Traditional Physical Model Research Articles

Longitudinal Research on the Application of Sport Education Model to College Physical Education

The application of latent growth model to compare the teaching effect of college physical education between sport education model and traditional physical education model provides reference for the application of sport education model to college physical education. It is considered in this study that sport education model is suitable for the application to college physical education and, in comparison with traditional teaching model, could enhance college students’ physical quality, improve sports enjoyment, and fulfill lifelong physical exercise to benefit the realization of sports participation, motor skills, and social adjustment in current college physical education. However, the promotion should take special background and time and space restrictions into account, rather than being suitable for all.

Modeling Wave Overtopping on a Seawall with XBeach, IH2VOF, and Mase Formulas

The advances in computational fluid dynamics have made numerical modeling a reliable complementary tool to the traditional physical modeling in the study of the wave overtopping phenomenon. This paper addresses overtopping on a seawall by combining the numerical models XBeach (non-hydrostatic and Surfbeat modes) and IH2VOF, and the Mase formulas. This work is structured in two phases: (i) phase I assesses the performance of numerical models and formulas in modeling wave run-up and overtopping on a seawall for a solid profile bottom and representative hydro-morphologic conditions of a study site in the Portuguese west coast; (ii) phase II investigates the effect of the profile bottom variation in the overtopping phenomenon for extreme maritime storm field conditions of the study site, considering a solid bottom and a varying sandy bottom. The results indicate that XBeach underestimates the wave energy, and the frequency and intensity of the overtopping occurrences predicted by IH2VOF; the numerical models’ run-up and overtopping discharge predictions are overestimated by the Mase formulas, in simplified and in storm field conditions; and the variation of the bottom morphology throughout the storm event greatly influences the XBeach predictions, while the Mase results are mostly influenced by the bottom roughness.

A novel method for solar panel temperature determination based on a wavelet neural network and Hammerstein-Wiener model

Accurate prediction of solar panel temperature can help keep on-orbit satellites in good condition. Traditional physical models have the ability to describe and predict temperature; however, the effect is not entirely satisfactory. To produce a better forecast, the panel current of the solar panel, which is strongly correlated with temperature signals, is chosen as the input, and a novel system identification model between the two signals is established. The model we propose is based on a Hammerstein-Wiener model and integrates wavelet neural networks that adopt self-constructed wavelet bases. In addition, a complete process of training and parameter optimization is designed to be less time consuming than previous methods. The result from a test set of real telemetry data demonstrates the efficiency and accuracy of our method. Moreover, the proposed prediction model, which is based on historical data, can be used in on board self-learning and our subsequent autonomous health management.

Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery

Hyperspectral images (HSIs) are widely used in various tasks such as mineral detection and food safety. However, during the imaging process, they are often contaminated by various noises. In this paper for HSIs restoration tasks, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN). For the physical prior of HSIs, a Tucker decomposition based low-rank tensor approximation can fully explore the global correlations in both the spatial and spectral domains. And for the implicit prior, a CNN based method can represent the prior which cannot be designed by mathematical theory tools. Then, we combine the advantages of the two methods to introduce the HSI restoration CNN with the low-rank tensor approximation based regularization in the flexible and extensible plug-and-play framework. The proposed model can be quickly solved using the alternating direction method of multipliers method. Experiments with simulated data and real data show that, compared with competitive methods, the proposed method achieves better HSI restoration results in various quantitative evaluation indicators.

A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators

We develop a data-driven, statistical control method for autonomous excavators. Interactions between soil and an excavator bucket are highly complex and nonlinear, making traditional physical modeling difficult to use for real-time control. Here, we propose a data-driven method, exploiting data obtained from laboratory tests. We use the data to construct a nonlinear, non-parametric statistical model for predicting the behavior of soil scooped by an excavator bucket. The prediction model is built for controlling the amount of soil collected with a bucket. An excavator collects soil by dragging the bucket along the soil surface and scooping the soil by rotating the bucket. It is important to switch from the drag phase to the scoop phase with the correct timing to ensure an appropriate amount of soil has accumulated in front of the bucket. We model the process as a heteroscedastic Gaussian process (GP) based on the observation that the variance of the collected soil mass depends on the scooping trajectory, i.e., the input, as well as the shape of the soil surface immediately prior to scooping. We develop an optimal control algorithm for switching from the drag phase to the scoop phase at an appropriate time and for generating a scoop trajectory to capture a desired amount of soil with high confidence. We implement the method on a robotic excavator and collect experimental data. Experiments show promising results in terms of being able to achieve a desired bucket fill factor with low variance.

Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels

The accurate prediction of tensile properties has great importance for the service life assessment and alloy design of RAFM steels. In order to overcome the limitation of traditional physical metallurgical models, machine learning algorithm was used to establish universal models for the prediction of RAFM steels’ yield strength and total elongation. A database with a wide range of compositions and treatment processes of RAFM steels was first established. Then, feature engineering methods were used to select the highly correlated features. With the reasonable selection of machine learning algorithm and test/training set partitioning strategy, random forests regressors were trained by the selected features. The prediction results proved that, compared with traditional physical metallurgical models, the feature engineering guided random forests regressors had advantages of accuracy and universality for the prediction of RAFM steels’ yield strength and total elongation. And the calculated process window for the balance of strength and plasticity could provide guidance for the further design and development of RAFM steels.

ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation

Abstract. Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment

For the distributed household generation technology, the operation status identification and prediction of multiple startup and shutdown solid oxide fuel cell (SOFC) system are of great significance to the optimization of power generation efficiency and long-term operation. The existing SOFC system research focuses on traditional physical models rather than actual SOFC system, which makes the system research too idealized and ignores the actual problems, such as health state prediction, voltage fluctuation and failure analysis of key equipment such as heat exchanger. Therefore, based on the data-driven actual natural gas SOFC system, this paper builds Elman neural network state prediction model under the entire stage (including system start-stop, long-term operation, hot-standby) to predict the future voltage, and infers the potential fault information of the system from the residual voltage. On this basis, combined with the dynamic response of the system, the essential causes of voltage fluctuation and heat exchanger breakage of the actual SOFC system are found out. And the improvement scheme of the system is proposed. The results show that the typical faults of the SOFC system can be accurately identified by combining Elman neural network state prediction model with multivariable dynamic response analysis of SOFC system.

Machine Learning in Seismology: Turning Data into Insights

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

A comparative study between a virtual reality heart anatomy system and traditional medical teaching modalities

The aim of using virtual reality (VR) as a medical training tool is to offer additional means to teach students and to improve the quality of medical skills. A novel system was developed to fulfil the requirements of modern medical education and overcome the challenges faced by both students and lecturers in the process of knowledge transfer. A heart three-dimensional model presented in a virtual reality (VR) environment has been implemented in order to facilitate a new educational modality. This paper reports the outcome of a comparative study between traditional medical teaching modalities and virtual reality technology. This study was conducted in the Faculty of Medicine in the University of Jordan. The participants were asked to perform system trials and experiment with the system by navigating through the system interfaces, as well as being exposed to the traditional physical model of the human heart that is currently used in the faculty during practical anatomy sessions. Afterwards, they were asked to provide feedback via a comparative questionnaire. The participants’ replies to the questions regarding the Physical Heart Model and VR heart anatomy system were assessed for reliability using Cronbach’s alpha. The first group’s (Physical Heart Model questions) α value was 0.689. The second group’s (VR heart anatomy system questions) α value was 0.791. Comparing students’ experience results between the traditional method (Physical Heart Model) and the VR heart anatomy system, the mean scores showed a distinct increase in the values. This indicates that the developed system enhanced their experience in anatomy learning and the provided tools improved their understanding of heart anatomy. Results demonstrated the usefulness of the system by showing a higher satisfaction rate for the provided tools regarding structure and visualisation.

