Environmental Data-Driven Performance-Based Topological Optimisation for Morphology Evolution of Artificial Taihu Stone

Smoothing topology optimization results using pre-built lookup tables

This paper investigates the development and optimization of artificial reefs through a new generative design method that integrates Computational Fluid Dynamics (CFD) with Bi-directional Evolutionary Structural Optimization (BESO). Since the 1950s, artificial reefs have been deployed to enhance marine ecosystems, and this study begins with a survey of existing reef designs. In response to limitations in current design approaches, we adopt a topology optimization strategy aimed at improving spatial allocation for polyphony expansion within reef structures. By coupling fluid-dynamic analysis with an iterative optimization loop, we evaluate the effectiveness of material exchange enabled by these artificial formations—an essential consideration given advanced manufacturing constraints and the need for rapid production of natural-like geometries. To extend artificial reef design into new possibilities, we propose a generative workflow in which reef morphology emerges from the interaction between CFD and BESO, iteratively removing and adding material in accordance with external loading conditions. The resulting reef is then compared with representative benchmarks from current artificial reef designs to assess material efficiency, structural performance, and geometric characteristics under complex underwater conditions.

Shell bridges have attracted extensive interest in engineering research and practice. This paper aims to evaluate the effects of longitudinal and transverse curvatures on the optimal design of the shell bridge. For this purpose, a slant-legged steel shell footbridge with the same initial and target volumes of steel was chosen to build parametric geometric models with different curvature radii, and then topology optimization was carried out using the bi-directional evolutionary structural optimization (BESO) technique to obtain optimized designs with high structural stiffness. Furthermore, linear static analysis and eigenvalue analysis demonstrate that the displacement, von Mises effective stress, and the first-order vertical vibration frequency satisfied all the requirements of design regulations. Numerical results indicate that not only the longitudinal curvature but also the transverse curvature have a significant effect on the optimized designs of steel shell footbridge. While the mean compliance increased with the transverse curvature radius, it first decreased and then increased with the longitudinal curvature radius.

The purpose of this study was to develop a new element removal method for ESO (Evolutionary Structural Optimization), which is one of the topology optimization methods, ESO starts with the maximum allowable design space and the optimal topology emerges by a process of removal of lowly stressed elements. The element removal ratio of ESO is fixed throughout topology optimization at 1 or 2%. BESO (bidirectional ESO) starts with either the least number of elements connecting the loads to the supports, or an initial design domain that fits within the maximum allowable domain, and the optimal topology evolves by adding or subtracting elements. But the convergence rate of BESO is also very slow. In this paper, a new element removal method for ESO was developed for improvement of the convergence rate. Then it was applied to the same problems as those in papers published previously. From the results, it was verified that the convergence rate was significantly improved compared with ESO as well as BESO.

Auxiliary Power Units (APU’s) are gas turbine engines which are located in the tail of most commercial and business aircraft. They are designed to provide electrical and pneumatic power to the aircraft on the ground while the main propulsion engines are turned off. They can also be operated in flight, when there is a desire to reduce the load on the propulsion engines, such as during an engine-out situation. Given an APU’s typical position in the back of an airplane, the intake systems for APU’s can be very complex. They are designed to provide sufficient airflow to both the APU and the cooling system while minimizing the pressure losses and the flow distortion. These systems must perform efficiently during static operation on the ground and during flight at very high altitudes and flight speeds. An APU intake system has been designed for a new commercial aircraft. This intake system was designed using the latest Computational Fluid Dynamics (CFD) techniques. Several iterations were performed between the APU supplier and the aircraft manufacturer since each of their components affects the performance of the other. For example, the aircraft boundary layer impacts APU intake performance and an open APU flap impacts aircraft drag. To validate the effectiveness of the CFD analysis, a full scale intake rig was designed and built to simulate the tailcone of the aircraft on the ground. This rig was very large and very detailed. It included a portion of the tailcone and rudder, plus the entire APU and cooling intake systems. The hardware was manufactured out of fiberglass shells, stereolithogrophy components and machined plastic parts. Three different airflows for the load compressor, engine compressor and cooling system had to be measured and throttled. Fixed instrumentation rakes were located to measure intake induced pressure losses and distortion at the APU plenum and cooling ducts. Rotating pressure and swirl survey rakes were located at the load compressor and engine compressor eyes to measure plenum pressure losses and distortion. Static pressure taps measured the flow pattern along the intake and flap surfaces. The intake rig was designed to be flexible so that the impact of rudder position, intake flap position, APU plenum baffle position and compressor airflow levels could be evaluated. This paper describes in detail the different components of the intake rig and discusses the complexity of conducting a rig test on such a large scale. It also presents the impact of the different component positions on intake performance. These results were compared to CFD predicted values and were used to calibrate our CFD techniques. The effectiveness of using CFD for APU intake design and its limitations are also discussed.

