A new theoretical approach to determine the air outlet temperature of an air-to-ground heat exchanger.

In this study, the control volume method is used to determine the air temperature at the outlet of an air-to-ground heat exchanger. Its implementation consists in dividing the duct of the ground-air heat exchanger into micro-volumes of identical size. An energy balance is then established for each micro-volume. The input parameters used to implement this model are related to the city of Yaoundé in the equatorial zone. The results show that when the total length of the air-to-ground heat exchanger duct varies between 0 and 100 m, the air temperature at the outlet also varies between 34.5 and 24 °C. The air-to-ground heat exchanger operates in cooling mode. As the length of the air-to-ground heat exchanger duct increases, the temperature of the air at the outlet of the air-to-ground heat exchanger decreases, approaching that of the ground. Based on the results obtained using the control volume model, the minimum total length of air-to-ground heat exchanger duct recommended for this zone is 40 m. Admittedly, air pressure drops, air humidity and the geometry of the air-to-ground heat exchanger are aspects that have not yet been taken into account in the implementation of this model. Nevertheless, the control volume method can be used to optimise the parameters influencing the thermal performance of an air-to-ground heat exchanger.•The control volume method is implemented here by dividing the air-to-ground heat exchanger duct into identical micro-volumes and then establishing an energy balance for each micro-volume;•In this work, the control volume method was used to optimise the total length of the duct of a ground air heat exchanger installed in an equatorial zone;•Some important aspects such as air pressure drops, air humidity, and the geometry of the air-to-ground heat exchanger are not yet taken into account in the implementation of the control volume method.

Physical activities aid in tumor prevention: A finite element study of bio-heat transfer in healthy and malignant breast tissues.

The objective of the present research is to explore the temperature diffusion in healthy and cancerous tissues, with a specific focus on how physical activity impacts on the weakening of breast tumors. Previous research lacked numerical analysis regarding the effectiveness of physical activity in tumor prevention or attenuation, prompting an investigation into the mechanism behind physical activity and tumor prevention from a bio-heat transfer perspective. The study employs a realistic model of human breasts and tumors in COMSOL Multiphysics® to analyze temperature distribution by utilizing Penne's bio-heat equation. The research examines their influence on tissue temperature by varying tumor diameter (10-20 mm) and exercise intensities (such as walking speeds and other activities like carpentry, swimming, and marathon running). Results demonstrate that cancerous tissues generate notably more heat than normal tissues at rest and during physical activity. Smaller tumors exhibit higher temperatures during exercise, emphasizing the significance of tumor size in treatment effectiveness. Tumor temperatures range between 40 and 43.2 °C, while healthy tissue temperatures remain below 41 °C during physical activity. High-intensity exercises, particularly swimming, walking at 1.8 m/s, and marathon running, display a therapeutic effect on tumors, increasing effectiveness with intensity. The temperatures of healthy and malignant tissues rise noticeably due to constant metabolic heat and decreased blood flow. The study also identifies the optimal duration of high-intensity exercise, recommending at least 20 min for optimal therapeutic outcomes. The outcomes of this research would help individuals, doctors, and cancer researchers understand and weaken malignant tissues.

Development of a novel geometrically-parametric patient-specific finite element model for anterior cruciate ligament reconstruction.

PURPOSE: A personalized model of the knee joint, with adjustable effective geometric parameters for the transplanted autograft diameter in Anterior Cruciate Ligament Reconstruction (ACLR) using the bone-patella-tendon-bone (BPTB) technique, has been developed. The model will assist researchers in understanding how different graft sizes impact a patient's recovery over time. METHODS: The study involved selecting a group of individuals without knee injuries and one patient who had undergone knee surgery. Gait analysis was conducted on the control group and the patient at various time points. A 3D model of the knee joint was created using medical images of the patient. Forces and torques obtained from the gait analysis were applied to the model to perform finite element analysis. RESULTS: The results of the finite element (FE) analysis, along with kinetic data from both groups, indicate that models with diameters of 7.5 mm and 12 mm improved joint motion during follow-up after ACLR. Additionally, a comparison of the stress applied to the ACL model revealed that a 12 mm autograft diameter showed a more favorable trend in patient recovery during the three follow-up intervals after ACL reconstruction surgery. CONCLUSION: The development of a personalized parametric model with adjustable geometric parameters in ACLR, such as the transplanted autograft diameter, as presented in this study, along with FE using the patient's kinetic data, allows for the examination and selection of an appropriate autograft diameter for Patella Tendon grafting. This can help reduce stress on the autograft and prevent damage to other knee joint tissues after ACLR.

