Physical Model Research Articles

Rapid sloshing-free transport of liquids in arbitrarily shaped containers

AbstractWe present a model-based feedforward control strategy suitable for designing swift rest-to-rest maneuvers for liquids in arbitrarily shaped containers. We employ the commonly used equivalent pendulum model to represent the sloshing dynamics and suggest a novel parameter identification scheme suitable for arbitrary container shapes and any number of sloshing modes. By computing natural modes and fluid reaction forces and torques for imposed harmonic container motions via a finite element model, we obtain data for the identification scheme. A fitting procedure then yields highly accurate parameters for a physical pendulum model, where each pendulum represents one sloshing mode. We also provide a thorough analysis of parameter identifiability and guidelines for obtaining robust parameter estimates. The proposed feedforward control method uses a virtual tray pendulum on which we place the container (in the form of its equivalent pendulum model). Designing the virtual tray such that the fluid’s dominant sloshing mode cannot be excited by horizontally moving the tray pendulum pivot effectively zeros out any sloshing motion in this mode. We then exploit the flatness property of the resulting system to design rest-to-rest maneuvers where any residual sloshing motion (in higher modes) can be exactly stopped at the end of the maneuver. The effectiveness of the proposed method is demonstrated through extensive simulations and experimental results using a Martini cocktail glass, whose shape is challenging in terms of sloshing. The experimental results show the successful, accurate suppression of sloshing, validating the efficacy of the proposed concept.

Enhancing Patient Comprehension in Skull-Base Meningioma Surgery through 3D Volumetric Reconstructions: A Cost-Effective Approach.

Understanding complex neurosurgical procedures and diseases, such as skull-base meningiomas, is challenging for patients due to the intricate anatomy and the involvement of critical neurovascular structures. Enhanced patient comprehension is crucial for satisfaction and improved clinical outcomes. Patient-specific 3D models have demonstrated benefits in patient education, though they are costly and time-intensive to produce. This study investigates whether the use of 3D volumetric reconstructions with anatomical segmentation, widely available via neuronavigation software, can improve patients' understanding of skull-base meningiomas, surgical procedures, and potential complications. This study included twenty patients with skull-base meningiomas. Three-dimensional volume reconstructions and anatomical segmentations were created using preoperative MRI sequences with neuronavigation software. These reconstructions were used during patient consultations where a surgeon explained key aspects of the disease, the surgical intervention, and potential complications. A questionnaire assessed the patients' perceptions of the utility of these 3D reconstructions. The majority of patients (75%) found the 3D volumetric reconstructions and anatomical segmentations to be more beneficial than MRI images for understanding their disease. Similarly, 75% reported improved comprehension of the surgical approach, and 85% felt that the reconstructions enhanced their understanding of potential surgical complications. Overall, 65% of patients considered the 3D reconstructions valuable in medical consultations. Our study indicates that using accessible, cost-effective, and non-time-consuming 3D volumetric reconstructions with anatomical segmentation enhances patient understanding of skull-base meningiomas. Further research is necessary to confirm these findings, compare these reconstructions with physical 3D models and virtual reality models, and evaluate their impact on patient anxiety regarding the surgical procedure.

Effect of slope angle on fractured rock masses under combined influence of variable rainfall infiltration and excavation unloading

Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites. The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation, considering the combined effects of precipitation and excavation activities. The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province, China. An engineering geological investigation was conducted, a physical model experiment was performed, numerical calculations and theoretical analysis were conducted using the matrix discrete element method (MatDEM), and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined. The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed. The experimental results indicate that as the slope angle increases, the combined effect of rainfall infiltration and excavation unloading is reduced. A novel approach to simulating unsaturated seepage in a rock mass, based on the van Genuchten model (VGM), has been developed. Compared to the vertical displacement observed in a similar physical experiment, the average relative errors associated with the slope angles of 45°, 50°, and 55° were 2.094%, 1.916%, and 2.328%, respectively. Accordingly, the combined effect of rainfall and excavation was determined using the proposed method. Moreover, the accuracy of the numerical simulation was validated. The findings contribute to the seepage field in a meaningful way, offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability, which can inform the development of more effective risk management strategies.

Revisiting the fermion-field nontopological solitons

Nontopological fermionic solitons exist across a diverse range of particle physics models and have rich cosmological implications. This study establishes a general framework for calculating fermionic soliton profiles under arbitrary scalar potentials, utilizing relativistic mean field theory to accurately depict the interaction between the fermion condensate and the background scalar field. Within this framework, the conventional “fermion bound states” are revealed as a subset of fermionic solitons. In addition, we demonstrate how the analytical formulae in previous studies are derived as special cases of our algorithm, discussing the validity of such approximations. Furthermore, we explore the phenomenology of fermionic solitons, highlighting new formation mechanisms and evolution paths, and reconsidering the possibility of collapse into primordial black holes.

