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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Radar Derived Rainfall and Rain Gauge Measurements at SRS

Over the years rainfall data for the Savannah River Site has been obtained from ground level measurements made by rain gauges. These instruments have inherent errors or biases that can impact the measured rainfall totals but are assumed as ground truth for most climatological and weather applications. With the development of weather radar technologies, various methods to derive rainfall totals from radar reflectivity values have been developed and have continued to improve. The Z-R relationship, which uses an exponential relationship to estimate rainfall rate based on radar reflectivity values, provides estimates of rainfall amounts (Z-R Level III) for locations within the radar’s detection range. The recently developed Multi-Radar Multi-Sensor (MRMS) dataset combines reflectivity-based estimates using the Z-R relationship with a network of gauges and other rainfall estimates to produce a refined set of precipitation estimates for each grid point within its domain. Comparisons were done between gauge observations and radar estimates for various SRS locations to assess whether radar derived estimates are representative of rainfall measurements at the site. Results obtained show good agreement between radar derived amounts and ground measurements, with MRMS showing stronger correlations and lower spread than Z-R Level III estimates. Outliers and errors observed appear to be related to hydrometeor classification schemes. In general, MRMS proved to provide sufficiently representative estimates of daily rainfall amounts to replace existing SRS rain gauge measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s -1 , respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m -2 with the benchmark microwave method.

Engineering↗
DOE OSTI2021

Methods and Comparisons Between Computer Vision and Radar Based Vehicle Location

Measuring vehicle locations relative to a driver's vehicle is a critical component in the analysis of driving data from both postanalysis (such as in naturalistic driving studies) or in autonomous vehicle navigation. In this work we describe a method to estimate vehicle positions from a forward-looking video camera using intrinsic camera calibration, estimates of extrinsic parameters, and a convolutional neural network trained to detect and locate vehicles in video data. We compare the measurements we achieve with this method with ground truth and with radar data available from a naturalistic driving study. We identify regions where video is preferred, where radar is preferred, and explore trade-offs between the two methods in regions where the preference is more ambiguous. We describe applications of these measurements for transportation analysis.

Aykac, Deniz↗
DOE OSTI2025

Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements

Abstract. A relative calibration technique has been developed for the US Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) user facility Ka-band ARM Zenith Radars (KAZRs). This method uses the signal attenuation caused by water on the radome to estimate reflectivity factor (Ze) offsets. The wet-radome attenuation (WRA) is assumed to follow a log-linear relationship with rainfall rate during light and moderate rain, as measured by a collocated surface disdrometer. The technique has an uncertainty of approximately 3 dB, due to factors such as disdrometer measurement error, rain variability between radar and disdrometer sample volumes, and the fitting function's uncertainty for the WRA behavior. A practical advantage of this WRA-based approach to shorter-wavelength radar monitoring is that, while it requires a reference disdrometer, it proves feasible for a wider range of collocated disdrometer measurements compared to traditional direct disdrometer comparison at the onset of light rain. This technique thus offers a cost-effective monitoring tool for remote or long-term radar deployments. This calibration technique was applied during the ARM Tracking Aerosol Convection Interactions Experiment (TRACER) from October 2021 through September 2022. The estimated Ze offsets were compared against traditional radar calibration and monitoring methods using available datasets from this campaign. Results show that the WRA-based offsets align closely with mean offsets found between cloud radars and from direct disdrometer comparison near the onset of rain, while also reflecting similar offset and campaign-long trends when compared to collocated, independently calibrated radar wind profilers. Nevertheless, overall, the KAZR Ze offsets estimated during TRACER remained stable at approximately 2 dB lower than the disdrometer estimates from the campaign start until the end of June 2022; afterward, the offsets increased to around 7 dB by the campaign's end. This increase is linked to a drop of about 1 dB in transmitter power toward the end of the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗
DOE OSTI2015

