Optimal Set Of Design Parameters Research Articles

Machine learning approaches to determining truck type from bridge loading response

The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.

Hybrid Optimization Approach for Improved Efficiency in Permanent Magnet Brushless DC Motor Design: Combining Cauchy Particle Swarm & Moth Flame Methods

This article proposes a novel design strategy for a PMBLDC motor through hybrid optimization, combining Cauchy Particle Swarm Optimization and Moth Flame Optimization approaches to find the optimal set of design parameters that yield the highest efficiency. The motivation is to improve the efficiency of PMBLDC motors, which are widely used in electric cars and renewable energy systems that place a premium on minimal power usage. The proposed work implements the hybrid optimization technique performs conventional single optimization strategies in terms of efficiency improvement. This study contributes in identifying the optimal values for the various design parameters of PMBLDC motors using the hybrid CPSO-MFO approach, which converged to the optimal design parameters. The performance of the PMBLDC motor was evaluated using the optimal design parameters, with an overall power loss of 49.1 W, and an efficiency of 97.88%. The study’s results provide valuable insights for design engineers into the optimal geometrical parameters of PMBLDC motors to achieve higher efficiency and lower power loss. The proposed novel hybrid optimization approach, which is faster and more efficient than conventional single optimization approaches, making it a useful tool for designing high-performance PMBLDC motors.

Superior mechanical properties by exploiting size-effects and multiscale interactions in hierarchically architected foams

Protective applications in extreme environments demand materials with superior modulus, strength, and specific energy absorption (SEA) at lightweight. They must also have the ability to attenuate intense stress waves and absorb kinetic energy from impact while providing thermally stable functionality. However, these properties typically have a trade-off. Hierarchically architected materials—such as the architected vertically aligned carbon nanotube (VACNT) foams—offer the potential to overcome these trade-offs and achieve synergistic enhancement in mechanical properties because of their multiscale origins of bulk properties derived from structural features that span nano to millimeter scales. Such architected materials with complex hierarchical structures require careful investigation of the effects of multitier design parameters and their interactions on the resultant bulk mechanical properties. Here, we adopt a full-factorial design of experiments (DOE) approach to identify an optimal set of design parameters to achieve synergistic enhancement in SEA, compressive strength, and modulus at lightweight in VACNT foams with mesoscale cylindrical architecture. We exploit size effects from geometrically-confined synthesis and highly interactive morphology of the CNTs to enable higher-order design parameter interactions that intriguingly disrupt the diameter-to-thickness (D/t)-dependent scaling laws found in common architected materials having steel and composite tubular structures. We show that exploiting complementary hierarchical mechanisms in architected material design can lead to superior and synergistic enhancement of mechanical properties and performance desirable for extreme protective applications.

Dynamic characteristics and adaptive design methods of enclosed courtyard: A case study of a single-story courtyard dwelling in China

In this study, the dynamic performance of an enclosed courtyard with a covered roof in a cold IIA region of China was explored, and the microclimate regulation and energy performance of its component, the courtyard adjustable ventilated roof (CsAVR), were emphasized. As a theoretical basis for thermal context exploration, adaptive thermal comfort content was introduced, and mean thermal sensation vote ( MTS ) was adopted as one of the evaluation metrics. The thermal environment was monitored, and subsequently, energy models and numerical models were constructed in DesignBuilder and the finite element program COMSOL, respectively, which were both validated by the measured data. The comparative experiments were applied to the energy model to validate the capabilities of CsAVR on thermal regulation and energy savings, while a morphological optimization procedure based on derivative-free optimization algorithms was implemented in COMSOL to obtain the optimal set of design parameters for CsAVR satisfying thermal comfort. The comparative experiments revealed that the CsAVR reduced the operative temperature in the courtyard and adjacent space by 3.9 °C and 1.6 °C, respectively, during the warm period; approximately 47.4% (courtyard) and 28.6% (adjacent space) of the occupied hours were extended to the comfort range during the cooling season; and approximately 8.2% of the energy was saved throughout the year. With the input of meteorological parameters, dwelling geometry parameters, and thermal adaptation objective functions, an optimization program with an embedded COBYLA algorithm was created to rapidly obtain a combination of morphological control parameters that approximates optimal thermal comfort. • Compare the performance of covered and uncovered courtyards. • The thermal and microclimatic functions of the courtyard are both considered. • A thermal intervention design that responds to the thermal comfort and behavior of occupants. • The MTS -restricted morphological optimization program is available.

