Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

GUR1050 is a medical grade variety of ultra-high molecular weight polyethylene (UHMWPE) intended for use on total joint prosthesis and implants. Probes of this material were characterized on a compression test following ASTM norms and lineaments. Available data from these mechanical tests is fitted on multiple material models. Achieved results on numerical solutions of finite element modeling (FEM) of the tests are discussed, looking for the best one available in order to simulate with accuracy GUR1050 behavior, with specific interest on the load curve results, showing the pertinence of using certain models on different conditions. It was found that the use of a bilinear isotropic hardening model assures the best fit for GUR1050 behavior in uniaxial compression under a constant strain rate.

Sleeping is one of the most important factors that influence the quality of human life, and this state of existence should be thoroughly investigated to improve the quality of the life. The mechanical design of bedding has great influence on the comfort of a mattress. Thus, objective and conventional techniques to evaluate the mechanics of mattress comfort could help improve the quality of sleep. In this report, an analysis technique for the assessment of the sleeping posture of humans is presented to facilitate the development of mattress design technology. Herein, an analytical model which imitates the human body has been formulated to determine the design parameters of a mass-spring-joint system on a soft underlay. The physical model is composed of five components that represent the head, chest, hip, femur, and calf, with each body part being represented by a simple ball model. The spring joint connecting the five parts reflects the neck, lumbar, hip, and knee joints. The specifications of the body model are determined by actual measurements and previous studies. In order to determine the physical properties of the mattress, two types of mattress urethane foam material are tested using the ball indenter method. The parameters include Young’s modulus, plateau stress, and other physical parameters. Variation due to the type of mattress has been observed in the laying test using a pressure distribution sensor sheet. In the analysis performed using the physical model, the variation in the lying posture and the extent of body sinking are observed to be the same during experiments. Both variations are compared using the change in force distribution in each body part. In conclusion, it was found that the observed changes in distribution are the same in the experimental and physical models. Therefore, the proposed model reliably reflects the design characteristics of the mattress.

Engineered tissue constructs are assembled through combining scaffolds, cells and biologically active molecules for restoring, maintaining, or improving damaged tissues or whole organs. Cells in engineered tissue constructs often experience mechanical forces during the fabrication process, maturation process, and under in vivo conditions. These mechanical forces/stimuli induce cellular responses and affect cell viability, proliferation, and differentiation. While it is critical to understand the mechanical milieu of cells in tissue constructs, it is also extremely challenging due to the time and length scale span. Multiscale modeling approaches have been emerged to provide linkage among different length scale. One of the approaches is continuum based multiscale modeling to link organ, tissue and cellular levels. A representative volume element (RVE) with periodic or random microstructure serves as a vehicle to connect different length scales. This study focuses on effects of RVE selection, microstructure, and boundary conditions on the mechanical environment at cellular level. In particular, cell embedded alginate tissue constructs were studied. Hyperelastic models were used for modeling alginate and cells. Multi-cellular FE models were generated. The results of the average properties and the stress/strain experienced by cells were compared under different conditions.

Mild traumatic brain injury (TBI) is a very common injury to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which can aid in the development of effective personal protective equipment. This paper attempts to correlate simulation results with clinical data from advanced techniques such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), MR spectroscopy and susceptibility weighted imaging (SWI), to identify TBI related subtle alterations in brain morphology, function and metabolism. High-resolution image data were obtained from the MRI scan of a young adult male, from a concussive head injury caused by a road traffic accident. The falling accident of human was modeled by combing high-resolution human head model with an articulated human body model. This mixed, multi-fidelity computational modeling approach can efficiently investigate such accident-related TBI. A high-fidelity computational head model was used to accurately reproduce the complex structures of the head. For most soft materials, the hyper-viscoelastic model was used to captures the strain rate dependence and finite strain nonlinearity. Stiffer materials, such as bony structure were simulated using an elasto-plastic material model to capture the permanent deformation. We used the enhanced linear tetrahedral elements to remove the parasitic locking problem in modeling such incompressible biological tissues. The bio-fidelity of human head model was validated from human cadaver tests. The accidental fall was reconstructed using such multi-fidelity models. The localized large deformation in the head was simulated and compared with the MRI images. The shear stress and shear strain were used to correlate with the post-accident medical images with respect to the injury location and severity in the brain. The correspondence between model results and MRI findings further validates the human head models and enhances our understanding of the mechanism, extent and impact of TBI.

