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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

What We Learned From Analyzing 18 Million Rows of Commercial Buildings’ HVAC Fault Data

To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.

Crowe, Eliot↗
DOE OSTI2026

Experimental and data-driven characterization of window-induced air leakage in residential buildings

Windows contributes up to 40% of envelope heat losses and around 9% of total building energy consumption due to air leakage. In the U.S., 48 million homes still use single-pane windows. Although the U.S. has an estimated 1.4 billion windows in its building stock and about 24 million windows are installed annually, only around 29 million individual window replacements (∼2%) occur each year. To address this gap, this study generates empirical evidence by (1) evaluating the contribution of windows to whole-building air leakage in 20 residential buildings using blower door tests before and after window replacement and (2) assessing whether building and window characteristics influence the measured change. Most simulation studies assume that replacing windows not only lowers the U-factor but also reduces air leakage by 10–20%. However, this assumption lacks empirical validation, highlighting the need for experimental analysis of air leakage specifically associated with windows. Using blower door tests in accordance with ASTM E779–19, the results indicated an average reduction in air infiltration of 6.1% within the range of 0.5–19.30% across all buildings and no significant correlations were found between air leakage improvements and any building/window characteristics. This research aims to help homeowners, and energy modelers to provide empirical data on importance of upgrading to more energy-efficient windows, supporting energy-efficient building standards.

Air leakage↗
DOE OSTI2022

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work here focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. In conclusion, our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Direct measurement of thermal Knudsen forces in rarefied gas environments

At micro- and nanoscales, momentum transfer between surfaces is influenced by various physical mechanisms, including quantum fluctuations, electromagnetic interactions, electric charges, and the dynamics of (rarefied) gases. Under non-isothermal conditions, rarefied gases give rise to thermal Knudsen forces whose magnitudes strongly depend on the gas species and surface characteristics. Knudsen forces are particularly relevant in nanotechnology, optical manipulation, and aerospace systems, where gas rarefaction occurs due to highly confined geometries, sub-micrometer length scales, and reduced particle densities. Despite their significance, predictive modeling of Knudsen forces is limited by a lack of comprehensive experimental data across diverse materials and surface morphologies. Here, in this work, we present a highly sensitive and adaptable measurement platform capable of directly quantifying Knudsen forces using a suspended, interchangeable micro-cantilever within controlled rarefied helium and nitrogen environments. The system integrates optical fiber interferometry to precisely capture out-of-plane displacements at sub-micrometer resolution, driven by Knudsen forces. From the empirical data, we derive a robust correlation linking the magnitudes of Knudsen forces to energy accommodation coefficients, offering deeper insights into the underlying gas–surface interaction mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Searching for fat tails in CRISPR-Cas systems: Data analysis and mathematical modeling

Understanding CRISPR-Cas systems—the adaptive defence mechanism that about half of bacterial species and most of archaea use to neutralise viral attacks—is important for explaining the biodiversity observed in the microbial world as well as for editing animal and plant genomes effectively. The CRISPR-Cas system learns from previous viral infections and integrates small pieces from phage genomes called spacers into the microbial genome. The resulting library of spacers collected in CRISPR arrays is then compared with the DNA of potential invaders. One of the most intriguing and least well understood questions about CRISPR-Cas systems is the distribution of spacers across the microbial population. Here, using empirical data, we show that the global distribution of spacer numbers in CRISPR arrays across multiple biomes worldwide typically exhibits scale-invariant power law behaviour, and the standard deviation is greater than the sample mean. We develop a mathematical model of spacer loss and acquisition dynamics which fits observed data from almost four thousand metagenomes well. In analogy to the classical ‘rich-get-richer’ mechanism of power law emergence, the rate of spacer acquisition is proportional to the CRISPR array size, which allows a small proportion of CRISPRs within the population to possess a significant number of spacers. Our study provides an alternative explanation for the rarity of all-resistant super microbes in nature and why proliferation of phages can be highly successful despite the effectiveness of CRISPR-Cas systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Accelerating the rate of discovery: toward high-repetition-rate HED science

