True Field Research Articles

Performance of buried pipeline under reverse fault in permafrost region

Long-distance pipelines may pass through areas of frozen soil sometimes, such as the Trans-Alaska pipeline and the Chinese Northeast pipeline. This work intends to analyze the performance of buried pipeline under reverse fault motion in such regions. A pipe-soil system model was established, and the thermo-mechanical coupling analysis was carried out. The temperature distribution of the soil near the ground and around the pipe is affected by them apparently, especially by ground. For the true temperature field (TTF) and the simplified one (STF), the pipe strain peak value and developing laws differ hugely, because of different soil mechanical properties distribution. The peak strains of the pipe are the largest at +0 °C of the ground, and the local buckling of the pipe appears earlier than the tensile failure. TTF should be obtained, and the peak compressive strain at +0 °C should be taken as the main criterion for seismic design or check calculation, for compressive bearing capacity usually determines pipe mechanical properties.

Numerical Analysis of Interbedded Anti-Dip Rock Slopes Based on Discrete Element Modeling: A Case Study

Varying geological conditions and different rock types lead to complex failure modes and instability of interbedded anti-dip rock slopes. To study the characteristics of failure evolution of interbedded anti-dip slopes, a two-dimensional particle flow code (PFC2D) based on the discrete element method (DEM) was utilized to establish an interbedded anti-dip rock slope numerical model for the Fushun West Open-pit Mine based on the true geological conditions and field investigations. The slope model with an irregular surface consists of interbedded mudstone and brown shale as two different rock layers, and a number of small-scale rock joints are randomly distributed in the rock layers. The influence of different inclination angles (20° and 70°) of the rock layer and slope angles (60° and 80°) on the stability of interbedded anti-dip rock slopes was considered. The evolution of the failure progress was monitored by the displacement field and force field. The simulation results showed that the rock joints in the rock stratum promoted crack initiation and increased the crack density but did not change its shear-slip failure mode. A large inclination angle of the rock layers and slope angle can lead to topping slip failure along the slip zone. However, shear-slip instability generally occurs in interbedded anti-dip rock slopes with small inclination angles of the rock layer and small slope angles. These results can contribute to a better understanding of the failure mechanism of interbedded anti-dip rock slopes under different geological conditions and provide a reference for disaster prevention.

The Force-freeness of the Solar Photosphere: Revisit with New Approach and Large Data Sets

Although it is generally believed that the solar photosphere is not magnetically force-free owing to its high plasma β, the estimations of force-freeness using observed magnetograms have produced disputable results. Some studies confirmed that the photosphere is largely not force-free whereas some authors argued that the photosphere is not far away from being force-free. In a previous paper of ours we demonstrated that, due to the fact that the noise levels of the transverse field in the magnetograms are much larger than those of the vertical field, wrong judgments on the force-freeness could be made: a truly force-free field could be judged as being not-force-free and a truly not-force-free field could be judged as being force-free. Here in this Letter, we propose an approach to overcome this serious problem. By reducing the spatial resolution to lower the noise level, the heavy influence of the measurement noise on the force-freeness judgment can be significantly suppressed. We first use two analytical solutions to show the success and effectiveness of this approach. Then, we apply this new approach to two large data sets of active region magnetograms, obtained with the Helioseismic and Magnetic Imager/Solar Dynamics Observatory and Spectro-Polarimeter (SP)/Hinode, respectively. Our analysis shows that the photospheric magnetic fields are actually far away from being force-free. Particularly, and most notably, the mean value of F z /F p (where F z is the net Lorentz force in the vertical direction and F p the total Lorentz force) is as low as −0.47, with more than 98% of the active regions having ∣F z /F p ∣ > 0.1 when using the SP/Hinode magnetograms of true field strength.

Shift Symmetries and AdS/CFT

Massive fields on anti-de Sitter (AdS) space enjoy galileon-like shift symmetries at particular values of their masses. We explore how these shift symmetries are realized through the boundary conformal field theory (CFT), at the level of the 2-point functions. In the alternate quantization scheme in which the dual conformal field gets the smaller ∆− conformal dimension, the shift symmetry is realized as a gauge symmetry in the dual CFT, so that only shift invariant operators are true conformal primary fields. In the standard quantization scheme the shift symmetry acts on the source, leading to Ward identities that take the form of integral constraints.

Computational modeling of hemodynamics and risk of thrombosis in the left atrial appendage using patient-specific blood viscosity and boundary conditions at the mitral valve.