Seismic rock physical modelling for gas hydrate-bearing sediments

There are ambiguities and uncertainties in the recognition of gas hydrate seismic reflections and in quantitative predictions of physical information of natural gas hydrate reservoirs from seismic data. Rock physical modelling is a bridge that transforms the seismic information of geophysical observations into physical information, but traditional rock physics models lack descriptions of reservoir micro-structures and pore-filling materials. Considering the mineral compositions and pore micro-structures of gas hydrates, we built rock physical models for load-bearing and pore-filling gas hydrate-bearing sediments, describe the mineral compositions, pore connectivity and pore shape using effective media theory, calculated the shear properties of pore-filling gas hydrates using Patchy saturation theory and Generalized Gassmann theory, and then revealed the quantitative relation between the elastic parameters and physical parameters for gas hydrate-bearing sediments. The numerical modelling results have shown that the ratios of P-wave and S-wave velocities decrease with hydrate saturation, the P-wave and S-wave velocities of load-bearing gas hydrate-bearing sediments are more sensitive to hydrate saturation, sensitivity is higher with narrower pores, and the ratios of the P-wave and S-wave velocities of pore-filling gas hydrate-bearing sediments are more sensitive to shear properties of hydrates at higher hydrate saturations. Theoretical analysis and practical application results showed that the rock physical models in this paper can be used to calculate the quantitative relation between macro elastic properties and micro physical properties of gas hydrate-bearing sediments, offer shear velocity information lacking in well logging, determine elastic parameters that have more effective indicating abilities, obtain physical parameters such as hydrate saturation and pore aspect ratios, and provide a theoretical basis and practical guidance for gas hydrate quantitative predictions.

Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (−0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.

A hybrid intelligent optimization approach to improving quality for serial multistage and multi-response coal preparation production systems

Modeling and optimization of multiple quality response characteristics (i.e., ash content and calorific value) has been a very important issue that can assure the quality conformance and stability for coal preparation system with multiple operating stages. Although extensive research works have been reported on multistage and multiple responses optimization problem, the traditional physical based model and statistical cause-effect model reaches its limits due to the fast increasing complexity, nonlinearity and high-dimensionality of modern coal preparation system. In addition, these conventional modeling and optimization methods have poor self-adaption and self-learning ability to different operation conditions. Internet-of-things and cyber manufacturing techniques make it convenient to collect larger volumes of sensor data that can provide powerful support for efficient data analytics. The combination of massive industrial data, advanced machine learning models and intelligent optimization algorithms bring new opportunities to deal with these problems. Therefore, this paper attempts to propose a hybrid intelligent technique based multistage and multi-response optimization approach for serial coal preparation system. Firstly, using the support vector regression theory, we construct a mechanism-data hybrid driven and forward-iteration model to reflect the change and propagation mechanism of quality response characteristics along the stage. Secondly, combining with the constructed response models and modified desirability function, the genetic algorithm based backward-iteration optimization search method is presented to determine the globally best processing setting conditions. A real life case study is used to verify the usefulness of proposed approach. Additionally, the performance of surrogate models based on different propagation modeling mode, kernel parameter optimization methods and modeling techniques are comprised and discussed. The results show the effectiveness and superiority of proposed approach.

