Traditional Design Approaches Research Articles

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

System reliability-based design of composite frames with concrete – filled steel plate shear walls

Nowadays, many high-rise buildings are constructed from concrete-filled steel tubular (CFST) columns and concrete-filled steel plate (CFSP) shear walls due to their superior merits. However, these composite buildings are still designed by traditional load and resistance factor design (LRFD) approach which is conservative and impossible to explicitly capture the complex behaviour of CFST structures. Hence, this paper develops an efficient framework for the system design of CFST structures utilizing nonlinear simulation and reliability analysis. Firstly, a C+ + source code for a new material model is developed in OpenSees to capture the behaviour of semi-rigid connections. This material is implemented into the numerical model of CFST structures. Secondly, a framework based on subset simulation is developed for the reliability analysis. This code rigorously considers uncertainties of variables such as numerical model, geometric and material properties or applied loads of both American (US) and Australian (AS) standards. Finally, case study and parametric study are conducted by using the developed framework to propose resistance reduction factors for the investigated frames. The results of the study show that the resistance reduction factor can be taken as 0.8 for both AS and US codes. This proposed factor can be a great source of reference for the system design of CFST buildings with composite shear walls.

A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention.

Research on the Application of Virtual Reality Technology in the Design and Evaluation of Interactive Interfaces for Renewable Energy Systems

In this investigation, we delve deeply into the visual interaction design of electric vehicle power management systems, particularly how we can integrate sustainable development principles to enhance the user experience and promote environmentally friendly travel. Initially, we take a look at the present situation and challenges faced by electric vehicle power management systems. We identify shortcomings in traditional design approaches that fail to prioritize sustainability, such as low energy efficiency and challenges in accessing user-friendly information. To validate the effectiveness of our proposed method, we conduct case studies and user tests. The results reveal that incorporating sustainable development elements into the visual interaction design not only significantly boosts user satisfaction and trust in the power management system but also helps reduce energy consumption and environmental pollution.

Investigation of approaches to designing complex database structures in systems of integrated monitoring of environmental, economic, energy and social parameters of the territory

The object of research is the traditional and universal approach of designing the database structure in systems of integrated monitoring of ecological, economic, energy and social parameters of the territory, which include diverse data from various subject areas. In the course of the study, an analysis was performed based on a set of criteria such as scalability, ease of updating data, absence of empty fields, volume of the database, number of tables and fields, ease and speed of execution of requests for a sample set of indicators of the research object. The comparison of these approaches took place on the example of water resources monitoring, since it has several subsystems and a large number of indicators that are used for assessment. It is established that the proposed universal approach to designing complex database structures made it possible to reduce the volume of the database by 2.25 times due to the absence of empty fields. In particular, in the considered example, the filling factor of the database with the traditional approach is 1.75 times less than with the proposed universal approach. It should be noted that the rate of table filling for the traditional design approach can vary depending on the number of indicator values, while the table filling rate for the universal approach is always close to 100 %. Also, the proposed database design approach makes it possible to speed up data loading and processing. For example, with the same volume of significant information, the minimum speed of sampling the characteristics of one research object is 3.87 times greater in a database developed according to the principles of the universal approach than according to the rules of the traditional approach. The proposed structure of the database is successfully used in the system of complex eco-energy-economic monitoring. The developed structure of the database can serve as an effective basis for the formation of an electronic data bank at the level of the enterprise, region and country.

A novel generative–predictive data-driven approach for multi-objective optimization of horizontal axis tidal turbine

Tidal turbines play a critical role in converting the kinetic energy of water into electricity, contributing significantly to energy conversion. However, the current optimization design of these turbines involves computationally intensive simulations, leading to higher design costs. Additionally, traditional parameterized modeling methods, constrained by predefined design parameters, limit the exploration of innovative designs. In response, this study introduces an innovative data-driven “generative–predictive” design approach comprising a generative model and a predictive model. The generative model autonomously learns feature representations from existing turbines and leverages this knowledge to generate a novel set of turbines with superior hydrodynamic performance. Subsequently, an efficient performance evaluation is conducted using a predictive model for the generated turbines. Compared to the current parameterized modeling approaches, the proposed approach is combined with multi-objective optimization algorithm to optimize the tidal turbine hydrodynamic performance. Research results demonstrate that the generative model, trained on gradients, can generate highly complex turbines with minimal latent vectors. Through transfer learning, the predictive model exhibits robustness and accuracy, effectively guiding the design process. In the final optimization comparison, the proposed generative–predictive design approach requires only 4% of the optimization time while achieving results similar to or surpassing traditional design approaches. This approach proves to be a powerful tool for guiding the efficient and optimized design of turbines.

