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Interaction with patient-facing medical devices requires integrated performance of both physical and cognitive tasks that are highly dependent on many user characteristics. Quantifying product use variability early in the design process is useful for device designers seeking to personalize medical device features to maximize performance and improve health outcomes, but three barriers make this difficult. First, patient populations often differ from the general population and can be difficult to recruit and access for human performance evaluations. Second, users of patient-facing devices are highly heterogeneous, leading to highly varied and complicated use-cases. Finally, there are numerous unique tasks for each medical device that make it resource prohibitive to perform comprehensive device use evaluations, particularly in the early stages of design. To address these challenges, a method for modeling highly prevalent patient user sub-populations to be used in targeted device personalization is proposed. In this study, Internal Medicine domain-expert input is used to identify patient characteristics critical to the performance of generic physical and cognitive tasks required for commonly prescribed medical devices. Then, a novel approach to quantify those characteristics is proposed, utilizing variables from the US National Health and Nutrition Examination Survey (NHANES) dataset, demonstrating a means to characterize specific patient populations. The data are statistically clustered to identify meaningful, task-specific device user sub-populations. The approach is demonstrated on the diabetes population for tasks related to hand-held self-management glucometer device use. The cluster results are then discussed, including their practical application to design personalization.

An arithmetic optimizer algorithm (AOA) is hybridized with slime mould algorithm (SMA) to address the issue of less internal memory and slow convergence at local minima which is termed as HAOASMA. Lens opposition-based learning strategy is also integrated with the hybrid algorithm which enhances the population diversity of the hybrid optimizer to accelerate the convergence. The local best ( $$P_{{{\text{best}}}} )$$ and global best ( $$g_{{{\text{best}}}} )$$ of SMA initializes the AOA’s search process. The $$P_{{{\text{best}}}}$$ obtained from AOA again initializes the SMA to further exploit the search space. In this way, the developed hybrid algorithm utilizes the exploitation and exploration capabilities of SMA and AOA, respectively. The developed HAOASMA has been compared on twenty-three benchmark functions at different dimensions with basic SMA, AOA and six renowned meta-heuristic algorithms. The HAOASMA has also been applied to classical engineering design problems. The performance of HAOASMA is significantly superior compared to basic SMA, AOA and other meta-heuristic algorithms.

PurposeRecently, children are no longer considered the passive users of their spaces; they are rather treated as active participants in those spaces design, with their own experiences. The present research aims to investigate the impact of selected architectural variables of kindergartens' design on the child's attachment to the place. It also examines the multi-complex approach in dealing with children in experiments.Design/methodology/approachSpatial analysis and photographing methods were used to analyse the educational spaces in two kindergartens in Greater Cairo, Egypt. In measuring children's place attachment, the research depended on a complex participatory approach, which comprised interviews with children, story completion, children's drawings and visual questionnaires.FindingsThe results of this qualitative study highlight that the design of a kindergarten classroom's details plays a significant role in strengthening the child's place attachment. The results also stress that connection and exposure to nature supports the child's spatial values.Originality/valueThe authors argue that this paper is considered a good basis for including children as the architectural design decision-makers for their buildings, by shedding light on the architect–user relationship and its impact on the design process. It demonstrates how architectural design of child-oriented spaces can promote a child's self-identity and perception.

The once exclusive technology empowering immersive and interactive training systems is now more affordable and accessible to mainstream use cases. Its adoption in the manufacturing industry can help reshape training processes as an intrinsic part of production routines and reduce the mental resources required to complete a task. Current academic literature does not integrate components to describe the skills and attributes of workers with impairments. In contrast, the research in this paper addressed the design and evaluation of a new immersive and interactive training system that can effectively provide new human augmentation opportunities for workers with impairments by reducing the mental resources required to complete a task. Automated machine interpretation of tasks and actions of workers with impairments is still a long way off and one of the reasons is that individual skills are still difficult to describe formally. Therefore, in this paper, skill transfer is assessed through external evaluation. The results of the preliminary evaluation of our Cross Reality (XR) prototype for training and error minimization in the manufacturing of electrical cabinets confirmed significant productivity gains and high adoption by participants, validating the suitability of the solution for workers in industrial manufacturing processes.

Liquefied natural gas (LNG)-fuelled ships provide a good option for the air pollution problem. However, safety and reliability have to investigate in the design process. A hypothetical LNG-fuelled ...

The shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.

Ever since the publication of the 99-line topology optimization MATLAB code (top99) by Sigmund in 2001, educational articles have emerged as a popular category of contributions within the structural and multidisciplinary optimization (SMO) community. The number of educational papers in the field of SMO has been growing rapidly in recent years. Some educational contributions have made a tremendous impact on both research and education. For example, top99 (Sigmund in Struct Multidisc Optim 21(2):120–127, 2001) has been downloaded over 13,000 times and cited over 2000 times in Google Scholar. In this paper, we attempt to provide a systematic and comprehensive review of educational articles and codes in SMO, including topology, sizing, and shape optimization and building blocks. We first assess the papers according to the adopted methods, which include density-based, level-set, ground structure, and more. We then provide comparisons and evaluations on the codes from several key aspects, including techniques, efficiency, usability, readability, environment, and compatibility. In addition, we conduct numerical experiments on the reviewed codes using the benchmark cantilever beam example to provide feedback on the overall user experience. With a systematic review and comparison, this paper aims to offer insights on the educational values and practicality for employing these codes. We try to provide not only guidance for beginners to approach various optimization methods, but also a dictionary to direct readers to effectively target the relevant codes and building blocks based on their demands. Finally, based on the findings in this review paper, we provide some perspectives and recommendations for future educational contributions.