Prediction of hippocampal electric field in time series induced by TI-DMS with temporal convolutional network.

Temporal interference deep-brain magnetic stimulation (TI-DMS) induces rhythmic electric field (EF) in the hippocampus to normalize cognitive function. The rhythmic time series of the hippocampal EF is essential for the assessment of TI-DMS. However, the finite element method (FEM) takes several hours to obtain the time series of EF. In order to reduce the time cost, the temporal convolutional network (TCN) model is adopted to predict the time series of hippocampal EF induced by TI-DMS. It takes coil configuration and loaded current as input and predicts the time series of maximum and mean values of the left and right hippocampal EF. The prediction takes only a few seconds. The model parameter combination of kernel size and layers is selected optimally by cross-validation method. The experimental results for multiple subjects show that the R2 of all the time series predicted by the model exceed 0.98. And the prediction accuracy is even higher as the input parameters approach the training set. These results demonstrate that the adopted model can quickly predict the time series of hippocampal EF induced by TI-DMS with relatively high accuracy, which is beneficial for future clinical applications.

Design and simulation of a compact polarization beam splitter based on dual-core photonic crystal fiber with elliptical gold layer.

For the polarization multiplexing requirements in all-optical networks, this work presents a compact all-fiber polarization beam splitter (PBS) based on dual-core photonic crystal fiber (PCF) and an elliptical gold layer. Numerical analysis using the finite element method (FEM) demonstrates that the mode modulation effect of the central gold layer effectively reduces the dimensions of the proposed PBS. By determining reasonable structural parameters of the proposed PCF, the coupling length ratio (CLR) between X- and Y-polarized super-modes can approach 2, achieving a minimal device length of 0.122 mm. The PBS exhibits a maximum extinction ratio (ER) of - 65 dB at 1.55 µm, with an operating bandwidth spanning 100 nm (1.5-1.6 µm) and a stable insertion loss (IL) of ~ 1.5 dB at 1.55 µm. Furthermore, the manufacture feasibility and performance verification scheme are also investigated. It is widely anticipated that the designed PBS will play a crucial role in the ongoing development process of miniaturization and integration of photonic devices.

Numerical simulation study on opening blood-brain barrier by ultrasonic cavitation.

Experimental studies have shown that ultrasonic cavitation can reversibly open the blood-brain barrier (BBB) to assist drug delivery. Nevertheless, the majority of the present study focused on experimental aspects of BBB opening. In this study, we developed a three-bubble-liquid-solid model to investigate the dynamic behavior of multiple bubbles within the blood vessels, and elucidate the physical mechanism of drug molecules through endothelial cells under ultrasonic cavitation excitation. The results showed that the large bubbles have a significant inhibitory effect on the movement of small bubbles, and the vibration morphology of intravascular microbubbles was affected by the acoustic parameters, microbubble size, and the distance between the microbubbles. The ultrasonic cavitation can significantly enhance the unidirectional flux of drug molecules, and the unidirectional flux growth rate of the wall can reach more than 5 %. Microjets and shock waves emitted from microbubbles generate different stress distribution patterns on the vascular wall, which in turn affects the pore size of the vessel wall and the permeability of drug molecules. The vibration morphology of microbubbles is related to the concentration, arrangement and scale of microbubbles, and the drug permeation impact can be enhanced by optimizing bubble size and acoustic parameters. The results offer an extensive depiction of the factors influencing the blood-brain barrier opening through ultrasonic cavitation, and the model may provide a potential technique to actively regulate the penetration capacity of drugs through endothelial layer of the neurovascular system by regulating BBB opening.

Numerical approximation for algorithmic tangent moduli for nonlinear viscoelastic model with CSDA method.

This work revisits the notion of complex step derivative approximation (CSDA) and presents its use in constitutive model of a class of nonlinear viscoelastic materials. The effectiveness of a CSDA is evaluated by putting it through a series of straightforward examples. After that, the idea of the CSDA is put to use in order to carry out a numerical evaluation of the algorithmic tangent moduli of a viscoelastic constitutive model. The performance of the constitutive models is evaluated through the use of three different numerical tests, and the results are compared to those that were achieved by the application of an analytical method. In comparison to other numerical differentiation techniques, It has been found that the CSDA scheme is the most computationally efficient and robust method of numerical differentiation, regardless of the size of the finite difference interval.

Slow sound mode prediction and band structure calculation in 1D phononic crystal nanobeams using an artificial neural network.