A contactless approach for respiratory gating in PET using continuous-wave radar

Purpose: Respiratory gating is commonly used to reduce motion artifacts in positron emission tomography (PET). Clinically established methods for respiratory gating in PET require contact to the patient or a direct optical line between the sensor and the patient’s torso and time consuming preparation. In this work, a contactless method for capturing a respiratory signal during PET is presented based on continuous-wave radar. Methods: The proposed method relies on the principle of emitting an electromagnetic wave and detecting the phase shift of the reflected wave, modulated due to the respiratory movement of the patient’s torso. A 24 GHz carrier frequency was chosen allowing wave propagation through plastic and clothing with high reflections at the skin surface. A detector module and signal processing algorithms were developed to extract a quantitative respiratory signal. The sensor was validated using a high precision linear table. During volunteer measurements and [{sup 18}F] FDG PET scans, the radar sensor was positioned inside the scanner bore of a PET/computed tomography scanner. As reference, pressure belt (one volunteer), depth camera-based (two volunteers, two patients), and PET data-driven (six patients) signals were acquired simultaneously and the signal correlation was quantified. Results: The developed system demonstrated a high measurement accuracy for movement detection within the submillimeter range. With the proposed method, small displacements of 25 μm could be detected, not considerably influenced by clothing or blankets. From the patient studies, the extracted respiratory radar signals revealed high correlation (Pearson correlation coefficient) to those derived from the external pressure belt and depth camera signals (r = 0.69–0.99) and moderate correlation to those of the internal data-driven signals (r = 0.53–0.70). In some cases, a cardiac signal could be visualized, due to the representation of the mechanical heart motion on the skin. Conclusions: Accurate respiratory signals were obtained successfully by the proposed method with high spatial and temporal resolution. By working without contact and passing through clothing and blankets, this approach minimizes preparation time and increases the convenience of the patient during the scan.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Environmentally Adaptive, Multiband Software-Defined-Radar for Monitoring of Item of Interest in a Dynamically Cluttered Room

High-resolution multi-frequency radars when networked together, form a robust and seamless monitoring system for high-valued items. The multi-look-direction radar network investigated by LLNL and DSI, operates at 77 GHz and 5.8GHz, and is capable of detection at centimeter resolution movement of objects of interest. This dual frequency radar (unlike optical methods) can operate under various environments with conditions such a smoke, dust, and other natural or manmade obscurations. In addition, the multiband network radar is based on low-cost FCC approved EM specifications and can be scaled with the dimensions of the room and objects under consideration by user-defined inputs. Furthermore, the underlying radar signal processing based on software-defined radar engine is adaptive to its operational environment, where operators can input the size of the facility and object of interest and the distance between the object and radar. Finally, this multi-frequency radar employs a host of change detection and machine learning algorithms to reach performance levels in terms of high probability of detection and low false alarm rates while the monitoring state of the object can be reported remotely.

42 ENGINEERING↗
DOE OSTI2014

Stratiform and Convective Precipitation Observed by Multiple Radars during the DYNAMO/AMIE Experiment

The motivation for this research is to develop a precipitation classification and rain rate estimation method using cloud radar-only measurements for Atmospheric Radiation Measurement (ARM) long-term cloud observation analysis, which are crucial and unique for studying cloud lifecycle and precipitation features under different weather and climate regimes. Based on simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two precipitation radars (NCAR S-PolKa and Texas A&M University SMART-R), and surface precipitation during the DYNAMO/AMIE field campaign, a new cloud radar-only based precipitation classification and rain rate estimation method has been developed and evaluated. The resulting precipitation classification is equivalent to those collocated SMART-R and S-PolKa observations. Both cloud and precipitation radars detected about 5% precipitation occurrence during this period. The convective (stratiform) precipitation fraction is about 18% (82%). The 2-day collocated disdrometer observations show an increased number concentration of large raindrops in convective rain compared to dominant concentration of small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also show two distinct structures for convective and stratiform rain. These indicate that the method produces physically consistent results for two types of rain. The cloud radar-only rainfall estimation is developed based on the gradient of accumulative radar reflectivity below 1 km, near-surface Ze, and collocated surface rainfall (R) measurement. The parameterization is compared with the Z-R exponential relation. The relative difference between estimated and surface measured rainfall rate shows that the two-parameter relation can improve rainfall estimation.