Android sangat di butuhkan bagi masyarakat pada umumnya khusunya milenial,apalagi saat sekarang ini Android adalah sangat banyak jenisnya dan hampir setiap orang memiliki smartphone sebagai media komunikasi, pembelajaran dan lain sebagainya.Dengan adanya Aplikasi pembelajaran aksara lampung berbasis android ini di harapkan dapat membantu siswa dan generasi muda masyarakat lampung (milenial) untuk mengetahui bentuk huruf aksara lampung hanya dengan mengakses smartphone mereka. Dalam hal ini penulis merancang rancang bangun aplikasi pembelajaran aksara lampung berbasis android untuk dapat mengetahui bentuk induk huruf, anak huruf, dan contoh penulisannya beserta cara penyebutannya. Dalam perancangan dan pengembangan aplikasi ini menggunakan metode pengembangan extreme programming dan dalam proses perancangan ini menggunakan software Unity 3D versi 5.6.7.Hasilnya adalah adanya suatu aplikasi pembelajaran aksara lampung yang dapat operasikan oleh mayarakat luas khususnya siswa dan generasi muda(milenial) masyarakat lampung dengan tujuan melestarikan kebudayaan peninggalan leluhur masyarakat lampung dan untuk untuk dapat mengetahui bentuk induk huruf, anak huruf, dan contoh penulisannya beserta cara penyebutannya, yang dapat berjalan atau diakses di smartphone android.

This paper reviews a novel method for integrated topology and packaging optimization in lightweight systems design. Presented as the component-existence model, this approach introduces a new class of packaging design variable coupled with traditional material pseudo-densities in a standard topology workflow. This method adopts a modified interpolation scheme compatible with standard finite element mesh discretizations, and through development of so-called design fields and variable mapping functions, applies gradient-based optimization and analytical sensitivity expressions for driving design updates. The resulting framework can consider multiple overlapping components, enhances effective component mobility in the design domain, and features numerical efficiency comparable to standard topology-only problems. This method is demonstrated in six numerical case studies and compared against solutions from full factorial analysis, with resulting integrated designs approaching or exceeding benchmark configurations within ±5%.

This paper is written to honor Raphael T. Haftka’s seminal contributions to multidisciplinary design optimization. We focus on those contributions that directly impacted our research, namely: the adjoint method for computing derivatives, wing aerostructural design optimization, and architectures for multidisciplinary design optimization. For each of these topics, we describe Haftka’s contributions, how they impacted our research, and examples of what they enabled us to do. The overarching theme of the contributions and developments described in this paper is the efficient computation of derivatives, which, together with gradient-based optimizers, enables the optimization with respect to large numbers of design variables, even when using costly high-fidelity models.

With the further exploration of the Moon, lunar landers need to land in complex and unexplored areas. However, current lunar landers are confined to basic landing conditions (low landing velocity and plain terrains). This research proposes a novel lunar lander with magnetorheological fluid (MRF) dampers that can adapt its damping forces to various complex landing conditions. The design process and coupled electromagnetic hydrodynamic model analysis of the lunar lander were systematically elucidated. According to the derived theory model of the damping force, a fuzzy control law is proposed. The controllable damping forces under different control currents are simulated according to the proposed fuzzy control law. Additionally, landing dynamic simulations of landers with different dampers are carried in MSC. Adams to compare with their landing performances. MRF dampers under fuzzy control can decrease the largest acceleration of the lander cabin by 63.6%. Finally, prototype drop simulations and experiments are performed, which results match each other well. By increasing the control current from 1 A to 2 A, the damping forces of the MRF damper increase approximately 20% in both the simulations and experiments. Simulations and experiments prove that MRF dampers can be used for energy impact absorption as variable dampers.

Design principles for vehicle transmissions are considered, with a view to ongoing functional diagnostics of the transmission components on the basis of thermometric data. Bench tests and finite-element modeling are employed in the analysis. An algorithm is developed for the design of vehicle components well suited to nondestructive thermometric testing.

This work presents an overview of the in-flight engineering data of the Energetic Particle Detector (EPD) instrument suite during its first year of operation. EPD is part of the in-situ scientific payload of the ESA/NASA Solar Orbiter mission which was launched in February 2020. After completion of its commissioning phase, Solar Orbiter started its cruise phase in June 2020, in coincidence with its first perihelion at 0.51 au. Six remote-sensing instruments and four in-situ instruments, including EPD, which is a suite of four individual sensors, make Solar Orbiter the most complete space laboratory flown into the inner heliosphere to date. In-flight engineering data of the different EPD units such as their power consumption and their temperatures are the key indicators to ascertain that the design, manufacturing and qualification of the EPD units on ground and throughout the different life cycles prior to launch were successful. On the other hand, these in-flight housekeeping data also reflect the general status of the units which are exposed to space radiation, stray light and a varying thermal environment. The reliability of the EPD suite was analyzed during the design phase and the in-flight status evaluation confirms this analysis. The first year of flight covers special milestones for the Solar Orbiter mission such as the launch, first perihelion, as well as the first Venus flyby. By evaluating the in-flight engineering data, it is demonstrated that the EPD suite successfully meets its design requirements and also satisfies its scientific objectives in flight.

Lataa OA, kun julkaistu. This article studies the use of quality factor Q as a tool to design multiport antenna structures with matching networks. Approximative formulas for computing the Q -factor from the port impedances of multiport antennas are presented. We show that the achievable performance level can be reliably estimated based on the Q -factor without the need for computationally heavy and time-consuming matching network optimization. The efficiency of the proposed method is confirmed with a practical two-port antenna cluster design for smartphones operating in the 0.7-1.0 GHz frequency band. The results show that the proposed port impedance-based Q -factor formulas provide an efficient tool to accelerate the design process of multiport antennas.

