Nonlinear structural optimization is fairly expensive and difficult, because a large number of nonlinear analyses is required due to the large number of design variables involved in topology optimization. In element density based topology optimization, the low density elements create mesh distortion and the updating of finite element material with low density elements has a severe effect on the optimization results in the next cycles. In order to overcome these difficulties, the equivalent static loads method for nonlinear response structural optimization (ESLSO) primarily used for size and shape optimization has been applied to topology optimization. The nonlinear analysis is performed with the given loading conditions to calculate equivalent static loads (ESLs) and these ESLs are used to perform linear response optimization. In this paper, the authors have presented the results of five case studies with material, geometric and contact nonlinearities showing good agreement and providing justification of the proposed method.

Aerodynamic lift force acting on the solar structure is important while designing the counterweight for rooftop-mounted solar systems. Due to their unique configuration, the load estimated for solar structures using international building codes can be either higher or lower than the actual. Computational Fluid Dynamics(CFD) simulations haveproven to be an efficient tool for estimating wind loads on solar panels for design purposes and identifying critical design cases. Computational Fluid Dynamics (CFD) simulations usually require high computation power, and slight changes in geometry to find optimum configuration can be time-consuming. An optimization method to minimize lift force effects on solar photovoltaic (PV) arrays installed on rooftops usesthe Computational Fluid Dynamics (CFD)and genetic algorithms proposed in this paper. The tilt angle and pitch between two rows of solar panels were parameterized, and a genetic algorithm was used to search for aconfiguration resulting in minimum wind lift force acting on the solar photovoltaic plant. Only combinations with a performance ratio >80% were considered. Three different rooftopphotovoltaic (PV) plant layout configurations were analyzed in this research. Two rows of photovoltaic (PV) panel arrays wereconsidered for optimization in the 2D domain using ANSYS Fluent. Results showed that the difference in wind-liftforce between optimized configurations against that with maximum lift force configuration for all three cases above was fifty percent.

Manufacturing process accuracy is obtained by proper arrangement of fixture elements known as fixture layout. A N-3-2-1 method is used for sheet metals which requires (N + 3) fixture elements to constrain deformation normal to surface. Genetic Algorithm (GA) is used for fixture layout optimization, but it requires high computational effort due to large number of populations. A new method for fixture layout optimization is proposed by integrating topology optimization into GA. In this method, topology optimization reduces the population for GA. The objective function is to reduce the population for GA and minimize total deformation normal to the plane of workpiece. The proposed approach comprised three stages. In the first stage, the initial number of clamps are determined. In the second stage, the population is reduced for GA and the feasible area of clamps are identified using the topology optimization technique. In the third stage, the number and position of clamps, earlier identified in stage one, are optimized using GA. Two different case studies are solved by varying applied load position and magnitude. The proposed method results 47.5% and 65% decreases in the population for subcase 1 and subcase 2, respectively. However, in subcase 3 and subcase 4 the population reduced was 90% and 80%, respectively. The 25% of reduced population is used as the convergence criteria. Similarly, total deformation normal to the plane is reduced in each subcase, with the highest reduction of 86.31% in subcase 1 and lowest of 59.85% in subcase 4. The experiment is performed on the first case study to validate results. This concludes that the proposed method is valid and that optimal results are found.

This contribution presents three-dimensional turning operation simulations exploiting the capabilities of finite element (FE) based software Abaqus/Explicit. Coupled temperature-displacement simulations for orthogonal cutting on an aerospace grade aluminum alloy AA2024-T351 with the conceived numerical model have been performed. Numerically computed results of cutting forces have been substantiated with the experimental data. Research work aims to contribute in comprehension of the end-burr formation process in orthogonal cutting. Multi-physical phenomena like crack propagation, evolution of shear zones (positive and negative), pivot-point appearance, thermal softening, etc., effecting burr formation for varying cutting parameters have been highlighted. Additionally, quantitative predictions of end burr lengths with foot type chip formation on the exit edge of the machined workpiece for various cutting parameters including cutting speed, feed rate, and tool rake angles have been made. Onwards, to investigate the influence of each cutting parameter on burr lengths and to find optimum values of cutting parameters statistical analyses using Taguchi’s design of experiment (DOE) technique and response surface methodology (RSM) have been performed. Investigations show that feed has a major impact, while cutting speed has the least impact in burr formation. Furthermore, it has been found that the early appearance of the pivot-point on the exit edge of the workpiece surface results in larger end-burr lengths. Results of statistical analyses have been successfully correlated with experimental findings in published literature.

Wind is an important renewable energy source. The majority of wind farms in Pakistan are installed in Jhimpir, Sindh Wind Corridor. At this location, downstream turbines encounter upstream turbines’, wake, decreasing power output. To maximize the power output, there is a need to minimize these wakes. In this research, a method is proposed to maximize the power output using a Genetic Algorithm (GA). Hub heights and inter-turbine spacing are considered variables in this method. Two wind farms located at Jhimpir, Sindh, namely, Second and Third Three Gorges Wind Farms (TGWFs), have been analyzed. Three different cases are considered to maximize the power output. In Case 1, thesame hub heights and inter-turbine spacing without wake effects are considered. In Case 2, the same hub heights and inter-turbine spacing with wake effects are considered. In Case 3, variable hub heights and inter-turbine spacing with wake effects are considered. The results revealed that TGWFs, with variable hub heights and inter-turbine spacing, produce more power output. It is also revealed that the increase in power output, in the case of two different hub heights, is greater in comparison to three different hub heights. Eventually, the proposed method may help in the layout optimization of a wind farm.