Optimization-based Design Research Articles

Optimization-based design of structure and operation parameters simultaneously for real-world distillation column based on rate-based model

Simultaneously optimizing the structure and operating parameters of distillation columns based on rigorous rate-based models is critical for various process intensification technologies to achieve their intended purpose in real-world distillation columns. However, efficiently optimizing a distillation column based on rate-based models while considering the column hydraulics is far from practical. The biggest obstacle in using rigorous models for distillation columns in an equation-oriented environment is ensuring a robust solution for large-scale, strongly coupled nonlinear models, along with the simultaneous optimization of integer and continuous variables. In this study, a new optimization framework was introduced. It incorporated a pseudo-transient simulation method to ensure convergence of the simulation in equation-oriented environment. Additionally, an efficient relaxation method transformed the mixed integer nonlinear programming problem into a nonlinear programming problem, facilitating simultaneous optimization of both the integer variable representing column structure and the continuous variables governing column operation. Three examples, including a complex intensified catalytic distillation column, were optimized to demonstrate the efficiency of the proposed framework. Specifically, its advantages in terms of mathematical optimality and industrial feasibility were illustrated.

Meta-heuristic optimization-based robust [formula omitted] controller design for active suspension systems subject to actuator saturation

This study focuses on designing robust H∞ controllers to mitigate actuator saturation-related issues of active suspension systems. Two design methods are presented: one utilizing full-state feedback and the other relying on measurable output feedback. The H∞ state-feedback control incorporates actuator saturation directly into the design, representing the control input with the saturation function in the state-space model. The resulting optimization-based design problem, derived from theoretical stability analyses, poses a non-convex NP-hard problem under bilinear matrix inequality (BMI) constraints. To address this challenge, the study proposes a meta-heuristic optimization-based technique, providing a straightforward yet effective method to manage the BMI problem. The scope extends to practical implementation considerations, where suspension systems may only utilize measurable output variables. The H∞ static output-feedback control case is explored by extending the design of a full-order state-feedback controller. Despite the BMI-related challenges entailed in this scenario, the proposed meta-heuristic optimization-based design technique demonstrates flexibility in managing BMI conditions, thus eliminating the need to modify the controller design methodology. A noteworthy contribution of this study is its consistent application of the same meta-heuristic optimization-based technique for designing both H∞ state-feedback and static output-feedback controllers. Experimental results validate the effectiveness of the proposed robust H∞ control strategies.

Topology Optimization-Based Design of Magnet System in Unilateral NMR Logging Sensor Considering Coil Efficiency

Topology Optimization-Based Design of Magnet System in Unilateral NMR Logging Sensor Considering Coil Efficiency

Optimization-based design exploration of building massing typologies—EvoMass and a typology-oriented computational design optimization method for early-stage performance-based building massing design

In the past decade, there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration. However, it remains challenging for designers to apply such optimization-based design explorations in practice. To address this issue, this paper introduces a design tool, called EvoMass, and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks. EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task, without necessitating advanced computational design skills. More importantly, it can provide architects with insights into the underlying performance implications, thereby enhancing early-stage performance-based design exploration. EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools, and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design. To demonstrate the efficacy of EvoMass, case studies derived from architectural design studio tasks, incorporating daylighting, solar exposure, and subjective design intents, and the result of a user survey are presented, which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.

Enhancing public transport accessibility: Exploring hybrid car sharing systems for improved connectivity

Transportation networks within a region are vital. They link individuals from their starting points to their destinations. Nonetheless, even highly effective transportation systems may not ensure optimal performance, service quality, or fairness for users unless they are extensively interconnected and accessible to as many people as possible. In this context, we present a planning framework for complementing public transit (PT) systems with other cost-effective mobility systems, such as car-sharing (CS) systems, to improve performance in terms of connectivity and accessibility. These metrics are evaluated by using the suitably introduced Connectivity and Accessibility Index (CAI). Specifically, in the present research, we first introduce a novel methodology for assessing the connectivity and accessibility values of existing PT systems. Then, the proposed approach provides an optimization-based design model for a hybrid CS system comprising both one-way (OW) and two-way (TW) models. To evaluate the capabilities of the proposed approach, a real-world case study of the PT system of the city of Trento (Italy) is evaluated.

Optimization-Based Design of Reinforced Concrete Cantilever Retaining Walls Considering Embodied Energy, Carbon Emissions, and Cost

Optimization-Based Design of Reinforced Concrete Cantilever Retaining Walls Considering Embodied Energy, Carbon Emissions, and Cost

An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study

The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11 % additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.