Due to the sudden transition towards online learning caused by COVID-19, we realized the urgency of deepening our understanding of the distributed collaborative design process. We conducted critical incident interviews to identify the challenges that design students and professional designers encountered and how they overcame them in their work. Based on the insights from this first study, we carried out an online survey to corroborate our insights. We found that the shift towards online meetings impacts the collaborative design process. Most notably it causes reduced co-exploration in the design process, especially on and with physical objects. This leads us to the conclusion that the current strategies are not adequate for facilitating designers who collaborate remotely. Following our insights, we provide suggestions for enhancing the supporting tools for the distributed collaborative design process. IEEE

Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10× more of the original molecules compared to the baseline DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP., A novel deep generative model combines convolution and graph neural networks to allow 3D-aware molecular design. We show how 3D pharmacophoric information can be incorporated into generative models and apply our model to both linker and R-group design.

Appropriate rock characterization is beneficial in providing a reliable judgment on rock properties which is crucial for the design process of rock engineering applications. However, it can be difficult to obtain consistent mechanical parameters due to substantial variations in rock properties. In this research, uniaxial compression tests on dolerite specimens collected from a gold mine in Western Australia showed substantial scatter in the results. Rock categorization based on the P-wave velocities is as accurate as the thin section analysis, which suggests that they can be used together to gain a more accurate initial understanding of the rock types before any laboratory testing. The quality of specimen preparation and rock–machine interaction greatly affect the test results. For instance, non-parallelness of loading platens can lead to considerable scatter of the testing results, which would be perceived as rock variability. It is suggested that the current testing standards should be modified towards a better control of the loading machine performance and equipment precision. Finally, the possibility of pre-existing microcracks in rock, neither detected by the thin section analysis nor by the ultrasonic measurement, must be examined by computed tomography (CT) scanning as they can affect the test results. This study will enhance our knowledge about the sources of variability in laboratory test results of rock which is essential for obtaining reliable data.

A pipe in an electromagnetic shutoff valve must meet high requirements on its surface roughness Ra (µm) and shape precision (quality classes IT8 and IT9). At the same time, reaming of the product is associated with self-oscillation that impairs the internal surface quality. An antivibrational reaming tool has been developed to address this problem.

This study proposes an intelligent hybridization between the multi-level cross-entropy optimizer (MCEO) and moth-flame optimization (MFO) algorithms to keep a good compromise between exploration and exploitation. The proposed hybrid multi-level cross-entropy-based moth-flame (MCMF) algorithm uses MCEO as a global search engine during the first phase of the optimization process that allows for fast approximation of the global best position (BP). The boundaries of the search space are then adaptively confined within the effective region around the current BP by applying the proposed search space boundaries confining factor (SSBCF). The modified MFO with two different moth generation patterns is then employed as a local search engine to simultaneously probe for new and proper BPs within the confined and overall search space. This prevents MCMF from becoming trapped in local optima while maintaining a balance between exploration and exploitation. The hybridization between both algorithms allows MCMF to accelerate throughout the early steps of the search process using the high exploration ability of MCEO, whereas, in the later stages of optimization, promising solutions will possess a high probability to be exploited using the higher exploitation power of MFO in the confined space. The competence of the MCMF is compared with other well-known state-of-the-art algorithms on 15 unconstrained benchmark functions and 5 constrained engineering design problems having a wide range of dimensions and varied complexities. The statistical results on the benchmark functions and the solved engineering examples verify that the proposed algorithm can provide very competitive and promising results. The results demonstrate the comprehensive superiority of the hybrid MCMF compared to both MCEO and MFO in terms of fewer function calls, high escaping ability from local optima, and fast convergence speed.

Given that many models of design-based learning in science education are grounded in forward engineering, in which students are challenged to use the engineering design process to achieve unknown solutions to engineering problems, this study aims to examine the potential of using reverse engineering, as an alternative approach to design-based learning, to facilitate students’ understanding of scientific concepts. Two classes of ninth-grade students, one with 21 students and the other with 24 students, participated in this study by engaging in reverse engineering activities where they dissected and investigated how a bimetal thermostat functions. The primary instrument is a conceptual test, comprising 12 multiple-choice questions about thermal expansion, which was completed by the students before and after the reverse engineering activities. A paired-samples t-test was used to analyse the data. Discourse analysis of students’ verbal interactions within small groups was also used to illustrate their learning process. The results indicate that both classes exhibited significant improvements in their understanding of thermal expansion, to a large or medium extent. Thus, it is recommended that reverse engineering can be used as a pedagogical approach to facilitating students’ learning of scientific concepts.

Immersive Virtual Environments (IVEs), particularly Mixed-Reality (MR) technologies, continue to have an increasing impact on design processes and design education. This study focuses on MR’s potentials in Basic Design (BD) education and its influence on novice designers’ design thinking abilities. In this study, through a newly developed MR-based design framework, DesignMR, a comparative analysis between design processes in MR and the physical environment is presented. A hybrid evaluation methodology is used in the context of three-dimensional (3D) BD tasks through two sets of protocol studies: Linkography and exit interviews. Linkography is a method to analyze the design process based on detecting design moves and their links with each other. In this study, linkographic analyses point to an increase in critical moves and link index values in MR as compared to the physical environment for all participants. This indicates that MR can trigger improved creativity, design productivity, and design exploration by idea generation. Exit interviews highlighted the positive impact of DesignMR on the motivation, work efficiency, and 3D perception of the participants. Further research will be pursued for improved hand gestures to increase the effectiveness of object interaction.