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP. The framework also identified feasible decision variable ranges and demonstrated the impact of different optimization strategies on system performance. Furthermore, the contributions of WWTP and CW in pollutant degradation were analyzed. Overall, the proposed framework offers a highly efficient and cost-effective solution for optimizing the design and operation of a combined WWTP and CW system.

Systematic Design and Implementation Method of Battery-Energy Comprehensive Management Platform in Charging and Swapping Scenarios

Batteries are one of the most crucial energy storage devices today, and battery-energy management technology has an extremely significant impact on the performance and lifespan of batteries. The traditional design approach for battery-energy management platforms often neglects considerations for charging and discharging scenarios. Additionally, functional modules are designed independently, leading to incompatibility issues between hardware and control units, thereby limiting the system’s performance. To address these challenges and enhance system coordination, this paper proposes a systematic design and implementation method for a battery-energy comprehensive management platform applied in charging and swapping scenarios. The method consists of four parts: hardware design, a dynamic load charging-balance control strategy, a composite micro-source hierarchical coordination control strategy, and a system emergency-response and protection strategy. The proposed method has been successfully applied to a design and has been used to build a battery-energy comprehensive management platform. Finally, through experiments, it has been demonstrated that this system can achieve energy scheduling, battery-energy balance, mode switching, and fault protection in a stable and reliable manner.

Deep-learning-based inverse design of colloidal quantum dots

Because optical properties of colloidal quantum dots (QDs) greatly depend on their layer thicknesses at the sub-nanometer scale, accurately predicting optical properties based on these structural parameters can help streamline the development process. However, most of traditional inverse design approaches require time-consuming numerical simulations and optimization processes. In this simulation-only paper, we propose a deep-learning-based inverse design model for colloidal InP/ZnSe/ZnS QDs based on the structure parameters of layer thicknesses and optical properties of the emission and absorption spectrum. Four different deep neural network (DNN) structures are implemented and compared. A tandem DNN structure with the simultaneous use of both emission and absorption spectra as input data shows the best accuracy due to its ability to alleviate the non-uniqueness problem in the inverse design of colloidal QDs. Our inverse design model enables the prediction of layer thicknesses for QDs that exhibit on-demand optical emission/absorption spectra, which are particularly enhanced at the specific target wavelength region. Moreover, the weights and biases of the well-trained tandem DNN for InP/ZnSe/ZnS QDs can be transferred and tuned to implement a transfer-learning-assisted inverse design model for CdSe/ZnSe/ZnS QDs, where the amount of training data to achieve the average prediction accuracy of 99.3 % is reduced by a maximum of 71.4 %.

The integrated multi-performance fast optimization strategy for battery thermal management system

Increased battery energy density is required to boost electric vehicle endurance; however, this also raises the possibility of thermal runaway and power battery explosion. Improving the cooling system performance requires optimization and enhancement of classical systems. Traditional design approaches struggle to simultaneously enhance multiple aspects of performance, while an optimization based on Computational Fluid Dynamics (CFD) methods is often inefficient. Therefore, by integrating a flow resistance network model (FRNM) with a weighted average optimization algorithm (INFO), an efficient optimization for the comprehensive performance of the system can be achieved. Five optimized systems under different airflow rates were obtained through optimization. A comparison with two existing systems validated the effectiveness of the optimized system. The results demonstrate that, compared to the two reference systems, the optimized system decreases the maximum temperature difference by 65.51 % and 39.07 %, respectively. Furthermore, the improvement in temperature uniformity is more significant, increasing by 63.76 % and 34.40 %, respectively.

Advancing wavefront shaping with resonant nonlocal metasurfaces: beyond the limitations of lookup tables

Resonant metasurfaces are of paramount importance in addressing the growing demand for reduced thickness and complexity, while ensuring high optical efficiency. This becomes particularly crucial in overcoming fabrication challenges associated with high aspect ratio structures, thereby enabling seamless integration of metasurfaces with electronic components at an advanced level. However, traditional design approaches relying on lookup tables and local field approximations often fail to achieve optimal performance, especially for nonlocal resonant metasurfaces. In this study, we investigate the use of statistical learning optimization techniques for nonlocal resonant metasurfaces, with a specific emphasis on the role of near-field coupling in wavefront shaping beyond single unit cell simulations. Our study achieves significant advancements in the design theoretical conception of resonant metasurfaces. For transmission-based metasurfaces, a beam steering design outperforms the classical design by achieving an impressive efficiency of 80% compared to the previous 23%. Additionally, our optimized extended depth-of-focus (EDOF) metalens yields a remarkable five-fold increase in focal depth, a four-fold enhancement in focusing power compared to conventional designs and an optical resolution superior to 600 cycle/mm across the focus region. Moreover, our study demonstrates remarkable performance with a wavelength-selected beam steering metagrating in reflection, achieving exceptional efficiency surpassing 85%. This far outperforms classical gradient phase distribution approaches, emphasizing the immense potential for groundbreaking applications in the field of resonant metasurfaces.