Electro-thermal-compliant mechanisms are mechanisms onto which electric voltage is applied as input. This mechanism is based on the thermal expansion properties of the materials. This study considered all of the convection effects according to layouts produced after each topology optimization process. A new material mixing method based on bi-directional evolutionary structural optimization (BESO) was suggested to obtain optimal topologies for compliant mechanisms composed of two materials. Reliability-based topology optimization (RBTO) based on BESO was also applied to the topology optimizations of the mechanisms. Performance measurement approach (PMA), which estimates has probabilistic constraints that are formulated in terms of the reliability index, was adopted to evaluate the probabilistic constraints. The procedure has been tested in numerical applications, and the results were compared with those obtained by the level set and solid isotropic material with penalization (SIMP) methods for validation of the proposed approaches.

In this paper, multi-objective optimization (MOO) of Al2O3-water nanofluid flow in microchannel heat sinks (MCHS) with triangular ribs is performed using Computational Fluid Dynamics (CFD) techniques and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid flow is solved numerically in various MCHS with triangular ribs using CFD techniques. Finally, the CFD data will be used for Pareto based multi-objective optimization of nanofluid flow in MCHS with triangular ribs using NSGA II algorithm. In the MOO process there are seven geometrical and non-geometrical parameters and the conflicting objective functions are to simultaneously maximize the amount of heat transfer and minimize the pressure drop. Five optimum designs are determined and discussed for both nanofluid and base fluid flows. Details of design variables for each of five optimum points are deeply discussed. It is shown that the achieved Pareto solution includes important design information on nanofluid flow in MCHS with triangular ribs.

The droplet drift during aerial spraying process of oilseed rape, which is induced by complex flow field including random lateral wind, is difficult to predict and suppress. In this study, the high-fidelity computational fluid dynamics (CFD) technique is employed to simulate the two-phase flow of droplets in the rotor flow field, and the influence of main operation parameters on spraying effect is investigated numerically. Furthermore, the mechanism of droplet deposition in various operation conditions is discussed according to the analysis of unsteady flow field characteristics. However, the simulation via CFD technique is time-consuming, and it is not suitable for multidisciplinary work and optimization design. To address such issue, a filter white Gaussian noise signal is used to mimic the random lateral wind, and the droplet drift distance is obtained numerically. Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. Numerical simulations show that the droplet drift distance can be predicted by measurable lateral wind speed via the reduced-order model approach, which agreed well with the results obtained via the CFD method. In addition, the reduced-order model could decrease computation cost by 6 orders of magnitude with an acceptable accuracy, which indicates that the proposed method could be used for the design of off-line closed-loop controller of a variable spraying system.

With the rapid development of computers and technology in the 20th century, the topological optimisation (TO) method has spread worldwide in various fields.This novel structural optimisation approach has been applied in many disciplines, including architectural form-finding.Especially Bi-directional Evolutionary Structural Optimisation (BESO), which was proposed in the 1990s, is widely used by thousands of engineers and architects worldwide to design innovative and iconic buildings.To integrate topological optimisation with artificial intelligence (AI) algorithms and to leverage its power to improve the diversity and efficiency of the BESO topological optimisation method, this research explores a non-iterative approach to accelerate the topology optimisation process of structures in architectural form-finding via conditional generative adversarial networks (GANs), which is named as OptiGAN.Trained with topological optimisation results generated through Ameba software, OptiGAN is able to predict a wide range of optimised architectural and structural designs under defined conditions.

