Holistic Design Optimization Research Articles

A Holistic Design Optimization Method for LLC Converters in Light Electric Vehicle Chargers

A Holistic Design Optimization Method for <i>LLC</i> Converters in Light Electric Vehicle Chargers

Isogeometric sizing and shape optimization of 3D beams and lattice structures at large deformations

A computational method for optimizing the shape of the centerline curve and the spatial variation of geometric and material sizing parameters of the cross-sections of elastic, 3-dimensional beams and beam structures subject to large deformations is presented in this work. The approach is based on the concept of isogeometric analysis, i.e., the representation of geometry and the discretization of the numerical solution using spline functions. Here, mixed isogeometric collocation methods are used to discretize the geometrically exact 3D beam model. These spline representations are extended to the parameterization of the design variables, which are the initial centerline curves of the beams, as well as cross-sectional sizing properties, which may be varying along the beam axis and can be functionally graded through the cross-sections. To tailor the mechanical deformation behavior of a beam or beam structure, a nonlinear optimization problem is formulated and solved using gradient-based methods. For this purpose, all required gradients and sensitivities are derived analytically. The potential of this holistic design optimization approach is demonstrated in application to tailoring of elastic metamaterials and beam lattice structures, as well as 4D printing of multi-material laminate beams.

A Holistic Multi-Objective Design Optimization Approach for Arctic Offshore Supply Vessels

This article presents a new holistic multi-objective design approach for the optimization of Arctic Offshore Supply Vessels (OSVs) for cost- and eco-efficiency. The approach is intended to be used in the conceptual design phase of an Arctic OSV. It includes (a) a parametric design model of an Arctic OSV, (b) performance assessment models for independently operating and icebreaker-assisted Arctic OSVs, and (c) a novel adaptation of the Artificial Bee Colony (ABC) algorithm for multi-objective optimization of Arctic OSVs. To demonstrate the feasibility and viability of the proposed optimization approach, a series of case studies covering a wide range of operating scenarios are carried out. The results of the case studies indicate that the consideration of icebreaker assistance significantly extends the feasible design space of Arctic OSVs, enabling solutions with improved energy- and cost-efficiency. The results further indicate that the optimal amount of icebreaking assistance and optimal vessel speed differs for different vessels, highlighting the motivation for holistic design optimization. The applied adaptation of the ABC algorithm proved to be well suited and efficient for the multi-objective optimization problem considered.

Conceptual Design Optimization of Autonomous Electric Buses in Public Transportation

Autonomous electric buses (AEB) have widely been envisioned in future public transportation systems due to their large potential to improve service quality while reducing operational costs. The requirements and specifications for AEBs, however, remain uncertain and strongly depend on the use case. To enable the identification of the optimal vehicle specifications, this paper presents a holistic design optimization framework that explores the impacts of implementing different AEB concepts in a given set of routes/network. To develop the design optimization framework, first, a multi-objective, multi-criteria objective function is formulated by identifying the attributes of bus journeys that represent overall value to the stakeholders. Simulation models are then developed and implemented to evaluate the overall performance of the vehicle concepts. A genetic algorithm is used to find the concepts with the optimal trade-off between the overall value to the stakeholders and the total cost of ownership. A case study is presented of a single bus line in Singapore. The results show an improvement in the waiting time with the use of a smaller sized AEB with a capacity of 20 passengers. However, the costs and emissions increase due to the requirement of a larger fleet and the increase in daily distance traveled compared to a 94-passenger capacity AEB.

Experimental validation and microstructure characterization of topology optimized, additively manufactured SS316L components

The integration of topology optimization (TO) and additive manufacturing (AM) has the potential to revolutionize modern design and manufacturing. However, few instances of manufactured optimized designs are documented, and even fewer examples of experimentally-tested designs are available. The lack of validation combined with the influence of AM process on material properties leaves a gap in our understanding of process-microstructure-property relationships that is essential for developing holistic design optimization frameworks. In this work, a functional design was topologically optimized and fabricated using both directed energy deposition (DED) and selective laser melting (SLM) methods. This is the first direct comparison of these AM methods in the context of TO. Mechanical properties of SS316L and the optimized components in as-fabricated and heat-treated conditions were investigated under uniaxial displacement-controlled tensile loading and compared to finite element modeling (FEM) predictions. Optimized samples provided regions of both compressive and tensile loading in the test specimen. Experimental results showed the FEM predictions to be conservative. Microstructural analysis revealed that this difference is due to refined microstructures formed during the additive manufacturing process that strengthen the material in regions with high stress levels. Moreover, SLM samples showed higher yield strength compared to DED samples due to a more refined grain size and denser dislocation structures. TO results are sensitive to the AM method, post-processing conditions, and differences in mechanical properties. Thus, a TO for AM framework can be best optimized with the incorporation of microstructure features to account for localized microstructural variations in fabricated components.

Design optimization and control approach for a solar-augmented industrial heating

Process level integration of solar energy could give an economically feasible solution if the industrial process allows its practical integration. The solar-augmented industrial process behaves as a complex system influenced by uncertainty of solar radiation, variability of demand temperature and process time schedule as well as possibility of thermal stratification in the storage. Addressing these issues to reach the most economical solution has two dimensions to it. First, the solar thermal system needs to be optimally designed. This requires the development of a performance criterion that will deliver maximum solar energy to the industrial process, avoid large variations of energy in the storage, and meet investment constraints. Second, the identified optimal system should be dynamically controlled to enable uniform heat distribution and efficient auxiliary heat utilization. This paper presents a holistic design optimization and control approach for a solar-augmented industrial process to facilitate decision support. The proposed solution is designed and optimized for a dyeing industrial process case study that resulted in a 5.7 year payback period, 56.3% solar fraction, and 252.2 tons equivalent carbon emission reduction. Furthermore by implementing dynamic control, about 12.4% increase in solar gain that led to a 5.6% reduction in payback period is identified.

