Search NASA⌕ Search

SEARCH · Search NASA

Results for “Mann 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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗
DOE OSTI2021

On turbulence models and lidar measurements for wind turbine control

Abstract. To provide comprehensive information that will assist in making decisions regarding the adoption of lidar-assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor-effective wind speed and lidar measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of lidar systems are examined to analyze the lidar measurement coherence based on commercially available lidar scan patterns. Finally, numerical simulations have been performed to compare the lidar measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the lidar measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the potential value creation of LAC based on simulations during the wind turbine design phase, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered uncertainties while evaluating the benefits of LAC.

17 WIND ENERGY↗
DOE OSTI2019

FAST.Farm Response to Varying Wind Inflow and Discretization

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

17 WIND ENERGY↗
DOE OSTI2019

FAST.Farm Response to Varying Wind Inflow Techniques: Preprint

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

17 WIND ENERGY↗
DOE OSTI2021

Effects of upstream rotor tilt on a downstream floating wind turbine

This work quantifies the relationship between the design tilt of an upstream rotor and the structural response of a spar floating offshore wind turbine located in its wake. Three wind speed scenarios are considered: below, at, and above rated operation. The inflow is generated with the Mann model and the wake and loads are simulated with FAST.Farm. As the upstream rotor tilt goes from 0° to 6°, we find that the mean wake is displaced upward by more than 0.2 rotor diameters (D) by a downstream distance of 7 D. The vertical velocities increase by up to 35 cm/s in the center of the wake. As a result, the downstream rotor is partially waked and experiences a rotated velocity vector. With a higher upstream rotor tilt, the velocities and moments on the downstream turbine increase their mean axial value and their lateral and vertical standard deviation. These changes affect the blade and tower loading and the floater motion primarily in the out-of-plane direction: the damage-equivalent loads for the tower pitch moment and blade-root moment increase by up to 10% because of higher variability at the first mode for the tower and at one blade passing frequency for the blade root. Lesser effects are observed for the roll moments and for floater sway and heave. When the joint effect of rotor tilt and platform motion is considered, the load response on the downstream system is amplified primarily for tower pitch and blade out-of-plane moment.

17 WIND ENERGY↗
DOE OSTI2025

Investigation of Main Bearing Fatigue Estimate Sensitivity to Synthetic Turbulence Models Using a Novel Drivetrain Model Implemented in OpenFAST

ABSTRACT A coupled medium‐fidelity drivetrain model is developed and implemented in OpenFAST for a 10‐MW land‐based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost and is useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is implemented for a monopile offshore wind turbine and used to investigate the sensitivity of main bearing basic rating life to different synthetic turbulence models. Large‐eddy simulations (LES) targeting stable, neutral, and unstable atmospheric conditions at below‐, near‐ and above‐rated wind speeds are used as a reference. The turbulence models recommended by the International Electrotechnical Commission, the Mann spectral tensor model, and the Kaimal spectral model with exponential coherence are fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb (short for Python Constrained Turbulence ), based on LES data, is applied in the aero‐hydro‐servo‐elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10% and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue by up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

17 WIND ENERGY↗
DOE OSTI2023

Sensitivity study of parameters important to Molten Salt Reactor Safety

This paper presents a molten salt reactor (MSR) design parameter sensitivity study using a nodal dynamic modelling methodology with explicitly modified point kinetics equation and Mann’s model for heat transfer. Six parameters that can impact MSR safety are evaluated. A MATLAB-Simulink model inspired by Thorcon’s 550MW th MSR is used for parameter evaluations. A safety envelope was formed to encapsulate power, maximum and minimum temperature, and temperature-induced reactivity feedback. The parameters are perturbed by ±30%. The parameters were then ranked by their subsequent impact on the considered safety envelope, which ranks acceptable parameter uncertainty. The model is openly available on GitHub.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Load response of a floating wind turbine to turbulent atmospheric flow

