Element-based connectivity approach for topology optimization of rib layout design

(Keywords) combinatorial optimization problem, rational construction, simulated annealing, agricultural facility, optimal layout design Abstract In this study, optimal layout design approach was used to solve problems related to site layout design of agricultural facilities such as refrigeration house, grading and packing house and warehouse. A basic program to support the rational construction of such facilities using Simulated Annealing (SA), one heuristic algorithm and one solution for combinatorial optimization problems, was coded in C language (Satake(1999)). Layout design simulations were carried out to obtain information for the development of a program for solving practical problems. And, to clarify the feasibility of applying SA to the optimal layout design problems, the layout design simulation using the hill climbing (HC), which is another heuristic algorithm, was carried out for comparison with SA. As a result of the comparison with the optimum solution by HC, the value of cost evaluation by SA using exponential annealing was lower than that of hill climbing. Furthermore, in addition to this simulation, preliminary layout design simulation was also carried out to determine the applicability of the optimal layout design simulator to practical design. In this study, the layout design for a new building and parking was carried out. In the layout design, the site is not rectangular. As a result, the optimal design satisfying our condition was obtained.

Fixture Layout Design (FLD) determines the specific position of locators and clamps to orient and holds the workpiece with respect to a machine tool. The FLD approaches that use Finite Element Analysis (FEA) have been widely used in previous works and have become computationally expensive and specific to a particular problem. Further, the FLD and clamping force optimization were often performed separately by ignoring their interdependence. In the present work, the locators' contact forces are uniformly distributed by suitably varying the fixture layout and clamping force to maximize the part dimensional and form quality. The parametric rigid body model is used to depict the behaviour of the workpiece-fixture system, and it is incorporated with the genetic algorithm to optimize the design variables. A prismatic workpiece with pocket milling operation is considered to validate the proposed methodology. Stability criterion and tool-fixture interference are considered constraints. Subsequently, FEA is used to verify the integrity of the proposed approach. The results infer that the uniform distribution of maximum elastic deformation is achieved due to the uniform distribution of contact forces. The suggested approach is proven effective for designing a milling fixture to manufacture components with high dimensional and form precision.

<div>This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function. As GA and PMP are global optimization methodologies under suitable conditions, the solution can be considered as a benchmark for the application under study. Five military drive cycles are used to evaluate the proposed approach. The simulation results show 5%-19% reduction in fuel consumption depending on the drive cycle compared to a baseline non-optimized case.</div>

In this paper, an optimum rib layout design method for reducing radiated noise is proposed based on topology optimization and acoustic contribution analysis. According to radiated noise depends on acoustic transfer vector (ATV) and normal velocity, the influence of rib layout on ATV is analyzed and it is found rib layout has little influence on ATV. Only if a region has maximum acoustic contribution, the normal velocities on this region can have the most remarkable influence on radiated noise. So the determination procedure of region with maximum acoustic contribution is introduced. Based on this, the topology optimization model is established to minimize the normal velocities on this region. Ribs can be arranged according to the optimum topologies to reduce the normal velocities, which in turn results in a reduction of radiated noise. The topology optimization model is used to obtain the optimal rib layout by taking a plate-like structure as an example. The plate is fixed along all side edges and excited by a time-harmonic external point load with different prescribed frequencies. The radiated noise is simulated using the finite element method and boundary element method. Four plates are manufactured according to the optimal rib layouts for different single frequency excitation. Modal test and sound measurement are conducted to validate the proposed method. The influence of loading position on the topology optimization results is also investigated and discussed.

Sandwich geometries, mainly panels and beams, are widely used in several transportation industries, namely aerospace, aeronautics, and automotive. They are known for some advantages in structural applications: high specific stiffness, low weight, and possibility of design optimization prior to manufacturing. This study aims to discover the dynamic behaviour of a model of novel sandwich type of beam simply supported-at-its-ends by use of finite element method. There are 12 geometries studied herein, with the same base configuration. The models were previously subjected to a design optimization routine. The dynamic behavior of the initial models in relation to their final versions is considered. The influence of the geometry on the characteristic frequencies is discussed, as well as its improvement in relation to the initial models. It is shown that the statically optimized models represent a significant improvement over the initial ones. In some cases, the improvement surpasses 20%. It can, therefore, be concluded that the design optimization approach, developed for static analysis, might be moderately effective in improving the modal behavior of the studied beams.

