MultiPEM Toolbox
Supports characterization of explosive events by providing estimates of device parameters such as yield, height of burst/depth of burial, geolocation, and event time with uncertainty quantification.
Williams, Brian↗
SEARCH · Engineering Papers
Results for “height estimation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Tethered Balloon System merged (TBSmerged) Value-Added Product Report
TBSmerged integrates data from instruments flown on ARM’s Tethered Balloon System missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary layer height from a surface-based ceilometer to improve the ease of use of TBS datasets. TBSmergedincloud includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.
54 ENVIRONMENTAL SCIENCES↗
ARM X-SAPR Velocity Azimuth Display
This dataset contains Velocity Azimuth Displays from the XSAPR radars in SGP. Velocity Azimith Displays provide estimates of the 2D wind direction with height from radar scans. For more information on how these Velocity Azimuth Displays are derived, see Michelson and Seaman (2000). Michelson, S.A. and N.L. Seaman, 2000: Assimilation of NEXRAD-VAD Winds in Summertime Meteorological Simulations over the Northeastern United States. J. Appl. Meteor., 39, 367–383, https://doi.org/10.1175/1520-0450(2000)039<0367:AONVWI>2.0.CO;2
54 ENVIRONMENTAL SCIENCES↗
Open-Loop Control of Adjustable Tuned Mass Dampers for Floating Wind Turbine Platforms
In this study, we demonstrate the ability of adjustable tuned mass dampers (TMDs) to reduce the platform motion of floating offshore wind turbines (FOWTs). The TMDs are located in the hull and provide control authority by varying the amount of water ballast and compressed air in a reservoir over time. The optimal TMD settings depend on the wind and wave conditions of the FOWT. We present an open-loop control scheme that changes the TMD natural frequency based on sea state and a simple method for estimating the peak wave period and significant wave height. The performance of this open-loop control is compared to ideal control of the TMDs (set knowing the exact sea state), a TMD with a constant natural frequency targeting the design load case with the greatest platform motion, and a baseline set of simulations of the platform without TMDs. The constant natural frequency case performs nearly as effectively as the actively controlled case, suggesting that the TMDs can be simply designed for extreme load conditions and provide consistent control over a range of environments; however, the methods used to find the optimal TMD parameters and estimate sea state statistics could be used in other aspects of FOWT control and design.
artificial intelligence↗
Boundary Layer Climatology at ARM Southern Great Plains
Operational since 1992, the Atmospheric Radiation Measurement (ARM) southern great plains (SGP) site at Oklahoma, USA has become a reference research site for meteorological studies. Due to an open data policy the ARM data are used by researchers all over the world. In this report, we review the long-term climatology of the atmospheric boundary layer, SGP instrumentations, the site and some site-specific atmospheric conditions which potentially effect wind turbines within the region. As the atmospheric boundary layer is bounded and influenced by the surface, observations of surface radiation components and heat fluxes are crucial in understanding land-atmosphere interactions. The entrainment of air, updrafts, downdrafts and boundary layer height characteristics is needed for understanding the structure and growth of the atmospheric boundary layer. Therefore, measurements from both surface in-situ and remote sensing observations at SGP provide an overall climatology and their interactions from surface up to the boundary layer. Measurements from a 60 m meteorological tower, surface flux stations, disdrometers, soil temperature and moisture flux plates, coherent Doppler lidar, Raman lidar, radiosondes, and satellite data at SGP central facility were analyzed. All the measurements were generally within a few square kilometers of each other at the central facility. This report focuses on data from January 2010 to June 2020 at SGP central facility. The various sections describe the ARM SGP site and surrounding wind turbines; in-situ and remote sensing instrumentation used in the report; provides mathematical equations to analyze fluxes, turbulence and other boundary layer parameters; a climatological analysis of surface winds, fluxes and thermodynamic parameters for several years; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; an analysis of the boundary layer winds and direction from a Doppler lidar; multi-year turbulence estimates through the boundary layer from a Doppler lidar; atmospheric boundary layer water vapor and relative humidity profiles from Raman lidar; cloud base height and boundary layer height from multiple sensors and satellite data; and finally site specific atmospheric conditions, such as nocturnal low-level jets. Diurnal, seasonal and yearly variations of surface, sub-surface and boundary layer quantities, such as wind