Jaya algorithm is one of the heuristic algorithms developed in recent years. The most important difference from other heuristic algorithms is that it updates its position according to its best and worst position. In addition to its simplicity, there is no algorithm-specific parameter. Because of these advantages, it has been preferred by researchers for problem-solving in the literature. In this study, the random walk phase of the original Jaya algorithm is developed and the Improved Jaya Algorithm (IJaya) is proposed. IJaya has been tested for success in eighteen classic benchmark test functions. Although the performance of the original Jaya algorithm has been tested at low dimensions in the literature, its success in large sizes has not been tested. In this study, IJaya's success in 10, 20, 30, 100, 500, and 1000 dimensions was examined. Also, the success of IJaya was tested in different population sizes. It has been proven that IJaya's performance has increased with the tests performed. Test results show that IJaya displays good performance and can be used as an alternative method for constrained optimization. In addition, three different engineering design problems were tested in different population sizes to demonstrate the achievements of Jaya and IJaya. According to the results, IJaya can be used as an optimization algorithm in the literature for continuous optimization and large-scale optimization problems.

An effective and straightforward method to implement topology optimization using high-level programming is presented. The method uses the LiveLink for MATLAB, which couples the commercial COMSOL Multiphysics software with MATLAB programming environment via COMSOL Application Programming Interface (API). The integrated environment allows one to implement advanced and customized functions and methods from scratch easily. Topology optimization of an acoustic-structure interaction problem with a mixed displacement–pressure (u/p) formulation is employed to demonstrate the effectiveness of the presented implementation method to design multiphysics problems systematically. The governing equations of the system are derived in a weak form, which is inserted directly in equation-based modeling in COMSOL Multiphysics via MATLAB programming environment. The tight integration of MATLAB and COMSOL Multiphysics allows one to easily pass the matrices and derivatives to perform design sensitivity analysis. A comprehensive code to perform the optimization of the acoustic-structure interaction problem is provided in Appendix. The well-structured code can be used as a platform for educational and research purposes, and it can be extended to other topology optimization applications involving various types of physical problems that use the equation-based modeling functionality of COMSOL.

Longitudinal barriers are considered as passive safety systems designed to shield hazards located at roadsides against motor vehicle impacts. Since these barriers are manmade obstacles, they also pose a threat to drivers using the road. Recent motorcyclist accidents with longitudinal barriers have proven that a particular barrier successfully protecting vehicle occupants may wound or kill motorcyclists due to its components. For this reason, sharp and blunt edges in steel longitudinal barrier parts, such as posts are usually shielded against contact from unprotected motorcyclists during a high-speed impact event. In recent years, more longitudinal barriers have been designed with motorcyclists in mind and these motorcycle protection barriers have become wide spread especially on urban high-speed roads. However, since the development of these barriers are fairly new compared to conventional longitudinal barriers, there is limited guidance on their design criteria, such as thickness, geometry, connections. For this purpose, this paper intends to provide an artificial neural network metamodeling-based design optimization methodology to an existing continuous motorcycle protection barrier design to make it more competitive in terms of weight and thus, cost. As a result of this study, the optimized barrier has become 34% more economical compared to its original design while its protection level remained intact.

Harris Hawk’s Optimizer (HHO) is a recently developed meta-heuristics search algorithm with inherent capability to explore global minima and maxima. However, the local search of the basic HHO algorithm is sluggish and has slow convergence rate due to its poor exploitation capability. In the present work, exploration and exploitation phase of HHO have been improved using a chaotic variant of the present optimizer. The proposed chaotic variant has been simulated and tested for 23 standard test functions and 10 different engineering design optimization problems of real life. To check the efficacy of the proposed algorithm, the test results of the proposed CHHO algorithm have been compared with others recently developed and well-known classical optimizers, such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, MMA, etc. The experimental results reveal that the suggested method outperforms on most of the test functions and engineering design challenges with superior convergence.

One of the popular creativity methods to solve technical problems is the Theory of Inventive Problem Solving, (TRIZ), however, requires a considerable investment of time. A teaching methodology for a simplified version of TRIZ (TRIZ10) has been developed for reducing learning time and improving the creative process in solving technical problems. This method has been applied in a small group of engineering design students as a proof of concept. The evaluation of the results obtained shows that students achieved adequate learning of this creative technique (75% overall efficiency). This method has led to the doubling of the number ideas developed by students compared to those generated before learning TRIZ10; further, it also improves a 10% the average degree of novelty for these ideas.

Aim. To consider matters of dependability of highly critical non-recoverable space products with short operation life, whose failures are primarily caused by design and process engineering errors, manufacturing defects in the course of single-unit or small-scale production, as well as to define the methodological approach to ensuring the required reliability.Methods. Options were analysed for improving the dependability of entities with short operation life using the case study of single-use mechanical devices and the statistical approaches of the modern dependability theory, special methods of dependability of actuated mechanical assemblies, FMEA, Stage-Gate and ground experiments on single workout equivalents for each type of effect. Results. It was concluded that additional procedures need to be conducted for the purpose of predicting, mitigation and (or) eliminating possible failures as part of the design process using exactly the same approaches that cause failures, i.e., those of design and process engineering. The engineering approaches to dependability are based on early identification of possible causes of failures, which requires a qualified and systemic analysis aimed at identifying the functionality, performance and dependability of an entity, taking into account critical output parameters and probabilistic indicators that affect the performance of the required functions with the allowable probability of failure. The solution is found using a generalized parametric model of operation and design engineering analysis of dependability.Conclusion. For highly critical non-recoverable space entities with short operation life, the reliability requirements should be considered primarily in terms financial, economic, safetyrelated and reputational risks associated with the loss of spacecraft. From a design engineer’s standpoint, the number of nines after the decimal point (rounded to a smaller number of nines for increased confidence) should be seen as the indicator for the application of the appropriate approaches to ensuring the required reliability at the stage of product design. In case of two nines after the decimal point it is quite acceptable to use analytical and experimental verification techniques common to the aerospace industry, i.e., dependability calculations using the statistical methods of the modern dependability theory and performance indicators, FMEA and Stage-Gate, ground experiments on single workout equivalents for each type of effect. As the required number of nines grows, it is advisable to also use early failure prevention methods, one of which is the design engineering analysis of dependability that enables designers to adopt substantiated design solutions on the basis of engineering disciplines and design and process engineering methods of ensuring quality and dependability. The choice of either of the above dependability strategies is determined solely by the developer’s awareness and understanding of potential hazards, which allows managing the risk of potential rare failures or reasonably refusing to do so.