Influenza is an important public health concern. Influenza leads to the death or hospitalization of thousands of people around the globe every year. However, the flu-season varies every year viz. when it starts, when it peaks, and the severity of the outbreak. Knowing the trajectory of the epidemic outbreak is important for taking appropriate mitigation strategies. Starting with the 2013–2014 flu season, the Influenza Division of the Centers for Disease Control and Prevention (CDC) has held a “Predict the Influenza Season Challenge” to encourage the scientific community to make advances in the field of influenza forecasting. A key observation from these challenges is that a simple average of the submitted forecasts outperformed nearly all of the individual models. Further, ongoing efforts seek ways to assign weights to individual models to create high-performing ensemble models. Given the sheer number of models, as well as variation in methodology followed among teams contributing influenza-risk forecasts, multiple forecasting models can be combined, by capturing human judgment, to outperform a simple average of these same models. This project exploits such a “wisdom of crowds” approach, using public votes acquired with the help of an R/Shiny based web-application platform in order to assign weights to individual forecasting models on a week-over-week basis, in an effort to improve overall ILI risk prediction accuracy. We describe a strategy for improving the accuracy of influenza risk forecast modeling based on a crowd-sourced set of team-specific forecast votes and the results of the 2017–2018 season. Our approach to assigning weights based on crowd-sourced votes on individual models outperformed an average forecasts of the individual models. The crowd was statistically significantly more accurate than the average model and all but one of the individual models.

Modeling human thermal behavior is important for applications involving medical device design, non-ionizing radiation dosimetry, and human comfort. Most thermal models use the finite element method (FEM) to represent the complicated domain structure. With the FEM, challenges in mesh and equation derivation limit rapid implementation. Finite difference (FDM) and finite volume (FVM) methods are alternatives to the FEM but have their own limitations. The FDM faces challenges in discontinuous domains at the boundaries. The FVM provides a possible solution to problems faced with FDM and FEM use. In a computational human phantom generated from medical imaging data, the finite volume structure is readily available in the form of two-dimensional pixels and three-dimensional voxels. However, geometric characteristics of rectangular prisms prevent acceptable surface-area convergence for curved surfaces, introducing an error that substantially impacts boundaries between regions such as a convection interface. The present work focuses on developing surface-area corrections for a domain generated from computed tomography scans. These geometric corrections are coupled with an FVM heat-transfer solution on a structured mesh. Solutions are demonstrated for thermoregulation in a domain similar to a section of the human forearm. The ultimate goal of this work is to evaluate human body temperature distributions under the influence of external stimuli and internal heat generation.

In this work, a computational model of PLLA (Poly L-lactic acid) stent was constructed to study the degradation behavior of the bioabsorbable stent in terms of the loss of mechanical integrity. A degradation model was improved based on experimental data from the literature, as well as a finite element (FE) model was constructed based on the model of the degradation behavior of PLLA material. The results showed that the degradation of the PLLA would switch the material property of stent from a uniform model to a heterogeneous model due to the decline of Young’s modulus locally at each location of the stent. Loss of mechanical integrity of the stent showed a bilinear behavior due to the decline of the Young’s modulus and the locale failure of the structure, respectively. The breakdown pieces of stent will stay a relative longer time in lesion after the loss of the mechanical integrity of the stent due to the nonlinear response of the degradation degree to the degradation time and strain in the material.

