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At least 1,495 records · Page 83

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NASA NTRSDate not supplied

An Application of CFD to Guide Forced Boundary-Layer Transition at Subsonic and Transonic Speeds for a Lambda-Wing Configuration

A hybrid transition trip-dot sizing and placement test technique was extended to support recent experimental research on a moderately-swept hybrid wing-body Uninhabited Combat Air Vehicle configuration under study in a North Atlantic Treaty Organization Applied Vehicle Technology task group. The approach combines traditional test techniques with Computational Fluid Dynamics. The application had three-dimensional boundary layers that were simulated with either fully turbulent or mixed laminar-turbulent flow models using established Reynolds-averaged Navier-Stokes methods. Forced transition effectiveness was verified experimentally using sublimating chemicals during low-speed and transonic wind tunnel tests. Assessments were performed between tripped low Reynolds number data and un-tripped high Reynolds number data. The hybrid test technique is based on fundamental flow physics and as such could be applicable to other configurations.

UCAV↗

NASA NTRSDate not supplied

Validation Experiments for Turbulent Separation over an Axisymmetric Body of Revolution

Historically, the flow physics involved with most turbulent separated flows have presented fundamental challenges to validating numerical approaches. As recognized by the CFD Vision 2030 study commissioned by NASA, validation of Reynolds-averaged Navier-Stokes (RANS) models and other scale-resolving methods for turbulent separated flow requires data from advanced, high-fidelity experiments designed specifically for CFD implementation. In accordance with this effort, a new test platform, referred to as the NASA Axisymmetric Afterbody, was designed to obtain detailed measurements of the flow field undergoing a smooth, adverse pressure gradient induced separation for a fixed Reynolds number, Re = 180,000. The parametric body offers a range of flow states progressing from fully attached, to incipient separation, and finally to small-scale separated flow based on variable afterbody geometries. In an initial effort to evaluate RANS turbulence model capabilities, the present configurations of the axisymmetric model host a mild adverse pressure gradient over the contoured boattail section, inducing incipient turbulent separation, as well as a slightly larger adverse pressure gradient, inducing a small-scale region of turbulent separation. Experiments include steady pressure measurements and 2-D PIV to provide the preliminary dataset for simulation studies, which examine the effect of variable grid domains and RANS turbulence models. This is done in an effort to understand and evaluate the critical variability between solutions for the present model configurations. Results indicate potential discrepancies may be due to the effect of the square tunnel test section walls, relatively large blockage ratio, and slight variability in reference parameters. Ongoing work will focus on higher fidelity experimental campaigns to obtain surface flow visualizations and Stereo-Particle Image Velocimetry (SPIV) to deliver higher spatial resolution of the three-dimensional flow field to aid turbulence modelers.

Validation↗

NASA NTRSDate not supplied

An Application of CFD to Guide Forced Boundary-Layer Transition at Subsonic and Transonic Speeds for a Lambda-Wing Configuration

Wind tunnel test technique↗

DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗

DOE OSTI2023

Characterizing Reactor Operations from Realistic Simulated Environmental Samples: Combining High-Performance Computing and Data Analytics

Environmental sampling is a common technique employed by inspectors and facility operators in nuclear safeguards, proliferation detection, and process monitoring contexts. Interpreting measurements performed on samples or collections of samples and ensuring the information extracted is accurate and precise is difficult. To date, these analyses have relied on simulated data to enable systematic studies; however, these models are inherently limited by the fidelity of the models and the implicit spatial averaging of isotopic composition or other signatures of interest. To advance this capability, we have refined the spatial discretization and expanded the range of physics in the simulation codes we use to perform reactor simulations and depletion calculations. This allows us to generate data that are more representative of real environmental samples, especially for the length scale of the isotopic composition and associated variation. Accordingly, these new data allow a more realistic assessment of traditional and new data analytic analysis methods. Here we present motivation for developing reactor simulations using high-performance computing methods and resources, impacts of these new simulations on our assessment of data analysis and interpretation methods, and initial results of developing and systematically testing data analytic methods designed to overcome the challenges expected of real-world samples. We also quantify the performance of these analyses using defensible statistical methods.