Hemodynamics play a vital role for the risk of thrombosis in the left atrial appendage (LAA) and left atrium (LA) for patients with atrial fibrillation. Accurate prediction of hemodynamics in the LA can provide important guidance for assessing the risk of thrombosis in the LAA. Patient specificity is a crucial factor in representing the true hemodynamic fields. In this study, we investigated the effects of blood rheology (as a function of hematocrit and shear rate), as well as patient-specific mitral valve (MV) boundary conditions (MV area and velocity profiles measured by ultrasound) on the hemodynamics and thrombosis potential of the LAA. Four scenarios were setup with different degrees of patient specificity. Though using a constant blood viscosity can classify the thrombus and non-thrombus patients for all the hemodynamic indicators, the risk of thrombosis was underestimated for all patients compared with patient-specific viscosities. The results with least patient specificities showed that patients prone to thrombosis predicted by three hemodynamic indicators were inconsistent with clinical observations. Moreover, though patients had the same MV inlet flow rate, different MV models lead to different trends in the risk of thrombosis in different patients. We also found that endothelial cell activation potential and relative residence time can effectively distinguish thrombus and non-thrombus patients for all the scenarios, relatively insensitive to patient specificities. Overall, the findings of this study provide useful insights on patients-specific hemodynamic simulations of the LA.

Prescribed performance LOS guidance-based dynamic surface path following control of unmanned sailboats

The unmanned sailboat path following control problem with model uncertainties and unknown external disturbances is addressed. Firstly, different sailing strategies are set for the sailboat under three different wind fields in nature for its vulnerability to the true wind field so that the sailboat can complete the path-following task under different wind directions. Secondly, a barrier Lyapunov function with prescribed performance is used to limit position errors and heading errors of the unmanned sailboat within prescribed constraints. A radial-based function neural network technique is then introduced to approximate model uncertainties. Then an adaptive law is introduced to estimate an upper bound on the sum of the external environmental disturbances and neural network approximation errors. A path-following control scheme for the unmanned sailboat is proposed by the backstepping method and combined with the dynamic surface control technique. Finally, stability analysis is performed by Liapunov functions, demonstrating that the proposed control method can guarantee that all error signals in the closed-loop system are semi-global uniform ultimate bounded and that the prescribed performance constraints are not violated. Finally, the effectiveness of the scheme is demonstrated by simulation.

A skeptic’s guide to “intercultural communication”—debunking the “intercultural” and rethinking “culture”

Abstract Starting with a critique of so-called intercultural communication, the present paper contests and challenges the prevalent and dominant essentialist views of “culture”. It is exposed that these views have a detrimental underlying logic that is both destructive and self-destructive. Instead, the paper proposes a radically new idea of culture, a minimalist approach supported by insights gleaned from contemporary semiotic inquiry. In this approach, culture is defined as a biological instinct to acquire information through modeling, that is, learning by models. This instinct is at work, or is realized, in specific acts of such modeling, resulting in cultural practices and cultural artifacts. In the case of humanity, a cultural practice is anything a human does that can be modeled by another human and a cultural artifact is any object that humans make and can model. The paper argues it is imperative to keep in mind that when we deal with the “intercultural”, we are only dealing with concrete yet different cultural practices or cultural artifacts. This is an effective way to completely refute essentialism. In a sense, the paper is meant to be a wake-up call, instead of a fighting talk. Its main objective is not to negate or obliterate the field of “intercultural communication”, among others, but rather to save them from themselves—a true and worthy field of “intercultural communication” is a field against essentialism, instead of an accessory to essentialism, whether the commission is “before the fact” or “after the fact”.

Physically consistent testing of large-frequency-band magnetic losses of non-oriented electric steel considering dynamic anisotropy

Non-oriented (NO) magnetic steels show gradually increasing relevance over an extended range of frequencies, in particular for electric drives. However, until now, data for corresponding characteristics like losses P are available in very restricted ways. A reason is that loss testers are standardized for technical low frequency (LF) until now. Here we report detailed loss data for three steel types, tested by broad-band single sheet testers that offer tests up to high frequency (HF), for values of f = 10 kHz, for steel samples of 500 × 170 mm2 (or 500 mm). In solving a specific problem of NO steel, very high values of H of magnetic field strength are considered by “True Field Air Flux Compensation.” For rising f, the three steels show strong, non-linear increases in losses P, up to about 100 W/kg. With respect to anisotropy, corresponding factors were defined for rolling direction and transverse direction (TD). The effective “field anisotropy” AH tends to show high values for LF but sinks dramatically for HF due to strong eddy currents. The analogously defined “loss anisotropy” AP is lower a priori. In addition, decreases in HF are less pronounced. The International Electrotechnical Commission-standardized “loss anisotropy factor” is generally lower by more than a factor of two, with values up to 30% for LF that may sink to zero for HF due to eddy current mechanisms that are rather independent from crystalline texture. In conclusion, for high dynamics, the effective anisotropy deviates completely from LF-based texture characteristics. Additional analyses of power functions prove that during a period of magnetization, strong anisotropy exists with respect to alignments of atomic moments, in particular for the TD.