speed, direction, temperature, atmospheric stability, soil temperature, and various atmospheric fluxes at SGP showed distinct trends useful for focused modeling studies. The applicability of surface similarity theory on ARM SGP data is also evaluated, which showed northerly flows are aligned with MO theory estimates compared to southerly flows. Boundary layer winds and direction profiles for several years from a Doppler lidar shows a consistent presence of a nocturnal low-level jet and predominant southerly wind directions through the boundary layer at SGP. The inter-annual variability at SGP is low (<3.5%), with a mean annual wind speed of approximately 7 m s -1 at 100 m above ground level. Boundary layer turbulence and moisture transport from Doppler and Raman lidars are evaluated, which provides evidence of increased water vapor mass flux into the great plains during nocturnal low-level jets. The moisture flux from nocturnal low-level jets is observed to be maximum during summer periods. A novel machine learning algorithm is implemented to accurately estimate the planetary boundary layer height, providing further insights into growth and destruction of the convective boundary layer height during various seasons and land-atmosphere conditions. The high frequency of low-level clouds during winter, spring and fall seasons is validated using the multi-sensor array and satellite estimates of cloud top height. Satellite vegetative fraction data provides insight into seasonal surface roughness and vegetation variability around SGP site.
54 ENVIRONMENTAL SCIENCES↗
Tool for Developing Integrated Strategies for Decontamination and Waste Management - 20291
Management of waste from chemical, biological, and radiological incidents is complicated by the fact that decontamination decisions have a profound impact on the characteristics of resulting waste streams. Wide-area biological and radiological incidents could produce massive quantities of waste that may need to be treated on-site and sent for subsequent disposal as non-contaminated materials, or else be directly disposed of as contaminated materials. The EPA has developed the Waste Estimation Support Tool (WEST) for characterizing and quantifying biological and radiological waste that may be generated from decontamination efforts. This paper focuses on the WEST's uses for radiological incidents. WEST combines Geographic Information System (GIS)-based analysis of externally-supplied plume data, infrastructure databases derived from the Federal Emergency Management Agency's (FEMA's) Hazus tool [1], and satellite imagery surface recognition algorithms to combine the composition and square footage of the buildings in the plume with estimates of the materials between the buildings in the plume. The resulting GIS data files are then imported into a Microsoft Access database application, where they are combined with information about the nature and concentration of contaminants, and then subjected to decontamination strategies for different contaminated surfaces. The tool provides estimates of the type and quantities of potential wastes resulting from simulated decontamination and/or demolition activities and includes estimates of the remaining contamination levels including residual contamination contained within each waste stream. Estimates are presented at several levels of detail, allowing users to obtain needed data at the desired resolution. These include estimates for the total affected area, estimates by contamination zone, estimates by decontamination method(s), and estimates by building type (occupancy classification). EPA is currently developing the next version of WEST which will include several substantial enhancements. The most significant improvement for the next version of WEST will include the ability for users to develop contamination scenarios and waste estimates based on previously developed, readily available, and geographically specific infrastructure data. Instead of using WEST's default infrastructure data based on FEMA's Hazus tool, users will be able to import their own building data specific to the geographically affected area. This capability may substantially decrease uncertainties in the resulting waste estimates because the results will be based on actual building data (numbers of each type, square footage, building height, etc.). Two other significant enhancements will be the ability to generate waste estimates for vehicles and biomass. In addition to building debris and building decontamination waste, vehicles and biomass will likely constitute a significant percentage of the total waste which may result from wide area contamination events. This presentation will present the most recent version of WEST, which includes such considerations as affected biomass (e.g., trees), vehicles, and the ability to replace the default Hazus infrastructure databases with custom infrastructure databases. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
New methods to improve the vertical extrapolation of near-surface offshore wind speeds
Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.