Phononic crystals, which are artificial crystals formed by the periodic arrangement of materials with different elastic coefficients in space, can display modulated sound waves propagating within them. Similar to the natural crystals used in semiconductor research with electronic bandgaps, phononic crystals exhibit the characteristics of phononic bandgaps. A gap design can be utilized to create various resonant cavities, confining specific resonance modes within the defects of the structure. In studies on phononic crystals, phononic band structure diagrams are often used to investigate the variations in phononic bandgaps and elastic resonance modes. As the phononic band frequencies vary nonlinearly with the structural parameters, numerous calculations are required to analyze the gap or mode frequency shifts in phononic band structure diagrams. However, traditional calculation methods are time-consuming. Therefore, this study proposes the use of neural networks to replace the time-consuming calculation processes of traditional methods. Numerous band structure diagrams are initially obtained through the finite-element method and serve as the raw dataset, and a certain proportion of the data is randomly extracted from the dataset for neural network training. By treating each mode point in the band structure diagram as an independent data point, the training dataset for neural networks can be expanded from a small number to a large number of band structure diagrams. This study also introduces another network that effectively improves mode prediction accuracy by training neural networks to focus on specific modes. The proposed method effectively reduces the cost of repetitive calculations.

Data analysis of thermal performance and irreversibility of convective flow in porous-wavy channel having triangular obstacle under magnetic field effect.

Mixed convective nanofluid flow has substantial importance in improvement of thermal performance, and thermal engineering to meet the global energy crisis. In this study, mixed convective nanofluid flow in a porous-wavy channel with an inner heated triangular obstacle under magnetic field effect is numerically examined. Nanofluid within the channel is heated and cooled from its bottom and top wavy-surfaces. A heated triangular cylinder is located at the centerline of the wavy-channel. Finite element method is utilized to solve the non-dimensional governing equations. The code is validated comparing present results with published numerical and experimental results. The response surface method is also implemented to analyze the obtained results and its sensitivity. The numerical results indicate that strength of flow velocity is accelerated with rising Reynolds number, Darcy numbers and inlet-outlet ports length but declined for Hartmann number and volume fraction. Heat transferring rate and heat transfer irreversibility are substantially increased for higher values of Reynolds number, inlet-outlet ports length, Darcy number and nanoparticle volume fraction but a reverse trend is occurred for magnetic field effect. The thermal performance is found significantly improved with simultaneous increment in Re, ϕ, Da and decrement in Ha. Positive sensitivity is achieved for input factors Re, ϕ, Da in computing N u a v while negative sensitivity to Ha. Heat transfer rate is found more sensitive to the impact of Re and ϕ compared to Da and Ha. 45.59 % more heat transmission potentiality is developed for using Al2O3-H2O nanofluid (vol.5 %) instead of using base fluid water. Heat transfer enhancement rate is decreased by 36.22 % due to impact of magnetic field strength. In addition, 84.12 % more heat transferring rate is recorded in presence of triangular obstacle. Moreover, irreversibility components are influenced significantly for the presence of heated triangular obstacle. Bejan number is also found declined for increasing physical parameters. The findings of this investigation may offer a guideline for finding experimental results to design high-performance convective heat exchangers.

Prediction of Welding Deformation Using the Thermal Elastic-Plastic Finite Element Method by Considering Welding Interpass Temperature.

In this study, we propose a method for predicting welding deformation caused by multi-pass welding using the thermal elastic-plastic finite element method (TEP-FEM) by considering the interpass temperature. This method increases the interpass temperature, which has not been considered in the existing TEP-FEM, from 200 °C to 1000 °C, and simultaneously performs thermal and mechanical analyses. In addition, this method can also evaluate temperature history and the time it takes to weld. By predicting the welding deformation using this method, angular distortion prediction was reduced from 16.75 mm to 10.9 mm compared to the case where the interpass temperature was cooled to room temperature. Additionally, the deformation error was significantly reduced from 6.14% to 2.92% compared to that of the strain as directed boundary method used in a previous study. Additionally, our research demonstrated that interpass temperatures above 800 °C can result in increased deformation errors. In conclusion, it is essential to select an appropriate temperature to minimize deformation error.

Adaptive Mesh Strategy for Efficient Use of Interface Elements in a 3D Probabilistic Explicit Cracking Model for Concrete.

