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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Verification and Validation of MFiX-PIC

This work is aimed at verification and validation (V&V) of particle-in-cell (PIC) approach in the National Energy Technology Laboratory’s (NETL) Multiphase Flow with Interphase eXchanges (MFiX-PIC) which was officially released in April 2019. Under verification, the results from MFiX-PIC are compared with exact or analytical solutions, while experimental results are used for benchmarking under validation. The test suite consists of the following cases: Terminal velocity. Particle advection in time-varying flow field. Particle settling in a dense medium. Evaporation. Rayleigh-Taylor instability. Minimum fluidization. Mini-circulating fluidized bed. The cases reported in a format consistent with the existing Verification and Validation Manual (Musser et al., 2018) explore a wide range of flow velocities and concentration regimes. Only one case considered in this work verifies the implementation of heat and mass transfer routines in MFiX-PIC, while a few other studies are currently underway to further expand the validation space in reacting flows. This study highlights the need for a rigorous analysis to objectively quantify the effect of model parameters. In addition, there is a scarcity of high-quality experimental data in the literature suitable for validating PIC methodology. Future efforts would include performing experiments which provide results for a valuable database. While designing such experiments, particle count should be significant to have sufficient particles per parcel while maintaining an optimal parcel to cell volume ratio. Also, datasets should have objectively assessed experimental uncertainty whereby confidence interval in those measurements become more explicit.

42 ENGINEERING↗
DOE OSTI2012

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was Less-Than-Or-Slanted-Equal-To 3 mm (95th percentiles within {+-}4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of Less-Than-Or-Slanted-Equal-To 5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

Absorption Properties of Mediterranean Aerosols Obtained from Multi-year Ground-based and Satellite Remote Sensing Observations

Aerosol absorption properties are of high importance to assess aerosol impact on regional climate. This study presents an analysis of aerosol absorption products obtained over the Mediterranean Basin or land stations in the region from multi-year ground-based AERONET and satellite observations with a focus on the Absorbing Aerosol Optical Depth (AAOD), Single Scattering Albedo (SSA) and their spectral dependence. The AAOD and Absorption Angstrom Exponent (AAE) data set is composed of daily averaged AERONET level 2 data from a total of 22 Mediterranean stations having long time series, mainly under the influence of urban-industrial aerosols and/or soil dust. This data set covers the 17 yr period 1996-2012 with most data being from 2003-2011 (approximately 89 percent of level-2 AAOD data). Since AERONET level-2 absorption products require a high aerosol load (AOD at 440 nm greater than 0.4), which is most often related to the presence of desert dust, we also consider level-1.5 SSA data, despite their higher uncertainty, and filter out data with an Angstrom exponent less than 1.0 in order to study absorption by carbonaceous aerosols. The SSA data set includes both AERONET level-2 and satellite level-3 products. Satellite-derived SSA data considered are monthly level 3 products mapped at the regional scale for the spring and summer seasons that exhibit the largest aerosol loads. The satellite SSA dataset includes the following products: (i) Multi-angle Imaging SpectroRadiometer (MISR) over 2000-2011, (ii) Ozone Monitoring Instrument (OMI) near-UV algorithm over 2004-2010, and (iii) MODerate resolution Imaging Spectroradiometer (MODIS) Deep-Blue algorithm over 2005-2011, derived only over land in dusty conditions. Sun-photometer observations show that values of AAOD at 440 nm vary between 0.024 +/- 0.01 (resp. 0.040 +/- 0.01) and 0.050 +/- 0.01 (0.055 +/- 0.01) for urban (dusty) sites. Analysis shows that the Mediterranean urban-industrial aerosols appear "moderately" absorbing with values of SSA close to approximately 0.94-0.95 +/- 0.04 (at 440 nm) in most cases except over the large cities of Rome and Athens, where aerosol appears more absorbing (SSA approximately 0.89-0.90 +/- 0.04). The aerosol Absorption Angstrom Exponent (AAE, estimated using 440 and 870 nm) is found to be larger than 1 for most sites over the Mediterranean, a manifestation of mineral dust (iron) and/or brown carbon producing the observed absorption. AERONET level-2 sun-photometer data indicate the existence of a moderate East-West gradient, with higher values over the eastern basin (AAEEast. = 1.39/AAEWest. = 1.33) due to the influence of desert dust. The North-South AAE gradient is more pronounced, especially over the western basin. Our additional analysis of AERONET level-1.5 data also shows that organic absorbing aerosols significantly affect some Mediterranean sites. These results indicate that current climate models treating organics as nonabsorbing over the Mediterranean certainly underestimate the warming effect due to carbonaceous aerosols. A~comparative analysis of the regional SSA variability has been attempted using satellite data. OMI and MODIS data show an absorbing zone (SSA approximately 0.90 at 470-500 nm) over Northeastern Africa that does not appear in the MISR retrievals. In contrast, MISR seems able to observe the East-West SSA gradient during summer, as also detected by AERONET. Also, the analysis of SSA provided by satellites indicates that the aerosol over the Mediterranean Sea appears less absorbing during spring (MAM) than summer (JJA).

