Analytical Model For Prediction Research Articles

Selection of piezoeloectric material and fiber volume fraction to maximize the electrical power produced by macro-fiber composite energy harvesters

A piezoelectric Macro fiber composite (MFC) materials developed in the last decade found widespread applications such as vibration sensing, actuation and structural health monitoring. Unlike conventional ceramic piezoelectric materials (PZT), these composites show many advantages such as flexibility, reliability and high actuation capacity. However, increasing their energy harvesting capabilities still remains a challenge. Modeling and simulation of electrical energy harvesters using MFC patches provide a mean for geometrical optimization and appropriate choice of the MFC composite. This paper proposes a linear analytical model for prediction of the electro-mechanical response of bimorph harvesters using MFC patches. Homogenization technique is used to describe the equivalent piezoelectric properties of the composite structure of the MFC patch. This paper investigates the effect of the volume fraction of the fibers and the material choice of the piezoelectric fibers and epoxy matrix on the generated electrical power. It has been found that an increased fiber volume fraction causes a decrease of the voltage, the power and the velocity amplitudes for a range of load resistance. However, an increase in the fiber volume fraction FVF is achieved by an increase of the current amplitude for a range of load resistance. Maximum amount of power is generated by the bimorph MFC harvester for an FVF of 86% which corresponds to the reference configuration. The piezoelectric fiber material has a significant influence on the output power. In fact, the SONOX-P502 generated the greatest electrical power. However, the material of the matrix has a negligible effect on the generated power.

A New Analytical Thermal Model of Distributed Winding Wheel Machine for Electric Vehicles

Due to the complex boundary conditions of the thermal analysis method (AM), the error of the calculation results is large, and the lumped-parameter thermal network (LPTN) mostly focuses on the solution of the average temperature. This paper proposes a thermal analytical model for wheel-motor windings, which includes LPTN and AM to solve the problems of boundary condition extraction and maximum temperature solution in AM. The heat transfers between the end caps of electric vehicle wheels and the outside air are considered by the model. The node temperature of LPTN can be adopted as the boundary condition of the proposed analytical method to predict and calculate the maximum temperature and average temperature of the wheel motor and improve the accuracy of temperature rise calculation. To further analyze the winding temperature, the armature winding is divided into active winding and end winding for calculation respectively. In the calculation of active winding, based on the equivalent area method, the active winding and other materials are layered into a series of equal-width rectangular rings through a novel layered strategy. The compensated thermal resistance (TR) is introduced into the analytical model of end winding, and the accuracy of the end temperature solution is improved by the iteration of compensated TR. Finally, the effectiveness and correctness of the proposed analytical thermal model for motor temperature rise prediction are verified on a 2kW distributed double-layer winding wheel motor.

Predicting particle orientation: Is an accurate flow field more important than the actual orientation model?

Analytical orientation prediction models (OPMs) are widely applied to efficiently predict the orientation of particles in suspensions or pastes. One application of these prediction models are extrusion processes where the model is used to identify combination of process and paste parameters that yield anisotropic orientation within the extruded filament. The accuracy of these predictions mainly depends on two elements that are the correct choice of the orientation model itself and the rheology model. The rheology of a paste is commonly modeled with the help of the Herschel Bulkley (HB) rheology model. The optimal OPM is still topic of today’s research. Which of the two elements, however, has a stronger influence on the orientation has not been addressed, yet. We make use of a wide range of HB parameters reported in literature with a particular focus on data that is reported for varying paste compositions. Based on this data, we use the HB model to predict the velocity and shear rate profile within a pipe and perform orientation predictions with the help of the models by Jeffery and Folgar and Tucker (FT). These predictions are used to estimate the sensitivity of the final orientation on the HB parameters and on the OPM. For our study, we assume that the paste can be described by the standard Herschel Bulkley rheology model. We assume no-slip boundary conditions and no influence of the particle orientation on the rheology. Second, we assume that the initial particle orientation before entering the pipe is random. Our results suggest that capturing the rheology model correctly is generally more important than capturing the OPM correctly. This applies in particular when densely filled states are approached. We also provide universal relations for predicting the degree of particle alignment and the mean flow velocity as functions of process parameters and material properties. • A suitable rheology model is found to be more important than the actual orientation prediction model in terms of particle orientation. • Normalized diagrams enable quick estimations of the evolution of particle orientation based on process parameters and paste properties. • The derived methodology is applied to a large set of published rheology data.