17 WIND ENERGY↗
NREL ASSIST Rhode Island / Thermodynamic retrievals TROPoe
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Upgraded Galion lidar #1212-60 and temperature, RH, and pressure from collocated PNNL met station. The lidar beta is used to estimate the cloud base height through a modified algorithm by Newsom et al., 2019 used at ARM SGP. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.
17 WIND ENERGY↗
Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population
Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.
Azevedo, Ana↗
Automated extraction of interfacial dislocations and disconnections from atomistic data
Here we introduce Interfacial Line Defect Analysis (ILDA), a method for identifying and extracting interfacial dislocations and disconnections with little-to-no input from the user on the nature of the interface. By simply providing the orientations and coherency strains for the crystals in a coherent reference state of the interface, ILDA provides exact Burgers vectors. Alternatively, these orientations and strains can be estimated using local atomic deformation gradients, making ILDA fully automated and providing estimated Burgers vectors. ILDA also determines the step height associated with each defect line segment, in case the associated defect is a disconnection. The heart of the method is the identification of atoms residing at co-incidence sites between the two crystals and the construction of a surface mesh connecting these sites that is used to compose Burgers circuits and insert defect line segments. We demonstrate the performance of ILDA in two test cases: a twist grain boundary and a phase boundary.
36 MATERIALS SCIENCE↗
Stability analysis of Monte Carlo burnup calculation based on PWR fuel rods
The existing Monte Carlo burnup algorithms may give some unstable results when using multiple burnable cells during burnup calculations. A very small time step is required to maintain the stability of the system, which pose some limits for the realistic applications. In this paper, we firstly repeat the instability issue by using a Monte Carlo code based on a typical PWR fuel rod model. Then, a theoretical stability analysis was carried out by using the perturbation theory in order to get a better understanding about the property of system stability. We determine a perturbation matrix which relates some main influence factors, such as the time step and the height of fuel rod, it can help people estimate the stability of burnup calculation system and the results are quantitatively consistent with the Monte Carlo code. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product Report
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product (VAP) integrates data from instruments flown on ARM’s tethered balloon system missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary-layer height from a surface-based ceilometer to improve the ease of use of tethered balloon system (TBS) data sets. The TBSMERGEDINCLOUD VAP, a variation of TBSMERGED, includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.
54 ENVIRONMENTAL SCIENCES↗
Productivity and Cost of Post-Tornado Salvage Logging in Upper Coastal Plain of South Carolina, USA
Salvage harvesting is common in the US South following natural disasters such as tornadoes and hurricanes; nevertheless, few studies have evaluated the productivity and costs of these harvests because of their geographic dispersion and the short interval between natural disasters and salvage harvesting. An Enhanced Fujita Scale 3 (EF3) tornado with winds in excess of 250 km per hour struck Aiken County, South Carolina in April of 2020, uprooting trees and severing other stems above breast height. The goal of this study was to estimate the productivity and cost of salvage harvesting in loblolly pine (Pinus taeda L.) stands following severe tornado damage. Salvage harvests were conducted with a rubber-tired drive-to-tree feller-buncher, grapple skidder, tracked loader, and chipper. All stems were chipped and used to produce energy; no roundwood was produced from the harvests. Elemental time-and-motion studies were conducted in three pulpwood-sized stands (<30 cm large-end diameter) and three sawtimber-sized stands (≥30 cm large-end diameter). Hourly harvesting costs were estimated using the machine rate method and per-ton costs were estimated using a modified version of the Auburn Harvesting Analyzer. Skidding productivity was low in each harvest unit, but especially so in the three pulpwood-sized stands because of stem breakage and low weight per stem. Harvesting costs averaged $\$$29.78 and $\$$19.97 (USD) per tonne (onboard truck) in the pulpwood- and sawtimber-sized stands, respectively. High salvage harvesting costs mean that landowners can expect significantly reduced stumpage prices from these harvests; nonetheless, landowners do benefit from reduced reforestation costs. Harvesting promptly after a tornado can reduce harvesting costs and increase timber value recovery.