satellite remote sensing observations↗
DOE OSTI2018

A method for processing the Confidential Carload Waybill Sample for railroad freight analysis

Freight transportation research is often constrained by the availability of appropriate data. Within the context of freight rail research, use of traditional freight datasets present some critical challenges. Aggregating freight data to geographic areas diminishes the ability to perform nuanced analysis. More challenging, the complexity of railroad operations limits the usability of publicly available freight flow data which do not account for which rail company is economically and legally responsible for the movement of the goods or for the contractual arrangements between infrastructure owners and carriers. Finally, detailed data are usually privately owned and not readily-accessible for analysis outside the individual rail companies. As part of their regulatory authority, the Surface Transportation Board has developed the Confidential Carload Waybill Sample, a stratified sample representing 1% to 3% of all rail traffic in the United States for a particular year. While the sample is rich with data, it is limited in spatial information as well as in its implementation in geospatial analysis. This paper introduces a methodology for geospatially enabling the Confidential Carload Waybill Sample which can then be used to generate rail routes based on a topological representation of the U.S. railroad network. As a result, this methodology is applied using two case studies, a freight diversion analysis and transporting hazardous materials.

42 ENGINEERING↗
DOE OSTI2016

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
NASA NTRS2024

Core Body Temperature Predictions Using Metabolic Energy Expenditure and Heart Rate During Simulated Extravehicular Activity

Long duration spaceflight missions will require crew to become more autonomous in conducting extravehicular activities (EVA) without direct communication with Mission Control for biomedical support. To enable such autonomy, we are developing a Crew State and Risk Model (CSRM) as a collection of key physiology domains that drive EVA crew capabilities and workloads. One model component of CSRM is human thermal regulation. In this paper, customized development of a baseline model to predict core body temperature is presented using physiology inputs of heart rate and metabolic rate. The model development dataset included a baseline study where participants (n=6, equal male and female) performed a 5-hour EVA in a two-part session while wearing a hybrid space suit simulator (HS3). The first session included an end-to-end EVA traversing 1500 meters to a geology site and traversing back to a habitat conducting geology, payload relocation, and maintenance operations every 500 meters. The second session included standalone tasks of a 2000-meter traverse followed by geology tasks. Thermal measures of core body temperature, local skin temperature, liquid cooling garment temperature, heart rate, and metabolic rate were collected through the test duration. Multiple regression was used to build a linear equation to predict core body temperature from inputs of heart rate and metabolic rate. Heat storage was calculated via predicted core body temperature plus LCG and skin temperatures. The model was tested against a dataset from a pressurized suited test (n=6, equal male and female) in the NASA Active Response Gravity Offload System (ARGOS) conducting similar end-to-end EVA and standalone tasks. Predicted error of the model against the raw test cases was 0.2±0.15 °C. The baseline prediction of core body temperature using heart rates and metabolic rates allows for simple real-time tracking from data collected in-flight to monitor crew consumables and thermal flight limits during EVA.

Bradley Hoffmann↗
DOE OSTI2024

Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning

This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an autoencoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulations (LES). The trained model takes the low-fidelity velocity field cost-effectively generated from the analytical engineering wake model as input and produces the high-fidelity velocity fields. The accuracy of the proposed ML model is demonstrated in a utility-scale wind farm for which datasets of wake flow fields were previously generated using LES under various wind speeds, wind directions, and yaw angles. Comparing the ML model results with those of LES, the ML model was shown to reduce the error in the prediction from 20% obtained from the Gauss Curl hybrid (GCH) model to less than 5%. In addition, the ML model captured the non-symmetric wake deflection observed for opposing yaw angles for wake steering cases, demonstrating a greater accuracy than the GCH model. The computational cost of the ML model is on par with that of the analytical wake model while generating numerical outcomes nearly as accurate as those of the high-fidelity LES.