LSTM neural network techniques-based analytical predictive models for wind energy and mechanical power

Nowadays, the importance of renewable energy is rapidly increasing. It is considered as an alternative clean source of energy due to environmental reasons. Therefore, this research presents a data analysis model to predict the generated electrical power based on wind energy and the long short-term memory (LSTM) model. The work focused on the Spring and Autumn seasons where wind speed has high variation and the data was collected every 15 min in a wide, open space area located in southeast Palestine. To investigate and validate the correctness and robustness of the work, three different scenarios were performed for each season to predict wind speed and direction, and mechanical power. Also, different performance metrics were applied. The results were very promising with an average error of less than 3% and an R-Squared value of 0.95. Since the price of electricity in Palestine is relatively high, the results showed also the possibility to generate electricity with lowered price of about 40% and a reasonable payback period of 11 years. The work confirms that wind energy is cost-effective and a good alternative to reducing global warming.

Analytical study on the dynamic response of foam-core sandwich plate under wedge impact

In this paper, the dynamic responses of sandwich plate with foam core subjected to wedge impact are theoretically investigated. An analytical model of fully clamped foam-core sandwich plate under low-velocity wedge impact is developed based on the approximate yield criteria and rigid-perfectly plastic theory. Besides, the horizontal impact experiments and numerical simulations of polyvinyl chloride (PVC) foam-core sandwich plates were conducted to verify the accuracy of analytical prediction model. Results show that the analytical prediction results of foam-core sandwich plate under wedge impact such as the permanent deformations and impact force as well as deformation distribution are consistent with the experimental and numerical results. Furthermore, the geometrical parameters such as wedge width and aspect ratio of sandwich plate as well as foam core thickness on the dynamic responses are theoretically performed. Results showed that the deformation reaches to maximum with aspect ratio of 1and the permanent deformation declines with the increase of wedge width and foam core thickness.

An analytical radiometric thermometry model for quantitative prediction and correction of the gas emission and absorption effects

An analytical radiometric thermometry model applied to predict quantitatively and correct for gaseous emission and absorption effects on spectral-band and ratio thermometer readings is presented. This model using numerical radiative transfer simulations takes into account the augmentation and attenuation of radiation in gas medium. The temperature predictions and corrections agree well with a series of measured data and the results of current best correction algorithms, which reveal that this model has good applicability in dealing with direct problems and inverse problems in radiation thermometry. The measurement uncertainty, along with the uncertainty component due to gaseous emission and absorption effects, for a thermometer interfered with absorptive gas is quite strongly dependent on the thermometer operating wavelength. If intervening gas is optically thin, the measured temperature errors of spectral-band and ratio thermometers will vary linearly with gas concentration or optical path length. Whereas for non-optically thin situations, the measured temperature errors of spectral-band thermometers vary as a second-order exponential function and the errors of ratio thermometers increase approximately as a biphasic hill function, with either increasing gas concentration or optical path length. Based on the theoretical calculations of present model, three new correction algorithms to eliminate the influence of gaseous absorption and emission are proposed.

A Wide Scale Survey on Weather Prediction Using Machine Learning Techniques

Several losses had been witnessed due to many natural calamities like earth quakes, storms, cyclones, etc. These natural calamities have direct or indirect effects on the lives of billions of people across the world. The prediction of environmental impact due to the changes in weather had been a critically challenging task. In countries like India, where agriculture is the livelihood of many people (49.5%) and rainfall is very essential for the cultivation of crops, rainfall is very much needed to all forms of lives. Extreme rainfall has its effects on the economy of any country. Heavy loss of lives and properties had been encountered due to havoc of flood in varying degrees. In this research work, the rainfall forecasting is highly focussed and it discusses on several models of weather prediction. Note that in the previous decades, many researchers have made some serious attempts to reach out with forecasting systems for weather prediction (which include statistical and analytical models for rainfall prediction) but maximum models proposed by the researchers are found to be unfit in terms of less accuracy, when these proposed prediction models are applied on a large scale. The research work presents the reviews of works that are proposed by many pioneers, who had taken lots of efforts arrive at a good prediction system. In this work, it is also found that that there had been a big gap between the prediction reports/weather news and the actually happening. This paper considers most of the features belonging to the models found from scientific articles published across the globe to find the factors that are widening the gap between the forecast data and the actual phenomenon.

