Surface Measurements Research Articles

Observational evidence of impact of heavy rainfall events on surface energetics and soil variables over a tropical station Tirupati

Two back-to-back heavy rainfall events (HREs) produced enormous rainfall over a pilgrim city, Tirupati, located in the southern peninsular India, during 10–12 November 2021 (149.2 mm) and 17–19 November 2021 (234.2 mm). Surface measurements at Yerpedu, Tirupati (13.74°, 79.60°E) showed a drop of 2–3 °C in temperature and 8 hPa in pressure during HREs, indicating that these HREs were associated with two landfalling tropical depressions from the Bay of Bengal. The sporadic HREs in the past three decades caused floods over Tirupati and devastated landslides in the sacred Tirumala hills especially during the second HRE. Fundamental understanding of diurnal cycle of surface radiation is critical to model the climate. The effect of HREs on the surface essential climate variables at diurnal scale indicate a reduction in peak solar and net radiations by 500 W m−2 (> 62.5 %). Similar to observations, ERA5 reanalysis also indicates a significant reduction in net surface solar and thermal radiations, which are nearly equal in terms of % but vary in magnitude. Net solar and thermal radiations show dominant diurnal cycles with a reduced (66.7 % and 74.4 % respectively) diurnal amplitudes from no-rain to HREs. Soil moisture drydown curves at 5- and 10-cm depths shows 3 h long e-folding decay time for HREs, indicating an enhanced soil moisture memory after HREs. The soil drying process takes longer time at 10 cm than at 5 cm depth. Soil temperatures are significantly low at 5 cm to 30 cm depths and less than the 50 cm depth with weak diurnal variation during the events. The reflectivity (Z)-rain rate (R) and shape (μ)-slope (Λ) relations show variations from HREs to normal rain events. Knowing the importance of land-atmospheric processes feedbacks in model predictions at diurnal scale, this study quantified the HREs impact on surface essential climate variables, especially the energy balance and soil variables.

Refining the Global Picture: The Impact of Increased Resolution on CO2 Atmospheric Inversions Using OCO-2 XCO2 Retrievals.

The threat posed by the increasing concentration of carbon dioxide (CO2) in the atmosphere motivates a detailed and precise estimation of CO2 emissions and removals over the globe. This study refines the spatial resolution of the CAMS/LSCE inversion system, achieving a global resolution of 0.7° latitude and 1.4° longitude, or three times as many grid boxes as the current operational setup. In a 2-year inversion assimilating the midday clear-sky retrievals of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from NASA's second Orbiting Carbon Observatory (OCO-2), the elevated resolution demonstrates an improvement in the representation of atmospheric CO2, particularly at the synoptic timescale, as validated against independent surface measurements. Vertical profiles of the CO2 concentration differ slightly above 22km between resolutions compared to AirCore profiles, and highlight differences in the vertical distribution of CO2 between resolutions. However, this disparity is not evident for XCO2, as evaluated against independent reference ground-based observations. Global and regional estimates of natural fluxes for 2015-2016 are similar between the two resolutions, but with North America exhibiting a higher natural sink at high resolution for 2016. Overall, both inversions seem to yield reasonable estimates of global and regional natural carbon fluxes. The increase in calculation time is less than the increase in the number of operations and in the volume of input data, revealing greater efficiency of the code executed on a graphics processing unit. This allows us to make this higher resolution the new standard for the CAMS/LSCE system.

Source distribution correlation enabled self-attention residual network for effective reconstruction of optical molecular tomography

As a promising preclinical imaging technique, optical molecular tomography (OMT) shows great potential in early detection and diagnosis of tumor diseases. However, its widespread application has been hindered by the limitations of traditional reconstruction methods, specifically the accuracy of optical transmission models and the ill-posed nature of inverse reconstruction. The development of deep learning has offered novel solutions for OMT, enabling efficient reduction of the ill-posed nature in reconstruction. The existing deep learning approaches employ conventional neural networks and objective functions, which retains significant scope for enhancing the accuracy of image reconstruction. In this paper, we propose a source distribution correlation enabled self-attention residual network (DCeSR network) to address the need for accurate OMT reconstruction. The DCeSR network leverages a residual learning strategy and a self-attention mechanism to effectively integrate the deep and shallow features, subsequently extracting highly informative surface measurements to accurately predict the three-dimensional distribution of light sources within tissues. The efficacy of the DCeSR network was validated through training and testing with two distinct numerical simulated datasets, each encompassing both single and dual source configurations. Both qualitative and quantitative analyses demonstrate the superior performance of the DCeSR network in achieving accurate OMT reconstructions.