Mechanics↗
DOE OSTI2025

Estimation of the radiation budget during MOSAiC based on ground-based and satellite remote sensing observations

An accurate representation of the radiation budget is essential for investigating the impact of clouds on the climate system, especially in the Arctic, an environment highly sensitive to complex and rapid environmental changes. In this study, we analyse a unique dataset of observations from the central Arctic made during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in conjunction with state-of-the-art satellite products from CERES (Clouds and the Earth's Radiant Energy System) to investigate the radiative effect of clouds and radiative closure at the surface and the top of the atmosphere (TOA). We perform a series of radiative transfer simulations using derived cloud macro- and microphysical properties as inputs to the simulations for the entire MOSAiC period, comparing our results to collocated satellite products and ice-floe observations. The radiative closure biases were generally within the instrumental uncertainty, indicating that the simulations are sufficiently accurate to reproduce the radiation budget during MOSAiC. Comparisons of the simulated radiation budget relative to CERES show similar values in the terrestrial flux but relatively large differences in the solar flux, which are attributed to a lower surface albedo and a possible underestimation of atmospheric opacity by CERES. While the simulation results were consistent with the observations, more detailed analyses reveal an overestimation of simulated cloud opacity for cases involving geometrically thick ice clouds. In the annual mean, we found that, during the MOSAiC expedition, the presence of clouds leads to a loss of 5.2 W m −2 of the atmosphere–surface system to space, while the surface gains 25.0 W m −2 and the atmosphere is cooled by 30.2 W m −2 .

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Investigation of Spiral Bevel Gear Condition Indicator Validation via AC-29-2C Using Fielded Rotorcraft HUMS Data

This report presents the analysis of gear condition indicator data collected on a helicopter when damage occurred in spiral bevel gears. The purpose of the data analysis was to use existing in-service helicopter HUMS flight data from faulted spiral bevel gears as a Case Study, to better understand the differences between HUMS data response in a helicopter and a component test rig, the NASA Glenn Spiral Bevel Gear Fatigue Rig. The reason spiral bevel gear sets were chosen to demonstrate differences in response between both systems was the availability of the helicopter data and the availability of a test rig that was capable of testing spiral bevel gear sets to failure. The objective of the analysis presented in this paper was to re-process helicopter HUMS data with the same analysis techniques applied to the spiral bevel rig test data. The damage modes experienced in the field were mapped to the failure modes created in the test rig. A total of forty helicopters were evaluated. Twenty helicopters, or tails, experienced damage to the spiral bevel gears in the nose gearbox. Vibration based gear condition indicators data was available before and after replacement. The other twenty tails had no known anomalies in the nose gearbox within the time frame of the datasets. These twenty tails were considered the baseline dataset. The HUMS gear condition indicators evaluated included gear condition indicators (CI) Figure of Merit 4 (FM4), Root Mean Square (RMS) or Diagnostic Algorithm 1 (DA1) and +/- 3 Sideband Index (SI3). Three additional condition indicators, not currently calculated on-board, were calculated from the archived data. These three indicators were +/- 1 Sideband Index (SI1), the DA1 of the difference signal (DiffDA1) and the peak-to-peak of the difference signal (DP2P). Results found the CI DP2P, not currently available in the on-board HUMS, performed the best, responding to varying levels of damage on thirteen of the fourteen helicopters evaluated. Two additional CIs also not in the on-board system, DiffDA1and SI1, also performed well responding to twelve and ten of the fourteen helicopters evaluated respectively. Of the three CIs currently available in the MSPU, DA1, FM4 and SI3, SI3, responded to eight, DA1 responded to six and FM4 responded to four of the fourteen helicopters evaluated. FM4, the poorest performing CI, was not as responsive to damage as the other five CIs. Conversely, when compared to the other two, it was the only CI that responded to damage on two helicopters. CI response could not be correlated to specific failure modes due to limited pictures and subjective descriptions found within the TDA. Flight regime did affect CI response to some gear faults. Due to the range of operating conditions for each regime, more studies are required to determine their sensitivity to regimes.

