Numerical Multi-objective Optimization Research Articles

Multi-objective optimization of surface texture shape in fluid mechanical face seals using mass-conserving cavitation boundary condition

Surface texture shape significantly influences the tribological and sealing properties of fluid mechanical face seals. Considering that it is a contradiction to simultaneously improve load capacity and reduce leakage, a numerical multi-objective optimization model is proposed herein to improve the load-carrying capacity while reducing the volume leakage rate via texture shape optimization. The mass-conserving cavitating boundary condition and the non-dominated sorting genetic algorithm II are employed for the optimization. The numerical results indicate that the optimal texture shape is spiral-like under the lowest volume leakage rate, which becomes an asymmetric flat-front chevron with increasing load-carrying capacity and volume leakage rate. The optimal shape outperforms four regular shapes (all obtained by multi-objective optimization) at different rotation speeds and sealing pressures. The cavitation within the optimal shape ensures zero leakage in fluid face seals. The comprehensive performance of the texture shape obtained by multi-objective optimization is superior to that obtained by single-objective optimization.

Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Multi-objective numerical optimization of 3D-printed polylactic acid bio-metamaterial based on topology, filling pattern, and infill density via fatigue lifetime and mass.

Infill parameters are significant with regard to the overall cost and saving material while printing a 3D model. When it comes to printing time, we can decrease the printing time by altering the infill, which also reduces the total process extent. Choosing the right filling parameters affects the strength of the printed model. In this research, the effect of filling density and infill pattern on the fatigue lifetime of cylindrical polylactic acid (PLA) samples was investigated with finite element modeling and analysis. This causes the lattice structure to be considered macro-scale porosity in the additive manufacturing process. Due to the need for multi-objective optimization of several functions at the same time and the inevitable sacrifice of other objectives, the decision was to obtain a set of compromise solutions according to the Pareto-optimal solution technique or the Pareto non-inferior solution approach. As a result, a horizontally printed rectangular pattern with 60% filling was preferred over the four patterns including honeycomb, triangular, regular octagon, and irregular octagon by considering the sum of mass changes and fatigue lifetime changes, and distance from the optimal point, which is the lightest structure with the maximum fatigue lifetime as an objective function with an emphasis on mass as an important parameter in designing scaffolds and biomedical structures. A new structure was also proposed by performing a structural optimization process using computer-aided design tools and also, computer-aided engineering software by Dassault systems. Finally, the selected samples were printed and their 3D printing quality was investigated using field emission scanning electron microscopy inspection.

Experimental investigation and optimization of the gasification parameters of macadamia nutshells in a batch-fed bubbling fluidized bed gasifier with air preheating

Gasification of biomass waste has a significant potential to reduce environmental impact and promote sustainability by producing syngas, which is considered as renewable energy. This work investigated the gasification of macadamia nutshells in air-preheated, batch-fed fluidized bed gasifier. The study conducted a parametric analysis to assess the effect of equivalence ratio (ER) and air temperature on the gasifier temperature profile and its performance based on gas composition, higher heating value (HHV), and gas yield. The research was conducted within the range of 0.15–0.35 for the ER and 25–825 °C for the air temperature. Multi-objective numerical optimization was conducted using response surface methodology (RSM). From the parametric study, a distinct temperature profile was observed along the gasifier height, with the peak temperature near the top of the fluidized bed section and the lowest temperature at the top of the gasifier. Air preheating mostly favored gasification temperature at the lower part of the gasifier and showed rare significance at the top. No improvement in gasifier performance was observed beyond an air temperature of 620 °C, which was identified as the ideal air-preheating temperature. Analysis of variance (ANOVA) revealed that the ER was the most influential parameter in the production of combustible gasses, syngas HHV and gas yield. Air preheating did not have a significant effect on methane production and gas yield. The most optimal values for ER and air temperature were obtained as 0.195 °C and 620 °C, respectively, producing optimal values of 9.54, 14.65%, 2.03, 4.02 MJ·Nm−3, and 1.82 Nm3·kg−1 for hydrogen, carbon monoxide, methane, HHV, and gas yield, respectively.

