Design Challenges Research Articles

Comparative study of optimization methods for building energy consumption and daylighting performance

When striving for sustainable development, designers encounter the formidable task of integrating the advantages of daylighting into energy-efficient designs. The process of investigating a multitude of design variables can be arduous and time-intensive. However, computational simulations provide indispensable insights into forecasting performance. A considerable number of these design challenges do not have explicit mathematical solutions; thus, black-box optimization techniques are favored for the purpose of determining the most effective design solutions. Popular meta-heuristic optimization algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), and Multi-Verse Optimizer (MVO), are described and thoroughly analyzed in this article. In addition, the present study run empirical studies to compare these optimization algorithms by applying them to tackle five distinct daylighting and energy optimization problems associated with enhancing building performance. In the test cases, the present study results demonstrate that the PSO and MVO algorithms exhibit better performance compared to the GA and WOA algorithms. The selection of the most suitable algorithm is contingent upon the distinctive attributes of the problem, and the intricacy of the optimization landscape.

A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is essential for tackling urban environmental challenges and facilitating renewable energy recycling. The MSWI process has characteristics of multiple variables, strong coupling, and complex nonlinearity, requiring advanced process control (APC) technology. Although there have been several reviews on the modeling and control of the MSWI process, there is a lack of focus on model predictive control (MPC), a widely used APC technology. This article aims to comprehensively review MPC strategies in the MSWI process. First, it describes MSWI process technology in detail, examining control issues and objectives to highlight the complexity and challenges in controller design while providing an overview of MPC methods and their benefits. Second, it reviews incinerator modeling for control, including traditional modeling techniques and machine learning technologies such as fuzzy neural networks. Third, it reviews the controllers used for MSWI process, emphasizing the advantages of MPC over existing control methods. Fourth, it discusses the current status of MPC design and online updates, covering the need for an accurate dynamic predictive model and objective function and the online updates components such as predictive modeling, rolling optimization, and feedback correction. Finally, the study concludes with a summary of the findings.

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Abstract. The microwave radiances are key observations, especially over data-sparse regions, for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements; however, the satellite field of view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at MEsoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study, indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures is computed over a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS tropospheric channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied. For AMSU-A radiances, the reduction is around 1 % for high-peaking channels and about 4 % for low-peaking channels. For MHS data, this reduction is somewhere between 1 %–2 % by the footprint observation operator.

ChatMol: interactive molecular discovery with natural language.

Natural language is poised to become a key medium for human-machine interactions in the era of large language models. In the field of biochemistry, tasks such as property prediction and molecule mining are critically important yet technically challenging. Bridging molecular expressions in natural language and chemical language can significantly enhance the interpretability and ease of these tasks. Moreover, it can integrate chemical knowledge from various sources, leading to a deeper understanding of molecules. Recognizing these advantages, we introduce the concept of conversational molecular design, a novel task that utilizes natural language to describe and edit target molecules. To better accomplish this task, we develop ChatMol, a knowledgeable and versatile generative pretrained model. This model is enhanced by incorporating experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages. Several typical solutions including large language models (e.g. ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement approach. Case observations and analysis offer insights and directions for further exploration of natural-language interaction in molecular discovery. Codes and data are provided in https://github.com/Ellenzzn/ChatMol/tree/main.

Compromise in Human-Robot Collaboration for Threat Assessment

Advancements in Artificial Intelligence (AI) will produce “reasonable disagreements” between human operators and machine partners. A simulation study investigated factors that may influence compromise between human and robot partners when they disagree in situation evaluation. Eighty-seven participants viewed urban scenes and interacted with a robot partner to make a threat assessment. We explored the impacts of multiple factors on threat ratings and trust, including how the robot communicated with the person, and whether or not the robot compromised following dialogue. Results showed that participants were open to compromise with the robot, especially when the robot detected threat in a seemingly safe scene. Unexpectedly, dialogue with the robot and hearing robot inner speech reduced compromise and trust, relative to control conditions providing transparency or signaling benevolence. Dialogue may change the human’s perception of the robot’s role in the team, indicating a design challenge for design of future systems.