Effect of Ball Burnishing Pressure on Surface Roughness by Low Plasticity Burnishing Inconel 718 Pre-Turned Surface.

The low plasticity burnished surface roughness is significantly affected by the low plasticity burnishing (LPB) parameters. This research proposed the analytical prediction model to predict the LPBed surface roughness and optimal LPB pressure based on Hertz contact mechanics and the slip-line field theory. In this study, the surface formatted process was divided into the smoothing stage (SS) and the indentation stage (IS). The smoothing mechanism of SS and the deterioration mechanism of IS were analyzed theoretically. The analytical prediction model for the LPBed surface roughness was proposed based on Hertz contact mechanics and slip-line field theory. The proposed analytical prediction model was validated by the LPBed surface roughness of AISI 1042, and the error of the analytically predicted results was less than 13.3%. After validation, the proposed model was applied to predict the LPBed surface roughness of Inconel 718. The single-factor experiments were conducted. The error between the proposed model prediction results and experimental results was less than 7% for the LPBed surface roughness of Inconel 718. The optimal LPB pressure interval was calculated to be [12.2 MPa, 17.5 MPa], corresponding to the experimental one as [12 MPa, 18 MPa]. It indicated that the proposed model could accurately predict the LPBed surface roughness and conduct the LPB processing.

Avoiding inelastic strains in solder joint interconnections of space electronics

AbstractA simple and physically meaningful analytical (“mathematical”) predictive model is developed for the evaluation of the sizes of the zones of inelastic deformations, if any, at the ends of a soldered electronics assembly in a packaged IC device. The emphasis is on the IC devices intended for space applications and on the incentive for using column‐grid‐array (CGA) rather than a ball‐grid‐array (BGA) technology for lower thermal interfacial stresses. Such devices, when employed outside spacecraft, experience extraordinarily low temperatures, and their solder joint interconnections, when used interchangeably inside and outside the craft, are subjected to significant and variable inelastic strains, possibly leading to low cycle fatigue conditions. Because of that, the useful lifetime of the device might be shorter than required for particular applications. There is an obvious incentive to avoid, if possible, the inelastic deformations in the solder material or, at least, to predict the sizes of the inelastic zones at the peripheral portions of the assembly. The developed model shows how this could be done. Both shearing and peeling interfacial stresses are addressed. The numerical examples are carried out for an IC‐package mounted on a ceramic (Al203) substrate using 3%–4%Ag0.51%Cu lead‐free solder alloy. In addition, an extreme response of an assembly of the type in question to the random temperature cycling is briefly addressed. It is concluded that several possible and independent ways to avoid or, at least, to minimize the inelastic deformations in the solder material could be employed: the use of high yield stress solder and/or a solder with a low melting temperature and/or a low expansion substrate and/or compliant interfaces, such as, for example, low‐modulus‐and‐compliant CGA design instead of high‐modulus‐and‐stiff BGA, and/or inhomogeneous attachment designs, such as, for example, those, when high‐modulus and/or high‐soldering‐temperature solder is used in the assembly's major mid‐portion, where heat‐transfer considerations play the major role, and low‐modulus and/or low‐melting‐temperature solder – at its peripheral portions, where mechanical strength is critical. It is concluded also that because, based on the calculated data, the peeling stresses are typically considerably lower than the shearing ones and are proportional to the shearing stresses, only the latter could be considered and possibly minimized. As to the random loading, whose effect was assessed by considering the extreme elastic shearing stress in an assembly with an extraordinarily high yield stress (so that inelastic deformations in it are impossible), it has been concluded that a probabilistic‐design‐for‐reliability of the solder joint interconnections in IC packages intended for space application should be considered in addition to the deterministic approach taken in this paper, but this analysis is beyond the scope of the present study and will be done as future work. Finally, it is concluded that the analytical ("mathematical") modeling should always be considered, in addition to computer simulations, in every important physical design related undertaking, such as the one addressed in this analysis: these two major modeling tools are based on different assumptions and employ different calculation techniques, and if the calculated data obtained using these tools are in agreement, then there is a good reason to believe that these data are accurate and trustworthy.