Normative values of non-radiological surface measurement of the lumbar lordosis curvature in the standing position and its association with age, sex, and body mass index: a cross-sectional study of 2,500 healthy individuals from Iran.

A cross-sectional study. This study aimed to define the normal values of the lumbar lordosis curve (LLC) and investigate its association with sex, age, and body mass index (BMI). The importance of the human spine's sagittal alignment, particularly in the lumbar region, has been argued from the physiological and pathophysiological points of view. The LLC size is an important predictor of lumbar pathologies. Lumbar curvature misalignment, particularly increased lumbar lordosis or hypolordosis, can, in the long term, lead to spinal instability and development of disorders such as low back pain and spondylolisthesis Therefore, knowledge about the normal LLC value and its association with age, sex, and BMI, appears crucial. The study recruited 2,497 asymptomatic volunteers (1,264 women and 1,233 men) aged 5-85 years. Participants were assigned to different groups based on their sex, age, and BMI. The LLC was measured using a Flexicurve. Normal LLC values were established for different sexes, age, and BMI groups. Overall, normal LLC ranges from 10.2° to 74.9° with a mean of 42.34°±13° (male, 38.57°±11.44°; female, 46°±13.38°). LLC was generally higher by 7.5° in women than in men. A significant three-way interaction of sex, age, and BMI with LLC was found. The association of age and BMI with LLC was also significant. Our results can be used as a reference by physicians, healthcare, etc., when the LLC values in different ages and sexes are measured noninvasively. In other words, this information can be used as reference when determining whether the obtained LLC is within the normal range.

Experimental characterization and similarity scaling of smooth-body flow separation and reattachment on a two-dimensional ramp geometry

The results of an experimental investigation of smooth-body adverse pressure gradient (APG) turbulent boundary layer flow separation and reattachment over a two-dimensional ramp are presented. These results are part of a larger archival smooth-body flow separation data set acquired in partnership with NASA Langley Research Center and archived on the NASA Turbulence Modeling Resource website. The experimental geometry provides initial canonical turbulent boundary layer growth under nominally zero pressure gradient conditions prior to encountering a smooth, two-dimensional, backward facing ramp geometry onto which a streamwise APG that is fully adjustable is imposed. Detailed surface and off-surface flow field measurements are used to fully characterize the smooth-body APG turbulent boundary layer separation and reattachment at multiple spanwise locations over the ramp geometry. Unsteady aspects of the flow separation are characterized. It is shown that the first and second spatial derivatives of the streamwise static surface pressure profile are sufficient to determine key detachment and reattachment locations. The imposed streamwise APG gives rise to inflectional mean velocity profiles and the associated formation of an embedded shear layer, which is shown to play a dominant role in the subsequent flow development. Similarity scaling is developed for both the mean velocity and turbulent stresses that is found to provide self-similar collapse of profiles for different regions of the ramp flow. Despite the highly non-equilibrium flow environment, a new similarity scaling proved capable of providing self-similar turbulent stress profiles over the full streamwise extent of flow separation and downstream reattachment.

Machine learning for modeling North Atlantic right whale presence to support offshore wind energy development in the U.S. Mid-Atlantic

The Mid-Atlantic region is set to be one of the first and largest contributors to the offshore wind energy goals of the United States. Yet, the same region is home to a diverse marine ecosystem comprising important marine species such as the critically endangered North Atlantic right whale (NARW). To support the responsible development and operation of the planned offshore wind farms, there is a need for high-resolution modeling of NARW presence, i.e., at the spatial and temporal resolutions relevant to farm-level operations. Towards this, we leverage highly localized observations from nine glider deployments in the Mid-Atlantic to propose a machine learning approach for modeling NARW presence conditioned on a diverse set of glider- and satellite-based oceanic, physical, and contextual information. We find that tree- and ensemble-based models achieve the highest levels of accuracy, while maintaining a sensible balance of missed and false alarms. Interpretation of the machine-learnt features reveals interesting insights on the relative value of well-resolved satellite surface measurements to well-resolved vertical information from glider sampling in explaining the species-habitat patterns of NARWs. We then discuss the value of the models proposed herein to offshore wind developers and operators in the United States and elsewhere. Our work constitutes the first machine learning attempt to jointly leverage glider- and satellite-based information for modeling of NARWs. Data and codes for producing the results of this work have been made freely available to promote the research on this timely topic.