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclusions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

This study examines the dynamics of spatial marketing and cultural experience in Chinese classical gardens through an empirical case of the Humble Administrator’s Garden in Suzhou. In the context of rapidly expanding digital tourism, we propose the Cultural Technological Experience (CTE) Triad Model to explain how spatial aesthetics, visitor engagement, and social media propagation jointly shape cultural identity and visitor satisfaction in heritage environments. Using 436 valid survey responses and Structural Equation Modeling (SEM), the findings demonstrate that all three dimensions exert significant positive effects on visitor satisfaction (p< .01), with visitor engagement exerting the strongest effect (β= .45). Cultural identity functions as a key mediator that transforms sensory impressions and participatory experiences into symbolic meaning, thereby elevating satisfaction. Theoretically, the study integrates Eastern spatial aesthetics with digital communication and the experience economy, extending current understandings of heritage-based experiential formation.Practically, the results underscore the value of participatory cultural programming, VR/AR-supported immersive interpretation, and high-quality user-generated content to reconcile authenticity with digital interaction. Although limited by on-site sampling and a cross-sectional design, the study provides a transferable analytical framework and actionable implications for the sustainable digital revitalization of Chinese classical gardens.

Detail control strategies for topology optimization in architectural design and development

A geometrically nonlinear topology optimization method is presented in this paper for steel I-section beams, incorporating large displacement analysis to capture realistic structural behavior under flexural loads. The developed framework aims to enhance structural performance and material efficiency by optimizing the web region while preserving critical load paths. The optimization process is driven by enhancing the Bi-directional Evolutionary Structural Optimization (BESO) algorithm implemented in MATLAB and coupled with ABAQUS. Three beam configurations are analyzed under identical boundary and loading conditions to compare the performance of the optimized layout against traditional designs. To further assess the robustness of the proposed algorithm, two additional load scenarios, including a four-point bending test and a uniformly distributed load, are investigated. The results demonstrate that the optimized beam configurations reduce web material by 60 % (volume fraction = 0.40) under 45 kN mid-span load while sustaining 55 % higher load than the circular-opening beam (45 kN vs. 29 kN) with mean stress of 203.1 MPa, achieving 20 % less web material than the conventional design and 18 % higher complementary work (1.89 × 10⁵ N·mm) than the plain-web beam. The findings underline the value of incorporating geometric nonlinearity into topology optimization for producing high-performance, lightweight steel structures suitable for real-world engineering applications.

Stress-based topology optimization and nonlinear structural topology optimization is gaining increasing attention in order to make topology optimization more realistic. Thus, this paper extends current concepts of topology optimization to the design of structures made of nonlinear materials. An extended bi-directional evolutionary structural optimization (BESO) method for stress minimization topology optimization of material nonlinear structures is proposed in this work. BESO method based on discrete variables can effectively avoid the well-known singularity problem in density-based methods with low-density elements. The maximum von Mises stress is approximated by the p-norm global stress. The sensitivity information for designing variable updates is derived in detail by adjoint method. As for the highly nonlinear stress behavior, the updated scheme takes advantages from two filters respectively of the sensitivity and topological variables to improve convergence. Moreover, the filtered sensitivity numbers are combined with their historical sensitivity information to further stabilize the optimization process. The effectiveness of the proposed method is demonstrated by several 2D benchmark design problems.

The topology optimization method has been widely applied study the optimization of the dynamic behavior of multiphysics systems, such as systems with fluid-structure interaction, in order to produce efficient and reliable designs. Within this context, this work aims to contribute to the study of the topology optimization of these coupled systems through the optimization of a fluid-structure system subject to a structural volume constraint, by maximizing the natural frequency of the system considering interpolations of the material domains. To this end, the methodology involves the application of algorithms developed in fluid-structure systems with a nonsymmetrical matrix formulation, in displacement u of the structure and pressure p of the fluid, with movable boundary interface. Topology optimization is developed using the BESO (Bi-Directional Evolutionary Structural Optimization) method and implemented in a numerical computing environment, while finite element analysis is performed using a parametric programming language to customize simulations in FEM software. The results obtained demonstrate the applicability of the codes developed for natural frequency optimization problems in fluid-structure coupled systems, using different material interpolations, and the influence of the participation of fluid terms and left eigenvectors in the sensitivity analysis, highlighting that interpolation using the SIMP method showed convergence with consistent final topologies.