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method.

This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.

Design Space of GaN Vertical Trench Junction Barrier Schottky Diodes: Comprehensive Study and Analytical Modeling

We report gallium nitride (GaN) vertical trench junction barrier Schottky (TJBS) diodes and systematically analyzed the effects of the key design parameters on the reverse and forward characteristics of the devices. By taking advantage of the shielding effects from both the trenches and pn junctions in the TJBS structure, the high electric field at the Schottky contact region can be effectively suppressed. We found that the doping concentration, thickness, and spacing of p-GaN, as well as the depth and angle of the trench sidewalls are closely associated with the electric field distribution and the reverse characteristics of the TJBS diodes. With an optimal set of design parameters, the local electric field crowding at either the corner of the trench or the edge of the p-GaN can also be alleviated, resulting in a boosted breakdown voltage of up to 1250 V in the TJBS diodes. In addition, an analytical model was developed to explore the physical mechanism behind the forward conduction behaviors. We believe that the results can provide a systematical design strategy for the development of low-loss, high-voltage, and high-power GaN power diodes towards an efficient power system.

Optimal Design of V-Shaped Fin Heat Sink for Active Antenna Unit of 5G Base Station

The active antenna unit (AAU) is one of the main parts of the 5G base station, which has a large size and a high density of chipsets, and operates at a significantly high temperature. This systematic study presents an optimal design for the heat sink of an AAU with a V-shaped fin arrangement. First, a simulation of the heat dissipation was conducted on two designs of the heat sink – in-line and V-shaped fins – which was validated by experimental results. The result shows that the heat sink with V-shaped fins performed better compared to conventional models such as heat sinks with in-line fins. Secondly, computational fluid dynamics (CFD) and the Lagrange interpolation method were applied to find out an optimal set of design parameters for the heat sink. It is worth noting that the optimal parameters of the orientation angle and fin spacing considerably affected the heat sink’s performance.

Parametric and design optimization investigation of a wavy fin and tube air heat exchanger using the T‐G technique

AbstractThe focus of this paper is to optimize the air‐side performance of a wavy fin and tube heat exchanger at different design parameters on an individual target response using the Taguchi method. However, a statistical concept, gray relational analysis, is also studied for combined optimization, considering all target responses at a time. Based on the heat exchanger requirement, parametric study for the air‐side is regarded as a more significant heat transfer and lower frictional factor. Experimental correlations were available and used for the 27 orthogonal runs. Investigation revealed the highest 47.06% fin pitch, 37.24% fin pitch, 25.46% air velocity, and 23.9% fin thickness contribution ratio for the target response of friction factor (TPF), heat transfer coefficient, and Colburn factor, respectively, with the application of the Taguchi method in a heat exchanger. GRG gives an optimum set of design parameters, A3B3C2D1E3F2G1, for wavy fin and tube of fin pitch of 6 mm, tube row number of 6, waffle height 1.8 mm, fin thickness 0.12 mm, and air velocity 5 m/s. Also, longitudinal tube pitch is 27.5 mm, and transverse tube pitch of 24.8 mm, at which TPF is maximum while the friction factor is minimal. The Colburn factor is the most significant, minor friction factor, and the heat transfer coefficient and TPF are the most considerable in GRG. Hence, an improved heat transfer performance design of a wavy fin and tube heat exchanger is achieved using the above techniques.