MULTI-CRITERIA NUMERICAL OPTIMIZATION OF MECHANICAL PROPERTIES IN ULTRASONIC WELDING PROCESS PARAMETERS OF PVC-COATED HYBRID TEXTILES FOR WEATHER PROTECTION

A series of research was carried out to determine the correlation between ultrasonic welding process parameters and weld seam mechanical properties. However, multi-objective numerical optimization of coated hybrid textiles for weather protection has not been addressed. To ensure a comprehensive evaluation of ultrasonic weld seams, the research investigates the optimal solution of the multi-objective function of ultrasonic welding process parameters and formulates a single criteria objective function. Lapped and superimposed types of seams were applied based on 33 factorial designs of experiments for 6 and 12 mm welding widths. Single-criteria objective functions instead of three independent problems were developed as a generalized utility function. A single-criteria optimization method was introduced through predetermined weight and normalization within the range of acceptable/unacceptable values. Numerical and graphical optimization methods were also applied to determine possible optimal solutions through generalized utility functions. The best optimal value of the generalized utility function (0.670425 and 0.944374) was attained at welding speed (2 and 2.01564 m/min), power (93.756 and 117.973 W), and pressure force (198.803 and 239.756 N) of 6 and 12 mm welding widths, respectively. The acceptable range of satisfactory values was determined for the roof and wall of awnings and camping tents through standard, in which seam performance level indicated. Nonlinear quadratic numerical models were formulated to estimate the generalized utility function, and their results were close to the regressed diagonal line against the actual points. The statistical analysis was shown a statistically significant effect of welding process parameters on the generalized utility function.

Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters.

Professionals in industries are making progress in creating predictive techniques for evaluating critical characteristics and reactions of engineered materials. The objective of this investigation is to determine the optimal settings for a 3D printer made of acrylonitrile butadiene styrene (ABS) in terms of its conflicting responses (flexural strength (FS), tensile strength (TS), average surface roughness (Ra), print time (T), and energy consumption (E)). Layer thickness (LT), printing speed (PS), and infill density (ID) are all quantifiable characteristics that were chosen. For the experimental methods of the prediction models, twenty samples were created using a full central composite design (CCD). The models were verified by proving that the experimental results were consistent with the predictions using validation trial tests, and the significance of the performance parameters was confirmed using analysis of variance (ANOVA). The most crucial element in obtaining the desired Ra and T was LT, whereas ID was the most crucial in attaining the desired mechanical characteristics. Numerical multi-objective optimization was used to achieve the following parameters: LT = 0.27 mm, ID = 84 percent, and PS = 51.1 mm/s; FS = 58.01 MPa; TS = 35.8 MPa; lowest Ra = 8.01 m; lowest T = 58 min; and E = 0.21 kwh. Manufacturers and practitioners may profit from using the produced numerically optimized model to forecast the necessary surface quality for different aspects before undertaking trials.

SINGLE- AND MULTI-OBJECTIVE NUMERICAL OPTIMIZATION OF DELTA WINGLET PAIR POSITIONED IN A DUCT

In the present work, three-dimensional numerical study was carried out to perform the single- and multi-objective optimization process of delta winglet pair (DWs) mounted on a rectangular duct with a hydraulic diameter of <i>D<sub>h</sub></i> = 0.0535 m. For the purpose of presenting the consistency of numerical model with experimental data, experimental work was also performed for DWs with an angle of attack of θ = 40° ranging from Re = 5000-20,000. The response surface method (RSM) with central composite design (CCD) was performed to construct numerical design parameters as well as to obtain responses for Nu,<i> f</i>, and thermal enhancement factor (TEF). On this basis, the DWs' angle of attack (θ), the ratio of the transverse distance between the trailing edges of DWs to the duct width (<i>L<sub>VG</sub>/W</i>), and the Reynolds number (Re) were considered to be design parameters. The selected single-objectives are maximizing Nu, minimizing <i>f</i>, and maximizing TEF. Moreover, the chosen multi-objectives are maximizing Nu while minimizing <i>f</i>; maximizing Nu and TEF while minimizing <i>f</i>. The findings indicate that the angle of attack (θ) and Re have the greatest influence on all responses compared to the <i>L<sub>VG</sub>/W</i> ratio. For single-objectives, increase in Nu, decrease in <i>f</i>, and increase in TEF were attained on the order of 45.67%, 14.89%, and 3.98%, respectively, in comparison to the reference case. Furthermore, maximizing Nu while minimizing <i>f</i> was selected as a multi-objective, increase in Nu and decrease in <i>f</i> were calculated to be 36.38&#37 and 12.76&#37, respectively, compared with the reference case.