Real-time 3D temperature field reconstruction for aluminum alloy forging die using Swin Transformer integrated deep learning framework

Temperature field distribution in forging dies is crucial for quality control and defect prevention, particularly for aluminum alloys. Current methods are limited to discrete points or surface measurements, making real-time three-dimensional temperature field acquisition challenging. In this paper, a novel Swin Transformer-integrated deep learning framework is proposed for real-time 3D temperature field reconstruction of forging dies, pioneering the application of transformer architecture in physical field prediction. In this framework, numerical simulations are first conducted to provide ground truth and fundamental insights into the temperature evolution, and then limited sparse thermal sensors are utilized to offer corrected real-time input parameters. The model for 3D temperature field reconstruction is developed through the combination of Swin Transformers with the U-shaped encoder-decoder structure, which is trained and tested with various sensor configurations, initialization methods, and datasets, including actual experiments. The results demonstrate that the proposed Swin-UNETR model achieves 3D temperature field prediction with time cost of 0.98 s per frame, mean absolute error of 0.8658 °C, showing a 17.23 % improvement over the next best CNN-based model (ResUNet3D at 1.0461 °C), and a 4.63 % improvement over the next best machine learning model (LightGBM at 0.9078 °C), which can be attributed to the Swin Transformer’s ability to capture both local and global contextual information and shifted window mechanism. The proposed method holds significant implications for ensuring the forming quality of forgings and propelling the development of digital twin technology in forging processes.

Smartphone‐based high durable strain sensor with sub‐pixel‐level accuracy and adjustable camera position

AbstractComputer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel‐level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)‐Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full‐stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub‐pixel level, with pixel‐based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub‐pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub‐pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS‐Silica features easy data acquisition, high precision, and long service term, offering a promising method for long‐term measurement of both surface and internal structures.

Chinese knot inspired isotropic linear scanning method for improved imaging performance in AFM

Atomic force microscope (AFM) is an important nano-scale surface characterization and measurement method. Raster scanning method (RSM), widely used in AFMs, faces limitations on scanning speed and imaging accuracy. In this paper, an isotropic linear scanning method (ILSM) is proposed to improve the AFM imaging performance. Inspired by Chinese knot, ILSM is constructed by integrating two iterative triangular scanning trajectories in X and Y axes, similar to triangular Lissajous. Compared with the other scanning methods, ILSM features isotropic scanning trajectory across the scanning region. It is also easy to increase either the scanning speed or scanning resolution using ILSM. Subsequently, to address the hysteresis associated with the piezoelectric actuator, a new tracking algorithm is proposed by combining adaptive Kalman filtering and direct inverse modeling approach. Finally, AFM imaging experiments are conducted to validate the effectiveness of the proposed method. It can be found that the artifacts in RSM can be efficiently eliminated using the proposed method, thus improving the imaging quality.

The two-phase problem for harmonic measure in VMO and the chord-arc condition

Let Ω + ⊂ R n + 1 \Omega ^+\subset \mathbb {R}^{n+1} be a bounded δ \delta -Reifenberg flat domain, with δ > 0 \delta >0 small enough, possibly with locally infinite surface measure. Assume also that Ω − = R n + 1 ∖ Ω + ¯ \Omega ^-= \mathbb {R}^{n+1}\setminus \overline {\Omega ^+} is an NTA (non-tangentially accessible) domain as well and denote by ω + \omega ^+ and ω − \omega ^- the respective harmonic measures of Ω + \Omega ^+ and Ω − \Omega ^- with poles p ± ∈ Ω ± p^\pm \in \Omega ^\pm . In this paper we show that the condition that log ⁡ d ω − d ω + ∈ VMO ⁡ ( ω + ) \log \dfrac {d\omega ^-}{d\omega ^+} \in \operatorname {VMO}(\omega ^+) is equivalent to Ω + \Omega ^+ being a chord-arc domain with inner unit normal belonging to VMO ⁡ ( H n | ∂ Ω + ) \operatorname {VMO}(\mathcal {H}^n|_{\partial \Omega ^+}) .