convective↗
DOE OSTI2016

Kinematic and Hydrometer Data Products from Scanning Radars during MC3E

Recently the Radar Meteorology Group at Colorado State University has completed major case studies of some top cases from MC3E including 25 April, 20 May and 23 May 2011. A discussion on the analysis methods as well as radar quality control methods is included. For each case, a brief overview is first provided. Then, multiple Doppler (using available X-SAPR and C-SAPR data) analyses are presented including statistics on vertical air motions, sub-divided by convective and stratiform precipitation. Mean profiles and CFAD's of vertical motion are included to facilitate comparison with ASR model simulations. Retrieved vertical motion has also been verified with vertically pointing profiler data. Finally for each case, hydrometeor types are included derived from polarimetric radar observations. The latter can be used to provide comparisons to model-generated hydrometeor fields. Instructions for accessing all the data fields are also included. The web page can be found at: http://radarmet.atmos.colostate.edu/mc3e/research/

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Methods for Estimating 2D Cloud Size Distributions from 1D Observations

Abstract The two-dimensional (2D) size distribution of clouds in the horizontal plane plays a central role in the calculation of cloud cover, cloud radiative forcing, convective entrainment rates, and the likelihood of precipitation. Here, a simple method is proposed for calculating the area-weighted mean cloud size and for approximating the 2D size distribution from the 1D cloud-chord lengths measured by aircraft and vertically pointing lidar and radar. This simple method (which is exact for square clouds) compares favorably against the inverse Abel transform (which is exact for circular clouds) in the context of theoretical size distributions. Both methods also perform well when used to predict the size distribution of real clouds from a Landsat scene. When applied to a large number of Landsat scenes, the simple method is able to accurately estimate the mean cloud size. As a demonstration, the methods are applied to aircraft measurements of shallow cumuli during the Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign, which then allow for an estimate of the true area-weighted mean cloud size.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo peptide identification infers peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

proteomics, mass spectrometry, forensics, de novo ↗
DOE OSTI2022

A Variational Method for Analyzing Vortex Flows in Radar-Scanned Tornadic Mesocyclones. Part III: Sensitivities to Vortex Center Location Errors

Abstract When the vortex center location is estimated from a radar-scanned tornadic mesocyclone, the estimated location is not error-free. This raises an important issue concerning the sensitivities of analyzed vortex flow (VF) fields by the VF-Var (formulated in Part I of this paper series and tested in Part II) to vortex center location errors, denoted by Δ x c . Numerical experiments are performed to address this issue with the following findings: The increase of |Δ x c | from zero to a half of vortex core radius causes large analysis error increases in the vortex core but the increased analysis errors decrease rapidly away from the vortex core especially for dual-Doppler analyses. The increased horizontal-velocity errors in the vortex core are mainly in the Δ x c -normal component, because this component varies much more rapidly than the other component along the Δ x c direction in the vortex core. The vertical variations of Δ x c distort the vertical correlation structure of Δ x c -dislocated VF-dependent background error covariance, which can increase the analysis errors in the vortex core. The dual-Doppler analyses have adequate accuracies outside the vortex core even when |Δ x c | increases to a half of vortex core radius, while single-Doppler analyses can also have adequate accuracies outside the vortex core mainly for the single-Doppler-observed velocity component. The sensitivities to Δ x c are largely unaffected by the vortex slanting. The above findings are important and useful for assessing the accuracies of analyzed VFs for real radar-observed tornadic mesocyclones. Significance Statement When the vortex center location is estimated from a radar-scanned tornadic mesocyclone, the estimated location is not error-free. This raises an issue concerning the sensitivity of analyzed vortex flow (VF) by the VF-Var (formulated in Part I of this paper series and tested with simulated radar observations in Part II) to vortex center location error. This issue and its required investigations are very important for the VF-Var to be applied to real radar-observed tornadic mesocyclones, especially in an operational setting with the WSR-88Ds. Numerical experiments are performed to address this issue. The findings from these experiments are important and useful for assessing the accuracies of VF-Var analyzed VF fields for real radar-observed tornadic mesocyclones.