Study on multi-objective search performance of production plan optimization in manufacturing field

There is a great demand for multi-objective optimization of production plans at the high-mix low-volume production of the manufacturing industry, and the importance of multi-objective numerical optimization technology will increase more and more in the future. In this research, we have developed a control method that can realize a wide range of Pareto solution at high speed for two object functions; cost and productivity, which are important in manufacturing fields but have a trade-off relationship. The key of present technique is a technology that can obtain a wide range of Pareto solutions, which are difficult to solve by each technology alone, by appropriately controlling a technology that has high search performance but is single objective optimization and a technology that can perform multi-objective optimization but has low search performance. This paper presents an example of solving a standard multi-objective optimization problem and the results of applying it to setup optimization problems which are often found in actual manufacturing fields.

Optimizing the Physical Properties of Waste Denim Fiber-Modified Rubberized Bitumen Through Response Surface Methodology

In this study, waste denim fiber (WDF) was used as a modifier to enhance the properties of bitumen incorporating tire pyrolysis oil (TPO). The designing of the experiment, analysis data, modeling and multi-objective optimization performed using response surface methodology (RSM). Base bitumen was modified with 0%, 5%, 10% and 15% TPO and 0%, 2%, 4% and 6% of WDF using high shear mixer. The interaction effects of TPO and WDF on the penetration, softening point and temperature susceptibility of bitumen were evaluated. Results showed that the addition of TPO and/or WDF has a significant effect on the consistency and temperature susceptibility of base bitumen. RSM analysis exhibited high degrees of correlation (R2) of 0.982, 0.973 and 0.947 for the dependent variables’ penetration, softening point and temperature susceptibility, respectively. That indicates there is an adequate agreement between models developed by RSM and experimental results. The numerical multi-objective optimization showed that optimum properties can be achieved with 8.4% of TPO and 6% of WDF.

A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing

Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with s -norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.

ROBUST MULTI-OBJECTIVE OPTIMIZATION IN PRODUCT DEVELOPMENT WITH RESPECT TO USER-SCENARIO AND MANUFACTURING UNCERTAINTIES

AbstractIn product development, user-scenarios are a way of tailoring requirements to defined customer groups. Furthermore, a product design often involves multiple conflicting objectives that are analyzed within an iterative process. The models typically used for the analysis often do not accurately reflect the real-world representation. This can be alleviated by finding robust product designs. While usually uncertainties due to manufacturing tolerances are investigated, we additionally consider uncertainties in the user-scenario. Therefore, we present a robustness evaluation in a multi-objective numerical optimization in product development. For this, we consider manufacturing tolerances using an adjusted Latin Hypercube Sampling as well as deviations in the user-scenario by means of a Gaussian distribution. In the case study, we present the robust development of a customer specific coffee machine, where we show the robustness evaluation and the impact of the proposed adjustments. The advantage of the presented process is a product design tailored to the customer's requirements under specified uncertainties. In addition, this enables a time benefit in the product development due to the automated analysis used in the optimization.

