Optimal Design Solutions Research Articles

Measuring the Robustness of Optimal Design Solutions for Wave Energy Converters via a Stochastic Approach

Among the challenges generated by the global climate crisis, a significant concern is the constant increase in energy demand. This leads to the need to ensure that any novel energy systems are not only renewable but also reliable in their performance. A viable solution to increase the available renewable energy mix involves tapping into the potential available in ocean waves and harvesting it via so-called wave energy converters (WECs). In this context, a relevant engineering problem relates to finding WEC design solutions that are not only optimal in terms of energy extraction but also exhibit robust behavior in spite of the harsh marine environment. Indeed, the vast majority of design optimization studies available in the state-of-the-art consider only perfect knowledge of nominal (idealized) conditions, neglecting the impact of uncertainties. This study aims to investigate the information that different robustness metrics can provide to designers regarding optimal WEC design solutions under uncertainty. The applied methodology is based on stochastic uncertainty propagation via a Monte Carlo simulation, exploiting a meta-model to reduce the computational burden. The analysis is conducted over a dataset obtained with a genetic algorithm-based optimization process for nominal WEC design. The results reveal a significant deviation in terms of robustness between the nominal Pareto set and those generated by setting different thresholds for robustness metrics, as well as between devices belonging to the same nominal Pareto frontier. This study elucidates the intrinsic need for incorporating robust optimization processes in WEC design.

Design of a multi-scale cylindrical porous fin based on constructal theory

Porous fin (PF) has obvious advantages in heat dissipation, so it is necessary to analyze and optimize its performance. In this paper, a multi-scale cylindrical porous fin (PF) model is built. Firstly, the fin length and height ratio are chosen as the optimization variables, constructal design is conducted with the objective of complex function (CF) composed of maximum temperature difference (MTD) and pumping power consumption (PPC). Then, an artificial neural network (ANN) model is established by using data samples, and multi-objective optimization (MOO) is performed using the NSGA-II method. The findings indicated that the optimal length and height ratio of the fin are 4.763 mm and 1.017 under the conditions that the total PF envelope volume and PF-material volume are fixed. The CF is reduced by 51.0% compared with that before optimization. The multi-scale cylindrical porous fin with different heights is superior to the single-scale one with equal height. For MOO, the lowest deviation index obtained by TOPSIS or LINMAP decision-making strategy (DMS) is 0.258. For this case, the MTD is reduced by 81.9%, and the PPC is reduced by 18.7%. Therefore, the relevant optimal structure herein can be used as the optimal design solution for the multi-scale cylindrical porous fin.

Optimization of the Air-Conditioning Energy Performance and Daylight Performance of AResidential Building According to the Bioclimatic Design Principles: An Application to the Moroccan Mediterranean

The bioclimatic architecture concept refers to an approach that takes into account the various characteristics of a building environment to make it more comfortable for its occupants. This work aims to improve the passive design parameters of an air-conditioned residential building located in the north of Morocco, in accordance with bioclimatic principles. The bioclimatic chart diagram is used to select the passive design measures that are the most appropriate for the north Morocco climate characteristics. Then, a set of design parameters are selected for more delimitation in the optimization study. The optimization problem is multi-objective and aims to find the design solution that simultaneously includes the best air-conditioning energy performance and daylight performance. The obtained results showed that the multi-objective optimum design solution is characterized by massive walls and roof, exterior insulation, double window glazing type, and a high summer ventilation rate. Also, a small glazing area with a large sun-shading covering is needed for the east facade. Ultimately, the building performance analysis revealed that the optimum bioclimatic design solution fully meets the requirements establishedby the Moroccan Building Thermal Regulation (MBTR), leading to an energy performance improvement of about 52%.