Path synthesis of a four-bar linkage using a teaching-learning-based optimization algorithm

This paper presents the performance of a teaching-learning-based optimization (TLBO) algorithm and its elite version named as an elitist teaching-learning-based optimization algorithm (ETLBO) to obtain the optimum set of design parameters for the path synthesis of a four-bar linkage. The minimization of the position error is considered as an objective function and four case studies are considered to verify the efficiency and accuracy of the TLBO and ETLBO algorithms. The synthesized mechanism is obtained for four case studies using the 2 D movable sketch method of SolidWorks. The results have shown that the performance of the TLBO and ETLBO algorithms are better or competitive to the other optimization methods considered by the previous researchers.

Parametric Optimization of Local Resonant Sonic Crystals Window on Noise Attenuation by Using Taguchi Method and ANOVA Analysis

Local resonant sonic crystals (LRSCs) window as a novel design has recently been proposed to achieve a good balance between noise mitigation, natural ventilation and natural lighting. In an effort to explore the feasibilities of such designs in civil residential buildings, an optimization methodology was proposed to develop a more compact LRSCs window with high noise attenuation performance in the present study. Specifically, the Taguchi method was adopted for the design of experiments on the parameters of interest and their corresponding levels, and SN ratio analysis was then applied for the parametric evaluations on the noise attenuation on specified frequencies in traffic noise (concentrated sound energy frequency range: 630–1000 Hz). Three optimal sets of design parameters on the interested frequencies, namely, 630 Hz, 800 Hz and 1000 Hz were obtained. ANOVA analysis was conducted to quantificationally identify the design parameters with statistical significance and remarkable contribution to the desired performance. Results indicate that the slit size has the most significant influence on the overall noise attenuation performance, followed by cavity width. An optimal set of design parameters to achieve the overall best noise reduction performance in the frequency range of 630–1000 Hz was finally determined by combining the SN ratio and ANOVA analysis. A prototype of the final optimized LRSCs window was then fabricated and tested in a semi-anechoic chamber. Good agreement was found between the experiment and numerical simulation. In comparison to the benchmark case, the final optimized design can achieve a further noise reduction by 2.84 dBA, 3.48 dBA and 5.56 dBA for the frequencies of 630 Hz, 800 Hz and 1000 Hz, respectively. The overall noise reduction for the interested frequency range can be promoted by 3.28 dBA. The results indicate that the proposed optimization methodology is practical and efficient in designing a high-performance LRSCs window or improving similar applications.

Evolutionary Computing based Neuron-Computational Model for Microstrip Patch Antenna Design Optimization

In this paper, a novel Evolutionary Computing named Adaptive Genetic Algorithm (AGA) based ANN model is developed for rectangular MPA (Microstrip patch antenna). Considering at-hand and Nextgeneration Ultra wideband application demands, the emphasis has been made on retaining optimal lowcost design with desired cut-off frequency. The proposed method employs multiple sets of theoreticallydriven training instances or patch antenna design parameters which have been processed for normalization and sub-sampling to achieve a justifiable and reliable sample size for further design parameter prediction. Procedurally, the input design parameters were processed for normalization followed by sub-sampling to give rise to a sufficient set of inputs to perform knowledge-driven (designparameter) prediction. Considering limitations of the major at-hand machine learning methods which often undergo local minima and convergence while training, we designed a state-of-art new Adaptive Genetic Algorithm based neuro-computing model (AGA-ANN), which helped to predict the set of optimal design parameters for rectangular microstrip patch antenna. The predicted patch antenna length and width values were later used for verification which achieved the expected frequency. The depth analysis revealed that a rectangular patch antenna with width 14.78 mm, length 11.08mm, feed-line 50 Ω can achieve the cut-off frequency of 8.273 GHz, which can be of great significance for numerous UWB applications.