X‐Reality and the Microsoft HoloLens: A Hollow Tool for Anatomical Education

Recent technological advancements in X‐Reality (XR) seem to create remarkably realistic models for anatomical education. Despite a lack of evidence regarding the efficacy of XR in this context, several institutions have adopted these technologies as primary educational tools in anatomy as an alternative to traditional cadaveric laboratories. In our earliest study, we evaluated a 3D, interactive projection on a 2D screen against a physical model of a female pelvis. This data demonstrated that those who learnt on the physical model performed significantly better during testing. Subsequently, we explored the efficacy of more intricate XR systems. Thus, we compared the efficacy of the Microsoft HoloLens, a mixed‐reality (MR) device, to a physical model in anatomy education. We recruited 20 McMaster University students and ran a preliminary study to gather qualitative data regarding the optimal MR environment. We gathered participant preference based on their experience observing several virtual objects against different coloured backgrounds and various lighting combinations. We used this data to build the testing environment for the MR model, such as adding black curtains and floor tiles to the room, and using a single light over the projection. These conditions were also used for the physical model, thus placing it at a slight disadvantage. We then recruited 40 McMaster University students with no prior anatomical education, and randomized them into two groups: one learning on a physical model of a female pelvis and one learning on the MR model of a female pelvis. We measured two possible covariates, spatial and stereoscopic ability, through two pretest assessments: a Mental Rotations Test (MRT) and a Titmus Fly Test, respectively. Our participants were then given 10 minutes to learn 20 structures using their respective models, and were tested on a female cadaveric pelvis on the basis of a 25‐question test with no time limit. This test included 15 nominal questions, which asked participants to name the indicated structure, and 10 functional questions, which asked participants to determine the function of a structure based on its location and form. We hypothesized that due to the realistic model that the MR system created, it should perform at least equivalent to the physical model in the context of anatomical education. Our assessments found that participants learning on the physical model performed significantly better in comparison to their MR counterparts on both nominal (65% vs 41%, respectively; p = 0.0051) and functional measures (42% vs. 31%, respectively; p = 0.0134). Additionally, when controlling for the aforementioned covariates, we found these results to remain consistent. Ultimately, our results indicate that the MR device is an inefficient tool for anatomical education when compared to traditional physical models. Our future directions involve exploring possible determinants influencing the superiority of the physical model, such as stereoscopic vision, as well as the assessment of other XR systems, such as virtual reality headsets, in the context of anatomy education.Support or Funding InformationSelf‐funded.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Self-consistent core-pedestal transport simulations with neural network accelerated models

Fusion whole device modeling simulations require comprehensive models that are simultaneously physically accurate, fast, robust, and predictive. In this paper we describe the development of two neural-network (NN) based models as a means to perform a snon-linear multivariate regression of theory-based models for the core turbulent transport fluxes, and the pedestal structure. Specifically, we find that a NN-based approach can be used to consistently reproduce the results of the TGLF and EPED1 theory-based models over a broad range of plasma regimes, and with a computational speedup of several orders of magnitudes. These models are then integrated into a predictive workflow that allows prediction with self-consistent core-pedestal coupling of the kinetic profiles within the last closed flux surface of the plasma. The NN paradigm is capable of breaking the speed-accuracy trade-off that is expected of traditional numerical physics models, and can provide the missing link towards self-consistent coupled core-pedestal whole device modeling simulations that are physically accurate and yet take only seconds to run.

Tidal and prism analysis of sea reclamation project in coastal bay

Mathematical model analysis is very important in investigating the water exchanges and tidal prism in the reconstruction engineering projects around coastal bay. Based on the accurate simulation of tides, the mathematical model method is more economical and efficient than traditional measurement method and physical experiment model in the first evaluation of initial design. A robust numerical model with high accuracy is explored in this study for a systematic consideration of hydraulic interactions within different water zones in coastal reconstruction project. Application of the developed tidal model to a reconstruction project in the Dalian Laohutan Bay of China is presented in details. The developed tidal model with a refined meshing algorithm has been validated through measured field data with reasonable agreement. Then, the validated model is used to examine water exchanges and the tidal prism under different engineering reconstruction scenarios. The engineering solutions that satisfy the requirements of both project demands and environmental protection have been obtained.

Reliable 3-D Clock-Tree Synthesis Considering Nonlinear Capacitive TSV Model With Electrical–Thermal–Mechanical Coupling

A robust physical design of 3-D IC requires investigation on through-silicon via (TSV). The large temperatures and stress gradients can severely affect TSV delay with large variation. The traditional physical model treats TSV as a resistor with linear electrical-thermal dependence, which ignores the fundamental device physics. In this paper, a physics-based electrical-thermal–mechanical delay model is developed for signal TSVs in 3-D IC. With consideration of liner material and also stress, a nonlinear model is established between electrical delay with temperature and stress. Moreover, sensitivity analysis is performed to relate the reduction of temperature and stress gradients with respect to dummy TSVs insertion. Taking the design of 3-D clock tree as a case study, we have formulated a nonlinear optimization problem for clock-skew reduction. By allocating dummy TSVs to reduce the temperature and stress gradients, the clock skew introduced by signal TSVs and drivers can be minimized. A number of 3-D clock-tree benchmarks are utilized in experiments. We have observed that with the use of dummy TSV insertion, clock skew can be reduced by 61.3% on average when the accurate nonlinear electrical-thermal–mechanical delay model is applied.