The low order Taylor’s series expansion was employed in this study to estimate the reliability indices of the failure criteria for reliability-based design optimization of a linear static structure subjected to random loads and boundary conditions. By taking the advantage of the linear superposition principle, only a few analyses of the structure subjected to unit-loads are needed through the entire optimization process to produce acceptable results. Two structural examples are presented in this study to illustrate the effectiveness of the proposed approach for reliability-based design optimization: one deals with a truss structure subjected to random multiple point constraints, and the other conducts shape design optimization of a plane stress problem subjected to random point loads. Both examples were formulated and solved by the finite element method. The first example used the penalty method to reformulate the multiple point constraints as external loads, while the second example introduced an approach to propagate the uncertainty linearly from the nodal displacement vector to the nodal von Mises stress vector. The final designs obtained from the reliability-based design optimization were validated through Monte Carlo simulation. This validation process was completed with only four unit-load analyses for the first example and two for the second example.

The application of Dr. Genichi Taguchi's approach for design optimization, called Robust Design, to the design of human-computer interface software is investigated. The Taguchi Method is used to select a near optimum set of interface design alternatives to improve user acceptance of the resulting interface software product with minimum sensitivity to uncontrollable noise caused by human behavioral characteristics. Design alternatives for interaction with personal micro-computers are identified. Several important and representative alternatives are chosen as design parameters for the Taguchi matrix experiment. A noise field with three human behavioral characteristics as noise factors were chosen as a representative noise array. Task accomplishment scenarios were developed for demonstration of the design parameters on an interactive human-computer interface. Experimentation was conducted using selected human subjects to study the effect of the various settings of the design parameters on user acceptance of the interface. Using the results of the matrix experiment, a near optimum set of design parameter values was selected. A verification experiment was developed and performed using the predicted near optimum design parameter values. Analysis of the follow-up experiment indicated improved levels of user acceptance with the near optimum values. This study suggests that the Taguchi Method of design optimization is applicable to human-machine engineering in general, and to the design of human-computer interface software in particular, as a means of selecting a near optimum set of design alternatives. This methodology is useful in reducing the number of total experiments required for optimization where several design alternatives exist in a richly interdependent context.

A compound parabolic concentrator (CPC) is a non-imaging device generally used in PV, thermal, or PV/thermal hybrid systems for the concentration of solar radiation on the target surface. This paper presents the geometric design, statistical modeling, parametric analysis, and geometric optimization of a two-dimensional low concentration symmetric compound parabolic concentrator for potential use in building-integrated and rooftop photovoltaic applications. The CPC was initially designed for a concentration ratio of “2×” and an acceptance half-angle of 30°. A MATLAB code was developed in-house to provoke the CPC reflector’s profile. The height, aperture width, and concentration ratios were computed for different acceptance half-angles and receiver widths. The interdependence of optical concentration ratio and acceptance half-angle was demonstrated for a wide span of acceptance half-angles. The impact of the truncation ratio on the geometric parameters was investigated to identify the optimum truncation position. The profile of truncated CPC for different truncation positions was compared with full CPC. A detailed statistical analysis was performed to analyze the synergistic effects of independent design parameters on the responses using the response surface modeling approach. A set of optimized design parameters was obtained by establishing specified optimization criteria. A 50% truncated CPC with an acceptance half-angle of 21.58° and receiver width of 193.98 mm resulted in optimum geometric dimensions.

The usual approach in structural design optimization requires that at each iteration of the numerical optimization algorithm, a set of finite element analysis equations are solved to compute the objective/constraint functions. Such a nested solution procedure is straightforward because optimization and analysis processes are kept separate, but wasteful since a complete analysis is performed for each intermediate non-optimal design (one at each iteration). This paper illustrates an economical alternative approach where the finite element equations are formulated as part of the optimization problem by treating the unknowns as design variables and equations as equality constraints. This integrated problem can be solved using a spreadsheet application which serves as a highly suitable platform for integrating and solving the optimization and analysis simultaneously since all spreadsheet cells are interlinked. Together with widely available spreadsheet software, these techniques represent a very convenient tool not just for engineers to learn about the subject of design optimization, but also to gain physical insight into their design problems and perform design optimization routinely in their work.