The two turbulence-generation models [Kaimal Spectrum Exponential Coherence (KSEC) and Mann] specified in the international standard for wind turbine design assume neutral atmospheric conditions and are based on statistical and spectral methods. Mainly due to the lack of physics, the flow fields simulated with these models ultimately differ in their underlying structure, especially in terms of the spatial coherence of longitudinal velocity perturbations. While this may not be critical for smaller wind turbine rotors, it becomes important when rotor sizes increase. Furthermore, it might be especially important in the context of floating technologies as they are more sensitive to large turbulent coherent structures. Previous work found that these differences between KSEC and Mann can propagate to loads predictions and thereby affect the design space of the entire wind turbine system. It is therefore crucial to determine in which ways these two models are underperforming. Up until now, validation of these models had only been done in the vertical direction because it is extremely difficult to obtain atmospheric turbulence measurements separated laterally, and sampled at heights relevant to wind energy. In this work, we address the lack of measurements by using high-fidelity, high-resolution simulation data as a reference. We perform hour-long, large-eddy simulations of turbulent velocity fields that are stability-dependent and contain three-dimensional coherent structures. These flow fields are then used to investigate which stochastic model is a better predictor of loads on a realistic spar-system floating offshore wind turbine, and to quantify how the assumption of neutral stratification propagates to short-term load estimates. Both stochastic turbulence models are found to overpredict fatigue loading in high-wind scenarios (in some cases, by more than 25%) and underpredict it when the wind speed is low (by as much as 20%). The KSEC model matches the high-fidelity flow fields more closely than Mann at high wind speeds, and the opposite is true at low wind speeds. Finally, turbine loading is found to be sensitive to atmospheric stability even when the turbulence intensity remains fairly constant. This sensitivity is most pronounced at low wind speeds, when fatigue load estimates on the spar system can differ by 40%.

17 WIND ENERGY↗
DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗
DOE OSTI2024

POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty Interim Field Campaign Report

Aerosols mediate the radiative fluxes in clear and cloudy skies and dominate the uncertainty in the radiative forcing of climate. Relatively dense networks of aerosol optical depth measurements have been used to effectively constrain simulated aerosol optical properties, but model diversity of aerosol microphysical properties is much larger (Mann et al. 2014, Myhre et al. 2009). A better understanding of aerosol microphysics is essential as they are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing of climate. Model representation of aerosol microphysical properties has lagged in part due to their inherently greater spatial variability, but also due to a lack of available observations. This project – the Printed Optical Particle Spectrometer Network (POPSnet)-Southern Great Plains (SGP) Pilot – launched the first spatially dense network of aerosol size distribution measurements over an area the size of a global model grid cell and demonstrated its use in providing valuable information regarding the spatial variability of aerosol microphysical properties and its drivers for improving model constraints.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CISME report on experiments with HMX powder, PBX 9501 prills, and heated PBX 9501

The ability to predict post-ignition explosive response is relevant to explosives safety, particularly weapons assembly/disassembly operations that must operate under a sufficiently conservative safety basis to meet risk mitigation criteria for inadvertent nuclear detonation (IND). In particular, hazard determinations hinge on whether the PBX 9501 main charge can ultimately attain detonation from an initial, non-shock, ignition stimulus. The CISME experiment uses a spherical explosive charge, which is thermally ignited in the center using a fiber-delivered infrared laser pulse. This ignition mechanism - i.e. a localized thermal hotspot - is representative of the ignition-causing insults typical in accident scenarios, including low-velocity impact, drop, skid, and punch events. In fiscal year 2018 CISME experiments were conducted with pristine PBX 9501 spheres, confined and unconfined. Sub-detonative, low-violence reactions were observed. Here we report experiments conducted since 2018. The explosive charge was varied: experiments were conducted with room temperature pour-density HMX powder and prills of PBX 9501, and heated, thermally damaged PBX 9501. Experiments were conducted in a confined configuration, with two 4 mm-thick stainless-steel hemispherical shells bolted around the sphere. Detonation, via DDT (deflagration-to-detonation transition) was observed in the 3-inch diameter powdered HMX and the 6-inch diameter heated and thermally-damaged PBX 9501 tests. No transition to DDT was observed in smaller diameters for those configurations, and no DDT was observed for pour-density prills of PBX 9501 of any diameter. The original test series demonstrated that the response of pristine, undamaged PBX 9501 to a central ignition is relatively benign. This new test series shows that increased porosity, lack of polymer binders and thermal damage can greatly increase the violence of the post-ignition response, up to and including detonation. These results bridge the explosive response of PBX 9501 from sub-detonative through detonation as a function of diameter and provide valuable test cases for modeling efforts that strive to capture the DDT threshold. By exploring explosive response in a more representative 3D geometry, we can also benchmark these new results to more plentiful DDT datasets generated from testing in 1D tube confinement (the historical test configuration)–the configuration upon which almost all DDT models were developed.