Underwater noise reduction of offshore wind turbine using compact circular liner

A lined layer used for suppressing the underwater noise radiation from the offshore wind turbine is developed in this study. The lined layer is made from nitrile rubber. It contains periodically distributed axial cavities and each of which consists of a circular truncated cone and a cylinder. The acoustic performance of the lined layer is analysed using the equivalent medium method and the transfer matrix method. Based on the analytical prediction model, an optimal configuration targeting for low-frequency range is designed and its absorption coefficient under 2500Hz is up to 0.96. The finite element method is employed to examine effects of the lined layer on a scaled tower-water model. The presence of the lined layer can lower the sound pressure level by up to 18 dB. Since the sound speed in the lined layer is lower than that in water, the radiation angle of the sound wave front is smaller than that for the baseline model. Practically, this study puts an early effort on underwater noise control of the offshore wind turbine and proposes a feasible solution for that.

An algorithm for obtaining 3D forms of draperies from visual images of used textiles

Adding economic value to textile waste, when developing fashion products from them, requires knowledge of their characteristics. In the present work, an algorithm for reproducing a three-dimensional shape from two-dimensional images of draperies is proposed. An analytical predictive model has been created for automated prediction of the step of the contours of draperies. This model depends on the execution time of the algorithm for creating three-dimensional shapes of draperies from used textiles. The optimal values of the step and the corresponding time for execution of the algorithm are determined. It has been proven that at step st=48 and execution time t=7s, sufficient accuracy of the three-dimensional simulation of used textile draperies is achieved. Software tools suitable for the study of draperies of used textile materials are proposed. The results of this study can be used to make interior textile accessories such as curtains, upholstery, tablecloths, napkins, blankets.

Progressive failure prediction of three-dimensional woven composites using a generic multi-scale analytical model

Three-dimensional (3D) woven composites with higher interlaminar fracture toughness, better damage tolerance and improved mechanical stability are being increasingly used in aviation and automotive industries. To expedite their analysis and design efficiency, developing simple and accurate modeling tools capable of quickly evaluating their mechanical properties and progressive failure is necessary. In this paper, a generic multi-scale analytical model for mechanical response prediction and failure behavior characterization of 3D woven composites is presented and validated by using the existing experimental and numerical results. Subsequently, the analytical model is adopted to evaluate the predictive capability of different unit cell model schemes and to investigate the effects of geometric parameters on stiffness and strength properties of composites. The results indicate that the interior-surface integrated cell model scheme has the overall best performance in mechanical response prediction of composites. In addition, an exponential model and a linear model are proposed to quantify the relationships between the number of weft layer and yarn density and mechanical properties, respectively. The present results demonstrate the analytical model can be a useful tool to provide an insight for the analysis and design of other 3D woven composites.

Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs. machine learning.