rotorcraft↗
DOE OSTI2016

Calculating Free Energy Changes in Continuum Solvation Models

We recently showed for a large dataset of pK a s and reduction potentials that free energies calculated directly within the SMD continuum model compares very well with corresponding thermodynamic cycle calculations in both aqueous and organic solvents (Phys. Chem. Chem. Phys. 2015, 17, 2859). In this paper, we significantly expand the scope of our study to examine the suitability of this approach for the calculation of general solution phase kinetics and thermodynamics, in conjunction with several commonly used solvation models (SMDM062X, SMD-HF, CPCM-UAKS, and CPCM-UAHF) for a broad range of systems and reaction types. This includes cluster-continuum schemes for pK a calculations, as well as various neutral, radical and ionic reactions such as enolization, cycloaddition, hydrogen and chlorine atom transfer, and bimolecular SN2 and E2 reactions. On the basis of this benchmarking study, we conclude that the accuracies of both approaches are generally very similar – the mean errors for Gibbs free energy changes of neutral and ionic reactions are approximately 5 kJ mol -1 and 25 kJ mol -1 respectively. In systems where there are significant structural changes due to solvation, as is the case for certain ionic transition states and amino acids, the direct approach generally afford free energy changes that are in better agreement with experiment. The results indicate that when appropriate combinations of electronic structure methods are employed, the direct approach provides a reliable alternative to the thermodynamic cycle calculations of solution phase kinetics and thermodynamics across a broad range of organic reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can easily cause problems if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases with highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Environmental Molecular Sciences Laboratory↗
NASA NTRS2013

Comparison of Four Precipitation Forcing Datasets in Land Information System Simulations over the Continental U.S.

The NASA Short ]term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real ]time configuration of the NASA Land Information System (LIS) with the Noah land surface model (LSM). Output from the SPoRT ]LIS run is used to initialize land surface variables for local modeling applications at select National Weather Service (NWS) partner offices, and can be displayed in decision support systems for situational awareness and drought monitoring. The SPoRT ]LIS is run over a domain covering the southern and eastern United States, fully nested within the National Centers for Environmental Prediction Stage IV precipitation analysis grid, which provides precipitation forcing to the offline LIS ]Noah runs. The SPoRT Center seeks to expand the real ]time LIS domain to the entire Continental U.S. (CONUS); however, geographical limitations with the Stage IV analysis product have inhibited this expansion. Therefore, a goal of this study is to test alternative precipitation forcing datasets that can enable the LIS expansion by improving upon the current geographical limitations of the Stage IV product. The four precipitation forcing datasets that are inter ]compared on a 4 ]km resolution CONUS domain include the Stage IV, an experimental GOES quantitative precipitation estimate (QPE) from NESDIS/STAR, the National Mosaic and QPE (NMQ) product from the National Severe Storms Laboratory, and the North American Land Data Assimilation System phase 2 (NLDAS ]2) analyses. The NLDAS ]2 dataset is used as the control run, with each of the other three datasets considered experimental runs compared against the control. The regional strengths, weaknesses, and biases of each precipitation analysis are identified relative to the NLDAS ]2 control in terms of accumulated precipitation pattern and amount, and the impacts on the subsequent LSM spin ]up simulations. The ultimate goal is to identify an alternative precipitation forcing dataset that can best support an expansion of the real ]time SPoRT ]LIS to a domain covering the entire CONUS.