A Design Challenge for Enhancing Cartographic Research, Education, & Outreach

A Design Challenge for Enhancing Cartographic Research, Education, & Outreach

Mechanistic insight into the synergy between nickel single atoms and nanoparticles on N-doped carbon for electroreduction of CO2

The synergy of single atoms (SAs) and nanoparticles (NPs) has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction (CO2RR); however, the rationalization of the SAs/NPs proportion remains one challenge for the catalyst design. Herein, a Ni2+-loaded porous poly(ionic liquids) (PIL) precursor synthesized through the free radical self-polymerization of the ionic liquid monomer, 1-allyl-3-vinylimidazolium chloride, was pyrolyzed to prepare the Ni, N co-doped carbon materials, in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing temperature. The catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO2 into CO. Operando Ni K-edge X-ray absorption near-edge structure (XANES) spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and NPs. The charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active sites. In the electroreduction of CO2, the co-existence of Ni SAs and NPs strengthened the CO2 activation and the affinity towards the key intermediate of *COOH, lowering the free energy for the potential-determining *CO2 → *COOH step, and therefore promoted the catalysis efficiency.

Finite element modelling of continuous fiber–reinforced composites produced by automated manufacturing

Abstract With the advent of automated, continuous filament placement technologies, the possibilities for reinforcement placement in composite manufacturing have been further expanded, as the reinforcement path can be continuously varied within the layer. This allows far more efficient structures to be created, resulting in a further reduction in the weight of the structure compared to conventional composites. At the same time, this manufacturing freedom also poses a major challenge for the structural design of composite components, as simulation methods based on meso-level homogenization are mostly not usable due to the lack of periodicity of the reinforcement structure. In our paper, we have established a structural modelling method using finite element analysis (FEA) for fused filament fabrication-based additive manufacturing of continuous fiber–reinforced composites. For modelling the reinforcement structure properly, we used the base-coordinate sweep (BCS) modelling method and for capturing progressive failure mechanisms we used Ansys LS-Dyna with the MAT54 material card. Based on the simulations and tests performed, we demonstrated that the method we present is suitable for the engineering modelling of continuous fiber–reinforced 3D printed composites.

Innovations and challenges in modern optical design

AbstractOptical designers are constantly pushing the boundaries of technology, developing new materials, techniques, and software tools to improve the design and optimization of optical systems – crucial for continued growth and innovation in areas such as virtual reality, autonomous vehicles, and quantum technologies.

Autonomous Building Design for Manufacturing and Assembly: A Systematic Review of Design Application, Challenges, and Opportunities

Autonomous Building Design for Manufacturing and Assembly: A Systematic Review of Design Application, Challenges, and Opportunities

Employing Vernacular Urbanism and Traditional Practices in Enhancing Contemporary Cities: Insights from India

Vernacular urbanism, which encompasses traditional architectural and planning practices rooted in local culture and environmental conditions, presents valuable insights for modern urban design. As modern cities face increasing challenges such as environmental sustainability, cultural homogenization and climate change, the significance of these traditional practices becomes more apparent. In this context, it is argued that understanding and integrating vernacular practices can enhance contemporary urban design by merging historical knowledge with modern needs. This paper examines the integration of vernacular urbanism— traditional architectural and urban planning practices—into contemporary city design by employing a qualitative approach through case studies across various Indian cities such as Mumbai, Kochi, Jaipur and Jaisalmer, drawing on various data sources including historical texts, architectural treatises. By examining these, the study uncovers the evolution of vernacular urban practices and their applicability to contemporary urban design challenges. The paper concludes that integrating vernacular principles into the design of contemporary cities creates a harmonious balance between heritage preservation and advancing modern developments. This approach effectively bridges the gap between historical knowledge and contemporary urban planning, resulting in more livable, sustainable urban environments.

‘Robinson’ Pedestrian Bridge in Budapest, Hungary

Abstract‘Robinson’ Bridge was built as part of the National Athletic Centre project in Budapest, Hungary. The 168 m long cable‐stayed pedestrian bridge has a 65 m tall inclined pylon standing on a small island. The 12,72 m wide main girder is suspended to a monopylon with a two‐plane fan‐shaped FLC stay cable system. The landmark bridge's stunning appearance was achieved with hybrid structures. Both the pylon and parts of the slender stiffening girder were made of S460 steel tubes filled with concrete to enhance their load‐bearing and dynamic characteristics. Due to very limited construction area, a special erection process was used for the pylon involving a 200t floating crane. The stiffening girder was built with incremental launching. Cable tensioning with a curved deck proposed special design challenges. Large displacements had to be accurately predicted. To meet the strictest requirements for pedestrian comfort, TMDs were applied and dynamic load tests carried out to prove the results.