This work improves our previous stress-constrained topology optimization method (Fan et al., in Struct Multidisc Optim 59:647–658, 2019) and provides an application of the improved method to a typical aircraft engine bracket design problem. The original method was built upon the bi-directional evolutionary structural optimization (BESO) method with an extension to account for stress constraints. In this work, we first improve the method by means of a more efficient and versatile self-adaptive scheme for the determination of the Lagrange multiplier. The improved method is then applied for the design of a typical aircraft engine bracket considering multiple practical load conditions. The resulting bracket topology from stress-constrained design is further smoothed and detailed using basic CAD (Computer-Aided Design) primitives. Numerical results show that the reconstructed bracket design evidently outperforms than the original bracket design in terms of weight, stiffness, and strength.

This paper presents a comprehensive state-of-the-art review of the research carried out on the energy-based structural seismic design methods. Since earthquake exerts energy to the structure, it is realistic to use the energy as the main design criteria of the structure. The energy-based seismic design method is based on the concept of energy balance in the structures, which states that the earthquake input energy to the structure must be less than its capacity to dissipate the energy; otherwise, local or global damage will occur. Although the energy-based design method has received increasing attention in recent years, it has not yet become an applicable engineering design procedure. This could be due to the lack of specified and reliable criteria for this method. This paper is intended to provide the reader with a thorough review of energy-based seismic design methods, highlighting the unresolved issues and identifying the gaps that will require attention in future research.

Wind energy is one of the fastest-growing energy sources due to its cleanness, sustainability, and cost-effectiveness. In the past, wind turbine design studies focused primarily on a sub-system or single-discipline design and analysis, including control, structural, aerodynamic, and electro-mechanical studies, for example. More recent studies formulated wind turbine design problems using multidisciplinary design optimization (MDO) strategies, with either static or dynamic system models, providing the potential for identifying system-level optimal designs. On the other hand, efforts have also been made to increase the reliability and robustness of wind turbines by accounting for various sources of uncertainty explicitly in the design process. In the presented study, the MDO formulation of wind turbine design problem has been extended to include both control system co-design and reliability considerations in an integrated manner. As a result, the optimal wind turbine design that has an optimal control solution and is robust to uncertainties can be obtained at an early design stage, which would benefit the controller design and maintenance design at latter phases. In this paper, the design of a horizontal axis wind turbine (HAWT) supported by a tubular tower is considered and formulated as a multi-objective control co-design problem with design parameter uncertainties and stochastic wind load. A physics-based multidisciplinary dynamic model of tubular-tower-supported pitch-controlled HAWT that captures the main design conflicts under extreme wind is provided and implemented, along with the necessary modifications to make nested control co-design comply with modern reliability-based design optimization structures, forming a new class of reliability-based co-design (RBCD) problems. In particular, we provide detailed discussions about RBCD problem formulations and implementation strategies, and with the HAWT design problem, we demonstrate the results and computational costs with integrated double-loop, single-loop, as well as decoupled methods.

Constraints widely exist in the structural optimization problems, including not only expensive constraints but also inexpensive constraints with explicit or implicit analytical expressions. Simply omitting the infeasible sampling points generated by conventional design of experiments (DoE) techniques leads to fewer feasible points than desired and to the remaining points distributed sub-optimally. This paper presents a novel constrained space-filling and non-collapsing sequential sampling (CSFSS) method for the arbitrarily constrained design space. To this end, an improved local density is firstly proposed to measure the spatial distribution of the sampling points located in the feasible region. Then, a novel formulation based on the improved local density is proposed for the criterion of sequential sampling strategies, which can guarantee the space-filling and non-collapsing properties both preferably optimal. Afterwards, several methods are proposed to tackle the challenging multimodal optimization problem posed by sequential sampling strategies. Finally, a replacement-based strategy is proposed to further elevate the quality of the design. Extensive numerical results, including an application for the lightweight design of cylindrical stiffened shells in aerospace engineering, highlight the satisfying performance of the proposed method not only in obtaining a high quality of constrained experimental designs but also in competitive contributions to constrained optimization gains.

Past present future (PPF) is a new stochastic optimization algorithm inspired by the phenomena of the way an individual learns from a successful person in society. PPF is based on the concept of “future improvement of a person’s life depending on his/her past experience and present work.” The influence of successful persons also affects the improvement of the future life of an individual. This work develops a mathematical model for PPF following the above facts. In this new algorithm, the population is divided into subpopulations and a switching mechanism is followed among the subpopulations to track the change in optimal positions of an individual thereby accelerating the convergence rate. In addition, this switching mechanism also prevents pre-mature convergence. PPF was found to possess low computational complexity with fast convergence characteristics. The proposed PPF is compared with 41 up-to-date meta-heuristic algorithms taking an extensive set of benchmark functions to verify the efficiency. In addition, five classical engineering design problems are simulated to estimate the efficacy of the PPF algorithm in optimizing engineering problems. The results confirm the superior performance of the proposed algorithm to get the optimal solution with less iteration and have shown the best competitive performance compared to all other algorithms.

