establish realistic room-by-room occupancy levels and usage patterns for connected devices. A series of event-driven hourly profiles were created, followed by adjustments based on application of HEMS control strategies to thermostats, interior lighting, and plug load schedules. EnergyPlus modeling was performed using these hourly schedules in three locations (Boston, Houston, and Phoenix) to examine climate dependence of energy savings. Total site energy savings ranged from 4.3 to 27.1 MBtu/year (7%-35%), and utility bill savings ranged from $\$$123 to $\$$670/year (6%-29%). The highest predicted savings was realized by occupants that were not energy conscious prior to HEMS installation, but highly engaged with the HEMS controls once the system was installed. The smart thermostat accounted for most of the savings, followed by the smart power strip. Smart lighting did not save a significant amount of energy in our analysis, based on an assumption that efficient LEDs with no standby power would normally be installed anyway.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

DOE OSTI2021

Lawrence Berkley National Laboratory Building 59

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Interface Problem Formulation Improvements with Application to Nuclear Fuel Performance Analysis

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation Program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This report documents new developments and robustness improvements in mechanical and thermal (gap heat transfer) contact formulations. The improvements range from the migration of industrial level (“assessment”) nuclear fuel model setups to the usage of mortar formulations, the addition of frictional contact to one-dimensional layered representations of fuel and cladding components, and the addition of the Petrov-Galerkin approach to dual mortar, which improves performance on curved, relatively coarse meshes. In addition, the Lagrange-multiplier enforcement of mechanical mortar contact constraints has been extended to two additional types of enforcement: penalty and augmented Lagrange-Uzawa. We show that the latter approach yields the same interface results as dual mortar in the Multiphysics Object-Oriented Simulation Environment, with the advantage of not worsening the condition number of the system matrix—thereby enabling the use of some general implementations of iterative preconditioners, at the expense of additional system evaluations (i.e., augmentations).

42 ENGINEERING↗

DOE OSTI2025

Structural uncertainty assessment for low-Mach wall-resolved large-eddy simulations: Plane Channel and Periodic Hill Use Cases

A structural uncertainty assessment is provided for a set of wall-resolved large-eddy simulations (WRLES) using a turbulent channel flow (𝑅𝑒 𝜏 395) and a periodic hill in the turbulent regime (𝑅𝑒 𝑏 2800) validation suite. The standard one-equation subgrid scale turbulent kinetic energy model (𝑘 𝑠𝑔𝑠 ) using both a static and dynamic coefficient approach along with Wall Adapting Local Eddy Viscosity (WALE) are exercised. The 𝑘 𝑠𝑔𝑠 model activated in this study also allows for a correction that supports theoretical turbulent viscosity and total kinetic energy scaling (cubit and quadratic, respectively) in the near-wall regime. Channel and hill results showcase very good agreement between prediction and simulation for the WALE and the dynamic coefficient 𝑘 𝑠𝑔𝑠 model. The study also demonstrates that near-wall damping improves predictivity for the static coefficient 𝑘 𝑠𝑔𝑠 model, while proving an unnecessary additional contribution for the dynamic coefficient 𝑘 𝑠𝑔𝑠 model as near-wall scaling is naturally captured. Overall conclusions are that this suite of WRLES models are well suited for production usage in the Sierra-based low-Mach codes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Lawrence Berkley National Laboratory Building 59 (Raw)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