A Red Flag Threat Index based on the Real-Time Mesoscale Analysis for use in the Warn-on-Forecast System

Abstract The increasing frequency of high impact wildfires has led to an emphasis on improving forecasts of the conditions that are favorable for wildfire initiation and rapid spread. The key atmospheric forecast products currently used are derived from low-level humidity and wind speed. One product that has seen widespread use by the National Weather Service (NWS) in the Southern Plains is known as the Red Flag Threat Index (RFTI). RFTI represents an index ranging from 0-10 where larger values indicate a more critical threat for favorable wildfire conditions. The current RFTI is based on 2-m humidity and 6-m windspeed climatologies from surface measurement sites located within a NWS forecast area resulting in a product that differs somewhat from office to office. RFTI forecasts are also only available from existing numerical weather prediction systems such as the Texas Tech Modeling system that do not output forecasts with the temporal resolution and latency to forecast rapidly evolving environmental conditions. To address these limitations, this work describes the creation of a grid-based RFTI using a 5-year, high resolution (3-km, hourly) reanalysis product known as the Real-Time Mesoscale Analysis (RTMA). Using RTMA data allows for continuous wind and humidity climatology fields to be developed for a larger W-CONUS domain, potentially expanding the use of RFTI. We combine these new climatologies with forecast output from the Warn-on-Forecast System (WoFS) which provides short-term (0-6 hour) probabilistic forecasts of high impact weather over a regional domain. RFTI forecasts from examples occurring in 2022 and 2024 will be discussed as well as its evaluation in the new Fire Weather Testbed.

Temperature measurements of high-temperature surface in environments with interfering radiation using luminescence lifetime thermometry

Temperature measurements of high-temperature surface in environments with interfering radiation using luminescence lifetime thermometry

Improvement of Coal Mining-Induced Subsidence-Affected (MISA) Zone Irregular Boundary Delineation by MT-InSAR Techniques, UAV Photogrammetry, and Field Investigation

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities.

Application of active piezoresistive cantilevers in high-eigenmode surface imaging

Abstract One of the most important limitations of the atomic force microscopy (AFM) is scanning speed, whose high values are required for contemporary high-resolution, long-range diagnostic applications. The measurement bandwidth of an AFM depends on several factors, but usually results from the time constant of the oscillating cantilever, which is correlated with its resonance frequency and quality factor. We propose a method to overcome this problem by performing the surface measurements when the cantilever is vibrating in higher eigenmodes. In this paper we demonstrate the application of active piezoresistive cantilevers operating in this mode. The active piezoresistive cantilever comprises a piezoresistive deflection sensor, a deflection actuator and a nanotip. It is a complete micro-electro-mechanical system, ensuring the highest reliability of cantilever vibration control and detection. Higher eigenmode operations are usually difficult to implement as they usually result in lower deflection and lower sensitivity of the probe vibration deflection. Here we present an experimental modification of the structure of an active piezoresistive cantilever using focused ion beam machining that mitigates both weaknesses. This has enabled the cantilever to scan the surface at a scanning rate of 10 lines s−1 with a maximum speed of 500 μm s−1 and a data acquisition rate of 10 kS s−1, when the probe is vibrating at 380 kHz in the second eigenmode. We also describe a traceable calibration routine (based on analysis of the response of the piezoresistive detector, the output of the HeNe interferometer and precise control of the deflection actuator), together with the cantilever modification process and the development of the measurement setup. We show measurement results of dedicated calibration samples and silicon carbide crystal lattice references.