A Dynamic Metaheuristic Network for Numerical Multi-objective Optimization

This research work proposes a dynamic metaheuristic network that is a layered interconnection of a number of multi-objective optimization (MOO) algorithms. Each node of the network corresponds to a MOO metaheuristic and interconnections between the nodes represent the flow of subpopulation elements in a feed-forward direction. The proposed method runs in consecutive sessions such that a session starts with the assignment of subpopulations to each of the individual nodes, proceeds with execution of node metaheuristics within their algorithmic framework and ends with feeding the improved subpopulations to the connected forward nodes. The network architecture is dynamic in the sense that nodes change their layers at the end of each session. At the end of each session, elements of the improved subpopulations are fed forward to nodes in subsequent layers which update their own subpopulations using uniform random sampling. The proposed method is evaluated on CEC2009, ZDT, DTLZ, WFG benchmarks and several real-world MOO problems using the experimental framework described for these problem instances. Comparative evaluations against a large set of state-of-the-art algorithms exhibited that the proposed method with its novel dynamic network architecture and subpopulation assignment strategy is promising both in the quality of the extracted Pareto fronts and in leading future research on ensembles of MOO algorithms.

A novel non-dominated sorting genetic algorithm for solving the triple objective project scheduling problem

Multi-objective project scheduling has attracted wide attention for approximately two decades. However, most of the existing research has focused on the double-objective project scheduling problem, while literature on project scheduling problems with more than two objectives is rather scarce. In this paper, the typical multi-mode resource-constrained project scheduling problem is extended to a new triple-objective multi-mode project scheduling problem (TOMPSP) with the objectives of minimizing the project duration, minimizing the resource investment and maximizing the robustness of the schedule. To solve the presented triple-objective problem, we resort to the latest version of the multi-objective genetic algorithm, the non-dominated sorting genetic algorithm III (NSGA-III). In the decoding process of the NSGA-III, a modified SSGS (serial schedule generation scheme), in which resource constraints are relaxed, is suggested by considering the delays of activities. Although the NSGA-III shows excellent performance in numerous multi-objective optimization problems with more than two objectives, it has a potential disadvantage in that it occasionally cannot find the intercept during the adaptive normalization process, and thus, the population cannot be normalized as expected. Since a case without an intercept is impossible in the NSGA-II, we adopt the NSGA-II normalization process rather than that of NSGA-III. The standard instances in PSPLib are modified to serve as the instances of the TOMPSP, and a computational experiment is conducted to test the algorithms. The results show that the presented algorithm not only greatly simplifies the implementation of the NSGA-III but also significantly improves the execution efficiency and calculation quality.

Numerical Investigation and Multi-Objective Optimization of Internal EGR and Post-Injection Strategies on the Combustion, Emission and Performance of a Single Cylinder, Heavy-Duty Diesel Engine

This work presents a numerical study that investigates the optimum post-injection strategy and internal exhaust gas recirculation (iEGR) application with intake valve re-opening (2IVO) aiming to optimize the brake specific nitric oxide (bsNO) and brake specific soot (bsSoot) trade-off with reasonable brake specific fuel consumption (BSFC) via 1D engine cycle simulation. For model validation, single and post-injection test results obtained from a heavy-duty single cylinder diesel research engine were used. Then, the model was modified for 2IVO application. Following the simulations performed based on Latin hypercube DoE; BSFC, bsNO and bsSoot response surfaces trained by feedforward neural network were generated as a function of the injection (start of main injection, post-injection quantity, post-injection dwell time) and iEGR (2IVO dwell) parameters. After examining the effect of each parameter on pollutant emission and engine performance, multi-objective pareto optimization was performed to obtain pareto optimum solutions in the BSFC-bsNO-bsSoot space for 8.47 bar brake mean effective pressure (BMEP) load and 1500 rpm speed condition. The results show that iEGR and post-injection can significantly reduce NO and soot emissions, respectively. The soot oxidation capability of post-injection comes out only if it is not too close to the main injection and its efficiency and effective timing are substantially affected by iEGR rate and main injection timing. It could also be inferred that by the combination of iEGR and post-injection, NO and soot could be reduced simultaneously with a reasonable increase in BSFC if start of main injection is phased properly.