Case, Jonathan L.↗
DOE OSTI2014

Coastal Ohio Wind Project

The Coastal Ohio Wind Project intends to address problems that impede deployment of wind turbines in the coastal and offshore regions of Northern Ohio. The project evaluates different wind turbine designs and the potential impact of offshore turbines on migratory and resident birds by developing multidisciplinary research, which involves wildlife biology, electrical and mechanical engineering, and geospatial science. Firstly, the project conducts cost and performance studies of two- and three-blade wind turbines using a turbine design suited for the Great Lakes. The numerical studies comprised an analysis and evaluation of the annual energy production of two- and three-blade wind turbines to determine the levelized cost of energy. This task also involved wind tunnel studies of model wind turbines to quantify the wake flow field of upwind and downwind wind turbine-tower arrangements. The experimental work included a study of a scaled model of an offshore wind turbine platform in a water tunnel. The levelized cost of energy work consisted of the development and application of a cost model to predict the cost of energy produced by a wind turbine system placed offshore. The analysis found that a floating two-blade wind turbine presents the most cost effective alternative for the Great Lakes. The load effects studies showed that the two-blade wind turbine model experiences less torque under all IEC Standard design load cases considered. Other load effects did not show this trend and depending on the design load cases, the two-bladed wind turbine showed higher or lower load effects. The experimental studies of the wake were conducted using smoke flow visualization and hot wire anemometry. Flow visualization studies showed that in the downwind turbine configuration the wake flow was insensitive to the presence of the blade and was very similar to that of the tower alone. On the other hand, in the upwind turbine configuration, increasing the rotor blade angle of attack reduced the wake size and enhanced the vortices in the flow downstream of the turbine-tower compared with the tower alone case. Mean and rms velocity distributions from hot wire anemometer data confirmed that in a downwind configuration, the wake of the tower dominates the flow, thus the flow fields of a tower alone and tower-turbine combinations are nearly the same. For the upwind configuration, the mean velocity shows a narrowing of the wake compared with the tower alone case. The downwind configuration wake persisted longer than that of an upwind configuration; however, it was not possible to quantify this difference because of the size limitation of the wind tunnel downstream of the test section. The water tunnel studies demonstrated that the scale model studies could be used to adequately produce accurate motions to model the motions of a wind turbine platform subject to large waves. It was found that the important factors that affect the platform is whether the platform is submerged or surface piercing. In the former, the loads on the platform will be relatively reduced whereas in the latter case, the structure pierces the wave free surface and gains stiffness and stability. The other important element that affects the movement of the platform is depth of the sea in which the wind turbine will be installed. Furthermore, the wildlife biology component evaluated migratory patterns by different monitoring systems consisting of marine radar, thermal IR camera and acoustic recorders. The types of radar used in the project are weather surveillance radar and marine radar. The weather surveillance radar (1988 Doppler), also known as Next Generation Radar (NEXRAD), provides a network of weather stations in the US. Data generated from this network were used to understand general migratory patterns, migratory stopover habitats, and other patterns caused by the effects of weather conditions. At a local scale our marine radar was used to complement the datasets from NEXRAD and to collect additional monitoring parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species. Our work focused on the design and development of custom built marine radar that used t-bar and parabolic dish antennas. The marine radar used in the project was Furuno (XANK250) which was coupled with a XIR3000B digitizing card from Russell Technologies for collection of the radar data. The radar data was processed by open source radR processing software using different computational techniques and methods. Additional data from thermal IR imaging cameras were collected to detect heat emitted from objects and provide information on movements of birds and bats, data which we used for different animal flight behavior analysis. Lastly, the data from the acoustic recorders were used to provide the number of bird calls for assessing patterns and peak passage rates during migration. The development of the geospatial database included collection of different data sources that are used to support offshore wind turbine development. Many different data sets were collected and organized using initial version of web-based repository software tools that can accommodate distribution of rectified pertinent data sets such as the lake depth, lake bottom engineering parameters, extent of ice, navigation pathways, wind speed, important bird habitats, fish efforts and other layers that are relevant for supporting robust offshore wind turbine developments. Additional geospatial products developed during the project included few different prototypes for offshore wind farm suitability which can involve different stakeholders and participants for solving complex planning problems and building consensus. Some of the prototypes include spatial decision support system (SDSS) for collaborative decision making, a web-based Participatory Geographic Information System (PGIS) framework for evaluating importance of different decision alternatives using different evaluation criteria, and an Android application for collection of field data using mobile and tablet devices . In summary, the simulations of two- and three-blade wind turbines suggested that two-bladed machines could produce comparable annual energy as the three-blade wind turbines but have a lighter tower top weight, which leads to lower cost of energy. In addition, the two-blade rotor configuration potentially costs 20% less than a three blade configuration that produces the same power at the same site. The cost model analysis predicted a potential cost savings of approximately 15% for offshore two-blade wind turbines. The foundation design for a wind turbine in Lake Erie is likely to be driven by ice loads based on the currently available ice data and ice mechanics models. Hence, for Lake Eire, the cost savings will be somewhat smaller than the other lakes in the Great Lakes. Considering the size of cranes and vessels currently available in the Great Lakes, the cost optimal wind turbine size should be 3 MW, not larger. The surveillance data from different monitoring systems suggested that bird and bat passage rates per hour were comparable during heavy migrations in both spring and fall seasons while passage rates were significantly correlated to wind directions and wind speeds. The altitude of migration was higher during heavy migrations and higher over water relative to over land. Notable portions of migration on some spring nights occurred parallel the shoreline, often moving perpendicular to southern winds. The birds approaching the Western basin have a higher propensity to cross than birds approaching the Central basin of Lake Erie and as such offshore turbine development might be a better option further east towards Cleveland than in the Western basin. The high stopover density was more strongly associated with migration volume the following night rather than the preceding night. The processed mean scalar wind speeds with temporal resolutions as fine as 10-minute intervals near turbine height showed that August is the month with the weakest winds while December is the month, which typically has the strongest winds. The ice data suggests that shallow western basin of Lake Erie has higher ice cover duration many times exceeding 90 days during some winters.