Xu, Qin↗
DOE OSTI2018

Compositing "Stand Off" Ground Penetrating Radar Scans of Differing Frequencies.

Methods have been developed to combine signals of various frequencies in a manner to produce clearer images in the presence of noise. Ground Penetrating Radar (GPR) scans at various frequencies are no exception. Methods using an optimization problem solver, the Expectation-Maximization (EM) Algorithm, define weights used to perform the task of combining GPR scans. In this paper, we explore using the Gaussian Mixture Model (GMM) feature of the EM Algorithm on GPR scans taken at various heights above ground ("Stand Off' GPR). This method demonstrates the same measured improvement toward producing a cleaner image as GPR scans taken at ground level using the same EM Algorithm method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

COMBLE Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility recently concluded its Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), with its campaign emphasis on marine boundary-layer clouds and mixed-phase clouds during cold-air outbreaks. The COMBLE campaign featured the deployment of the first ARM Mobile Facility (AMF1) to northern Scandinavia (Andenes, Norway), including its standard complement of ARM cloud radars. In keeping with user demands stemming from the previous AMF Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, a post-campaign radar mentor effort was initiated for COMBLE. This activity was intended to improve the usability of the ARM cloud radar data sets in response to overall demands for calibrated, corrected data sets for downstream studies and retrieval applications. In addition to the terrain complexities previously important to CACTI data sets (e.g., clutter designation and/or removal), COMBLE presented new challenges to existing ARM radar mentor capabilities. These included the extension of existing methodologies (e.g., relative calibration adjustment [RCA] target techniques) to frozen environments and the potential issues with their applicability when considering mixed-phase precipitation conditions. As in the previous CACTI documentation (Hardin et al. 2020), the overall calibration and conditioning process in ARM nomenclature is referred to as generating a “b1” datastream. For the radars, these “b1” standards refer to a datastream that has been calibrated (and cross-calibrated), with effort to deliver the highest-quality (well-characterized) data possible. The “b1” radar mentor reporting (this current document) is intended to detail (i) the status/quality of the original “a1” (raw) data sets during the COMBLE AMF campaign, (ii) the corrections and calibrations that are applied to generate the b1 datastreams available on ARM’s Data Discovery, and (iii) the details of the applied methods, e.g., how radar offset/calibration numbers were determined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Recover Act. Verification of Geothermal Tracer Methods in Highly Constrained Field Experiments