ObjectiveEctopic pregnancy (EP) is well known for its critical maternal outcome. Early detection could make the difference between life and death in pregnancy. Our aim was to make a prompt diagnosis before the rupture occur. Thus, the predictive analytical models using both conventional statistics and machine learning (ML) methods were studied.Materials and methodsA retrospective cohort study was conducted on 407 pregnancies with unknown location (PULs): 306 PULs for internal validation and 101 PULs for external validation, randomized with a nested cross-validation technique. Using a set of 22 study features based on clinical factors, serum marker and ultrasound findings from electronic medical records, analyzing with neural networks (NNs), decision tree (DT), support vector machines (SVMs), and a statistical logistic regression (LR). Diagnostic performances were compared with the area under the curve (ROC-AUC), including sensitivity and specificity for decisional use.ResultsComparing model performance (internal validation) to predict EP, LR ranked first, with a mean ROC-AUC ± SD of 0.879 ± 0.010. In testing data (external validation), NNs ranked first, followed closely by LR, SVMs, and DT with average ROC-AUC ± SD of 0.898 ± 0.027, 0.896 ± 0.034, 0.882 ± 0.029, and 0.856 ± 0.033, respectively. For clinical aid, we report sensitivity of mean ± SD in LR: 90.20% ± 3.49%; SVM: 89.79% ± 3.66%; DT: 89.22% ± 4.53%; and NNs: 86.92% ± 3.24%, consecutively. However, specificity ± SD was ranked by NNs, followed by SVMs, LR, and DT, which were 82.02 ± 8.34%, 80.37 ± 5.15%, 79.65% ± 6.01%, and 78.97% ± 4.07%, respectively.ConclusionBoth statistics and the ML model could achieve satisfactory predictions for EP. In model learning, the highest ranked model was LR, showing that EP prediction might possess linear or causal data pattern. However, in new testing data, NNs could overcome statistics. This highlights the potency of ML in solving complicated problems with various patterns, while overcoming generalization error of data.

Modeling of Over-Tip-Rotor Liners for the Suppression of Fan Noise

Fan noise is one of the dominant sources of aircraft engine noise, both at approach and at takeoff. Improved attenuation of fan noise with acoustic liners and the reduction of fan noise at the source remain key technology challenges for civil aviation in the foreseeable future. Over-tip-rotor (OTR) acoustic treatments have been investigated experimentally during the last decade, and significant fan noise reductions have been measured, most recently using a single rotor and multiple lined circumferential grooves. This paper describes an analytical OTR prediction model in which the fan noise is modeled with point or distributed, static or rotating monopole and dipole sources based on Green’s functions for infinite hard or lined cylindrical ducts containing uniform mean flow; these are combined with an anechoic or unflanged inlet termination and an embedded finite length lined section in which the rotor-alone source is located with its OTR liner. OTR liner insertion loss predictions are obtained for comparison with data from the W-8 NASA experimental rig. These yield peak broadband in-duct noise reductions of up to 4 dB, in line with the measurements.

Full-scale experimental study on shear behavior of multiple-keyed epoxy joints in precast concrete segmental bridges

Precast concrete segmental bridges (PCSBs) have become a trend in bridge industry, the joint behavior between segments is critical to the mechanical performance of PCSBs. Compared with dry joints, epoxy joints are more widely used in practical engineering, but the researches on epoxy joints are relatively few. In this paper, an experimental investigation of the shear behavior of full-scale multiple-keyed epoxy joints under direct shear are presented, considering the parameters of joint type, confining stress, epoxy thickness, and key geometry. The crack patterns and failure modes of the test specimens were obtained and the influences of the parameters on the shear capacity were discussed. The existing formulas of joint shear capacity were assessed by the test results, and a new analytical prediction model that fully considers the uneven distribution of shear stress in the shear plane was derived based on Mohr’s stress circle, which can accurately predict the shear strength of epoxy joints.

Simulation of gradient grain size and hardness in ultrasonic burnishing process through multi-physics of contact mechanic and microstructure evolution

Hardening of surface layers are one of main goals of surface sever plastic deformation (SSPD) process. Grain refinement is majorly a dominant mechanism that results in hardening of surface layers when the material is subjected to room temperature SSPD. Accordingly, development of predictive models of microstructure refinement is gaining interest. In the present study, analytical predictive model is developed to predict the through depth grain size and hardness in one of famous SSPD technique namely ultrasonic burnishing process. Here, firstly the deformation parameters (i.e. strain and strain rate) are modeled by means of the concept of elasto-plastic contact mechanics taken into account the superposition of static and dynamic loads. Then, grain refinement is predicted through sequence of dislocation density evolution (DDE) followed by dynamic recrystallization (DRX). In order to verify the results derived by analytical model, series of confirmatory experiments were carried out on aluminum 6061-T6. The X-ray diffraction (XRD) method was utilized to obtain the grain size in different layers from the surface of the burnished sample. The obtained results indicated that the developed model of microstructure evolution were consistent well with experimental measurement in terms of gradient grain size and micorhardness distribution. The confirmed simulation model was further utilized to understand how the process factors i.e. static force, vibration amplitude and ball size influence the evolution of microstructure. It was found the finer grain structures distributed at further depth can be obtained while static force and vibration amplitude adopt their maximum range value; while the ball diameter and feed rate are set on minimal values.