Dayman, Ken J.↗

DOE OSTI2023

IP Protection in TinyML

Tiny machine learning (TinyML) is an essential component of emerging smart microcontrollers (MCUs). How- ever, the protection of the intellectual property (IP) of the model is an increasing concern due to the lack of desktop/server-grade resources on these power-constrained devices. In this paper, we propose STML, a system and algorithm co-design to Secure IP of TinyML on MCUs with ARM TrustZone. Our design jointly optimizes memory utilization and latency while ensuring the security and accuracy of emerging models. We implemented a prototype and benchmarked with 7 models, demonstrating STML reduces 40% of model protection runtime overhead on average.

42 ENGINEERING↗

DOE OSTI2024

Estimating Electrification Potential for Class 8 Regional-Haul Trucks

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. As part of the North American Council for Freight Efficiency's (NACFE's) Run on Less Depot data workshop, NREL sought to understand how Tesla semi-trucks would perform in real-world regional haul applications. Analysis reveals that the modeled Tesla trucks, with an average efficiency of 1.78 kWh/mi, struggle to achieve full operational coverage using current battery and charging configurations assuming operations remain unchanged. However, in an extreme case where ubiquitous charging exists, 100% EV coverage is possible for the given drive cycles. These findings highlight the trade-off between battery size and charge rate in electrification potential and emphasize the necessity for advancements in charging infrastructure to enable electric trucks for regional haul operations.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Progressive matrix cracking in off-axis plies of a general symmetric laminate

A generalized shear-lag model is derived to determine the average through-the-thickness stress state present in a layer undergoing transverse matrix cracking, by extending the method of Lee and Daniels (1991) to a general symmetric multilayered system. The model is capable of considering cracking in layers of arbitrary orientation, states of general in-plane applied loading, and laminates with a general symmetric stacking sequence. The model is included in a computer program designed for probabilistic laminate analysis, and the results are compared to those determined with the ply drop-off technique.

Thomas, David J.↗

NASA NTRS1988

Comparison of OGO 6 measured thermospheric temperatures with the MSIS-86 empirical model

The thermospheric temperatures measured by the Fabry-Perod interferometer on the OGO 6 satellite are found to be reasonably represented by the mass spectrometer/incoherent scatter 1986 (MSIS-86) empirical model except for two anomalies, one in the South Atlantic and the other near noon local time. These anomalies are likely due to measurement problems. The OGO 6 temperature data were not used in the generation the MSIS models, so this is an independent comparison of measured and model temperatures. The measurements were made primarily during daytime at mid to low latitudes and throughout the day at high latitudes. On average, the measured temperatures are 16 K below the MSIS-86 model temperatures. Latitude gradients during solstices as well as for the yearly average are well represented by the model, as are high-latitude longitudinal and magnetic activity variations.

Hedin, A. E.↗

DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1978

Estimation of the sea surface's two-scale backscatter parameters

The relationship between the sea-surface normalized radar cross section and the friction velocity vector is determined using a parametric two-scale scattering model. The model parameters are found from a nonlinear maximum likelihood estimation. The estimation is based on aircraft scatterometer measurements and the sea-surface anemometer measurements collected during the JONSWAP '75 experiment. The estimates of the ten model parameters converge to realistic values that are in good agreement with the available oceanographic data. The rms discrepancy between the model and the cross section measurements is 0.7 db, which is the rms sum of a 0.3 db average measurement error and a 0.6 db modeling error.

Wentz, F. J.↗

NASA NTRS1996

Validation of Aircraft Noise Models at Lower Levels of Exposure

Noise levels around airports and airbases in the United States arc computed via the FAA's Integrated Noise Model (INM) or the Air Force's NOISEMAP (NMAP) program. These models were originally developed for use in the vicinity of airports, at distances which encompass a day night average sound level in decibels (Ldn) of 65 dB or higher. There is increasing interest in aircraft noise at larger distances from the airport. including en-route noise. To evaluate the applicability of INM and NMAP at larger distances, a measurement program was conducted at a major air carrier airport with monitoring sites located in areas exposed to an Ldn of 55 dB and higher. Automated Radar Terminal System (ARTS) radar tracking data were obtained to provide actual flight parameters and positive identification of aircraft. Flight operations were grouped according to aircraft type. stage length, straight versus curved flight tracks, and arrival versus departure. Sound exposure levels (SEL) were computed at monitoring locations, using the INM, and compared with measured values. While individual overflight SEL data was characterized by a high variance, analysis performed on an energy-averaging basis indicates that INM and similar models can be applied to regions exposed to an Ldn of 55 dB with no loss of reliability.