Development and verification of real-time hybrid model test delay compensation method for monopile-type offshore wind turbines

The real-time hybrid model (RTHM) test is adept at addressing the scale contradiction, the lack of fidelity in wind modelling in hydrodynamic testing facilities and spatial constraints inherent in conventional monopile-type offshore wind turbine (OWT) model testing methods, thus emerging as an effective avenue for conducting physical model tests of Monopile-type OWTs. This method entails the reproduction of aerodynamic loads or platform motions using loading device or vibration tables. Time delays in the physical attributes of the loading device and signal transmission processes within the system can result in error accumulation, with the potential to impact overall system stability. Moreover, time delay compensation algorithms for hybrid model test systems with force control loading can easily generate excessive noise, leading to system divergence. As a result, time delay has emerged as a technical challenge in the RTHM test. To address this issue, this paper has developed second-order and third-order polynomial extrapolation algorithms, alongside an adaptive compensation algorithm. The adaptive compensation algorithm employs the least squares method to identify parameters of the loading system, enabling it to address variations in the time delay of the experimental system caused by the nonlinearity of the loading system and changes in the physical properties of the model. The feasibility and effects of time delay compensation for various algorithms are validated through numerical simulation. Results indicate that the adaptive compensation algorithm surpasses second and third-order polynomial extrapolation compensation algorithms in terms of accuracy and compensation effectiveness. To validate the applicability of the adaptive compensation algorithm, a RTHM test was conducted. Across rotor thrust force (RotThrust) and tower top displacement, there was an average reduction of approximately 5 % and 9 % in the maximum and minimum synchronization errors, respectively. This highlights the efficacy of the delay compensation algorithm in practical applications, notably diminishing time delay errors within the experimental system. The adaptive compensation algorithm continuously adjusts and updates parameters, enhancing the adaptability of the compensation process to time-varying systems.

Effect of Lateral Load to the Behavior of Single Piles Subjected to Earthquake Load in Sand

In fact, pile usually carry static and dynamic load in same time, the case of simulations lateral and dynamic load is possible occurred. This case is not cover enough by previous researches especially by laboratory model. Therefore, the purpose of this study is to determine the extent to which altering the horizontal lateral loads impacts the behavior of individual piles during seismic events. An experimental test was devised using a physical model with an advanced shaking table that using El Centro earthquake. Sandy soil with 65% relative density used raining technique. Lateral loads were applied at levels of 0%, 50% and 100% from allowable lateral load capacity. It was observed that when lateral load increased from 0% to 50% and 100%, the lateral displacement response decreased by 32.10% and 49.59% respectively. While the vertical displacement increased by 49.96% and 76.95%. Finally, the acceleration decreased by 34.39% and 41.03%. This is possibly due to the load acts at an angle opposite to the earthquake as a restraining factor for the pile. led to a decrease in lateral displacement, vertical displacement, and peak ground acceleration.

Investigating the Energy Dissipation Mechanism of Piano Key Weir: An Integrated Approach Using Physical and Numerical Modeling

The enormous energy carried by discharged water poses a serious threat to the Piano Key Weir (PKW) and its downstream hydraulic structures. However, previous research on energy dissipation in PKWs has mainly focused downstream effects, and the research methods have been largely limited to physical model experiments. To deeply investigate the discharge capacity and hydraulic characteristics of PKW, this study established a PKW model with universally applicable geometric parameters. By combining physical model experiments and numerical simulations, the flow pattern of the PKW, the discharge at the overflow edges, and the variation in the energy dissipation were revealed for different water heads. The results showed that the discharge of the side wall constitutes the majority of the total discharge at low water heads, resulting in a relatively high overall discharge efficiency. As the water head increases, the proportion of discharge from the inlet and outlet keys increases, while the proportion from the side wall decreases. This change results in less discharge from the side wall and a consequent reduction in the overall discharge efficiency. The PKW exhibits superior energy dissipation efficiency under low water heads. However, this efficiency exhibits an inverse relationship with an increasing water head. The overall energy dissipation efficiency can reach 40% to 70%. Additionally, the collision of the water flows inside the outlet chamber and the mixing of the overflow jet play a primary role in energy dissipation. The findings of this study have significant implications for hydraulic engineering construction and PKW operational safety.