Advanced analytical model for orbital aerodynamic prediction in LEO

The growing interest in low earth orbit (LEO) applications demands for accurate modeling of orbital aerodynamics. But classical analytical models of aerodynamic coefficients in free molecule flow, such as the Sentman’s model, Schamberg’s model and Schaaf-Chambre model, were built upon over simplistic gas-surface interaction models, which degrade the fidelity of aerodynamic prediction. This work presents a new analytical model of orbital aerodynamic coefficients based on the state-of-the-art Cercignani–Lampis–Lord (CLL) gas-surface interaction model, where lobular quasi-specular scattering pattern and separate accommodation degree for different velocity components can be well captured. A key component of the new model is a rigorous function approximation solution of the reflected normal momentum flux based on the CLL model which is derived for the first time and is validated within 1% for any hypothermal flow and surface accommodation conditions. Closed-form analytical solutions of aerodynamic coefficients for simple convex geometries are obtained and exhibit high accuracy (within 0.1%) in typical LEO scenarios. The new analytical model surpasses the classical models in some important aspects, such as overcoming the diffuse scattering hypothesis constraint, considering the variation of normal momentum exchange with the surface incidence angle and being applicable in any hypothermal flow situation. In virtue of the advanced CLL model and feasibility of coupling with the panel method technique, the new analytical model is promising to provide more accurate predictions on the orbital aerodynamic coefficients for LEO applications.

Bond-slip behavior of self-consolidating rubberized concrete

• Study of the bond-slip behavior in self-consolidating rubberized concrete (SCRC) • Overview of the available studies on bond-slip behavior in SCRC. • Results show, the bond strength depends on the diameter of reinforcement bar and rubber content. • Proposition of the analytical model for predicting bond strength and corresponding slip. • Ultimate bond strength as a function of the reinforcement diameter, compressive strength and rubber content. A sustainable solution was found for the use of waste material such as used tires by partially replacing the natural aggregate in the concrete with recycled rubber particles. This resulted in improved ductility, but at the expense of reduced strength and stiffness. To further investigate the behavior of rubberized concrete, this paper investigates the bond-slip behavior of reinforcement in self-consolidating rubberized concrete. A total of 27 cubic specimens with reinforcement bars and embedded confinement were constructed. Two parameters affecting the bond-slip behavior were investigated: the reinforcement bar diameter (∅12, ∅16, ∅20 mm), the content of rubber particles in the self-consolidating concrete (0%, 10%, 15%), and silica fume (0%, 5%). From the experimental results, it can be concluded that both a higher rubber content and a larger reinforcement diameter have a negative effect on the bond coefficient, reducing it by up to 28.87% and 18.78%, respectively, while the bond strength decreases by 33% for a rubber replacement of 15%. In the case of rubberized concrete, a more ductile behavior is observed due to the rubber content. Based on the obtained results, a parametric analysis was performed and an analytical prediction model was proposed.

3-D Hybrid Analytical Prediction Model for Air-Cored Linear-Rotary Induction Machine

This article presents a 3-D hybrid analytical prediction model for an air-cored linear-rotary induction machine (LRIM) in which its columnar movable armature surfaced with the conductor. The analytical magnetic distributions in the conductor and air gap of the LRIM are predicted by employing a group of H-formulations in the conductor and the Laplacian equations with magnetic scalar potential (MSP) in the others in the Cartesian coordinate. The correctness of the magnetic field distributions and the eddy-current distributions is verified by the numerical computational results using the 3-D finite-element method (FEM).

Analytical Model for Prediction of Tunnel Deformations in Soft Rocks Considering the Softening and Expansion Effects

Analytical Model for Prediction of Tunnel Deformations in Soft Rocks Considering the Softening and Expansion Effects