Numerical optimization of a heat exchanger with slit fins and vortex generators using genetic algorithms

Abstract Heat exchangers are devices very commonly found in heating and air conditioning appliances. This work is about the optimization of compact heat exchangers with three rows of tubes and slit fins, using built-in longitudinal vortex generators on the fins. The Colburn factor and the friction factor were obtained out of numerical simulations from a universe of 32 geometries. Geometries were selected using the Latin hypercube design, and were numerically simulated. Two objective functions were employed as surrogate models for the Colburn and friction factor. The functions were determined by multiple regression with a level of R square higher than 99.5%. A multi-objective numerical optimization was made, to find the best location for delta winglet vortex generators and its angle of attack; searching for an increase of heat transfer and a low value in pressure penalty. Group of optimal geometries was obtained, because of the optimization process using genetic algorithms. The geometry, with the best performance was simulated, and the results were used to validate the optimization process. This geometry, when compared to the slit fin without vortex generators (smooth condition), increased by 8.74% the heat transferred and augmented by 14.77% the pressure drop. The angle of attack, module, and module angle of the optimized geometry were 49.86°, 5.08 mm and 22.51° respectively.

Optimization of an Asymmetric Twin Scroll Volute Turbine under Pulsating Engine Boundary Conditions

<div class="section abstract"><div class="htmlview paragraph">Future CO<sub>2</sub> emission legislation requires the internal combustion engine to become more efficient than ever. Of great importance is the boosting system enabling down-sizing and down-speeding. However, the thermodynamic coupling of a reciprocating internal combustion engine and a turbocharger poses a great challenge to the turbine as pulsating admission conditions are imposed onto the turbocharger turbine. This paper presents a novel approach to a turbocharger turbine development process and outlines this process using the example of an asymmetric twin scroll turbocharger applied to a heavy duty truck engine application. In a first step, relevant operating points are defined taking into account fuel consumption on reference routes for the target application. These operation points are transferred into transient boundary conditions imposed on the turbine. These pulsating admission conditions to the turbocharger turbine are analyzed and subsequently discretized using the method of quasi-steadiness to avoid numerically very expensive unsteady CFD simulations. Following, an automated in-house developed workflow based on a parameterized model of the entire turbine stage is introduced and described. The parameterization is based on design parameters linked to aerodynamic properties, hence it is not limited to one specific geometry but rather able to represent a large variety of designs with comparatively few input parameters. Concluding, a meta-model based multi-disciplinary and multi-objective numerical optimization is performed to obtain the best geometry possible. The optimization objectives are linked to a perfect turbine-compressor matching with an existing benchmark compressor stage and regards to the engine’s air fuel ratio and exhaust gas recirculation requirements. The entire optimization is based on numerical methods, that is, a CFD study. However, the numerical models used throughout the paper are validated against experimental data to ensure the quality and accuracy of the predictions regarding its behavior in an experimental setup.</div></div>

Numerical Study and Multi-objective Optimization of an Energy Absorbing Connector with Curved Plate and Aluminum Foam

This paper presents numerical works and multi-objective optimization of the energy absorbing connectors which was employed to dissipate blast/impact energy through curved plates and aluminum foam. Finite element models of the energy absorbing connectors under dynamic crushing load were established and validated with the impact loading tests. Then, the parametric studies were conducted to reveal the influences of crushing velocity, angle θ, curved plate thickness as well as length and height of aluminum foam on the energy absorbing characteristics. Increasing crushing velocity was shown to improve the absorbed energy of the connector owing to the strain rate enhancement and change of deformation mode. In addition, the absorbed energy could also be increased via increasing curve plate thickness, length and height of aluminum foam as well as decreasing angle θ. The multi-objective optimization was conducted by employing the non-dominated sorting genetic algorithm II. It was noted that the specific energy absorption and peak crushing force were conflicting with each other.