Numerical study using the finite volume method on the thermal and energy performance of a regulated underfloor heating system incorporating PCM in a ventilated room

A physical model is performed to assess the thermal and energetic advantages of using PCM in controlling and regulating the indoor temperature of a ventilated room with an underfloor heating system incorporating a PCM. The heating source is regulated to keep the cavity air temperature in a small interval assuring a certain level of comfort without wasting too much energy. By contrasting our numerical results with numerical data from the literature, the suggested model was validated. Physical processes are solved numerically using computational fluid dynamics by a detailed 2D transient simulation integrating real climatic data of Agadir, Tangier and Casablanca. The simulations carried out justified the suitability of using the PCM with Agadir and Casablanca climate types by a saving of heating power reaching 18.80 % and 17.33 %, respectively.

Thermal Mixing Analysis in a Ladle Utilizing Physical and Numerical Modeling through Planar Laser-induced Fluorescence (PLIF) Technique

Thermal Mixing Analysis in a Ladle Utilizing Physical and Numerical Modeling through Planar Laser-induced Fluorescence (PLIF) Technique

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

ObjectivesCT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methodsIn this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. ResultsIn the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. ConclusionExperimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net.

A Data-Driven Multi-Step Flood Inundation Forecast System

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving the performance of LIBS

Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters. Further, the deep learning methods were used to fully excavate all the effective features in the plasma images and spectra to invert the complex high-dimensional plasma parameters according to the physical models. Finally, the estimation models of spectral bias and spectral error were established based on these features, to realize the high-precision correction of spectral intensity. To verify the feasibility of SBESC-PISF, the spectra of aluminum alloy samples obtained under three complex detection conditions were used for analysis. Under the experimental condition of laser energy fluctuation, after correction by SBESC-PISF, R2 of the three calibration curves was all increased to 0.999, RMSE and STD of the validation set (RMSEV, STDV) were reduced by 55.246 % and 50.167 %, respectively. Under the experimental condition of defocusing amount fluctuation, R2 was also all increased to 0.999, RMSEV and STDV were decreased by 58.201 % and 51.006 %, respectively. When the laser energy and defocusing amount fluctuate simultaneously, R2 was increased to 0.999, 0.996 and 0.988, RMSEV and STDV were reduced by 58.776 % and 54.397 %, respectively. These experimental results demonstrate that the spectral fluctuation correction of SBESC-PISF under complex detection conditions is effective and has wide applicability.

Viability of fracture characterization using seismic attributes through seismic physical modeling

Abstract Seismic attributes have been widely utilized for fault detection; however, accurately characterizing mesoscale fractures (1/10 to 1/4 wavelength) using these attributes remains challenging, particularly in complex geological setting. To explicitly reveal the effectiveness of seismic attributes in fracture characterization, we constructed a realistic physical model based on the stratigraphic structure of a real shale-gas play. This model incorporates several faults and six fracture zones containing vertically aligned mesoscale fractures of varying fracture densities, embedded in a target layer of 40 m equivalent thickness. Through a comprehensive evaluation of various seismic attributes, we demonstrate that, while these attributes effectively map faults or macroscale fractures, they are less reliable for characterizing mesoscale fractures. Although mesoscale fractures induce anomalies in seismic amplitude and attributes, the magnitude of these anomalies does not correlate with the fracture density. To address this limitation, we introduce the amplitude at the dominant frequency (ADF) attribute, designed to characterize fracture density in thin-beds with mesoscale fractures. A strong relationship is observed between fracture density and the ADF attribute, with a correlation coefficient of 0.8067. Thus, the ADF attribute serves as a valuable tool for interpreting seismic data in the context of mesoscale fracture density.

Experience in Team Multidisciplinary Training of an Ambulance Station Using a Physical Model of the City

The most difficult part of training young professionals is the skills of decision-making, communication, and role allocation. To develop these skills, Kursk State Medical University conducts business games simulating the operation of an ambulance and disaster medicine station. Starting with oral exercises and board games, the authors aim to diversify the mechanics of the game and increase engagement. The result was a model of an entire region with a city, villages, enterprises and natural areas, where participants physically move by special transport and fully provide the entire pre-hospital stage in normal and emergency conditions.