We consider modeling of single phase fluid flow in heterogeneous porous media governed by elliptic partial differential equations (PDEs) with random field coefficients. Our target application is biotransport in tumors with uncertain heterogeneous material properties. We numerically explore dimension reduction of the input parameter and model output. In the present work, the permeability field is modeled as a log-Gaussian random field, and its covariance function is specified. Uncertainties in permeability are then propagated into the pressure field through the elliptic PDE governing porous media flow. The covariance matrix of pressure is constructed via Monte Carlo sampling. The truncated Karhunen–Loève (KL) expansion technique is used to decompose the log-permeability field, as well as the random pressure field resulting from random permeability. We find that although very high-dimensional representation is needed to recover the permeability field when the correlation length is small, the pressure field is not sensitive to high-oder KL terms of input parameter, and itself can be modeled using a low-dimensional model. Thus a low-rank representation of the pressure field in a low-dimensional parameter space is constructed using the truncated KL expansion technique.

There is a need for better 3-D model representations of cerebrovasculature particularly on the order of arterioles. Such a model would have many applications and could be a useful tool for those conducting studies involving the brain and its function. The load bearing effects of the vasculature can be better studied with such a model, such as in the case of large strains. In addition, by having a continuous hollow structure, studies involving flow properties can be conducted at a whole scale rather than in a segmented view. Such studies are critical to the advancement of knowledge about the brain and its mechanics which can lead to advancements in preventative and curative care, as well as preventative safety measures. The model developed in this paper could serve as a tool in such studies. A fractal L-system is used to define the branching nature of the model. As such a growing tree structure is developed and characterized by its bifurcation at the end of a vessel segment. The index of bifurcation, α , is a parameter that controls the behavior of the two generated daughter vessels. The model presented here grows from a single parent branch into a bifurcation each of which then bifurcates as many times as specified. The length and diameter of the two daughter vessels will be a function of the respective parent’s length and diameter as well as a value α . The branching angle of the two daughter vessels will be entirely controlled by α. The hollow continuous nature of the model allows for it to be used as a representation of the arteriole structures in the brain. There is also use for such a model in other areas of the body, however, this study will focus on the representation of the cerebrovasculature. The end result is a branching tree model generated in Abaqus which is continuous, hollow and capable of extensive generation with uses in modeling complex cerebrovascular mechanics.

Torrefaction is a thermal pretreatment process which usually takes place at temperatures between 200–300°C. Torrefied biomass has been proven in numerous studies to have superior combustion properties compared to raw biomass. The objective of this study is to develop a model to estimate solid energy yield, elemental compositions and enthalpy of solid and volatile yield. Formation enthalpy of raw and torrefied biomass is calculated using the correlations developed for elemental compositions and HHV of torrefied biomass. Solid yield is determined by anhydrous weight loss model for torrefied wood. Specific heat correlations for raw biomass and char are used to calculate the sensible heat required for torrefaction process. Sensible heat and formation enthalpy give the total enthalpy for raw and torrefied biomass. During torrefaction, a mixture of volatile compositions is released. Experimental mass fractions of the volatiles components are taken from published literature, which allowed us to determine the enthalpy of formation and specific heat of the volatiles. Finally, the model results associated with the torrefaction process are compared with experimental data.

Reverberatory furnaces improper burners and chimney location would cause a significant scape of hot gases and shorten their residence time in the furnace and therefore reduce the convective heat transfer opportunity to the metal and walls surfaces. Appropriate burners location and orientation, as well as the chimney location, are very expensive to adjust in practical furnaces by trial and error to maximize the furnace performance. This study aimed to develop a validated 3-D CFD furnace model for studying the effect of burners’ location and orientation, chimney location and flow momentum on the hot gases residence time, heat transfer, flow and temperature distribution as well as the overall exergetic efficiency of the furnace. The results reflect the optimum design parameters for maximizing the furnace performance.