An integrated-hull design assisted by artificial intelligence-aided design method

To mitigate the high cost of integrated ship design, in this paper, we propose an innovative integrated hull design methodology utilizing artificial intelligence-aided design technology. The AIAD methodology leverages a deep neural network model constructed from multiple fidelities datasets to curtail the exponential growth in data requirements resulting from the proliferation of design variables caused by integration. Combining the proposed bump-free deformation method and optimization algorithms with the multi-fidelity deep network, the AIAD-based design approach facilitates the achievement of optimal hull design solutions with remarkable fidelity while minimizing design workload. Utilizing the developed design method, we performed a numerical test on integrated-hull design for container ships.Through a comparative analysis and sensitive analysis of design results and workload, the optimized results obtained demonstrated the effectiveness of the proposed AIAA integrated-hull design methodology in achieving optimal designs while preserving high fidelity and low workload. The proposed new approach has shown promises to expedite ship integrated-hull design, addressing the longstanding issue of its high cost.

Research on Intelligent Design of Geometric Factor Encoding for Aircraft Engine Turbine Structures

In recent years, with the rapid development of computer technology and artificial intelligence design technology, multiple possible design solutions can be quickly generated by transforming the experience and knowledge of structural design into computer executable rules and algorithms. To achieve intelligent design of aircraft engines, this paper proposes an encoding model for the turbine rotor structure of aircraft engines using geometric encoding technology. The turbine rotor structure of aircraft engines is divided into several units according to geometric similarity types, these units continue to be divided into attribute sets according to their functional types, connection relationships, and material properties. These attribute sets can be encoded using geometric encoding technology. The experiment simulated that these codes, for the point cloud modeling of turbine rotor structure, can be quickly achieved and they combine various algorithms to display the point cloud model of the turbine rotor in the Microsoft Visual studio MFC class library. The results show that by creating geometric codes for the turbine rotor of aircraft engines, it is possible to quickly create and display point cloud models of the turbine rotor structure, laying the foundation for subsequent application of machine learning to solve and find the optimal design solution.

Study of Robot Path Planning with Improved A* And DWA Algorithm Fusion

For some problems of the traditional A* algorithm in robot path planning, such as inefficient search, many and complex inflection points in the designed and realized paths, as well as the inability to complete real-time dynamic path planning for obstacle avoidance, this paper proposes an improved A algorithm and combines it with Floyd for path rounding, and finally with DWA (Dynamic Window Approach) algorithm to be Fusion. In the improved A* algorithm, a carefully normalized random obstacle environment is used and the search process is optimized by reducing the search direction and redundant nodes. Meanwhile, the evaluation function is adjusted to achieve the optimal global design solution, and path optimization and obstacle avoidance are performed by the DWA algorithm. The experimental results finally show that when the robot is in a complex environment, the fusion algorithm can ensure the completion of the globally optimal route while avoiding obstacles in a timely and effective manner, which realizes more efficient path planning.

Production of α-olefins from biomass gasification: Process development and multi-objective optimization for techno-economic and environmental goals

Efficient utilization of biomass as a substitute for fossil fuels holds great promise for meeting future greener chemical demands in a sustainable manner. This study presents a process simulation and multi-objective optimization for an innovative process of biomass-based Fischer-Tropsch synthesis of value-added linear α-olefins. Optimal design and operation solutions with heat-exchange network integration taken into account are obtained that minimize total annual costs (TAC), generalized energy consumption (GEC), greenhouse gas emissions (GHG), and maximize product yields (η). Compared to the benchmark case, this optimization leads to a 9.93 % reduction in TAC, a 21.76 % reduction in GEC, a 19.23 % reduction in GHG, and increases η by 1.41 %. Additionally, a comprehensive factor analysis of the optimal design and operation parameters in the Pareto front helps to discover patterns related to energy consumption, environmental impact, or economy. This allows to maximize a specific key process performance metric while minimizing the loss in the other, thus enabling flexible process design and operation under diverse manufacturing environments.