As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 H z ) come online around the world, the high energy density (HED) science they enable will experience a radical paradigm shift. The >10 3 increase in shot rate over today's shot-per-hour drivers translates into dramatically faster data acquisition, more experiments, and the ability to exploit machine learning, and thus the potential to significantly accelerate the advancement of HED science. A wide range of HED experiments, from opacity investigations to secondary source generation to plasma nuclear physics, will benefit from the increased statistics, precision, and exploration of phase space. Besides increasing the rate at which scientific experiments can be performed, HRR also allows for the rapid delivery of optimal experiments supported by simulations and modeling augmented by close coupling to empirical data. To fully realize such an HRR framework, numerous subsystems must be developed and brought together, including feedback laser control loops, high-throughput targetry and diagnostics, cognitive simulation, enhanced HED codes, and advanced data analytics. This paper describes the vision for an integrated HRR laser experimental HED system and outlines some of the major considerations and challenges for realizing it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

State-Space Model to Estimate Salmon Escapement Using Multiple Data Sources

Abstract Accurate estimates of salmonids passing Lower Granite Dam on the Snake River, by species and origin, are a critical input to assessing the status and trends of various populations as well as successful management of fisheries in the Snake River basin. Here, we describe a state-space model that estimates such escapement past a dam by using window counts, PIT tag observations, and data from an adult fish trap, accounting for issues such as nighttime passage, fallback and reascension, potential observation error at the window, and uncertainty in the adult trap rate. We tested the approach using a simulation framework that mimicked several levels of observation error, differences between nighttime passage and reascension rates, and the possibility of the adult trap being closed for some period of time. Our results demonstrate that the model produced unbiased estimates across all tested scenarios. We also applied this model to empirical data from Lower Granite Dam to produce estimates of wild, clipped hatchery, and unclipped hatchery spring/summer-run Chinook Salmon Oncorhynchus tshawytscha and steelhead O. mykiss from spawn years 2010–2019.

See, Kevin E. (ORCID:0000000297626442)↗
DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Metabolic tradeoffs and heterogeneity in microbial responses to temperature determine the fate of litter carbon in simulations of a warmer world

Abstract. Climate change has the potential to destabilize the Earth's massive terrestrial carbon (C) stocks, but the degree to which models project this destabilization to occur depends on the kinds and complexities of microbial processes they simulate. Of particular note is carbon use efficiency (CUE), which determines the fraction of C processed by microbes that is anabolized into microbial biomass rather than lost to the atmosphere and soil as carbon dioxide and extracellular products. The temperature sensitivity of CUE is often modeled as an intrinsically fixed (homogeneous) property of the community, which contrasts with empirical data and has unknown impacts on projected changes to the soil C cycle under global warming. We used the Decomposition Model of Enzymatic Traits (DEMENT) – which simulates taxon-level litter decomposition dynamics – to explore the effects of introducing organism-level heterogeneity into the CUE response to temperature for decomposition of leaf litter under 5 ∘C of warming. We found that allowing the CUE temperature response to differ between taxa facilitated increased loss of litter C, unless fungal taxa were specifically restricted to decreasing CUE with temperature. Litter C loss was exacerbated by variable and elevated CUE at higher temperature, which effectively lowered costs for extracellular enzyme production. Together these results implicate a role for diversity of taxon-level CUE responses in driving the fate of litter C in a warmer world within DEMENT, which should be explored within the framework of additional model structures and validated with empirical studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Certifying the Performance of Fixative Technologies under Open Air Demolition Activities for D and D - 20096