Large-scale data processing platform for laser absorption tomography

Abstract Laser absorption tomography (LAT) has been widely employed to capture two/three-dimensional reactive flow-field parameters with a penetrating spatiotemporal resolution. In industrial environments, LAT is generally implemented by measuring multiple, e.g. 30 to more than 100, wavelength modulated laser transmissions at high imaging rates, e.g. tens to thousands of frames per second (fps). A short-period LAT experiment can generate extensive load of data, which require massive computational source and time for data post-processing. In this work, a large-scale data processing platform is designed for industrial LAT. The platform significantly speeds up LAT signal processing by introducing a parallel computing architecture. By identifying the discrepancy between the measured and theoretical spectra, the new platform enables indexing of the laser-beam measurements that are disturbed by harsh-environment noise. Such a scheme facilitates effective removal of noise-distorted beams, which can lead to artefacts in the reconstructed images. The designed platform is validated by a lab-based LAT experiment, which is implemented by processing the laser transmissions of a 32-beam LAT sensor working at 250 fps. To process a 60-second LAT experimental dataset, the parallelism enabled by the platform saves computational time by 40.12% compared to the traditional single-thread approach. The error-detection scheme enables the successful accurate identification of noise-distorted measurements, i.e. 0.59% of overall laser-beam measurements that fall out of the physical model.

Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models

Abstract Artificial neural network models have been developed to predict rotation velocity and ion temperature profiles on the EAST tokamak based on spectral measurements from the X-ray crystal spectrometer. Both Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models have been employed to infer line-integrated ion temperatures. The predicted results from these two models exhibit a strong correlation with the target values, providing an opportunity for cross-validation to enhance prediction accuracy. Notably, the computational speed of these models has been significantly increased, surpassing traditional methods by over tenfold. Furthermore, the investigation of input data range and error prediction serves as the foundation for future automated calculation process. Finally, CNNs have also been employed to predict line-integrated rotation velocity profiles and inverted ion temperature profiles for their robustness in the training process. It is noted that these algorithms are not restricted to any specific physics model and can be readily adapted to various fusion devices.

Statistical physics insights in the Allura Red adsorption on chitosan: Effects of adsorbents features, and experimental conditions

This study presents an innovative approach using statistical physical modeling to analyze the effect of experimental conditions and adsorbent features on removing Allura Red dye by chitosan. Five models based on statistical physics were employed to understand the removal mechanism under different experimental conditions, including variations in pH, particle sizes, chitosan deacetylation degrees, and temperatures. The best models were determined through rigorous statistical analysis and a detailed evaluation of the estimated parameters, considering their physical meaning and standard deviations. The models demonstrated high predictive capacity (coefficients of determination greater than 0.9 and low mean squared error values < 110). Analysis of the effects of experimental conditions revealed that a pH of 6.6, particles smaller than 0.10 mm, a high degree of deacetylation (84 %), and temperatures of 298 K and 308 K present the greatest potential for dye removal by chitosan. Furthermore, it was observed that, in general, the dye removal mechanism involves the formation of a double layer with two adsorption energies under the best experimental conditions, and the process occurs predominantly by physical forces (adsorption energy below 35 kJ/mol). This work contributes significantly to understanding how experimental conditions influence the mechanism of dye adsorption by chitosan, offering valuable and new insights for future applications and optimizations in the treatment of effluent-containing dyes.

Developing discharge-head equation for radial-gated spillways based on physical modeling

Determination of the discharge through the spillways with radial gates is essential, depending on the application of these spillways in check dams and flood discharge systems in larger dams. On the other hand, physical modeling is often required due to the complexity and impact of various factors. This study investigated the efficiency of various methods in estimating the discharge through the spillways with radial gates using 958 experimental datasets from physical models of flood discharge systems in 17 large reservoir dams in Iran. In addition to dimensional analysis of the parameters, affecting the discharge of spillways with radial gates, new equations were proposed to determine the gate opening and the deviation angle of the gate. The mean absolute relative error in estimating the discharge at every flow condition showed a decreasing trend from about 9.1 % (for the previous methods) to about 3.9 % (the proposed equation in the present study). Later, the effects of different factors on the discharge through the spillways with the radial gate in large dams were evaluated. The results showed the errors of the previous methods in estimating the discharge for transient flow conditions, non-standard spillways, increase in the number of gates, and operation of only one gate with up to 22.2 %, 11.8 %, 21.5 %, and 10.5 %, respectively. However, under the abovementioned conditions, the mean absolute relative error of the proposed method error of discharge estimation, in the present study, decreased to about 4 %, 4.3 %, 3.3 %, and 3.5 %, respectively. Accordingly, using the new proposed equation involving the different factors to determine the discharge through the spillways with the radial gates in larger dams is recommended.

Wind farm power optimization using system identification

The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.