Key Issues and Solutions in the Study of Quantitative Mechanisms for Tropical Islands Zero Carbon Buildings

Faced with the challenges of global climate change, zero-carbon buildings (ZCB) serve as a crucial means to achieve carbon peak and carbon neutrality goals, particularly in the development of tropical island regions. This study aims to establish a ZCB technology system suitable for the unique climatic conditions of tropical islands. By employing methods such as energy flow boundaries, parametric design, and data-driven optimization algorithms, the research systematically analyzes the integrated mechanisms and optimization solutions for energy utilization, energy conservation, energy production, and intelligent systems. The study identifies and addresses key technical challenges faced by ZCB in tropical island regions, including the accurate identification of system design parameters, the precise quantification of the relationship between design parameters and building performance, and the comprehensive optimization of technical and economic goals for zero-carbon operational design solutions. The research results not only provide a comprehensive theoretical framework, promoting the development of architectural design theory, but also establish a practical framework for technology and methods, advancing the integration and application of ZCB technology. The study holds significant practical implications for the green transformation of the tropical island construction industry and the realization of national dual-carbon strategic goals. Future research should further explore the applicability of the technology system and the economic feasibility of optimized design solutions, promoting continuous innovation and development in ZCB technology.

Coupled and collaborative optimization model of impervious surfaces and drainage systems from the flooding mitigation perspective for urban renewal

Urban pluvial flooding mitigation is a significant challenge in city development. Many mature methods have been used to reduce the risk of flood. The optimal design of impervious surfaces (ODIS) is an adaptive solution to urban flooding from the perspective of urban renewal planning. However, existing ODIS models are limited because they do not consider the drainage systems. To address this issue, this study proposes an elastic and controllable optimization model based on assumptions about rainstorm and drainage capacity, nondominated sorting genetic algorithm-II (NSGA-II), multivariate linear programming (MLP) and soil conservation service curve number model (SCS-CN) in a case study of the old town of Guangzhou city, China. The model not only coupled the drainage systems, but also collaboratively optimized the impervious surfaces and the drainage systems. The results show that the proposed model achieved an optimized efficiency of 5.70 %, which is more than a tenfold improvement compared to existing ODIS models. The study emphasizes that the optimization of the drainage system should be the focus and the optimization of impervious surfaces should be supplementary, and different flood risk areas require different optimization strategies. Furthermore, transforming impervious surfaces into a “high-low-high” spatial distribution of impervious surface densities is the optimal design solution for impervious surfaces. In general, this study offers a novel perspective and approach to urban flooding mitigation, enabling comprehensive control of flooding from a global perspective.

Collaborative Optimized Design of Glazing Parameters and PCM Utilization for Energy-Efficient Glass Curtain Wall Buildings

Glass curtain walls (GCWs) have become prevalent in office buildings, owing to their lightweight and modular characteristics. However, their lower thermal resistance, compared to opaque walls, results in increased energy consumption. Incorporating phase-change materials (PCMs) provides a viable solution through which to address the susceptibility of GCWs to external conditions, thus enhancing thermal performance and mitigating energy concerns. This study delves into the influences of the glazing solar heat gain coefficient (SHGC), the glazing heat transfer coefficient (U-value), and PCM thickness on the energy performance of buildings. Using Design Builder (DB) software version 6.1.0.006, a multi-story office building was simulated in different climatic zones in China, covering the climatic characteristics of severe cold, cold, hot summer and warm winter, cold summer and winter, and mild regions. The simulation results quantitatively elucidated the effects of the glazing parameters and the number of PCMs on thermal regulation and energy consumption. A sensitivity analysis identified the glazing SHGC as the most influential factor in energy consumption. Additionally, by employing Response Surface Methodology (RSM), the researchers aimed to achieve a balance between minimal building energy consumption and economic cost, ultimately determining an optimal design solution. The results demonstrated significant energy savings, ranging from 20.16% to 81.18%, accompanied by economic savings, ranging from 15.78% to 79.54%, across distinct climate zones in China.