Department of Energy (DOE) facilities undergoing deactivation and decommissioning (D and D) activities struggle with safety concerns of residual radioactive contamination leftover after gross decontamination efforts have concluded. The positive effects of implementing fixative technologies for decommissioning and maintenance is known across the DOE complex. However, there is no standardized metric in place that quantifies the performance of these technologies such as incombustible fixative platforms that immobilize the contamination in a solid polymer material under normal operating conditions or when exposed to stressors. The primary focus of this research is to provide the empirical data necessary to properly characterize the effects of implementing fixative technologies on mitigating the release of contamination. In addition, this effort has been used to identify and establish uniform testing protocols to quantify operational parameters of fixating platforms and properly credit such systems under a variety of operational conditions and stressors that can arise during open air demolition efforts (e.g. impact, water, thermal, etc.). The established protocols will be provided to the ASTM International E10.03 Subcommittee on Radiological Protection for Decontamination and Decommissioning of Nuclear Facilities and Components for the potential of formal standardization. Attributes to be addressed during testing are material compromise from impact stress, thermal stress for incombustible fixative materials, and immobilization factors (how well an immobilizing platform is capable of retaining fixated material during thermal and impact stressors). In developing these test methods, a greater understanding of the material's behavior under anticipated and unanticipated events, such as decommissioning activities and contingency events as outlined in the Basis of Interim Operations (BIO), can be successfully characterized. There are several key components of the experimental methodology that is essential in empirically certifying fixative technologies. A surrogate contaminant with a unique signature has been utilized with the implementation of a uniform contamination process of test coupons that is quantifiable and replicable for direct fixative comparisons. A modular test chamber has also been used to apply the various stressors of interest. Collection of released contamination that includes airborne and resettled contaminant particles and analyzed using mass spectrometry. This research directly supports DOE complex-wide concerns in terms of final disposition of nuclear facilities and the considerations made during their time between ceasing facility operation and final disposition. The results of this effort aims to provide a standardized road map and logical decision flow to better facilitate fixative technologies into D and D activities required to achieve the desired facility end state. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Tree Diversity, Site Index, and Carbon Storage Decrease With Aridity in Douglas-Fir Forests in Western Canada

Forests are important for biodiversity, timber production and carbon accumulation, but these ecosystem services may be impacted by climate change. Field data collected from individual forest types occurring across a climatic gradient can contribute to forecasting these consequences. We examined how changes in temperature, precipitation and aridity affect ecosystem services in 23 mature Douglas-fir (Pseudotsuga menziesii) forests in nine climatic regions across a 900 km gradient in British Columbia, Canada. Using Canadian National Forest Inventory methodology, we assessed richness and diversity of plant functional groups, site index, and above- and below-ground carbon stocks. As aridity increased, ecosystem-level tree species richness declined on average from four to one species, Douglas-fir site index declined from 30 to 15 m, and ecosystem carbon storage decreased from 565 to 222 Mg ha –1 . Tree species richness was positively and herb species richness negatively correlated with carbon storage. Carbon storage by ecosystem compartment was largest in aboveground live tree biomass, declining in the following order: mineral soils > coarse woody debris and dead standing trees > forest floor > small and fine woody debris > understory plants. Mineral soil carbon at depths of 0-15 cm, 15-35 cm, and 35-55 cm increased with increasing mean annual precipitation and decreasing aridity. Our results indicate that as aridity increases and precipitation decreases, tree species richness, site index and carbon storage in existing Douglas-fir forests declines. However, assisted or natural migration of Douglas-fir into more humid regions could be associated with more diverse, productive, carbon-rich forests. This study informs carbon stock vulnerability and provides empirical data essential for carbon stock forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Root anatomy helps to reconcile observed root trait syndromes in tropical tree species

Premise Studying the organization of functional traits in plant leaves and stems has revealed notable patterns linking function and form; however, evidence of similarly robust organization in root tissues remains controversial. We posit that anatomical traits in roots can provide insight on the overall organization of the root system. We hypothesized that size variation in the tissue outside the stele is related in a nonlinear fashion with functional traits associated with direct resource uptake, including a negative relationship with root architectural traits, and that similar relationships detected in tropical areas also hold true in other biomes. Methods We addressed our hypotheses using empirical data from 24 tropical tree species in French Guiana, including anatomical measurements in first order roots and functional trait description for the entire fine root system. In addition, we compiled a global meta‐analysis of root traits for 500+ forest species across tropical, subtropical, and temperate forests. Results Our results supported the expected nonlinear relationships between cortical size and morphological traits and a negative linear trend with architectural traits. We confirmed a global negative relationship among specific root length (SRL), diameter, and tissue density, suggesting similar anatomical constraints in root systems across woody plants. However, the importance of factors varies across biomes, possibly related to the unequal phylogenetic representation across latitudes. Conclusions Our findings imply that the rhizocentric hypothesis can be a valuable approach to understand fine root trait syndromes and the evolution of absorptive roots in vascular plants.