Structural topology optimization for nonlinear structures and stress-based design problems have been well developed and extensively studied. However, topology optimization of Simultaneous Geometrical and Material Nonlinear (SGMN) structures under stress constraints, requires significant investigation. Hence, based on an extended Bi-directional Evolutionary Structural Optimization (BESO) method, this work proposes a novel topology optimization method to maximize the stiffness of SGMN structures subject to both volume fraction and maximum von Mises stress constraints. The extended BESO method based on discrete variables can avoid the stress singularity problem. The global von Mises stress is established with the p-norm function, and the adjoint sensitivity analysis is derived. The nonlinear finite element equations are established by the Lagrangian method and the large deformation problem is solved by the Total Lagrangian method. A series of comparison studies have been conducted to validate the effectiveness and practicability of the method on several benchmark design problems. It concludes that the proposed approach can well control the stress level and efficiently solve the stress concentration effect at the critical stress areas of SGMN structures.

In this paper, a cross-section contribution analysis (CSCA) method is proposed to optimize the size of a truss frame. A finite element model of the initial truss frame was established to analyze the static-dynamic stiffness and the full load strength. The reliability of the model was then verified by experiments. An improved wireframe model was obtained from the secondary design of the topological conceptual model, and the contribution rate (CR) of each tube to the performance was analyzed. A “modular line” was defined to modularize the frame tubes based on the cross-section CR (CSCR). The performance and light weight of the modular frames (MFs) with different tube layout schemes were studied. The results of modular size optimization show that the optimized truss frame for small electric vehicles (EVs) reduces the weight and improves the performance significantly. In addition, this method can have better results in continuous variable optimization (with non-standard tube size) as a reference for industry. In discrete variable optimization (with standard tube size for manufacturing feasibility), the method could greatly shorten the development cycle and lead to production rapidly.

Sensitivity analysis of coupled acoustic-structural systems under non-stationary random excitations is investigated in this paper. The combined method of direct differentiation method (DDM) and pseudo excitation method (PEM) in previous work is computationally expensive for problems with large-scale design variables. To circumvent this difficulty, a fast sensitivity analysis method integrating the adjoint variable method (AVM) and PEM for coupled acoustic-structural systems under non-stationary random excitations is proposed. In this framework, the sensitivity analysis of coupled systems subject to non-stationary random excitations is transformed into a sensitivity analysis under pseudo-transient excitations with the help of PEM. On this basis, the computing efficiency of random response sensitivity is improved directly by solving the adjoint equations involved in the AVM. Numerical examples fully demonstrate the accuracy and efficiency of the proposed method.

Eco-feedback is a design strategy that reminds users of their resource consumption as they use a product. The ultimate goal is to promote pro-environmental behaviors in users by making them aware of the environmental impact of their consumption. The way these designs present resource usage information can significantly impact user perceptions. This paper investigates two aspects of resource usage presentation, quantitative feedback and emotional evocativeness, by evaluating 32 designs spanning electricity, materials, transportation, and water via surveys of 619 university students in the US and Saudi Arabia. Both these aspects are positively correlated with perceived product appeal and perceived effectiveness in encouraging sustainable behavior. It was found that presenting quantitative resource usage information was more helpful to respondents who could better estimate resource consumption, while the emotional evocativeness of a design aided respondents with lower and higher resource consumption knowledge to a similar degree. In addition, we found that images of living creatures and strong visual cues evoked strong emotions in users. Female participants, in general, responded more strongly than males to this emotional evocativeness. These experimental findings cast light on how to better design eco-feedback products to be more widely accepted.

This paper presents a Python wrapper and extended functionality of the parallel topology optimization framework introduced by Aage et al. (Topology optimization using PETSc: an easy-to-use, fully parallel, open source topology optimization framework. Struct Multidiscip Optim 51(3):565-572, 2015). The Python interface, which simplifies the problem definition, is intended to expand the potential user base and to ease the use of large-scale topology optimization for educational purposes. The functionality of the topology optimization framework is extended to include passive domains and local volume constraints among others, which contributes to its usability to real-world design applications. The functionality is demonstrated via the cantilever beam, bracket and torsion ball examples. Several tests are provided which can be used to verify the proper installation and for evaluating the performance of the user's system setup. The open-source code is available at https:// github. com/thsmit/, repository TopOpt_in_PETSc_wrapped_in_Python., Structural and Multidisciplinary Optimization, 64 (4), ISSN:1615-147X, ISSN:1615-1488

It is a challenging task to effectively estimate the upper bound as well as the lower bound of failure probability under epistemic and aleatory uncertainties in a dynamic environment. This paper proposes a novel approach to estimating both the lower and upper bounds of the time-variant hybrid failure probability by combining the proposed box moments and improved maximum entropy methods. The sparse grid sampling technique is first employed to simplify the time-variant hybrid performance function for the integration. A new continuous maximin algorithm based on the improved particle swarm optimization is then proposed for estimating the box moments. The probability density function and the time-variant hybrid failure probability can be finally estimated with the improved maximum entropy method by introducing the nonlinear transformation with an adjustable parameter. Two engineering examples are employed to testify the effectiveness of the proposed method.