Intelligent computer-aided design (CAD) emulates the human activity of design so that production planning decision making and inventive design can be performed by computers. Based on the history of human experience in engineering design, a formalized approach to design methodology should include procedures from (1) conceptual design, (2) layout design, and (3) numerical optimization design. The highest design level in such a system should be responsible for generating skeleton structures of entities within the design process which are eventually to be specified uniquely, and to be optimized. Planning plays a key role in such a system. Planning has been utilized as a tool for process organization within the knowledge domains of chemical engineering electrical engineering and manufacturing as well as for general problem formulation and solution. State estimation, subtask scheduling and constraint propagation are factors of prime importance in this type of problem. A methodology for planning and the problems associated with it within large scale design are discussed. The generality of our hypothesis for the design optimization process is examined within the context of a prototypical mechanical design model. An example which demonstrates the applicability of this approach to mechanical power transmission design and which is representative of a large scale design problem, is provided.

Genetic algorithms (GA) have been applied in many fields to solve multivariable, non linear optimization problems. There are lots of different GA techniques which are used to improve the performance of the algorithm. In this paper, the implementation of GA in optimization of electrical system design for offshore wind farms is proposed and several different GA techniques are investigated. The main problems of GA implementation in this field are addressed and the suitable GA techniques are proposed. The GA method is applied to a real offshore wind farm and the results are evaluated. It is concluded that the genetic algorithm is suitable for the optimization of offshore wind farm layout design as long as proper GA techniques are employed

<div class="htmlview paragraph">This paper presents an enriched performance measure approach (PMA+) for reliability-based design optimization (RBDO) to substantially improve computational efficiency when applied to large-scale applications. Three aspects of PMA+ are presented: as a way to launch RBDO at a deterministic optimum design, as an efficient probabilistic feasibility check, and as a fast reliability analysis under the condition of design closeness. It is found that deterministic design optimization helps improve numerical efficiency by reducing some RBDO iterations. Unlike deterministic design optimization, a significant computational burden is imposed on the feasibility check of constraints in the RBDO process due to the costs of a reliability analysis. Such difficulties can be effectively resolved by using a mean value (MV) first-order method with an allowable accuracy for the purpose of feasibility identification, and by carrying out the refined reliability analysis using the hybrid mean value (HMV) first-order method for <i>ε</i>-active and violate constraints in the RBDO process. In addition, the fast reliability analysis method is proposed by reusing some of the information obtained at the previous RBDO iteration to efficiently evaluate probabilistic constraints at the current design iteration under the condition of design closeness. Other RBDO methods have recently been developed to enhance numerical efficiency of RBDO while maintaining numerical accuracy and stability. Thus, the PMA+ is compared to existing RBDO methods from a numerical efficiency and stability point of view. For a numerical understanding of the RBDO process, several numerical examples are provided, including a large-scale multi-crash application.</div>

This study explored the use of AI technology in architectural design, with a particular focus on AI design tools and their impact on the AEC industry in Pakistan. This study explored how AI technology variations. This research studied the impact of AI technology on traditional design workflows, productivity enhancement, and the transformational influence of AI on decision-making processes of design professionals in Pakistan. This research examined a variety of AI design tools, such as generative design, machine learning, and parametric modeling, to assess the AI technology relevance in optimization of spatial layout, structural analysis, and sustainable design in the Pakistani context. This research considered the time and cost efficiency, as well as the enhancement of creativity and overall design quality, the research evaluated the AI construction tools impact on the design process. The research findings emphasized the AI tools impact on design process efficiency, decreasing idle time, and empowering architects to assess numerous design options in a short time. The study also examined the specific AI tools challenges in the AEC Pakistan sector: inadequate tech infrastructure, learning challenges, workflow assimilation, and design originality loss. The research finally perceived the architectural practice AI technology on integration as a construction industry revolution and highlighted the importance of local industry dynamics. Such insights were helpful in guiding Pakistani AEC professionals in appreciating the value of AI technology and its effective utilization.

In the realm of indoor space design, achieving an optimal layout is crucial for enhancing functionality, efficiency, and user experience. Traditional approaches often rely on manual assessments or simplistic algorithms, lacking the precision and adaptability required for modern architectural challenges. This paper presents a novel framework for optimizing indoor space layout and design leveraging advanced machine vision algorithms. By harnessing the power of computer vision techniques, our methodology offers automated analysis and optimization of indoor environments, considering factors such as spatial utilization, traffic flow, and aesthetic appeal. Through a comprehensive exploration of machine learning and optimization techniques, we demonstrate significant improvements in space efficiency and usability, paving the way for intelligent design solutions in various architectural contexts. This research contributes to the intersection of computer vision and architectural design, offering a paradigm shift towards data-driven and adaptive approaches for optimizing indoor spaces.

Multi-criterion optimization of integrated photovoltaic facade with inter-building effects in diverse neighborhood densities