The supercritical carbon dioxide (sCO 2 ) power generation system holds tremendous potential in nuclear, chemical and renewable energy utilization fields due to its compactness, security and high efficiency. However, the dramatic variation in the physical property of sCO 2 complicates the system analysis and optimization. Recent researches usually took simple stack of all governing equations of individual components as the physical model of system. Besides, based on the traditional heat transfer modeling method, some researches apply the segmentation method to take fluid property variation into consideration. These methods exacerbate the multivariate nonlinearity of the system and are not suitable to analyze complex sCO 2 thermal systems. Moreover, taking the consideration of the strong nonlinearity of sCO 2 system, most researches adopt single parameter analysis to obtain the optimum solution, which may not achieve global optimization. In this contribution, introduction of a new definition of thermal resistance of heat exchanger disassembles the original implicit nonlinear properties of heat transfer processes as the linear relation between inlet temperature difference of fluids and heat flow rate, and the explicit nonlinear expression of thermal resistance. For the nonlinearity caused by the variable properties of sCO 2 , segmentation is also used in heat exchanger modeling. However differently, the introduction of new defined thermal resistance enables the elimination of most intermediate variables produced by segmentation, which contributes to the connection of all segments in heat exchanger into a heat exchanger network. Furthermore, based on the system layout, the equivalent power flow diagram of the system is built to derive the corresponding governing equations revealing the overall transfer and conversion laws of heat. Combining the flow resistance balance equations of all components and the accompanying power flow processes constraints offers the inherent physical constraints among operating parameters. Benefit from the conciseness of system model, the genetic algorithm can be used for the model optimization. Taking thermal efficiency of the system as the optimization objective, the optimal matching of the operating parameters under variable working conditions is obtained.

Biomass gasification is the devolatilization and incomplete combustion of biomass resulting in the production of a combustible gas mixture including carbon monoxide (CO), hydrogen (H 2 ) methane (CH 4 ), and traces of other hydrocarbons (C n H m ), and referred to as producer gas. Producer gas can be cleaned and then used in various engines or can be converted to various biofuels. This paper presents an experimental and simulation-based evaluation of producer gas quality resulting from corn kernel gasification in a two-stage downdraft gasifier. Test conditions were selected, based on the results of previous studies, to yield high conversion efficiency and low tar production. Experimental tests were performed with an air flow of 25 Nm 3 /h and with 80% of the air supplied to the first gasification stage. Simulations based on a chemical and thermal equilibrium model were carried out to examine the effect of equivalence ratio (ER) changes. Both the experimental and modelling results show that using a two-stage air supply leads to a significant reduction in the tar content of the producer gas, while maintaining a high gasification efficiency.

Following head trauma caused by traffic accidents, many patients are unable to completely recover their social functions due to higher brain dysfunction although they are able to return home. To predict the onset and severity of post-traumatic higher brain dysfunction, the visualization of responsible injury is considered urgent. In this study, we focused on five patients with higher brain dysfunction following head trauma caused by traffic accidents to establish a method for quantitatively evaluating higher brain dysfunction. The injury conditions were reproduced on the basis of multibody dynamic and collision analyses using finite element (FE) modeling of the human head to determine mechanical responses inside the cranium of these patients. The strain on the frontal lobe generated by an injury condition was suggested to contribute to the onset of attention disturbance during the chronic phase of medical treatment. Reproduction analysis of the injury conditions using FE modeling of the head could predict the onset and severity of traumatic higher brain dysfunction.

During metal cutting, it is well known that the cutting temperature has great influence on the machined surface integrity, especially on the residual stress and machining defects. At present, a lot of analytical modeling work has been done on the cutting temperature of tool, chips and workpiece machined by the side cutting edge during end milling process. To the workpiece surface machined by the bottom cutting edge, the study of temperature modeling is rarely reported. Besides, as a new kind of particulate metal matrix composites (MMCs) with improved mechanical and physical properties, the machining study of in-situ TiB 2 /7050Al MMCs is not many and no analytical temperature modeling of MMCs has been published up to now. Our study aims to establish an analytical cutting temperature model of workpiece machined by the bottom cutting edge in end milling in-situ TiB 2 /7050Al MMCs. In this model, the moving heat source method was applied. To meet the actual cutting process, the effect of heating time was also taken into account. With validation, the temperature model shows good agreement with experimental results. It was found that the heat partition ratio conducted from the shear plane heat source to the workpiece increased linearly as thermal number increased, due to the influence of increasing heat conducted into chip by the side cutting edge. The proposed cutting temperature model was of great significance for both the temperature modeling work of end milling and study of Al-MMCs.