Measuring the photostability of agrochemicals on leaves: understanding the balance between loss processes and foliar uptake.

Photostability is an important property in agrochemicals, impacting their biological efficacy, environmental fate and registrability. As such, it is a property that is routinely measured during the development of new active ingredients and their formulations. To make these measurements, compounds are typically exposed to simulated sunlight after application to a glass substrate. While useful, these measurements neglect key factors that influence photostability under true field conditions. Most importantly, they neglect the fact that compounds are applied to living plant tissue, and that uptake and movement within this tissue provides a mechanism to protect compounds from photodegradation. In this work, we introduce a new photostability assay incorporating leaf tissue as a substrate, designed to run at medium throughput under standardized laboratory conditions. Using three test cases, we demonstrate that our leaf-disc-based assays provides quantitatively different photochemical loss profiles to an assay employing a glass substrate. And we also demonstrate that these different loss profiles are intimately linked to the physical properties of the compounds, the effect that those properties have on foliar uptake and, thereby, the availability of the active ingredient on the leaf surface. The method presented provides a quick and simple measure of the interplay between abiotic loss processes and foliar uptake, supplying additional information to facilitate the interpretation of biological efficacy data. The comparison of loss between glass slides and leaves also provides a better understanding of when intrinsic photodegradation is likely to be a good model for a compound's behaviour under field conditions. © 2023 Society of Chemical Industry.

The development of a multispectral pyrometer for achievable true temperature field measurements of the explosion flame

The temperature measurement of explosion flames is a key challenge in the field of weapons research. Many pyrometers have been developed to determine the accurate temperature of the explosion flame. However, these pyrometers can only measure the true temperature curve and the brightness temperature field of explosion flames, not their true temperature field. A multispectral pyrometer that measures the true temperature field of the explosion flame is developed in this paper. The multispectral pyrometer acquires brightness temperature field images at four different wavelengths of the explosion flame at the same moment using synchronous spectral-splitting technology. The pixels of the obtained four brightness temperature field images are then aligned using an edge feature-based image matching algorithm. The true temperature value is finally calculated for each pixel using multispectral radiation thermometry to construct the true temperature field of the explosion flame. Based on the experimental results, the true temperature field of the explosion flame can be measured with the proposed multispectral pyrometer.

Understanding contact acoustic nonlinearity through coupled synchrotron x-ray imaging and dynamic acoustoelastic measurements under stress

Dynamic acoustoelastic testing (DAET) is a vibro-acoustic technique, which has been used to measure acoustic nonlinearity in disparate solid media. DAET is a pump-probe approach, where the strain pump-induced changes in the elastic properties of the medium are monitored by a pair of ultrasonic probes at frequencies much higher than that of the strain pump. In a fractured/cracked medium, the measured changes in the elastic properties are attributed to fracture breathing (opening/closing) or shearing caused by the low-frequency pumping. In a set of unprecedent experiments, we use time-lapse synchrotron x-ray imaging in tandem with quasi-static loading/unloading and DAET to visualize the fast dynamics mechanisms in four different materials systems under various stress levels. These include two samples with localized CAN: fractured Westerly granite and Berea sandstone and two samples of granular media with distributed CAN: glass beads and sand. The synchrotron x-ray images are analyzed to obtain true contact areas and changing strain fields under stress. The ultrasonic signals are analyzed in relation to the changes in contact areas during both the quasi-static and dynamic loading to examine the state-of-the-art understanding of ultrasonic transmission in fractured media as well as acoustoelasticity and dynamic acoustoelasticity.

A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties.

In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.

Encapsulation for Sensing Element and Its Application in Asphalt Road Monitoring

The internal pavement structure is a “black box”; an accurate strain response for the pavement interlayer structure under vehicle load is hard to obtain by conventional road surface detection methods. This is due to the true strain field of the pavement structure, which means that the service state of the pavement cannot be accurately evaluated. This paper proposes an innovative strain sensor based on a carbon nanotube and epoxy (CNT/EP) composite to solve the current strain monitoring problem in asphalt pavement health monitoring. The CNT/EP composite encapsulation method was proposed, and the I-shaped strain sensor for asphalt pavement structure was developed. The strain–resistance response characteristics of the self-developed sensor were further investigated using a universal testing machine. The encapsulated sensor was used to monitor the strain of the asphalt mixture by means of a laboratory asphalt concrete beam and a practical pavement field. The results showed that the encapsulation method proposed in the study is suitable for CNT/EP material, which could guarantee the survivability and monitoring effectiveness of the self-developed sensor under the harsh environment of high temperature and pressure of asphalt mixture paving. The resistance of encapsulated sensor presents a linear relationship with strain. The laboratory and practical paving verified the feasibility of the self-sensor for strain monitoring of asphalt pavement. Compared to other post-excavating buried sensors, the self-developed sensor can be embedded in the pavement interlayer as the asphalt mixtures paving process, which can obtain the real strain response of pavement structure and reduce the perturbation of the sensor to the dynamic response of the pavement.