In this paper, the development of a 3D adaptive probabilistic explicit cracking model for concrete is reported. The contribution offered herein consists in a new adaptive mesh strategy designed to optimize the use of interface elements in probabilistic explicit cracking models. The proposed adaptive mesh procedure is markedly different from other strategies found in the literature, since it takes into account possible influences on the redistribution of stresses after cracking and can also be applied to purely deterministic cracking models. The process of obtaining the most appropriate adaptive mesh procedure involved the development and evaluation of three different adaptivity strategies. Two of these adaptivity strategies were shown to be inappropriate due to issues related to stress redistribution after cracking. The validation results demonstrate that the developed adaptive probabilistic model is capable of predicting the scale effect at a level similar to that experimentally observed, considering the tensile failure of plain concrete specimens. The results also show that different softening levels can be obtained. The proposed adaptive mesh strategy proved to be advantageous, being able to promote significant reductions in the simulation time in comparison with the classical strategy commonly used in probabilistic explicit cracking models.

Temperature-Stress Coupling Fatigue Behavior of Film-Cooling Holes in Complex Temperature Fields.

This research investigates the complex temperature distribution and fatigue behavior of single film-cooling holes manufactured by lasers with different pulse widths in a real flow field. The aerodynamic and heat transfer characteristics of film-cooling holes manufactured using lasers with different pulse widths were analyzed through laser drilling experiments, conjugate heat transfer simulations, and crystal plasticity finite element methods. The study investigated the relationship between changes in the geometric accuracy of the film-cooling holes and the corresponding flow and temperature fields during the film-cooling process. Additionally, the effects of temperature and structural variations on the stress around the holes in a flat plate composed of the second-generation nickel-based single-crystal superalloy DD6 in real flow and temperature fields were studied. The coupling effect of the temperature and stress fields around the holes on the fatigue behavior of the film-cooling holes was examined, and the fatigue damage mechanism of film-cooling holes in complex temperature fields was analyzed. It was found that changes in the blowing ratio do not affect the temperature and stress distributions around the holes but only alter the temperature peak. An increase in the temperature peak results in a decrease in the stress peak. Additionally, the fatigue damage of single film-cooling holes is determined by both the structural defects of the holes and the changes in material behavior due to the temperature around the holes, with the structural influence being more significant.

Numerical Investigation of Fatigue Behavior in Ti-6Al-4V Orthopedic Hip Implants Subjected to Different Environments.

In this paper, hip implants made of Ti-6Al-4V titanium alloy are analyzed numerically using Extended Finite Element Method XFEM. The combined effect of corrosion and fatigue was considered here since this is a common cause of failure of hip implants. Experimental testing of Ti-6Al-4V alloy was performed to determine its mechanical properties under different working environments, including normal, salty, and humid conditions. The integrity and life of the hip implant were assessed using the Linear Elastic Fracture Mechanics (LEFM) approach. For this purpose, the conditional fracture toughness Kq using CT specimens from all three groups (normal, humid, salty conditions) were determined. This provided insight into how different aggressive environments affect the behavior of Ti-6Al-4V alloy; i.e., how much its resistance to crack growth would degrade depending on conditions corresponding to the real exploitation of hip implants. Next, analytical and XFEM analyses of fatigue behavior in terms of the number of cycles were performed for all three groups, and the obtained results showed good agreement, confirming the validity of the integrity assessment approach shown in this work, which also represented a novel approach since fatigue and corrosion effects were investigated simultaneously.

X-IGA Used for Orthotropic Material Crack Growth.

In this paper, we propose a new approach for numerically simulating the growth of cracks in unidirectional composite materials, termed extended isogeometric analysis, evaluating the maximum stress intensity factor and T-stress. To validate our approach, we used a small anisotropic plate with two edge cracks, beginning with formulating the governing equations based on the energy integral method, Stroh's Formula, and the Elastic Law describing the behaviour of anisotropic materials, while considering boundary conditions and initial states. A MATLAB code was developed to solve these equations numerically and to post-process the tensile stress and the stress intensity factor (SIF) in the first mode. The results for the SIF closely match those obtained using the extended finite element method (X-FEM), with a discrepancy of only 0.0021 Pa·m0.5. This finding underscores the credibility of our approach. The extended finite element method has demonstrated robustness in predicting crack propagation in composite materials in recent years, leading to its adoption by several widely used software packages in various industries.

Finite element analysis of fatigue life of commercially pure titanium clasps additively manufactured with different building orientations.

The geometrical accuracy of additively manufactured pure titanium clasps depends on the building orientation. The aim of this study is to compare the geometrical accuracy and the fatigue lives predicted by finite element analysis (FEA) among three clasps manufactured with different building orientations. Besides, this paper proposed a calculation method of the moment of inertia of area and cross-sectional area along with the arm as the geometrical parameters. One of the clasps manufactured with a cylindrical chucking part for the fatigue test had almost the same geometrical parameters with the CAD design. Also, the authors' fatigue life prediction method using the CAD based FEA was verified through comparison with micro-CT image-based FEA. The other two clasps had larger geometrical parameters than the CAD design, resulting in longer fatigue lives. The results implied the importance of calculating the moment of inertia of the area in the design of the clasp arm.