60 APPLIED LIFE SCIENCES↗
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble
To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.
17 WIND ENERGY↗
A three-dimensional model of terrain-induced updrafts for movement ecology studies
Abstract Background Spatially explicit simulation models of animal movements through the atmosphere necessarily require a representation of the spatial and temporal variation of atmospheric conditions. In particular, for movements of soaring birds that rely extensively on vertical updrafts to avoid flapping flight, accurate and reliable estimation of the vertical component of wind is critical. The interaction between wind and complex terrain shapes both the horizontal and vertical wind fields, highlighting the need to model the coupling between local terrain features and atmospheric conditions at scales relevant to animal movement. Methods In this work, we propose a new empirical model for estimating the orographic updraft field. The model is developed using computational fluid dynamics simulations of canonical atmospheric conditions over moderately complex terrain. To isolate buoyancy and thermal effects, and focus on terrain-induced effects, we use only simulations of a neutrally stratified atmosphere to develop the model. The model, which we name Engineering Vertical Velocity Estimator (EVVE), is simple to implement and is a function of the underlying terrain elevation map, the desired height above ground level (AGL), and wind conditions at a reference height (80 m). We validate the model with data from the Alaiz mountain (Spain) field campaign. Results Compared to observations, the proposed improved model estimates the updrafts at 120 m AGL with a mean error of 0.11 m/s ( $$\sigma =0.28$$ σ = 0.28 m/s), compared to 0.85 m/s ( $$\sigma =0.58$$ σ = 0.58 m/s) for its baseline. For typical land-based wind turbine hub heights of 80 m AGL, the proposed model has a mean error of 0.04 m/s ( $$\sigma =0.25$$ σ = 0.25 m/s), compared to baseline 0.54 m/s ( $$\sigma =0.45$$ σ = 0.45 m/s) estimations. We illustrate an application of the model in movement ecology by comparing simulated tracks and presence maps of golden eagles ( Aquila chrysaetos ) moving across two distinct landscapes. The tracks and presence maps are obtained using a simple heuristic-based movement model, with the updraft field given by the proposed model and a wind vector-based estimation approach that is currently in wide use in movement ecology studies of raptors and other soaring birds. Conclusions We highlight that movement model results can be sensitive to the underlying orographic updraft model, especially in studies of fine-scale movements in regions of complex topography. We suggest adopting the proposed model rather than the wind vector estimation method for studies of soaring bird movements.
17 WIND ENERGY↗
Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands
Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.
17 WIND ENERGY↗
Short-term impacts of 2017 western North American wildfires on meteorology, the atmosphere’s energy budget, and premature mortality
Abstract Western North American fires have been increasing in magnitude and severity over the last few decades. The complex coupling of fires with the atmospheric energy budget and meteorology creates short-term feedbacks on regional weather altering the amount of pollution to which Americans are exposed. Using a combination of model simulations and observations, this study shows that the severe fires in the summer of 2017 increased atmospheric aerosol concentrations leading to a cooling of the air at the surface, reductions in sensible heat fluxes, and a lowering of the planetary boundary layer height over land. This combination of lower-boundary layer height and increased aerosol pollution from the fires reduces air quality. We estimate that from start of August to end of October 2017, ∼400 premature deaths occurred within the western US as a result of short-term exposure to elevated PM 2.5 from fire smoke. As North America confronts a warming climate with more fires the short-term climate and pollution impacts of increased fire activity should be assessed within policy aimed to minimize impacts of climate change on society.
Bernstein, Diana N. (ORCID:0000000308279336)↗
The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries
This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.
49 EE - Wind and Water Power Program - Wind (EE-4W↗