The system failure possibility of multi-mode structural system (referred to as system) under fuzzy uncertainty is the joint membership function of input vector at the system fuzzy design point, and it can reasonably measure the safety degree of the system. The system fuzzy simulation (S-FS) can be combined with adaptive Kriging model (AK-S-FS) to solve the system failure possibility. In the current AK-S-FS method, the system fuzzy design point is searched in the maximum value region of the fuzzy input vector corresponding to the 0 membership level, and its computational efficiency still needs to be improved. Thus, a hierarchical system fuzzy simulation combined with adaptive Kriging model (AK-HS-FS) method is proposed to improve the efficiency of searching the system fuzzy design point in this paper. The efficiency of the proposed AK-HS-FS method comes from the innovative strategies of three aspects. The first is the strategy of the hierarchical system fuzzy simulation (HS-FS). Compared with the S-FS with the system fuzzy design point searched roughly in the maximum possible value region, the strategy of the HS-FS is to exploratively expand the search region by transferring from a larger membership level to a smaller one. The overall search region of the system fuzzy design point can be reduced without losing the search accuracy in the HS-FS. The second is the strategy of the hierarchical training. Compared with training the system Kriging model in the combined candidate sample pool (CSP) of all layers, it is more time-saving to train the system Kriging model layer by layer in the hierarchical CSP. The third is the strategy of iteratively reducing the CSP. According to the properties of the system fuzzy design point and the probability properties of the Kriging prediction, the required time of training the system Kriging model can be further reduced by iteratively reducing the CSP, and the reduction of the CSP can ensure the accuracy without introducing any computational cost and complexity. The results of case studies fully verify that the AK-HS-FS is much more efficient than the AK-S-FS under satisfying the computational accuracy.

In the programming course of our engineering design degree tutorials are the focal point of learning. This is especially so since we employ a tinkering based educational approach, in which students explore, from the very beginning, the material by self-defined projects. The assignment defines ingredients to use and sets expectations, but students are free to set their own design goals. In this setting tutorials are an important place of feedback and learning, and we developed an online platform that supports tutors during tutorials. This paper reports on the educational philosophy and underpinnings, and results from applying the tool in two first-year courses.

Surrogates have been widely used for replacing computationally costly computer simulations. To improve the prediction accuracy with less sampling cost, sequential sampling is often used to seek targeted training samples for surrogate modeling. Previous methods often integrate gradient information to improve the sampling effect, but the increase of prediction accuracy is still limited for complicated problems. It is intractable how to better exploit the useful gradient information to sample potential highly nonlinear regions. In this paper, an effective gradient and geometry enhanced Kriging sequential sampling approach is proposed to handle this problem. First, a novel improvement function, which combines the approximate gradient and prediction variance, is constructed to measure the improvement of a candidate sampling point. Second, the design domain is partitioned into multiple Voronoi subdomains according to design points, and an adaptive and efficient cross-validation-based criterion is introduced to determine the candidate sampling regions and reduce the optimization cost. Finally, the improvement function is maximized in the selected Voronoi subdomains to sample a new point, which can effectively achieve great accuracy improvement in the nonlinear area. The proposed approach is tested on several highly nonlinear functions and an application-related example, and the experimental results confirm its effectiveness.

Multi-fidelity (MF) surrogate models have been widely adopted in simulation-based engineering design problems to reduce the computational cost by fusing data with diverse fidelity levels. Most of the MF modeling methods only apply to the problems with hierarchical low-fidelity (LF) models. However, the LF models obtained from different simplification approaches often vary in fidelity levels throughout the design space, namely, the multiple LF models are non-hierarchical. To address this challenge, a MF surrogate modeling method based on variance-weighted sum (VWS-MFS) is developed to flexibly handle multiple non-hierarchical LF data in this work. Firstly, each set of the non-hierarchical LF data is allocated diverse weights according to uncertainties quantified by variances of constructed Kriging models, which enables all the LF data to be fused and contribute to the trend function reflecting the response trend of the true model. Secondly, for more precise scaling factor between HF and LF models and mean square error (MSE) estimation, an improved hierarchical kriging (IHK) model is introduced to construct the MF surrogate model enabling the LF model scaled by a varied scaling factor to capture the characteristics of the HF model. The performance of the proposed VWS-MFS method is compared to three MF surrogate models through several numerical examples and one engineering case. Results show that the proposed method provides more accurate MF surrogate models under the same computational cost. Additionally, the proposed method saved the computational cost by more than 59.61% with the same model accuracy compared to the Kriging model built with HF data for the engineering case.

This paper implements a novel integration of the polynomial dimensional decomposition (PDD), topology derivative, and level-set method for robust topology optimization subject to a large number of random inputs. With this method, the influence of a large number of random inputs can be easily captured in an accurate manner. In addition, the stochastic moments and their sensitivities can be obtained from analytical expressions based on the PDD approximation of response functions and the deterministic topology derivative. Only a single stochastic analysis is required for evaluating the moments and their sensitivities in each iteration. The topology is described by the level-set function and its evolution is driven by solving the reaction-diffusion equation of the level-set function. An augmented Lagrange penalty formulation dovetails the stochastic topology derivatives of objective and constraints into the reaction term in the reaction-diffusion equation, which generates a new topology during the iteration process. The practical examples illustrate that the proposed method can render meaningful optimal designs for structures subject to several or a large number of random inputs.