Advancing healthcare through piezoresistive pressure sensors: a comprehensive review of biomedical applications and performance metrics

Abstract Piezoresistive pressure sensors have transformed biomedical applications, enabling precise diagnostics and monitoring. This concise review explores the fundamental principles, key components, and fabrication techniques of piezoresistive pressure sensors, focusing on critical performance metrics such as sensitivity, accuracy, and response time. Biomedical design challenges, including biocompatibility and long-term stability, are examined, offering insights into solutions for optimal sensor integration. In diverse biomedical applications, piezoresistive pressure sensors play pivotal roles, from blood pressure monitoring to implantable medical devices. The paper emphasizes their versatility in enhancing patient care through continuous and accurate monitoring. Looking forward, the review discusses emerging trends and potential research directions, positioning piezoresistive pressure sensors as central contributors to the future of biomedical technology, promising improved patient outcomes and advanced healthcare delivery through precise and continuous monitoring.

Concurrent topology optimization of sandwich structures with multi-configuration and variable-diameter lattice infill

The superior stiffness-to-weight and strength-to-weight mechanical advantages of sandwich structures can be fully exploited through concurrent design of entire topology, infill configuration and density, where the high-performance yet complicated structure can be fabricated through additive manufacturing. However, the emerging design challenges are concurrent design updating related to sandwich topology, infill configuration and density, which is a design problem with continuous and discrete variables mathematically. In this paper, a concurrent topology optimization is proposed for sandwich structures with multi-configuration and variable-diameter lattice infill. Three design variable fields are employed to describe the fundamental topology considering sandwich structural topology, infill configuration and density simultaneously. Corresponding material interpolation model is developed by combining DSP-based shell-infill description and multi-response latent-variable surrogate model based effective material property calculation. Two-stage design model is formulated as a rough design model considering all design variables followed by a refined design model with only infill density variables, which is developed to strictly satisfy the material allowance constraint due to the mapping between discrete infill configuration variables and continuous latent configuration variables. Corresponding sensitivities of compliance and constraint with respect to the structural topology, infill configuration and density variables are derived, and the method of moving asymptotes (MMA) is employed to solve the design model efficiently. Several numerical examples are presented to systematically demonstrate the effectiveness of the proposed approach.

State of-The-Art Multi-Input DC-DC Converter Topologies and their Control Techniques: A Review

Environmental awareness and technological advancement have made renewable energy sources such as fuel cell,solar photovoltaic (PV), wind energy an emerging research area. Power electronics converters are used as interface betweenthe renewable energy sources and the load unit, aimed at matching these sources with the load demand and improving thesteady-state and dynamic characteristics. Traditional method of using multiple single-input single-output dc-dc converter tointegrate several renewable energy sources, comes with challenges in controller design, poor efficiency, low power densityand increase in cost. To address these limitations, the use of an integrated multi-port converter is a preferable solution.Recently, several multi-port dc-dc converter topologies have been reported each having its peculiarity in terms of number ofcomponent count, relationships between input and output, voltage conversion ratio, control algorithm and efficiency. Thisarticle reviewed multi-port dc-dc converters presented by different research groups from the point of view of topology, inputand output relation and structure. Furthermore, control algorithms proposed to solve various control problems are alsoreviewed.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discoveryprocess.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications.

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.

Numerical Simulation Study on Coupled Heat Transfer During the Heating Process of Hot Isostatic Pressing Equipment

Abstract Hot isostatic pressing (HIP) technology is currently the primary process for manufacturing high-performance advanced materials, powder metallurgy, and diffusion bonding. With the increasing demand for enhanced material performance and a wider range of materials, the HIP process now requires reaching temperatures of up to 2000 °C and pressures of 200 MPa, with a temperature range of less than ±5 °C. This presents significant challenges in equipment design. This article utilizes engineering data inversion methods to accurately determine material parameters under high temperatures and establishes a visual simulation model for the core area of HIP equipment. By comparing the calculated temperatures with actual equipment temperature uniformity during experimental processes, the study found that after 120 min of heating, the simulated temperature in the working area closely matched the actual engineering temperature of 2105.36 K, with an error of less than 5%. Under empty furnace conditions, the minimum temperature difference in the working area is 12 K, and effective temperature uniformity can be easily achieved through insulation. Simulation results indicated that the processed product may decrease temperature uniformity in the furnace, suggesting that adding bottom insulation could increase temperature uniformity in the effective heat zone by 25%. This research provides a theoretical foundation for HIP equipment design and process optimization through simulation, ultimately enhancing process quality in a cost-effective and cost-efficient manner.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.