The structural health monitoring by piezoelectric wafer active sensor (PWAS) using electromechanical impedance method used for monitoring of structure. In present work impedance method of elasto-plastic beam structure is studied. In order to model the effect of a plastic in beam, the moment-curvature relationship for elasto-plastic region for loading and unloading is used. The finite difference method is used to discretize beam with piezoelectric. The piezoelectric actuator is modeled by equivalent moment. Then output current of piezoelectric sensor is calculated. Firstly, elastic modeling of beam is considered that this is leads to linear system equation. In linear system, time domain system equations are calculated and Fourier transform of current output obtained, and then impedance of PWAS in frequency domain is calculated. Secondly, the elasto-plastic of beam is modeled. This phenomenon leads to the nonlinear system equations. These nonlinear equations are solved using finite difference method for any harmonic voltage applied to actuator. Then impedance of PWAS is calculated. Two methods are used to detect elasto-plastic modeling on PWAS impedance. At the first, frequency response of elastic beam as intact model is compared with elasto-plastic results in a desired frequency range. Second, only frequency response of one harmonic is computed with its super-harmonics. Finally, the detection method of linear is compared with nonlinear model.

The paper presents a new solution for the wind turbine profile shape modeling based on the concept of the maximum lift force, capable to be produced at different values of the wind velocities. The profile is designed and realized in accordance with the new concept emerged in the last decade, on the operation of the wind turbines with maximum lifting force. The purpose is to provide a low-noise during operation because a negative effect on the medium and long-term operation of the wind turbines (wind farms) is the noise that affects the flight of birds, terrestrial animal life, and especially human communities. Various sources generate independent acoustic emissions on wind profiles, such as the turbulent flow, the interaction of the turbulent boundary layer area of the trailing edge, the flow separation, and the boundary layer separation of vortices formed in the zone of the trailing edge. There is also considered the influence of the apparent wind on the incidence variation of the profile. In order to maintain an optimum angle of attack relative to the wind velocity, a fixed blade inclination must increase its speed to be proportional to the wind. Thus, to maximize the aerodynamic performance, the rotor must spin faster when the wind intensity increases. Measurement of the acoustic signal requires electronic devices that operate on electric signals obtained from the conversion of the pressure variations in voltage or variations in electrical current. The noise caused by the turbulent flow is generated primarily by the sharply pointed leading edge and cannot be diminished. There are presented some numerical results correlated with the measurements made in the field.

We derive formulas for the gradients of the total scattering cross section (TSCS) with respect to positions of a set of cylindrical scatterers. Providing the analytic form of gradients enhances modeling capability when combined with optimization algorithms and parallel computing. This results in reducing number of function calls and time needed to converge, and improving solution accuracy for large scale optimization problems especially at high frequencies and with a large number of scatterers. As application of the method we design acoustic metamaterial structure based on a gradient-based minimization of TSCS for a set of cylindrical obstacles by incrementally re-positioning them so that they eventually act as an effective cloaking device. The method is illustrated through examples for clusters of hard cylinders in water. Computations are performed on Matlab using parallel optimization algorithms and a multistart optimization solver, and supplying the gradient of TSCS.

A multiscale modeling that integrates electronic scale ab initio quantum mechanical calculation, atomic scale molecular dynamics simulation, and continuum scale two-temperature model description of the femtosecond laser processing of nickel film at different thicknesses is carried out in this paper. The electron thermophysical parameters (heat capacity, thermal conductivity, and electron-phonon coupling factor) are calculated from first principles modeling, which are further substituted into molecular dynamics and two-temperature model coupled energy equations of electrons and phonons. The melting thresholds for nickel films of different thicknesses are determined from multiscale simulation. Excellent agreement between results from simulation and experiment is achieved, which demonstrates the validity of modeled multiscale framework and its promising potential to predict more complicate cases of femtosecond laser material processing. When it comes to process nickel film via femtosecond laser, the quantitatively calculated maximum thermal diffusion length provides helpful information on choosing the film thickness.