Page, Juliet A.↗

NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗

NASA NTRS1998

A Monte Carlo Uncertainty Analysis of Ozone Trend Predictions in a Two Dimensional Model

We use Monte Carlo analysis to estimate the uncertainty in predictions of total O3 trends between 1979 and 1995 made by the Goddard Space Flight Center (GSFC) two-dimensional (2D) model of stratospheric photochemistry and dynamics. The uncertainty is caused by gas-phase chemical reaction rates, photolysis coefficients, and heterogeneous reaction parameters which are model inputs. The uncertainty represents a lower bound to the total model uncertainty assuming the input parameter uncertainties are characterized correctly. Each of the Monte Carlo runs was initialized in 1970 and integrated for 26 model years through the end of 1995. This was repeated 419 times using input parameter sets generated by Latin Hypercube Sampling. The standard deviation (a) of the Monte Carlo ensemble of total 03 trend predictions is used to quantify the model uncertainty. The 34% difference between the model trend in globally and annually averaged total O3 using nominal inputs and atmospheric trends calculated from Nimbus 7 and Meteor 3 total ozone mapping spectrometer (TOMS) version 7 data is less than the 46% calculated 1 (sigma), model uncertainty, so there is no significant difference between the modeled and observed trends. In the northern hemisphere midlatitude spring the modeled and observed total 03 trends differ by more than 1(sigma) but less than 2(sigma), which we refer to as marginal significance. We perform a multiple linear regression analysis of the runs which suggests that only a few of the model reactions contribute significantly to the variance in the model predictions. The lack of significance in these comparisons suggests that they are of questionable use as guides for continuing model development. Large model/measurement differences which are many multiples of the input parameter uncertainty are seen in the meridional gradients of the trend and the peak-to-peak variations in the trends over an annual cycle. These discrepancies unambiguously indicate model formulation problems and provide a measure of model performance which can be used in attempts to improve such models.

Considine, D. B.↗

NASA NTRS1977

An empirical model for the solar wind velocity

An analytic expression for the average radial component of the Solar Wind velocity between 1 solar radius and 1 AU is developed. The model is constructed by assuming the conservation of particle flow in the Solar Wind and application of a twelve-year average measured value of the Solar Wind radial velocity at 1 AU.

Berman, A. L.↗

DOE OSTI2022

An Evaluation of Tropical Cyclone Rainfall Structures in the HighResMIP Simulations against Satellite Observations

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. Here, we specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Transition Analysis for the CRM-NLF Wind Tunnel Configuration

This paper reports the results of a comprehensive linear stability analysis of the boundary layer flow over the common research model with natural laminar flow (CRM-NLF) aircraft configuration. The flow conditions match selected test conditions from a recent wind tunnel experiment in the National Transonic Facility at the NASA Langley Research Center. Previous work has shown that the measured onset of laminar-turbulent transition during the experiments can be correlated with the linear amplification of Tollmien-Schlichting (TS) and stationary crossflow (CF) instabilities in the swept wing boundary layer. However, a significant scatter ( N ∈ (4,9)) was observed in the values of the logarithmic amplification factor along the measured transition front. This previous analysis was based on an approximate basic state (based on a boundary layer code with conical flow approximation) and parallel stability computations without surface curvature effects. Here, we examine the effects of these various approximations with the goal of quantifying the resulting changes in the N-factor correlations. Specifically, both linear stability theory (LST) and the parabolized stability equations (PSE)are used in conjunction with an accurate definition of the laminar boundary layer flow as computed with a Navier-Stokes solver with a Reynolds-Averaged-Navier-Stokes (RANS) based turbulence model within the turbulent parts of the flow. Furthermore, the effects of instability wave propagation within a fully three-dimensional boundary layer are also evaluated by integrating the disturbance growth rates along suitably chosen, curvilinear (i.e., nonplanar) propagation trajectories. The results of this analysis are also used in an accompanying paper by Venkatachari et al. to develop improved, physics based transition predictions for the same CRM-NLF configuration.

Boundary layer transition↗