AC Current Ripple in Three-Phase Four-Leg PWM Converters with Neutral Line Inductor

This paper presents a comprehensive study of peak-to-peak and root-mean-square (RMS) values of AC current ripples with balanced and unbalanced fundamental currents in a generic case of three-phase four-leg converters with uncoupled AC interface inductors present in all three phases and in neutral. The AC current ripple characteristics were determined for both phase and neutral currents, considering the sinusoidal pulse-width modulation (SPWM) method. The derived expressions are simple, effective, and ready for accurate AC current ripple calculations in three- or four-leg converters. This is particularly handy in the converter design process, since there is no need for heavy numerical simulations to determine an optimal set of design parameters, such as switching frequency and line inductances, based on the grid code or load restrictions in terms of AC current ripple. Particular attention has been paid to the performance comparison between the conventional three-phase three-leg converter and its four-leg counterpart, with distinct line inductance values in the neutral wire. In addition to that, a design example was performed to demonstrate the power of the derived equations. Numerical simulations and extensive experimental tests were thoroughly verified the analytical developments.

Optimisation of friction stir welding parameters using combined Taguchi L9 and genetic algorithm

'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

Optimization of a shell and tube heat exchanger with staggered baffles using Taguchi method

In this work, Taguchi method is applied on the numerical simulation results of a shell and tube heat exchanger with staggered baffles (STHX-ST) for the purpose of optimizing its design parameters corresponding to maximum heat transfer rate and minimum pressure drop. An STHX-ST integrates the characteristics of a shell and tube heat exchanger with segmental baffles as well as helical baffles. The baffles in these heat exchangers are oriented in such a way that a constant clockwise or counter clockwise angle is maintained between the adjacent baffles. The analysis of the shell side of these heat exchangers in this work is carried out using computational fluid dynamics (CFD) by varying its design parameters. The rate of heat transfer obtained and total pressure drop across the shell side of the heat exchanger is calculated using CFD. A set of experiments are designed as an orthogonal array as per Taguchi method through combinations of design parameters such as shell inside diameter, tube outside diameter, baffle cut, baffle spacing and baffle orientation angle. CFD Simulations are carried out on each parameter combination in this array. Finally signal to noise ratio analysis is conducted on this array and two set of optimum design parameters corresponding to maximum heat transfer and minimum pressure drop is obtained. The optimum combination of variables for maximum heat transfer yields a maximum heat transfer rate of 141252 W and the optimum parameter combination for minimum pressure drop yields a minimum pressure drop of 58352.6 Pa.

Optimisation of friction stir welding parameters using combined Taguchi L9 and genetic algorithm

'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

Bone fracture healing under external fixator: Investigating impacts of several design parameters using Taguchi and ANOVA

In this paper we aim to improve the understanding of the relationship between unilateral-uniplanar external fixator design parameters and their influences on fixator performance. Stability and strength of bone-fixator construct as well as the quality of healing were defined as our major concerns in order to evaluate the performance of fixator. The roles of six key design parameters were assessed during the early stage of healing by using finite element models. Tissue differentiation within the callus was predicted through the implementation of a mechanoregulation theory of bone healing. Taguchi and ANOVA methods were used to achieve optimal design sets for outputs and to determine contribution percentage of each design parameter on outputs. For improving overall fixator performance, optimal set of design parameters consisting of 2 mm, 8 mm, 120 mm, 20 GPa, 50 mm and 20 mm were determined by Taguchi for pin diameter, rod diameter, rod elevation, fixator Young’s modulus, distance of the nearest pin to fracture site and distance between adjacent pins, respectively. Also, results of ANOVA revealed that rod elevation is the most important design parameter, with 43 % effectiveness on overall fixator performance, which was followed by fixator material and pin diameter with 28 % and 19 %, respectively. Results of this study can assist orthopedic surgeons to achieve an optimal fixator device with respect to the patient’s condition and give insight into the importance of different design parameters.