Convolutional neural network optimisation to enhance ESPI fringe visibility

The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI). The proposed approach is based on quick and light trainings to select the architecture parameters (network depth and kernel sizes) to maximize the performance of the neural network improving the visibility of ESPI images. To measure the performance, the structural similarity index (SSMI) will be the lead indicator, and the need for large datasets to train neural networks, unavailable for ESPI images, forces the use of a simulated ESPI image dataset along the process. This dataset is computed using Zernike polynomials to simulate local surface deformations in the specimen under test and simulated true speckle fields for the reference and object field involved in ESPI techniques.

Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose.

The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source pose estimation models (AlphaPose, BlazePose, and OpenPose) across varying movements, camera views, and trial lengths. Second, this study aimed to assess the suitability and interchangeability of keypoints detected by each pose estimation model when used as inputs into machine learning models for the estimation of GRFs. The keypoint detection rate of BlazePose was distinctly lower than that of AlphaPose and OpenPose. All pose estimation models achieved a high keypoint detection rate at the centre of an image frame and a lower detection rate in the true sagittal plane camera field of view, compared with slightly anteriorly or posteriorly located quasi-sagittal plane camera views. The three-dimensional ground reaction force, instantaneous loading rate, and peak force for running could be estimated using the keypoints of all three pose estimation models. However, only AlphaPose and OpenPose keypoints could be used interchangeably with a machine learning model trained to estimate GRFs based on AlphaPose keypoints resulting in a high estimation accuracy when OpenPose keypoints were used as inputs and vice versa. The findings of this study highlight the need for further evaluation of computer vision-based pose estimation models for application in biomechanical human modelling, and the limitations of machine learning-based GRF estimation models that rely on 2D keypoints. This is of particular relevance given that machine learning models informing athlete monitoring guidelines are being developed for application related to athlete well-being.

WITHDRAWN: Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

The Bonnor-Melvin magnetic 2 + 1 + p-brane solution in gravity coupled to nonlinear electrodynamics

In this work, we present a 2 + 1 + p-brane solution in Einstein’s gravity coupled to the nonlinear electrodynamic with the Lagrangian density powered by a true uniform magnetic field. The three and four-dimensional solutions coincide with the recently reported Bonnor-Melvin universe with a cosmological constant in gravity coupled with Maxwell’s linear Lagrangian in [50] and [7], respectively. The magnetic fields are produced by electric currents through coil-type wires located periodically with the discreet radii. The invariants of the spacetime are regular/constant everywhere but nonzero and proportional to Maxwell’s invariant.

Thermo-Hydro-Glycol Ageing of Polyamide 6,6: Microstructure-Properties Relationships.

The microstructural evolutions occurring during the thermo-hydro-glycol ageing of an injection molded PA66 were studied. They were correlated to the evolutions of its mechanical properties. The aged samples were immersed in an antifreeze fluid—mainly composed of water and ethylene glycol—at varying times and temperatures. The aim was to combine an as exhaustive as possible microstructural investigation and a rigorous mechanical analysis. Consequently, the microstructure of the aged and unaged PA66 was assessed through the average molar mass, the diameter of the spherulites, the lamellae thickness, the crystallite’s apparent size, a crystal perfection index, and a crystallinity index. Moreover, a core-skin approach was set up. The mechanical consequences of the microstructural changes were investigated by DMA and tensile testing. The local true strain fields were measured with a digital image correlation system. The temperatures and strain rates of the tests were chosen by referring to the time-temperature superposition principle. It is concluded that the water and ethylene glycol intake resulted in an intense plasticization, the loss of the molar mass resulted in the embrittling of the polymer, and finally, it was identified that the changes of the crystalline structure have an influence on the stiffness of PA66.

A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image reconstruction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Networks (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distribution. The results show that the proposed DNN can accurately describe the distribution of electromagnetic field and boundary shape of phantom compared to traditional algorithms (LBP, FBP, Noser and Tikhonov), regardless of the size and number of phantoms within the monitoring area. Hence, the proposed DNN is more robust and has a high degree of generalization.