Cost Benefits of Net Zero Energy Homes in Australia

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance.

Optimization of a parametric product design using techno-economic assessment and environmental characteristics with application to an electric traction motor

Accelerating the development of clean energy technologies is required to move towards a sustainable future. New design approaches are being proposed to help accelerate these developments. This study proposes and improves a novel design framework that incorporates life cycle assessment (LCA) and techno-economic assessment (TEA) with an optimization-based design model of a discrete product at early stage. Objective functions for mass, energy consumption, and two metrics relating to manufacturer and consumer economic interests are considered; these objectives are minimized using a Pareto-like approach. An electric traction motor for an electric vehicle is used as a discrete product case study. The approach demonstrates the utility of considering four-objectives in the design problem, uncertainty analysis via Monte Carlo simulation, and the power of employing LCA and TEA methods. Future work may explore a broader range of objective functions to cover different challenges that inhibit the development of clean energy technologies.

Optimal design of a sector-coupled renewable methanol production amid political goals and expected conflicts: Costs vs. land use

Methanol is an important bulk chemical which can be produced from renewable resources with the currently available technologies and doing so has a high potential for greenhouse gas (GHG) emission reductions for the chemical sector. Reaching the targets for climate change mitigation and biodiversity protection will require designing not only renewable, but also land-efficient chemical production systems. However, under the current economic imperative of cost minimization, the efficient use of land is often not considered during production system design and compromise solutions are left undiscovered, the potential of land-use intensifying technologies stays unknown and the impact of their exclusion, e.g. due to political reasons, is not quantified. This study addresses these issues for renewable production of methanol through the biogas- and power-to-methanol production pathways, which are considered concurrently with processes allowing intensified land use (agrivoltaics, wheat straw anaerobic digestion, heat pumps, wind turbines, etc.) in a previously unexplored technological scope. The designs, as well as the effect of coupling with other production systems (residential heat, electricity, and food) as a strategy to reduce the total annualized costs (TAC) and direct land use, are investigated using the FluxMax approach, an optimization-based design methodology. It simultaneously accounts for the, often neglected, dynamics of renewable energy harvesting, waste-heat utilization and biomass production under the fluctuating renewable resource and product-demand conditions of an example location in Saxony-Anhalt, Germany. Reductions of the TAC of 19 %, direct land use of 9 % and GHG emissions of 12 % for the production system by coupling all four of these products were determined. Furthermore, Pareto fronts were constructed to quantify the trade-off between the conflicting objectives of minimizing TAC and direct land use, demonstrating that land use can be reduced by up to 10 % with minimal extra costs among the available technologies. Political conflicts and goals, which may influence the deployment of the considered technologies, are discussed from a political science perspective and highlight the need for further interdisciplinary collaboration.

ToRoS: A Topology Optimization Approach for Designing Robotic Skins

Soft robotics offers unique advantages in manipulating fragile or deformable objects, human-robot interaction, and exploring inaccessible terrain. However, designing soft robots that produce large, targeted deformations is challenging. In this paper, we propose a new methodology for designing soft robots that combines optimization-based design with a simple and cost-efficient manufacturing process. Our approach is centered around the concept of robotic skins---thin fabrics with 3D-printed reinforcement patterns that augment and control plain silicone actuators. By decoupling shape control and actuation, our approach enables a simpler and cost-efficient manufacturing process. Unlike previous methods that rely on empirical design heuristics for generating desired deformations, our approach automatically discovers complex reinforcement patterns without any need for domain knowledge or human intervention. This is achieved by casting reinforcement design as a nonlinear constrained optimization problem and using a novel, three-field topology optimization approach tailored to fabrics with 3D-printed reinforcements. We demonstrate the potential of our approach by designing soft robotic actuators capable of various motions such as bending, contraction, twist, and combinations thereof. We also demonstrate applications of our robotic skins to robotic grasping with a soft three-finger gripper and locomotion tasks for a soft quadrupedal robot.

Parametric design, life cycle assessment, and optimization of a discrete product with application to an electric traction motor

Humans are surrounded and even dependent on discrete products for their daily activities. Product design processes have been vastly studied towards a healthy economic prosperity and other technical objectives (e.g., low risk). However, recent studies show that these methods have been lacking in terms of the environmental challenges humanity faces in the present and future. This study proposes a novel design framework that incorporates a parametric life cycle assessment (PLCA) as part of the optimization-based design model of a discrete product at early stage. Electric traction motor design applied to an electric vehicle is used as a case study. The results of performing a multi-objective design problem and an uncertainty analysis using Monte Carlo simulation show the usefulness of incorporating the PLCA in a transparent manner, as required by the International Standards Organization. Future work suggests investigating the expansion of this technique to support a broader range of objective functions.