17 WIND ENERGY↗
DOE OSTI2025

Uncertainty-Based Design: Finite Element and Explainable Machine Learning Modeling of Carbon–Carbon Composites for Ultra-High Temperature Solar Receivers

Design under uncertainty has significantly grown in research developments during the past decade. Additionally, machine learning (ML) and explainable ML (XML) have offered various opportunities to provide reliable predictable models. The current article investigates the use of finite element modeling (FEM), ML and XML predictions, and uncertain-based design of carbon-carbon (C-C) composites for use in ultra-high temperatures. A C-C composite concentrating solar power (CSP) as a microvascular receiver is considered as a case study. These C-C composites are fiber composites with directly integrated carbonized microchannels to form a lightweight, high-absorptivity material that includes an embedded microvascular network of channels. The topology of these microchannels is engineered to optimize heat transfer to a supercritical carbon dioxide (sCO2) heat transfer fluid. The mechanical characterization of C-C composites is highly challenging. Thus, designing every component made of C-C composites for ultra-high temperature applications needs an uncertainty-based analysis. As a part of a comprehensive project on the development of a novel carbonized microvascular C-C composite, this paper explores C-C composite sensitivity analysis, FEM, ML prediction, and XML analysis. The resulting composite can then be carbonized and coated with an oxidation-resistant coating to form a thermally efficient and mechanically robust C-C composite. An ANSYS 3-D-FE model was used to analyze the CSP’s stress/strain. To consider the variability in the mechanical and thermal properties of C-C composites, various mechanical properties are considered as the ANSYS FEM’s input. A synthetic dataset from 730 ANSYS runs was produced to feed into the ML and XML algorithms for uncertainty analysis and prediction. The ML and XML algorithms could accurately predict the CSP stresses/strains.

Daghigh, Vahid (ORCID:0000000298941620)↗
NASA NTRS2019

Investigating Meteorological Conditions Associated with Valley Fever Outbreaks in Southwestern United States

According to CDC, a sharp increase in reported Valley fever cases (Coccidioidomycosis) has been found between 2009 and 2012. Environmental conditions play an important role for Valley fever outbreaks. For example, dust storms can significantly increase the amount of the fungus Coccidioides in the air and associated strong winds can transport the fungus to other areas. In last year’s AGU session, we reported a preliminary study to investigate hydrometeorological conditions and their connection with dust storm activities in southwestern United States. We found wind is a major contributing factor for the seasonal variation of dust storm activities. Interannual variation of the regional hydrometeorological conditions are closely linked to the large-scale environment such as the Pacific Decadal Oscillation (PDO). Strong winds are linked with a number of weather events such cold front passages, thunderstorms that produce downbursts and strong winds, the Santa Ana winds, etc. In this presentation, we will report the results of our latest investigation on meteorological conditions associated with Valley fever outbreaks in southwestern United States, using NLDAS (North American Land Data Assimilation System), GPM (Global Precipitation Measurement), and MERRA-2 (Modern Era Retrospective-analysis for Research and Applications, Version 2) hourly datasets, from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).

Liu, Zhong↗
DOE OSTI2013

Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both contexts. One of the most interesting models we propose attempt to marry the two by obtaining a coarse global representation using prediction models, and a detailed local representation based on topology. Our methods are validated on several analytical test functions as well as a small nuclear simulation dataset modeled after a simplified Pressurized Water Reactor.

97 MATHEMATICS AND COMPUTING↗