Leveraging Additive Manufacturing to Fabricate High Temperature Alloys With Co-Designed Mechanical Properties and Environmental Resistance

Abstract A paradigm shift in the traditional sequential design approaches is critically essential to create application-specific hierarchical and multifunctional materials with superior long-term performance for next-generation energy technologies involving extreme environments. In the current work, we aim to leverage the flexibility and geometric/compositional complexity offered by additive manufacturing to demonstrate this new approach by codesigning a compositionally graded Ni-based alloy for molten salts\sCO2 heat exchangers to enable mitigation of environmental degradation of surfaces exposed to molten halide salts, while simultaneously suppressing the consequent deterioration in mechanical stability. Thermokinetic modeling describing the underlying physics of thermally- and environmentally induced spatiotemporal compositional and microstructural evolution will be employed to predict the parameter space of material deposition processes and precisely identify the required composition gradient. Preliminary corrosion and mechanical testing of the dual material demonstrated the potential of the material to replace existing solid solution strengthened materials for this application.

Optical color routing enabled by deep learning.

Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.

UDGAN: A new urban design inspiration approach driven by using generative adversarial networks

Abstract The morphological design of urban space affects the quality of the environment. The traditional experience-based design approach was greatly improved by introducing computational design tools. However, the existing urban design tools are mostly developed on pre-set rules or given targets, which have few contributions to enhance creativity or generate inspiring schemes. Therefore, this paper proposes a new computational urban design approach named UDGAN, integrating generative adversarial networks (GANs) and multi-objective optimization algorithms. This model utilizes urban design scheme plans over the past 20 years from a particular designer as training datasets. Four preference models were trained to autonomously generate stylized urban design schemes. Eight morphological parameters were used to analyze the model performance by comparing generated results with the ground truth. This GAN-based surrogate approach is combined with a morphological indicator alignment process using multi-objective optimization model to obtain better results. The result shows that the R2 predicted by the improved Pix2Pix model reaches 0.798, and the similarity of the generated results can be stably distributed between 0.7 and 0.8, so the design scheme of this preferred style can be effectively learned. At the same time, the pre-trained model greatly reduces the time consumption of the design scheme generation, taking 5 min approximately to complete a generation process. This approach quickly generated the design scheme with preferred features, supporting the designer with creativity and greatly saving the time of design creation, transforming computational design into an inspiration-driven process.

Integrating irregular inventories: Accessible technologies to design and build with nonstandard materials in architecture

Circular principles are a promising method to reduce embodied carbon in construction. Material reuse has the potential to reduce waste and environmental impacts of the extraction, production, and transportation of new materials. However, its implementation can be limited by the challenging nature of irregular materials that do not fit traditional design approaches, fabrication tools, and standards. In response, this paper proposes a generalized, accessible framework for working with irregular material sources in architecture. This framework includes computational tools and fabrication strategies that enable, expand access to, and scale up this approach in the AEC industry. The methodology is demonstrated through four design-build examples that leverage the irregularity of different materials into new architectural assemblies.

Efficient co-planar adder designs in quantum dot cellular automata: Energy and cost optimization with crossover elimination

Quantum dot cellular automata (QCA) stands as a highly promising nanotechnology, capable of outperforming conventional CMOS technology. Leveraging its advantages, this paper addresses the challenges in adder circuits related to size, delay, and cost by integrating QCA technology. A novel layout for a co-planar full adder in QCA is introduced, featuring a half-adder design with 23 cells in a single layer without any crossover, achieving a delay of 0.25 clock cycles. By utilizing inter-cellular effects inherent in QCA technology, the presented designs offer significant improvements in a variety of design metrics, and a cost efficiency of 64.3%, distinguishing them from traditional logic design approaches. Additionally, in order to ensure an efficient routing channel and a consistent structure of the adder design, a Universal, Scalable, and Efficient (USE) clocking scheme has also been employed. The adder design is further used to build 4-bit and 8-bit Ripple Carry Adders. An extensive study on energy dissipation using QCADesigner-E and QCAPro tools is conducted for three tunneling energies (0.5 Ek, 1 Ek, and 1.5 Ek). The full adder design showcases substantial improvement in energy to delay cost when compared to the closest reported design, achieving improvements of 91.66%, 89.03%, and 88% at 0.5 Ek, 1 Ek, and 1.5 Ek, respectively. The results highlight enhanced circuit efficiency and streamlined layouts, outperforming existing models and offering substantial benefits to high-performance and energy-efficient computing systems.