A holistic two-stage decision-making methodology for passive and active building design strategies under uncertainty

In the last decade, many studies focused on finding optimal design solutions considering trade-offs between different aspects of building design. Accordingly, multi-objective optimization (MOO) approaches have been increasingly applied in the building industry. However, certain aspects must be deepened to ensure a more effective decision-making process in the early planning phase. On the one hand, uncertainties should be considered before making decisions to ensure the robustness of the optimal solutions; on the other hand, decisions are made at different times in building planning, and the sequential order of making decisions should be modeled. This paper presents a holistic two-stage multi-objective stochastic optimization (MOSO)-II framework to minimize the environmental impact of global warming potential (GWP) and cost throughout the entire life cycle of a building (phases of A1-A3, B4, B6 and C3-C4), considering passive and active design strategies in two consecutive stages, under uncertainty. Herein, individual/use and political/market uncertainties are considered. As a proof of concept, the proposed framework is applied in a case study for a generic zone in a multi-family terraced residential building type with solid brick construction. The advantages of the proposed framework are validated by comparing it with alternative single-stage MOSO frameworks. Results show that the proposed two-stage MOSO-II framework can deliver a smaller range of solutions with a better performance in terms of lower GWP and cost. It indicates that the proposed framework can effectively assist planners in decision-making by reducing the effort in choosing the proper solution. Secondly, the results also emphasize the importance of passive design strategies in sustainable building planning. In addition, the energy mix structure and cost of energy sources should be carefully adjusted in the future to promote a more ecologically sustainable building design.

Assessment of the validity of using a radiant panel in the low-height floor heating

This paper examines the viability of using a radiant panel in lightweight low-height floor heating. In this structure, a dry screed with high thermal resistance is replaced by a low-resistance adhesive layer with reinforcement mesh. The absence of the radiant panel results in a reduction in heat output of 5%–17% for floor finishes with a thermal resistance of 0.001 m2K/W, 50%–54% for 0.05 m2K/W and 60%–62% for 0.1 m2K/W. A structure with a panel is characterised by a lower surface temperature amplitude (maximum 2.4 K) compared to a structure without it (8.8 K), which improves the comfort of floor users. The annual heating cost in a building equipped with the analysed structures was determined. The absence of the panel resulted in a cost increase of 13%, 37% and 79% for floor finish resistances of 0.001, 0.05 and 0.1 m2K/W, respectively. The SPBT for the purchase of radiant panels is 40 years for a floor resistance of 0.001 m2K/W, demonstrating the unprofitability of this solution. For resistances of 0.05 and 0.1 m2K/W, SPBT was 14 and 6 years, respectively. The choice of floor heating system should take into account the required heat output, thermal comfort and economic aspects. Practical application This paper assesses the validity of using radiant panels in a low-height floor heating structure, where the dry screed layer is replaced by an adhesive layer with reinforcement mesh. This structure is gaining popularity due to its quick installation and low height but has the disadvantage of the high panel cost. The paper evaluates the effect of panels on heat output and identifies the conditions under which their use is economically justified. Recipients of results are designers and contractors of heating systems. The recommendations can be the basis for selecting the optimum design solution for a lightweight floor heating system.

Machine Learning Aided Optimization of Non-Metallic Seals in Downhole Tools

Machine learning-based optimization analysis is conducted to improve the performance and reliability of non-metallic seals that undergo dynamic loading. Non-metallic seals, mostly made from elastomeric materials such as rubber, are widely used in the oil and gas industry due to their combination of hyper-elastic behavior and lack of plastic deformation under pressure and temperature cycles. However, these elastomeric materials could soften at elevated temperatures or during cyclic loading which makes them more prone to extrude from the seal region. For simple seals such as O-rings, thermoplastic anti-extrusion backup rings can be used to resist rubber extrusion. For more complex molded or bonded seals subject to cyclic loads and temperature cycles, minimizing the rubber damage and maintaining the seal integrity becomes more challenging as there may be inadequate room for backup rings. Additionally, the rubber may also experience repeated long stroke lengths during service which can cause surface abrasion. Non-metallic seals are extensively used in Sand Control Tools, a class of completion equipment. In the development of these molded and bonded seals, advanced optimization analysis techniques are important to make the seal design sufficiently robust to meet the stringent requirements of industry codes and regulations. Machine learning-based optimization analysis allows for increasing the seal performance and reliability. The Mullins effect is included in the elastomeric material model to capture the material softening effects under cyclic loading. Machine learning-based optimization starts with a feasible design space that is defined based on the controlling design parameters related to the geometry of the seal. Comprehensive Finite Element Analysis is performed on each design sample under the target cyclic load and temperature cycles. The key response parameters of the non-metallic seal are selected as the principal strain and the contact stress in the rubber which together indicate the damage and seal-ability states of the design. A Performance Objective Index is defined as a function of the key response parameters. The FEA results covering the entire design space provide the simulation-driven training data to build the surrogate model using multi-layer neural networks. The optimum design solution is found from the surrogate model which correlates the Performance Objective Index to the design parameters. The results of this study reveal that the performance of the seal is greatly affected by the ratio of the elastomeric seal volume to the gland volume into which the seal is molded. As opposed to the base design, the optimum design shows a solid performance with no signs of extrusion when the principal strain of the rubber is kept below the elongation limit throughout the load cycles, and when sufficient contact stress is developed to maintain the seal.