Investigating the thermodynamic optimization of naturally convective flow in a corrugated enclosure: The influence of gap spacing and orientation of split baffles.

The natural convection in cavities is frequently used in fluid mechanics and heat transfer engineering, such as heat exchangers, electronics, solar collectors, and growing crystals. However, the physics of natural convection flow and heat transfer in cavities with split baffles is least understood. The fundamental aim of this research is to investigate the impact of heated split baffles positioned at various locations on steady-state free convection within a sinusoidal corrugated star cavity. In this model configuration, the outer wavy enclosure is maintained at a constant temperature of T c , while the inner split baffles are heated at a constant temperature of T h . The finite element method is employed to discretize and solve the governing equations describing the fluid flow and heat transfer within the enclosure. This numerical approach aimed to analyze the effects of baffle inclination angles, baffle spacing, Rayleigh number on the fluid dynamics and convective thermal transport characteristics. The variation in velocity and temperature profile is illustrated through the streamlines and isotherm contours. Moreover, the numerical result is displayed in term N u a v g of the heat transfer, which are analyzed for inside space of baffles and angles of the baffle ( θ = 0 0 , 45 0 , 90 0 ) . The key finding demonstrates that increasing the Rayleigh numbers and the different positions (up, central, down) of inner vertical split baffles enhances the magnitude of the velocity by 88.1 % , 85.9 and 89.6 % respectively. Furthermore, for the inner rectangular split baffle angles of 0 ° , 45 ° , and 90 ° , and within the Rayleigh number range of 10 4 to 10 6 , the N u a v g exhibits significant variations, with maximum increases of 71.9 % , 46.7 % and a subsequent decrease of 45.9 % .

Computational investigation of the role of ventricular remodelling in HFpEF: The key to phenotype dissection.

Recent clinical studies have reported that heart failure with preserved ejection fraction (HFpEF) can be divided into two phenotypes based on the range of ejection fraction (EF), namely HFpEF with higher EF and HFpEF with lower EF. These phenotypes exhibit distinct left ventricle (LV) remodelling patterns and dynamics. However, the influence of LV remodelling on various LV functional indices and the underlying mechanics for these two phenotypes are not well understood. To address these issues, this study employs a coupled finite element analysis (FEA) framework to analyse the impact of various ventricular remodelling patterns, specifically concentric remodelling (CR), concentric hypertrophy (CH), and eccentric hypertrophy (EH), with and without LV wall thickening on LV functional indices. Further, the geometries with a moderate level of remodelling from each pattern are subjected to fibre stiffening and contractile impairment to examine their effect in replicating the different features of HFpEF. The results show that with severe CR, LV could exhibit the characteristics of HFpEF with higher EF, as observed in recent clinical studies. Controlled fibre stiffening can simultaneously increase the end-diastolic pressure (EDP) and reduce the peak longitudinal strain (ell) without significant reduction in EF, facilitating the moderate CR geometries to fit into this phenotype. Similarly, fibre stiffening can assist the CH and 'EH with wall thickening' cases to replicate HFpEF with lower EF. These findings suggest that potential treatment for these two phenotypes should target the bio-origins of their distinct ventricular remodelling patterns and the extent of myocardial stiffening.

CutFEM-based MEG forward modeling improves source separability and sensitivity to quasi-radial sources: A somatosensory group study.

Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial.

Real-time simulation for multi-component biomechanical analysis using localized tissue constraint progressive transfer learning.

In virtual surgical training, it is crucial to achieve real-time, high-fidelity simulation of the tissue deformation. The anisotropic and nonlinear characteristics of the organ with multi-component make accurate real-time deformation simulation difficult. A localized tissue constraint progressive transfer learning method is proposed in this paper, where the base-compensated dual-output transfer learning strategy and the localized tissue constraint progressive learning architecture are developed. The proposed strategy enriches the multi-component biomechanical dataset to fully represent complex force-displacement with minimal high-quality data. Meanwhile, the proposed architecture adopts focused and progressive model to accurately describe tissues with varied biomechanical properties rather than singular homogeneous model. We made comparison with 4 state-of-the-art (SOTA) methods in simulating multi-component biomechanical deformations of organs with 100 pairs of testing data. Results show that the accuracy of our method is 50% higher than other methods in different validation matrix. And our method can stably simulate the deformations in 0.005 s per frame, which largely improves the computing efficiency.

Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns.

This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.