Measuring nearshore waves at break point in 4D with Stereo-GoPro photogrammetry: A field comparison with multi-beam LiDAR and pressure sensors

Measuring nearshore waves remains technically challenging despite wave properties are being used in a variety of applications. With the promise of high-resolution and remotely-sensed measurements of water surfaces in four dimensions (spatially and temporally), stereo-photogrammetry applied to video imagery has grown as a viable solution over the last ten years. However, past deployments have essentially used costly cameras and optics, requiring fixed deployment platforms and hindering the applicability of the method in the field.Focusing on close-range measurements of nearshore waves at break point, this paper presents a detailed evaluation of a field-oriented and cost-effective stereo-video system composed of two GoProTM(Hero 7) cameras capable of collecting 12-megapixel imagery at 24 frames per second. The so-called ‘Stereo-GoPro’ system was deployed in the surf zone during energetic conditions at a macrotidal field site using a custom-assembled mobile tower. Deployed concurrently with stereo-video, a 16-beam LiDAR (Light Detection and Ranging) and two pressure sensors provided independent data to assess stereo-GoPro performance. All three methods were compared with respect to the evolution of the free-surface elevation over 25 min of recording at high tide and the wave parameters derived from spectral analysis. We show that stereo-GoPro allows producing digital elevation models (DEMs) of the water surface over large areas (250 m2) at high spatial resolution (0.2 m grid size), which was unsurpassed by the LiDAR. From instrument inter-comparisons at the location of the pressure transducers, free-surface elevation root-mean square errors of 0.11 m and 0.18 m were obtained respectively for LiDAR and stereo-GoPro. This translated into a maximum relative error of 3.9% and 12.5% on spectral wave parameters for LiDAR and stereo-GoPro, respectively. Optical distortion in imagery, which could not be completely corrected with calibration, was the main source of error. Whilst stereo-video processing workflow remains complex, cost-effective stereo-photogrammetry already opens new opportunities for deriving wave parameters in coastal regions, as well as for various other practical applications. Further tests should try to address specifically challenges associated to variable ambient conditions and acquisition configurations, affecting measurement performance, to guarantee a larger uptake of the technique.

Oscillatory integrals and weighted gradient flows

We investigate estimating scalar oscillatory integrals by integrating by parts in directions based on (x_{1} \partial_{x_1}f(x) ,\dots, x_{n} \partial_{x_n}f(x)) , where f(x) is the phase function. We prove a theorem which provides estimates that are uniform with respect to linear perturbations of the phase and investigate some consequences. When the phase function is quasi-homogeneous the theorem gives estimates for the associated surface measure Fourier transforms that are generally not too far off from being sharp. In addition, the theorem provides a new proof, up to endpoints, that the well-known oscillatory integral estimates of Varchenko when the Newton polyhedron of the phase function is nondegenerate extend to corresponding bounds for surface measure Fourier transforms when the index is less than 1/2 . A sharp version of this was originally proven by the author [J. Funct. Anal. 262 (2012), no. 5, 2314–2348].

In-Situ Form Metrology of Structured Composite Surfaces Using Hybrid Structured-Light Measurement with a Novel Calibration Method

Accurately measuring the form of structured composite surfaces in situ is critical for advanced manufacturing in various engineering fields. However, challenges persist in achieving precision, miniaturization, and calibration using current structured light techniques. In this work, a hybrid structured light with compact configuration is proposed for the in-situ and embedded form metrology of structured composite surfaces. The proposed technique contains three subsystems: phase-measuring deflectometry (PMD), fringe projection profilometry (FPP), and stereo vision. The PMD subsystem accurately reconstructs the form data of specular surfaces based on the principle of structured light reflection, and the FPP subsystem measures rough surfaces by projecting structured light onto them. Output data from these subsystems are then stitched to reconstruct a complete form of the measured composite surfaces. The compact configuration is explored to reduce the system volume to improve the technique’s portability and embedded measurement ability. With the stereo vision subsystem as an intermediary, a novel calibration method is applied for calculating the relations among the subsystems to improve the hybrid structured light system’s calibration and data stitching accuracy between PMD and FPP subsystems. Three calibration tools are designed and manufactured for the proposed calibration technique. A portable metrology prototype based on the proposed hybrid structured light technique’s principle and configuration is also developed and then calibrated using the novel calibration method. An embedded measurement experiment in a diamond turning machine demonstrates that the proposed techniques can achieve 400 nm form accuracy in specular surface measurement.