РАЦИОНАЛЬНЫЕ КОНСТРУКТИВНЫЕ РЕШЕНИЯ КРУГЛОГОДИЧНЫХ АНГАРНЫХ ТЕПЛИЦ

The subject of the study is year–round greenhouses of the hangar type for individual and farm farms. The costs of the construction and operation of greenhouses significantly affect the cost of agricultural products grown in them. Optimization of the design solutions of hangar greenhouses is important to reduce internal forces in the frame elements, reduce the material consumption and cost of greenhouses. The purpose of the study is to determine the optimal design solution for hangar greenhouses up to 6 m wide. The study was conducted with the help of the Russian certified software package "Lira-CAD". Calculated models of hangar greenhouses with arched, gable-roofed and lancet-shaped greenhouses with a width of 3 to 6 m have been developed. The design solutions of the greenhouses were developed taking into account the requirements of the code of rules SP 107.13330 "Greenhouses and greenhouses", based on the possibility of using polycarbonate sheets in enclosing structures. Comparative calculations of greenhouse structures in the Lira-CAD software package have been performed. The calculation results are analyzed. Conclusions have been drawn on optimizing the design solutions of hangar greenhouses up to 6 m wide. The study showed that among the considered types of greenhouses, the most rational in terms of metal consumption are lancet-shaped greenhouses. The specific consumption of metal in lancet greenhouses is 42..53% lower than in greenhouses with an arched roof, and 34...64% lower compared to greenhouses with a pitched roof. The specific consumption of metal decreases with an increase in the width of greenhouses, the greatest decrease in the specific consumption of metal is observed in greenhouses with a pitched roof. The most technologically advanced from the point of view of manufacturing and installation are greenhouses with a pitched roof, the manufacture of the frame of such greenhouses from galvanized steel pipes is possible in building conditions, and both polycarbonate and glass can be used for translucent fences.

ПРОЕКТИРОВАНИЕ СИСТЕМЫ ВОДОСНАБЖЕНИЯ И ВОДООТВЕДЕНИЯ C МИНИМАЛЬНЫМИ КАПИТАЛЬНЫМИ ЗАТРАТАМИ НА ПРИМЕРЕ ТЕПЛОВОЙ ЭЛЕКТРИЧЕСКОЙ СТАНЦИИ

The work is devoted to the design of water supply and sanitation systems for a low-power thermal power plant within the framework of an energy saving policy with minimal capital costs. The purpose of this work was to analyze the station’s needs for water resources, as well as identify sources of water supply with the selection of cost-effective methods of water treatment and recycling. As part of this work, the set goal was achieved through detailed design of the system on the AutoCAD software platform, which made it possible to accurately visualize all elements of the system. The most important and significant result in this work is the calculations and recommendations for optimizing design solutions taking into account economic feasibility. The significance of the results obtained will allow the implementation of these design solutions at mini stations with low power.

Selecting a modular mining laboratory in the design life cycle

Modular laboratories are becoming increasingly popular in mining operations due to their flexibility, mobility and cost-effectiveness. However, selecting a suitable modular laboratory can be a challenging task as many factors need to be considered throughout the project life cycle. This article provides a comprehensive analysis of the design phase of a temporary research facility, as well as consideration of management and organization to identify the optimal design solution. The authors discuss various project management methodologies such as lean manufacturing and quality management that can help optimize processes and improve efficiency. Key factors such as testing requirements, infrastructure availability, mobility, durability, and environmental considerations are examined. In addition, the article explores the benefits of temporary buildings to permanent buildings in a mining environment. Finally, the authors provide recommendations for selecting a modular laboratory that will provide efficiencies on the shop floor.