The prediction of the geothermal system efficiency is strong linked to the character of the flow system that connects injector and producer wells. If water flow develops channels or “short circuiting” between injection and extraction wells thermal sweep is poor and much of the reservoir is left untapped. The purpose of this project was to understand how channelized flow develops in fracture geothermal reservoirs and how it can be measured in the field. We explored two methods of assessing channelization: hydraulic connectivity tests and tracer tests. These methods were tested at a field site using two verification methods: ground penetrating radar (GPR) images of saline tracer and heat transfer measurements using distributed temperature sensing (DTS). The field site for these studies was the Altona Flat Fractured Rock Research Site located in northeastern New York State. Altona Flat Rock is an experimental site considered a geologic analog for some geothermal reservoirs given its low matrix porosity. Because soil overburden is thin, it provided unique access to saturated bedrock fractures and the ability image using GPR which does not effectively penetrate most soils. Five boreholes were drilled in a “five spot” pattern covering 100 m2 and hydraulically isolated in a single bedding plane fracture. This simple system allowed a complete characterization of the fracture. Nine small diameter boreholes were drilled from the surface to just above the fracture to allow the measurement of heat transfer between the fracture and the rock matrix. The focus of the hydraulic investigation was periodic hydraulic testing. In such tests, rather than pumping or injection in a well at a constant rate, flow is varied to produce an oscillating pressure signal. This pressure signal is sensed in other wells and the attenuation and phase lag between the source and receptor is an indication of hydraulic connection. We found that these tests were much more effective than constant pumping tests in identifying a poorly connected well. As a result, we were able to predict which well pairs would demonstrate channelized flow. The focus of the tracer investigation was multi-ionic tests. In multi-ionic tests several ionic tracers are injected simultaneously and the detected in a nearby pumping well. The time history of concentration, or breakthrough curve, will show a separation of the tracers. Anionic tracers travel with the water but cationic tracer undergo chemical exchange with cations on the surface of the rock. The degree of separation is indicative of the surface area exposed to the tracer. Consequently, flow channelization will tend to decrease the separation in the breakthrough. Estimation of specific surface area (the ration of fracture surface area to formation volume) is performed through matching the breakthrough curve with a transport model. We found that the tracer estimates of surface area were confirmed the prediction of channelized flow between well pairs produced by the periodic hydraulic tests. To confirm that the hydraulic and tracer tests were correctly predicting channelize flow, we imaged the flow field using surface GPR. Saline water was injected between the well pairs which produced a change in the amplitude and phase of the reflected radar signal. A map was produced of the migration of saline tracer from these tests which qualitatively confirmed the flow channelization predicted by the hydraulic and tracer tests. The resolution of the GPR was insufficient to quantitatively estimate swept surface area, however. Surface GPR is not applicable in typical geothermal fields because the penetration depths do not exceed 10’s of meters. Nevertheless, the method of using of phase to measure electrical conductivity and the assessment of antennae polarization represent a significant advancement in the field of surface GPR. The effect of flow character on fracture / rock thermal exchange was evaluated using heated water as a tracer. Water elevated 30 degrees C above the formation water was circulated between two wells pairs. One well pair had been identified in hydraulic and tracer testing as well connected and the other poorly connected. Temperature rise was measured in the adjacent rock matrix using coiled fiber optic cable interrogated for temperature using a DTS. This experimental design produced over 4000 temperature measurements every hour. We found that heat transfer between the fracture and the rock matrix was highly impacted by the character of the flow field. The strongly connected wells which had demonstrated flow channelization produced heat rise in a much more limited area than the more poorly connected wells. In addition, the heat increase followed the natural permeability of the fracture rather than the induced flow field. The primary findings of this work are (1) even in a single relatively planar fracture, the flow field can be highly heterogeneous and exhibit flow channeling, (2) channeling results from a combination of fracture permeability structure and the induced flow field, and (3) flow channeling leads to reduced heat transfer. Multi-ionic tracers effectively estimate relative surface area but an estimate of ion-exchange coefficients are necessary to provide an absolute measure of specific surface area. Periodic hydraulic tests also proved a relative indicator of connectivity but cannot prove an absolute measure of specific surface area.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Three Dimensional Characterization of Defects by Ultrasonic Time-of-Flight Diffraction (ToFD) Technique

Ultrasonic time-of-flight diffraction (ToFD) technique has high accuracy in locating and sizing discontinuities. The primary reason for this accuracy is the use of time instead of amplitude for measurement of the depth and size of discontinuities. Despite the many advantages of ToFD, it suffers from a number of shortcomings, the most notable one being its two-dimensional character. The goal of this paper is to develop an algorithm, as a prerequisite, to extend ToFD measurements to a three-dimensional space. For this purpose, a combination of multiple transmitting and receiving probes is proposed instead of just one pair commonly used in ToFD measurements. The approach for locating and sizing defects in a 3D space follows the methods used in radar and acoustic positioning systems. Non-iterative techniques are used for positioning a single source (defect) based on signals collected by several transducers. The estimation formula, in the form of a closed-form solution, is derived by linear least-squares minimization. In addition to existing conventional passive algorithms, a new active algorithm is also proposed for the general arrangement of transducers. This algorithm is tested on a steel specimen having an artificially implanted discontinuity and the three-dimensional location of the defect is estimated.

42 ENGINEERING↗