Homoepitaxial growth of device-grade GaAs using low-pressure remote plasma CVD

We have achieved the growth of high-quality, homoepitaxial 100 GaAs thin films at 0.5 mbar and 500 °C using a Remote Plasma Chemical Vapor Deposition (RP-CVD) reactor. With this process, we demonstrate a film growth rate up to 3 μm/h, comparable to the conventional MOCVD technique. The resulting films exhibit structural characteristics close to those of commercial GaAs wafers, with excellent crystalline quality as confirmed by SAED patterns and XRD rocking-curve measurements for the 004 peak with a FWHM of 0.004°. AFM measurements reveal a surface roughness of 0.2 nm, similar to that of a polished wafer. Analysis of the chemical composition – as determined through XPS surface and depth-profiled measurements – indicates that the film is homogeneous, with a constant III/V ratio of 1 throughout the whole layer, and has no detectable carbon or oxygen contamination. Additionally, the films demonstrate a sharp photoluminescence peak (FWHM of 55 meV), a p-type doping concentration of 1.1018 cm−3, and a hole mobility of 172 cm2 V⁻1.s⁻1. This work thus demonstrates a cost-effective growth method for III-V devices, enabled by the reduced gas consumption (only a few sccm, compared to tens of L/min in MOCVD) in RP-CVD operation at low pressure.

Assessment of soil gas radon migration and transport through the estimation of radon diffusion length and diffusion coefficient in the soil matrix

Naturally occurring radioactive gas, radon is a decay product of radium present in the soil. Radon flux reaches the atmosphere through exhalation from soil surface. This study aims to estimate the radon diffusion length and diffusion coefficient in soil matrix on the premises of HNB Garhwal University campus at Tehri Garhwal, India. Soil gas radon concentration was measured at different depths (15, 30, 45, 60, 75, and 90 cm) using a Smart RnDuo (Scintillation-detector) at 14 different locations in the university campus. Simultaneous measurement of surface and mass exhalation rates was also carried out for each location. Gamma-ray spectrometry was used to estimate the radium content in soil samples. Results of the present study suggest that soil gas radon concentration increases with increasing sampling depth. The diffusion coefficient and length were calculated using the soil gas radon concentration gradient within the soil matrix. The average value of diffusion length (l) and diffusion coefficient (D) were obtained as, ls = 0.70 ± 0.22 m, Ds = 0.0054 ± 0.0049 m2 sec-1 and la = 0.77 ± 0.63 m, Da = 0.0073 ± 0.014 m2 sec-1 by Fick’s diffusion model and analytic models, respectively.

A study of measurement of raceway direct measurement of rolling bearings

Demands for improved fuel efficiency in automobiles and other vehicles have led to smaller, lighter power transmission device which result in high surface contact stress and a thin oil film, which in turn tends to cause the temperature of rolling bearings to rise. The most common temperature measurement method is to touch a thermocouple against the inner and outer rings, and this method has been used for many years. However, the method using thermocouples can only measure temperatures in a limited range near the measurement point. The authors applied the Seebeck effect, a phenomenon in which an electromotive force is generated when different metals are connected and a temperature difference is applied to bearings, to a method of measuring bearing raceway temperatures called the dynamic thermocouple method. In the dynamic thermocouple method, the average value of each contact points between the different metals generates the emf (electromotive force), so the temperature rise of all the each rolling elements in contact becomes the average value, and the exact point of temperature rise is not clear. Therefore, all but one rolling element was changed to electrically insulating zirconia balls. With this method, the contact points between many different metals became one, making it possible to identify the locations of temperature rises on the raceway surface. This method makes it possible to directly measure the temperature change of the raceway. The results of temperature measurements of the raceway surface using two types of bearings with different raceway accuracy showed a clear difference of temperature. The bearing with a poor raceway accuracy showed a temperature rise in the unloaded zone, and slippage was observed when the behavior of the rolling element was checked with a high-speed camera. Furthermore, in bearings with good raceway accuracy, the temperature of the raceway surface remained almost constant even in the non-load zone. By using the dynamic thermocouple method and observing the rolling elements with a high-speed camera, it was possible to correlate the bearing temperature rise with the behavior of the rolling elements.