Design optimization of micromixer with circular mixing chambers (M-CMC) using Taguchi-based grey relational analysis

Abstract The aim of the present study is to optimize the micromixer with circular mixing chambers (M-CMC) using Taguchi-based grey relational analysis approach. Simulations are performed to investigate the effect of design parameters viz. chamber diameter, transverse offset and width of constriction channel on performance characteristics for parameter sets corresponding to Orthogonal Array (OA) L9. Further, grey relational grade is used to identify the optimal set of design parameters. In depth study of flow and mixing dynamics are carried out to visualize the mixing mechanism for optimal and other two cases of proposed micromixer design configuration for Re in the range from 0.1 to 50. In order to assess the response of each design variable at each level, analysis of variance (ANOVA) is also performed to analyse influence of design parameters on mixing index and pressure drop.

Mathematical Modeling on Statics and Dynamics of Aerostatic Thrust Bearing with External Combined Throttling and Elastic Orifice Fluid Flow Regulation

As aerostatic bearings are used in high-speed metal-cutting machines to increase machining accuracy, there is the need to improve their characteristics, including compliance, which is usually high. In practical applications, a significant reduction of bearing compliance is often necessary, sometimes down to zero and even negative values, to ensure automatic compensation of the elastic deformation in the machine technological system. A decrease in compliance leads to deterioration in the dynamic performance of the bearing, so it is necessary to develop new designs that meet the above requirements. This article considers an aerostatic bearing, in which decrease in compliance is ensured by the use of air throttling with elastic orifices. To ensure its stability, the principle of combined external throttling was applied, which can substantially improve the dynamics of conventional aerostatic bearings. A mathematical model of the elastic orifice deformation was developed, together with the flow rate performance calculation method. The method ensured full qualitative and satisfactory quantitative agreement with the experimental data. The model was used in the mathematical modeling of the aerostatic bearing movement. The article also proposes a method to calculate the static load capacity and compliance of a bearing, as well as a numerical method for fast computation of its dynamic performance, which allows for real-time multi-parameter optimization by the bearing dynamic performance criteria. The study showed that there is an optimal set of design parameters for which low, zero, and negative static compliance of the bearing is ensured, with the necessary stability margin, high speed, and the non-oscillatory nature of the transient processes.

Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer.

In this study, we report on a numerical study on design optimization for a microfluidic crossflow filtration system incorporated with the staggered herringbone micromixer (SHM). Computational fluid dynamics (CFD) and the Taguchi method were employed to find out an optimal set of design parameters, mitigating fouling in the filtration system. The flow and the mass transfer characteristics in a reference SHM model and a plain rectangular microchannel were numerically investigated in detail. Downwelling flows in the SHM model lead to backtransport of foulants from the permeable wall, which slows down the development of the concentration boundary layer in the filtration system. Four design parameters — the number of grooves, the groove depth, the interspace between two neighboring grooves, and the interspace between half mixing periods — were chosen to construct a set of numerical experiments using an orthogonal array from the Taguchi method. The Analysis of Variance (ANOVA) using the evaluated signal-to-noise (SN) ratios enabled us to identify the contribution of each design parameter on the performance. The proposed optimal SHM model indeed showed the lowest growth rate of the wall concentration compared to other SHM models.

Fully coupled modeling and design of a piezoelectric actuation based valveless micropump for drug delivery application

The precise control over the drug delivery involved in several vital applications including healthcare is required for achieving a therapeutic effect. For such precise control/manipulation of the drugs, micropumps are used. These micropumps are basically of two types viz. check valve-based and valveless micropumps. The valveless micropumps are preferable due to the congestion-free operation of diffuser/nozzle valves. In this paper, design optimization of a valveless piezo-electric actuation based micropump is carried out using COMSOL Multiphysics 5.0 by coupling two Multiphysics interface modules namely fluid–structure interaction and piezoelectric physics modules. Using simulation studies, the influence of pump design parameters including diffuser angle, diffuser length, neck width, chamber depth, chamber diameter and diaphragm thickness on net flow rate is studied. An optimal set of design parameters for the proposed micropump is identified. Further, the influence of actuation frequency on the flow rate is analysed. It is found that the proposed micropump is capable to deliver a net flow rate of 20 µl/min and a maximum back pressure attainable is 200 Pa.