Multi-use of small performance halls – case studies of modern cultural facilities in Poland

Contemporary architecture is expected to meet not only functional goals and high aesthetic quality, but also to take into account changing social needs, which is in line with the trend towards sustainable building. It is therefore important to consider the idea of multifunctionality when implementing new developments, especially in small towns. The subject of this paper is the analysis of three buildings in Wieliczka, Dąbrowa and Pszczyna, in which the aspect of multifunctionality was considered differently. At the same time, the discussed examples will benefit researchers, investors and designers alike, as they present specific architectural aesthetic and technological solutions related to the realization of small auditoriums – a subject that is rarely dealt with in scientific publications. The main aim of this paper was to draw attention to the need for an individual approach to the design process and a multi-criteria search for optimal design solutions for the hall, moving away from standardization. Finally, the realization of small multifunctional performance space fits in with the idea of sustainable development, which is based on combining the needs of the present generation with the ability to meet the needs of future generations.

Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.

Evaluation of the casing strength reliability in deep gas well by taking into account the cement quality and strength uncertainty: Method, analysis and application

Casing damage is one of the most challenging engineering issues in deep gas wells, leading to significant economic losses and severe incidents such as gas leaks or high annular pressure. According to field data analysis, in addition to traditional factors like stress, temperature, or corrosion, cement sheath quality also impacts casing damage. Hence, this paper establishes a three-dimensional finite element model for casing-cement sheath -formation considering cement sheath quality. It discusses the impact of different cement sheath defects (such as void and eccentricity) on casing stress distribution under the influence of in-situ stresses, high-pressure fluids, and annular pressure. The analysis reveals that under adverse conditions, poor cement sheath quality exacerbates stress on the casing, increasing the risk of casing failure, with the degree of impact being cement void angle > cement eccentricity > cement void thickness. Based on this, a nonlinear fitting of changes in casing equivalent stress is conducted to establish a model for calculating casing external loads under cement void or eccentricity conditions. Considering the dual uncertainty of casing external load and strength parameters, a casing reliability evaluation method is established based on the external load calculation model and stress-strength interference theory. The reliability of casing strength is assessed and analyzed using a practical well in an oil field as an example. The results indicate that compared to traditional safety factor methods, the reliability evaluation method established in this paper can more intuitively and accurately display variations in casing reliability, offering a scientific basis for optimized design solutions. Optimizing cement sheath performance (such as reduced cement elastic modulus and Poisson's ratio) and enhancing cementing quality (by preventing cement voids and eccentricity) effectively reduce the risk of casing failure.

Levelized-cost optimal design of long-distance CO2 transportation facilities

With the need to develop carbon-neutral technologies and reduce greenhouse gas emissions to avert climate change, carbon capture, storage and utilization (CCS/CCUS) techniques have emerged as effective pathways to achieve these targets. Despite being a critical aspect of CCS/CCUS chains, carbon dioxide transport is an astonishingly under-researched domain. This study presents an efficient and robust computational model to cost-optimally design of long distance carbon dioxide transport pipelines (trunk) connecting a source-sink pair. Several physical and topographical conditions have been accounted for to propose optimal design solutions for different case studies. This involves right-sizing of pipes and booster stations and placing the latter at the right locations. The implications of uncertainties in modelling of the costs have also been studied. The model is very flexible and general in the sense that it can work with a variety of cost models and design constraints. Further, unlike most studies, an appropriate hydrodynamic model for pressure and density calculations has been incorporated. Genetic algorithm and interior point methods have been employed to find optimal design parameters that minimize the “per ton levelized transportation cost”, and their performances have been compared. Results demonstrate that pipe diameters and elevation changes have the most significant impact on the pressure drop, optimal design parameters and the cost. Lastly, several improvements have been made over our previous study in this domain.