Valverde‐Barrantes, Oscar J.↗
DOE OSTI2024

Quantum optimization algorithms: Energetic implications

Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. Here in this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2x to 4x more energy efficient than their classical counterparts; why feedback-based quantum optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥2 0 x. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Abiotic and biotic factors jointly influence the contact and environmental transmission of a generalist pathogen

The joint influence of abiotic and biotic factors is important for understanding the transmission of generalist pathogens. Abiotic factors such as temperature can directly influence pathogen persistence in the environment and will also affect biotic factors, such as host community composition and abundance. At intermediate spatial scales, the effects of temperature, community composition, and host abundance are expected to contribute to generalist pathogen transmission. We use a simple transmission model to explain and predict how host community composition, host abundance, and environmental pathogen persistence times can independently and jointly influence transmission. Our transmission model clarifies how abiotic and biotic factors can synergistically support the transmission of a pathogen. The empirical data show that high community competence, high abundance, and low temperatures correlate with high levels of transmission of ranavirus in larval amphibian communities. Discrete wetlands inhabited by larval amphibians in the presence of ranavirus provide a compelling case study comprising distinct host communities at a spatial scale anticipated to demonstrate abiotic and biotic influence on transmission. We use these host communities to observe phenomena demonstrated in our theoretical model. These findings emphasize the importance of considering both abiotic and biotic factors, and concomitant direct and indirect mechanisms, in the study of pathogen transmission and should extend to other generalist pathogens with the capacity for environmental transmission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Engineering evaluation of the EQSIM simulated ground-motion database: The San Francisco Bay Area region

Ground-motion simulations for infrequent earthquake scenarios are gaining increasing interest in the engineering community for their potential to inform performance-based structural design and assessment, particularly in regions where major earthquakes are expected, but recordings from consistent historical events are not available. However, the absence of empirical data makes the evaluation of such scenarios and the interpretation of the simulation results very challenging. In this context, this paper presents the evaluation of the first EQSIM (v.1.1.0) ground-motion simulations generated for the San Francisco Bay Area (SFBA) region. The current database, which is at the first stages of development, includes eight realizations of a Hayward Fault M w 7 event. The evaluation of the simulated motions is first conducted on the average ground-motion amplitudes through comparison against the NGA-W2 GMPEs and a selected population of real records. A methodology for selecting a population of real records consistent with the simulated event is proposed. The objective of the proposed evaluation is twofold: (1) build confidence in the realistic character of the simulated motions for their use in engineering applications and (2) offer a critical physics-based interpretation of the simulation results that can help improve key features of the simulation models. To further investigate the implications of using the simulated ground motions for site-specific structural assessments, the ASCE7-16 approach is employed for the analysis of two hazard-consistent near-field sites in the SFBA. Results are discussed. Furthermore, this study offers a critical review of the EQSIM (v.1.1.0) SFBA ground-motion simulations and suggestions for improving the earthquake rupture models.

58 GEOSCIENCES↗
DOE OSTI2025

On Master-Length Scale Formulations for Stable Conditions in Turbulence Closure Models

Three formulations of the turbulence-length scales used in numerical modelling of atmospheric flows are compared. The comparison is made using the Mellor–Yamada–Nakanishi–Niino turbulence closure model within the stable boundary layer local similarity framework. With an appropriate choice of model constants, the model predictions are barely discernible and compare well with the empirical data obtained from the SHEBA campaign.

54 ENVIRONMENTAL SCIENCES↗