A topology optimisation-based design method for 3D Voronoi porous structures and its application for medical pillows

ABSTRACT This study introduces a novel approach to design non-uniform porous structures with gradient density through the integration of the Topology Optimisation (TO) method and the Voronoi porous structure design technique. With the homogenisation method of Voronoi structures, the density data derived from the TO process is converted into seed point distribution for Voronoi diagrams. The porous structure with controlled mechanical properties is constructed based on Voronoi diagrams using the surface mesh superposition method. Compared with uniform Voronoi porous structures, TO Voronoi porous structures exhibit improved strength and stability. The proposed method for generating non-uniform Voronoi structures in this study exhibits notable advantages in terms of simplicity of implementation and robustness. The surface mesh superposition method has advantages in model generation efficiency and accuracy. In addition, the TO Voronoi porous structure design method is applied to design medical pillows, showing significant advantages in shape retention, weight reduction, and personalisation.

Simulation-Driven and Optimization-Based Design of an Architectural Building: A Case Study of a Space Tourism Building in the UAE

In the constantly changing field of architectural design and meeting the evolving demands of the space tourism industry, this study presents a case study focused on the design of a space tourism building in Sharjah, United Arab Emirates (UAE). The main objective of this study is to showcase the integration of simulation-driven and optimization-based methodologies in architectural design to anticipate forthcoming challenges and resolve design-related issues or constraints. This study employs a range of computational tools to optimize various design parameters, including sun path, radiation, shadow, outdoor comfort, and wind simulation, to conduct a more thorough assessment in the process of choosing the best-optimized design. In addition, a finite element analysis (FEA) was carried out to gain insights into the structural behavior of the chosen design under diverse physical conditions. This integration marks a paradigm shift in how architectural projects are conceptualized, developed, and realized by addressing complex challenges and enhancing the functionality, sustainability, and performance of architectural buildings. The findings of this case study demonstrate and prove the potential of simulation-driven and optimization-based design approaches in determining the future of architectural designs. As the UAE strives to become a center for space-related activities, this research helps to drive the realization of cutting-edge, sustainable, and user-centric architectural solutions, paving the way for the next generation of space tourism facilities.

IDESS: a toolbox for identification and automated design of stochastic gene circuits.

One of the main causes hampering predictability during the model identification and automated design of gene circuits in synthetic biology is the effect of molecular noise. Stochasticity may significantly impact the dynamics and function of gene circuits, specially in bacteria and yeast due to low mRNA copy numbers. Standard stochastic simulation methods are too computationally costly in realistic scenarios to be applied to optimization-based design or parameter estimation. In this work, we present IDESS (Identification and automated Design of Stochastic gene circuitS), a software toolbox for optimization-based design and model identification of gene regulatory circuits in the stochastic regime. This software incorporates an efficient approximation of the Chemical Master Equation as well as a stochastic simulation algorithm-both with GPU and CPU implementations-combined with global optimization algorithms capable of solving Mixed Integer Nonlinear Programming problems. The toolbox efficiently addresses two types of problems relevant in systems and synthetic biology: the automated design of stochastic synthetic gene circuits, and the parameter estimation for model identification of stochastic gene regulatory networks. IDESS runs under the MATLAB environment and it is available under GPLv3 license at https://doi.org/10.5281/zenodo.7788692.

Topology optimization-based design, development and testing of steel-reinforced epoxy granite vertical machining centre column

The structural vibrations experienced by traditional machine tools made up of cast iron (CI) or cast steel material lead to premature failure of the cutting tool and poor surface finish on the manufactured components. Steel-reinforced epoxy granite (SREG), an alternate material to CI, improves the static and dynamic characteristics of the machine tool structure. However, the addition of steel reinforcement in SREG affects the damping characteristics of epoxy granite (EG). The present work focuses on optimizing both EG and steel reinforcement of the vertical machining centre column to achieve favourable damping characteristics. Initially, parametric analysis is performed to arrive at an optimum configuration of only the EG column. Then, steel reinforcement is integrated into the EG column, and topology optimization is carried out with the objective of maximizing the stiffness and natural frequencies of the SREG column while minimizing the mass of the reinforcement. The multiple design configurations for steel reinforcement evolved from topology optimization are numerically investigated using finite element analysis. The scaled-down prototype of the finalized model was fabricated, after which the dynamic characteristics of the proposed SREG column are measured and compared with that of existing CI and SREG columns. The results revealed six-fold and three-fold improvements in dynamic stiffness along X- and Y-directions, respectively, and a 33% reduction in the mass of steel reinforcement than the existing SREG column.