Multiobjective optimization of a flat-panel airlift reactor designed by computational fluid dynamics

The optimization and scale-up process of industrial reactors maintaining optimum and homogeneous reaction conditions is a great challenge to engineers. For each final product, process and facility is necessary elaborate suitable strategies in order to allow a comprehensive and detailed process characterization to identify the most relevant process parameters influencing product yield and quality. Despite several research efforts to develop ideal reactors, these studies usually focus on trial-and-error optimization approach. This work was aimed to propose a numerical multiobjective optimization method and its application to the design of a flat-panel airlift (FPA) reactor. A preliminary study of the hydrodynamics of the reactor was conducted in a 61L FPA reactor. Experiments and computational fluid dynamics (CFD) simulations were compared to demonstrate the viability of usage of the simplifications applied to CFD model in order to reduce the computational effort. The inner structure key parameters of a FPA reactor, including the ratio of the cross-section area of the downcomer to the cross-section area of the riser, are optimized vis-à-vis the flow aspects portrayed by the performance parameters, such as shear stress and residence time variations. An in-house computer package was used for the automatic CFD simulations and the optimization process. A Pareto-optimal front with possible solutions to the problem and a detailed investigation of the mixing performance of the possible optimal geometries were presented in the study herein. The results also showed the high influence of the geometric parameters on the hydrodynamics of the FPA reactor, that have to be taken in account in the scaling-up process.

Experimental and numerical analysis and multi-objective optimization of quasi-static compressive test on thin-walled cylindrical with internal networking

ABSTRACTThe present study aimed at quasi-static compressive test on 6061-O aluminum cylindrical absorbers with internal networking. The cylindrical cellularization was in such a way that a square is welded to a thin-walled cylindrical at the middle of the sides. As one of the design of experiments (DOE) techniques the response surface method (RSM) was used to examine the effect of the parameters on energy absorption, peak force (PF), specific energy absorption per unit mass (SEA) and energy absorption per length (EA). The variables of thickness, height and length of square of the thin-walled cylindrical were considered in three levels. The samples were performed under a quasi-static compressive test at a constant speed of 10 mm/min. Then, the numerical simulation was performed using the finite element software (ABAQUS). The results obtained from the experiments and simulations were consistent. In accordance to the equations obtained from the experimental results, the multi-objective optimization was conducted considering maximum SEA conditions, EA conditions, and minimum PF conditions. The results indicated a direct relationship between thickness and length of square with responses, nonlinear relationship between height and responses, and the effect of thickness was greater than other parameters in all responses. Except for PF response, there was an interaction between the parameters in EA and SEA responses.

Assessment of alkaline peroxide-assisted wet air oxidation pretreatment for rice straw and its effect on enzymatic hydrolysis

Biomass recalcitrance is considered to be one of the impediments in bioconversion of lignocellulosic biomass (LCB) to sugars. Rice straw, a potential lignocellulosic waste, owing to the surplus availability, renewability and high carbohydrate content was used as a model LCB in the present study. The alkaline hydrogen peroxide-assisted wet air oxidation (APWAO) was evaluated as plausible pretreatment for rice straw based on the 23—factorial experimental design with a goal to reduce biomass recalcitrance by enhancing the cellulose recovery, hemicellulose solubilization, lignin removal and concomitantly generating limited degradation products. APWAO resulted in an overall cellulose recovery ranging from 80.54 to 93.02%, hemicellulose solubilization of 36.44–82.08% and lignin removal of 65.95–81.11% (all on w/w basis) respectively and absence of potent inhibitors viz. furfural and 5-hydroxymethylfurfural. The statistically significant pretreatment factors that affected each of these responses were assessed and optimum pretreatment conditions were determined to be biomass soaking in 0.5% H2O2 for 14 h succeeded by wet air oxidation (WAO) at 190 °C, 6 bar, 20 min by multi-objective numerical optimization. Further, the morphological and structural changes occurring as a result of pretreatment were scrutinized using SEM and FT-IR. APWAO further ensued in enhanced cellulose accessibility during enzymatic saccharification indicated by the glucose yield ranging from 113.97 to 200.34 g/kg untreated rice straw. Thus, the combined pretreatment (APWAO) i.e. pre-soaking in alkaline H2O2 followed by WAO was shown to enhance glucose yields owing to significant delignification.