42 ENGINEERING↗
DOE OSTI2018

Magnetic Inflation and Stellar Mass. II. On the Radii of Single, Rapidly Rotating, Fully Convective M-Dwarf Stars

Main-sequence, fully convective M dwarfs in eclipsing binaries are observed to be larger than stellar evolutionary models predict by as much as 10%–15%. A proposed explanation for this discrepancy involves effects from strong magnetic fields, induced by rapid rotation via the dynamo process. Although, a handful of single, slowly rotating M dwarfs with radius measurements from interferometry also appear to be larger than models predict, suggesting that rotation or binarity specifically may not be the sole cause of the discrepancy. We test whether single, rapidly rotating, fully convective stars are also larger than expected by measuring their Rsin i distribution. We combine photometric rotation periods from the literature with rotational broadening (vsin i) measurements reported in this work for a sample of 88 rapidly rotating M dwarf stars. Using a Bayesian framework, we find that stellar evolutionary models underestimate the radii by 10%--15%{sub −2.5}{sup +3}, but that at higher masses (0.18 < M < 0.4 M {sub Sun}), the discrepancy is only about 6% and comparable to results from interferometry and eclipsing binaries. At the lowest masses (0.08 < M < 0.18 M {sub Sun}), we find that the discrepancy between observations and theory is 13%–18%, and we argue that the discrepancy is unlikely to be due to effects from age. Furthermore, we find no statistically significant radius discrepancy between our sample and the handful of M dwarfs with interferometric radii. We conclude that neither rotation nor binarity are responsible for the inflated radii of fully convective M dwarfs, and that all fully convective M dwarfs are larger than models predict.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Regional Manufacturing Cost Structures and Supply Chain Considerations for Medium Voltage Silicon Carbide Power Applications

Bottoms-up cost analysis has been a mainstay of commoditized industry and manufacturing processes for years, however a holistic objective supply chain analysis to inform research and investment in the development of early stage technologies has not. The potential for rapid adoption of wide bandgap (WBG) semiconductors, specifically silicon carbide (SiC), highlights a need to understand the drivers of location-specific manufacturing cost, global supply chains, and plant location decisions. Further, ongoing research and investment, necessitates analytical analysis to help inform the roadmap of SiC technologies. In collaboration with PowerAmerica the project explores the bottoms-up cost analysis of wafers, devices, modules, and motor drives at the anticipated manufacturing levels. Leveraging these models, it outlines how the cost reduction potential of proposed research advances can be quantified.

42 ENGINEERING↗
DOE OSTI2018

Regional Manufacturing Cost Structures and Supply Chain Considerations for SiC Power Electronics in Medium Voltage Motor Drives

With the growth in wide bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), the technology has matured enough to highlight a need to understand the drivers of manufacturing cost, regional manufacturing costs, and plant location decisions. Further, ongoing research and investment, necessitates analytical analysis to help inform development of wide bandgap technologies. The paper explores the anticipated device, module, and motor drive cost at volume manufacturing. It additional outlines the current regional contributors to the supply chain and proposes how the base models can be used to evaluate the cost reduction potential of proposed research advances.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Cost Modelling for Energy Efficient Window Replacements

This paper detailed data collection and cost-modeling efforts to understand the hardware and non-hardware (soft) cost of residential, energy efficient window replacements. NREL created a bottom-up cost model that details costs associated with window materials, installation labor, company overhead, and administrative costs. NREL did not find a single cost area for improvement but observed several non-cost barriers to increased window replacements. The non-cost barriers associated with window replacements are consumer education, lack of communication of the value of window replacement to consumers, and difficulty for consumers to identify quality products and installation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A Techno-Economic Look at SiC WBG from Wafer to Motor Drive

Techno-economic analysis helps benchmark and deliver supply chain and manufacturing insights that can be leveraged by decision-makers to inform investment strategies, policy, and other decisions to promote economic growth and competitiveness. Silicon Carbide (SiC) wide-band gap (WBG) technologies is poised to be an integral contributor to the clean energy economy. We use bottoms-up regional manufacturing cost models to show SiC power electronics, manufactured in volume, could result in final product cost parity with those manufactured with silicon. The models are further leveraged to show innovation pathways to lower cost and potentially expanded technology adoption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