Approaching low-energy high-rise building by integrating passive architectural design with photovoltaic application

Building envelopes can highly impact the building energy demand and indoor environmental quality, so that the application of novel façade systems such as photovoltaics has been widely investigated. However, few study has addressed the interactive effect between photovoltaic (PV) application and traditional passive architectural design strategies, which is thoroughly discussed in this comparative study using a holistic design optimization process. The holistic design optimization approach combines screening-based and variance-based sensitivity analyses with the non-dominated sorting genetic algorithm-II (NSGA-II) and hybrid generalized pattern search particle swarm optimization (HGPSPSO). The impact of the light-to-solar gain ratio (LSG) is evaluated as one of the key factor to combine the passive design and PV glazing based on a comprehensive glazing database. Through an exhaustive sensitivity analysis (SA), the Morris method is proved to be efficient and robust in factor prioritizing only when the required minimum sampling size is satisfied. The window to ground ratio showed much greater impact on the net building energy demand when PV applications are coupled with all available vertical façades. Furthermore, the necessary particles for specified design input dimensions are determined for the optimal performance of HGPSPSO. With the optimum design configuration, the net building demand can be reduced by up to 71.36% under the hot summer and warm winter condition of Hong Kong. Research findings from this study can be used to develop low-energy building guidelines and building integrated PV applications in early planning stages.

Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context

This research thoroughly explored the impact of archetypes and confounding factors on a proposed holistic design optimization approach for high-rise office buildings with integrated photovoltaic (PV) facades. The design optimization adopts the hybrid generalized pattern search particle swarm optimization (HGPSPSO) algorithm, which is incorporated with qualitative and quantitative sensitivity analyses for factor prioritizing and fixing. Different archetypes are modelled by changing floor plan sizes and shapes, while diverse urban contexts and internal load (heat gain) levels are investigated as major confounding factors beyond designers' control. Variation of these four simulation scenarios are then used to examine the uncertainty of sensitivity indices and optimization potential for passive architectural design parameters. The window geometry, thermal and optical properties are proved to be most important to the reference PV envelope design. The building plan shape is found to have little impact on the weighting of different design parameters, while the shape coefficient (SC) is determined to be almost linearly correlated with the HVAC (heating, ventilation and air-conditioning) demand. The office design with the highest shape coefficient can therefore achieve a net energy demand reduction up to 48.77%. The floor plan size also has minor impact on the sensitivity index for each design factor, but the energy-saving potential grows with decreasing floor sizes. On the contrary, confounding factors can greatly change the sensitivity analysis (SA) result. The window U-value becomes more important with an increasing internal load level and urban context density whereas the impact of the window light-to-solar gain ratio is reduced by peripheral shading. Furthermore, varying confounding factors can even change the dimension of optimization problems based on different factor fixing results. This research can provide early-stage design guidance for energy efficient buildings with a comprehensive analysis of pragmatic building archetypes, background contexts and operation scenarios.

Multidisciplinary design optimization architecture to concurrent design of satellite systems

The design of space systems is a complex and multidisciplinary process with multiple conflicting objectives, large number of design variables, and constraints that limits application of the existing multidisciplinary design optimization architectures to this class of design problems. This paper presents an enhanced multidisciplinary design optimization architecture to concurrent holistic design optimization of a satellite system. The proposed multidisciplinary design optimization architecture extends concepts of multidiscipline feasible and bi-level integrated system synthesis into a unified architecture using metamodels. The proposed architecture was evaluated and compared with the existing multidisciplinary design optimization architectures that include all-at-once, bi-level integrated system synthesis, and multidisciplinary design optimization using a remote sensing small satellite in low earth orbit. The satellite design optimization problem deals with the minimization of the total mass of the satellite, involving disciplines of mission analysis, payload, structures, attitude determination and control, communication, command and data handling, power and thermal. The computational performance and accuracy of the proposed architecture were compared with multidisciplinary design optimization benchmark problems. Then the proposed architecture is successfully applied to the satellite system design problem. The results obtained show that metamodel-based bi-level integrated system synthesis-multidisciplinary design optimization architecture presented in this paper provides an effective way of solving large-scale design problems.

Holistic Design Optimization in Mechatronics

Design of modern mechatronic systems can be an intimidating task. The underlying problem lies in that several different engineering domains merge into one product, creating integration issues. Commonly used design methodologies are based on optimizing the different domains separately; hence creating a suboptimal final system. This paper contributes to the field by describing and discussing a holistic approach to design and optimize mechatronic products, especially useful in an early design phase. Specifically, this paper extends previously published work by taking control aspects into account, as well as enabling the use of multiple optimization criteria. The design approach described is based on using simplified, static models, to dimension and describe physical properties of structure and transducer components, while transfer function models are used for control design and behavioral modeling. To enable time-efficient optimization, computationally inexpensive ways to evaluate structural and behavioral properties are sought. An evolutionary optimization algorithm is used to evaluate the component models and by doing that derive an optimal solution.