Interplay between collective behavior and spreading dynamics on complex networks

There are certain correlations between collective behavior and spreading dynamics on some real complex networks. Based on the dynamical characteristics and traditional physical models, we construct several new bidirectional network models of spreading phenomena. By theoretical and numerical analysis of these models, we find that the collective behavior can inhibit spreading behavior, but, conversely, this spreading behavior can accelerate collective behavior. The spread threshold of spreading network is obtained by using the Lyapunov function method. The results show that an effective spreading control method is to enhance the individual awareness to collective behavior. Many real-world complex networks can be thought of in terms of both collective behavior and spreading dynamics and therefore to better understand and control such complex networks systems, our work may provide a basic framework.

Dark Parameterizations, Equivalent Partner Fields and Integrable Systems

After introducing dark parameters into the traditional physical models, some types of new phenomena may be found. An important difficult problem is how to directly observe this kind of physical phenomena. An alternative treatment is to introduce equivalent multiple partner fields. If use this ideal to integrable systems, one may obtain infinitely many new coupled integrable systems constituted by the original usual field and partner fields. The idea is illustrated via the celebrate KdV equation. From the procedure, some byproducts can be obtained: A new method to find exact solutions of some types of coupled nonlinear physical problems, say, the perturbation KdV systems, is provided; Some new localized modes such as the staggered modes can be found and some new interaction phenomena like the ghost interaction are discovered.

The Emergence of Immersive Low-Cost 3D Virtual Reality Environments for Interactive Learning in Materials Science and Engineering

ABSTRACTMaterials science is an interdisciplinary field that examines the structure-property relationships in matter for its applications to many areas of science and engineering. Providing a means for intuitive development of understanding of these relationships by young learners and university undergraduates alike is critical. The effectiveness of an immersive low-cost 3D virtual reality (VR) environment was evaluated during a pilot study sponsored by the Center of Integrated Nanomechanical Systems (COINS) program. The 3D VR environment involves the use of a specialized display, sensors, computers, and immersive visual technology equipment. In collaboration with Cognitive Science investigators, our research focused on understanding the impact of the 3D VR environment on the visual ability to perceive structures in three dimensions and on quantifying the learning of COINS participants. The premise was to measure the learning of undergraduate participants in activities designed to evaluate the quality of the learning environment. Our investigation consisted of three stages in which participants learned about carbon nanotubes (CNTs) via traditional methods, physical models and virtual models. Traditional methods (2D projection graphs) were not appealing to participants and did not facilitate depth perception. Physical (ball-and-stick) models motivated participants by allowing interactivity but bond distance/angle measurements were tedious. Virtual models (3D models) offered complete manipulation, real-time measurements and the capability of mimicking realistic atomic forces (attractive/repulsive), giving the user a better insight into the structure of CNTs compared to previous methods. While immersive environments offer virtual models with some of the same benefits of physical models, it is the extended features (e.g. accurate distance representation, computer simulations capability and analysis tools for further investigations) that suggest such environments as effective learning tools for materials science education. Preliminary data analysis suggests that highly accurate perception of a molecular structure is facilitated by the use of immersive environments in which the operator may manipulate and measure important intrinsic information about the structure. Moreover, computer simulations of materials are of great scientific interest for technological progress. We are presently working on the development of the immersive 3D VR environment to perform atomistic simulations to enable scientists to perform accelerated calculations to solve problems with performance enhancements over conventional methods. Another important value in the immersive 3D VR environment lies in its expanded use for multi-disciplinary research, influencing structure-dependent applications, science learning, and design of nanodevices in fields such as materials science, chemistry, engineering, cognitive science, nanotechnology, and computer science among others.