FAT-integrated pedagogy: a case study of footwear product design instruction in higher education

ABSTRACT Facing the drawback of the traditional product design approach and educational courses, this study aimed to explore an innovative approach through which synthesised knowledge and skills of product design in Function (F), Aesthetics (A) and Technology (T) can be integrated into the instruction of art and design subjects in higher education. With shoe modelling as a study case, experiments with the involvement of 19 participants were conducted to test a traditional shoe design method and a FAT-integrated shoe design method in terms of efficiency, flexibility and accuracy. The results demonstrated that the FAT-integrated shoe design method was more advantages than the traditional method. The study can conclude that the FAT-integrated shoe design method can not only propel the efficiency of product design but also the quality. It can enhance the quality of higher education in art and design subjects and promote the new technology application in traditional labour-intensive industries.

Creating Diverse Play-Style-Centric Agents through Behavioural Cloning

Developing diverse and realistic agents in terms of behaviour and skill is crucial for game developers to enhance player satisfaction and immersion. Traditional game design approaches involve hand-crafted solutions, while learning game-playing agents often focuses on optimizing for a single objective, or play-style. These processes typically lack intuitiveness, fail to resemble realistic behaviour, and do not encompass a diverse spectrum of play-styles at varying levels of skill. To this end, our goal is to learn a set of policies that exhibit diverse behaviours or styles while also demonstrating diversity in skill level. In this paper, we propose a novel pipeline, called PCPG (Play-style-Centric Policy Generation), which combines unsupervised play-style identification and policy learning techniques to generate a diverse set of play-style-centric agents. The agents generated by the pipeline can effectively capture the richness and diversity of gameplay experiences in multiple video game domains, showcasing identifiable and diverse play-styles at varying levels of proficiency.

An ultra-fast method for designing holographic phase shifting surfaces

Holographic phase-shifting surfaces (PSSs) have been proven to offer a cost-effective solution for enabling passive arrays to mechanically steer their beams toward desired directions. However, even though the principle of operation of PSSs is straightforward, designing a PSS is very challenging, because it involves an extremely high computational time, which in turn limits their usage and development. Notably, traditional design approaches of PSSs, with N number of layers that have M different variations of conductive patches, need M^N full-wave simulations to be properly characterized. To address these challenges that are associated with the design of PSSs and reduce the needed computational effort, we present here a semi-numerical approach that enables the efficient design of holographic PSSs. Specifically, by representing an N-layer PSS unit-cell as N cascaded networks, where each network represents one layer of the PSS that has M different designs of sub-wavelength resonators, we only need to conduct N times M full-wave simulations to collect all the required data needed to analyze the performance of the PSS. In turn, by utilizing the multiplication property of ABCD parameters we can evaluate very efficiently all the M^N different combinations that characterize our PSS. To validate the accuracy of our design methodology, a 1-D beam steerable antenna system is designed that is comprised of a circularly polarized holographic metasurface antenna (HMA) and a hybrid PSS, both operating at 30 GHz. Comparisons between our semi-numerical results, full-wave simulations, and measurements demonstrate an angular error of less than 0.7^{circ }.

Seismic risk-based optimization of tension-only shape-memory alloy device for steel moment-resisting frames

This study introduces the seismic risk-based design of SMA devices for steel moment-resisting frame buildings. The investigation begins with the numerical representation of the device which includes SMA wires exhibiting super-elastic stress–strain response, along with steel components. The model is then compared against an actual test result. Accordingly, several four-story steel-frame buildings are numerically characterized, all designed to withstand similar seismic loads. This includes a control frame building (Bare-SMF), a frame building with SMA devices using a traditional design approach (SMA-D), and a frame with buckling restrained braces (BRB-D) for comparison. As the traditional design method relies on the first modal shape and static loads, the pushover analysis results for all structures possessed similar amount of strength and deformation capacity. However, the nonlinear time history analysis revealed inconsistencies in the responses of the retrofitted buildings (SMA-D and BRB-D) when compared to Bare-SMF. To address this issue, a seismic risk-based design approach that combines nonlinear time history analysis on a steel moment-resisting frame and the particle swarm optimization algorithm is employed. By using this approach, the SMA brace quantities were determined such that the cost of SMA devices can be minimized while the life-cycle probability of collapse and demolition is smaller than that of Bare-SMF. As a result of the risk-based design approach, three brace quantities are determined according to a decision maker’s attitude against the seismic risk. As a result, the cost of SMA devices could be reduced by more than 40% while maintaining a significant reduction in the life-cycle probability of collapse and demolition.