In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in many disciplines, the problems in vehicle crashworthiness pose severe difficulties for standard, gradient-based approaches, due to high noisiness, multi-modality, and discontinuous nature of the nonlinear simulation responses considered typically as objectives and constraints. In this article, we propose to use Evolutionary Algorithms (EAs) together with a suitable low-dimensional representation in an extended version of the Evolutionary Level Set Method (EA-LSM), able to address complex 3D crash TO problems. The method is used to optimize a 3D-printed metal joint in a hybrid S-rail structure under axial crash loading, inspired by novel frame design concepts in industry. The obtained results show that the method is capable of handling optimization problems with multiple constraints, including challenging acceleration responses, and yields significantly better solutions than the state-of-the-art methods. Finally, the robustness of the obtained designs is studied, demonstrating the ability of EA-LSM to find designs of low sensitivity w.r.t. small variations of the loading conditions, which is crucial from the perspective of industrial applications of the proposed method.

Material engineers continuously make every effort for the evolution of novel and prevailing production performances to supply our biosphere with resource-proficient, economical, and hygienic substances with superior package operation. The mitigation of energy depletion and gas releases as an utmost significance worldwide is a renowned datum; which also needs the improvement of delicate substances employing budget-proficient and ecologically pleasant methods. Consequently, copious exploration has been aimed in the study of methods retaining a potential to wrestle these widespread essentials. Material engineering processes have advanced as a feasible substitute for conventional steel fragment construction methods. CE has experienced an extraordinary advancement throughout the previous three decades. It was originally utilised uniquely as a state-of-the-art reserve of the paradigm. Referable to the expertise development which permits merging countless engineering procedures for the output of a modified portion that employed intricate configurations, CE expertise has got cumulative responsiveness. As such, this article intends to furnish a comprehensive appraisal of chemical fabrication progressions for steel substance evolution utilised in different applications. The inspection encompasses the current advancement of CE know-hows, a detailed taxonomy and classification of manufacturing operations. The focal point of the upcoming perspective of CE in substance investigation and application is further deliberated

A topology optimization method for coupled vibro-acoustic problem based on hybrid finite element-wave based method is proposed. The hybrid finite element-wave based method is used to model the coupled free layer damping structure-acoustics system, and a gradient-based topology optimization formulation is developed by solid isotropic microstructures with penalization method, to obtain optimized layout of free layer damping with the objective of minimizing sound pressure in the acoustic cavity and with constraint defined as the amount of free layer damping. The adjoint variable method for the coupled vibro-acoustic model based on the hybrid finite element-wave based method is also derived as a part of the sensitivity analysis. Numerical example shows that the optimized distribution reduce sound pressure effectively and the calculation time of proposed method is less than the conventional method for topology optimization base on finite element method. Experimental results verify the accuracy of the hybrid finite element-wave based method and the effect of optimized damping material layout is also validated by experiment.

Since the emergence of digital design techniques in combination with so-called responsive materials, the concept of organic forms in architecture seems to be gaining a new quality. The resemblance to an organism should no longer apply only superficially but be inscribed in the materiality as well as in the history of origin and functioning. This article addresses these new transformative effects between architecture and biology. They are presented primarily in relation to the structural architecture of the 1960s and the computational architectural systems since the 1990s. One focus of architecture is on dynamic forms that adapt themselves to their environment by means of flexible materials and generative algorithms. Here, architecture as technically animated matter no longer involuntarily competes with creative nature but is seen as part of a reciprocal relationship. This reciprocal relationship is specified by recourse to various architectural models. The models’ approaches suggest that organic-looking forms are generated by simulated biological processes. The article examines this claim of the models from the perspective of the history of architecture and design. It shows how, since the mid-twentieth century, a renewal of architectural design practice has been sought by reformulating morphological questions at the intersection of biological and cybernetic discourses.

Topology optimization as a computational approach is used to find an optimal structure with specific objectives such as an ultimately lightweight design. The optimization is normally performed under a series of constraint functions to ensure expected structural performance for safety and resilience. To promote the use of topology optimization in structural engineering, it is crucially important to identify and apply suitable constraints for realistic construction. In this paper, a conceptual attempt aiming for a construction-oriented topology optimization framework is presented. An isogeometric analysis module using NURBS curves for geometric description and discretization is employed in stress analyses, which incorporates the density-based SIMP optimization approach to generate the optimized topology. A Drucker–Prager criterion is adopted to constrain the stresses within the unequal limits of tension and compression for the use of concrete type material. To prevent slim components of potentially high complexity in construction, a minimum-width control is applied as an additional geometric control. In this paper, the conceptual framework is demonstrated using a prestressed concrete beam, while the optimization is performed to find suitable density distribution of concrete and a NURBS-described tendon profile after imposing the mapped prestressed action. The iterative optimization processes are presented and demonstrated to investigate the effect of tensile-compressive strength ratio and the minimum width, which reflects the construction-oriented vision pursuing a more regularized topology after applying the constraints in optimization.

This paper proposes an efficient methodology for concurrent multi-scale design optimization of composite frames considering specific design constraints to obtain the minimum structure cost when the fundamental frequency is considered as a constraint. To overcome the challenge posed by the strongly singular optimum and the weakness of the conventional polynomial material interpolation (PLMP) scheme, a new area/moment of inertia–density interpolation scheme, which is labeled as adapted PLMP (APLMP) is proposed. The APLMP scheme and discrete material optimization approach are employed to optimize the macroscopic topology of a frame structure and microscopic composite material selection concurrently. The corresponding optimization formulation and solution procedures are also developed and validated through numerical examples. Numerical examples show that the proposed APLMP scheme can effectively solve the singular optimum problem in the multi-scale design optimization of composite frames with fundamental frequency constraints. The proposed multi-scale optimization model for obtaining the minimum cost of structures with a fundamental frequency constraint is expected to provide a new choice for the design of composite frames in engineering applications.