Technoeconomic assessment of solar technologies for the hybridization of industrial process heat systems using deterministic global dynamic optimization

The industrial process heat (IPH) sector provides an opportunity to replace terawatts of fossil fuel energy with solar alternatives, such as parabolic trough collectors (PTC) or photovoltaics (PV) with resistive heating and energy storage (ES) for off-peak deployment. PTCs offer significantly higher thermal efficiency, but have stagnated in cost and efficiency improvements over the past decade. In contrast, PVs have seen significant improvements in cost and efficiency. Additionally, the energy generated by PV can be stored electrochemically in batteries. Current optimization-based approaches for design and investment decision making lack guarantees of global optimality that are necessary to ensure the best-possible solutions are obtained for these general nonconvex dynamical models. We present dynamical models for the formal deterministic global optimization-based design and technoeconomic assessment of solar systems to hybridize IPH and reduce natural gas combustion that serve as a general and flexible framework. We compare hybridization strategies of PTC with thermal ES, PV with thermal ES, and PV with battery ES and explore the impacts of natural gas pricing and process scale. For the low- to medium-temperature IPH, PTC with thermal ES is currently at least four times more economically favorable than alternative technologies regardless of location or process scale.

Topology Optimization-Based Design of Topological Edge-State Wavelength Division Multiplexer

Previous research processes on topological insulators were mainly based on the manual adjustment of a large number of fine parameters to achieve the desired properties. Here, we propose to treat the design process of photonic crystals as an objective function through topological optimization theory, allowing the desired properties to arise spontaneously with the optimization. Through topology optimization, we have designed and implemented three topological photonic crystals with different topological characteristics and dual-band protection common bandgap. The results show that functional edge modes with desired propagation properties can be generated within these two bandgaps by constructing domain walls between photonic crystal structures with different topological phases. The unidirectional transmission characteristics of the wave can also be observed, which provides an easy way to implement a wavelength division multiplexer.

Optimization-Based Design and Control of Dynamic Systems

Dynamic systems such as robots, autonomous vehicles, and process plants require careful design and control to achieve optimal performance. This paper presents an integrated framework for the simultaneous optimization of system design parameters and control policies. The underlying mathematical formulation utilizes optimization techniques to find the best system configuration and control strategy according to specified objectives. These objectives may include metrics such as tracking error, energy consumption, safety margins, and cost. Both model-based and data-driven techniques are explored for learning the system dynamics and synthesizing optimal controllers. The optimization algorithms leveraged include gradient-based methods, evolutionary algorithms, and reinforcement learning. The benefits of the joint optimization approach are demonstrated through case studies on representative dynamic systems. Results show that the integrated design and control framework outperforms sequential optimization, leading to improved efficiency, responsiveness, and robustness. This underscores the importance of co-optimizing design and control parameters, especially for complex, uncertain systems. The proposed methods provide an effective tool for next-generation automated design of smart, adaptable systems.

Convex Optimization-Based Design of Sparse Arrays for 3-D Near-Field Imaging

A convex optimization-based method of sparse array synthesis (SAS) for wideband near-field millimeter-wave imaging is proposed by extending our previous work. We construct a monostatic sparse array synthesis optimization model from the electromagnetic propagation formula. The reweighted l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm decoding algorithm is utilized to enhance the sparsity. A modified iterative element weighting merging method is also proposed to put constraints on the minimum element spacing to synthesize a practicable sparse layout. Through the proposed SAS method, the customized sparse monostatic array for near-field imaging can be generated with different schemes, such as 1-D linear arrays, 2-D planar arrays, etc. The imaging performance of the synthesized planar sparse array, is studied by examining the properties of focusing, sidelobe-suppression, and grating lobe suppression both theoretically and by simulation. It is shown that the optimized array is superior to the arrays with equally spaced antennas or randomly spaced antennas, using approximately the same number of antenna elements. Experimental results further indicate the advantages of the optimized wideband sparse array through the three-dimensional imaging reconstruction.