Efficient Process for Multidisciplinary Optimization of Electric Drive Units: A Case Study on Enhancing Design Efficiency

<div class="htmlview paragraph">In this paper the development of statistical metamodels and statistical fast running models is presented first. They are utilized for propagating uncertainties in a multi-discipline design optimization process. Two main types of uncertainty can be considered in this manner: uncertainty due to variability in design variables or in random parameters; uncertainty due to the utilization of metamodels instead of the actual simulation models during the optimization process. The value of the new developments and their engagement in multi-discipline design optimization is demonstrated through a case study. An underwater vehicle is designed under four different disciplines, namely, noise radiation, self-noise due to TBL excitation, dynamic response due to propulsion impact loads, and response to an underwater detonation. The case study also demonstrates the value of the multi-discipline design optimization in identifying a system level optimum, and it emphasizes the importance of including uncertainties in the design optimization process.</div>

The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

<div class="htmlview paragraph">Cost reduction is an essential and continuous process to meet the competitive environment of product pricing and to increase the profitability of an organization. Integrated cost reduction exercise encompass all the possible avenue., e.g. Alternate material & it's procurement system, batch size, material handling, manufacturing process improvements, logistics and finally review & make changes to derive an optimum design and cost reductions to maximum extent.</div> <div class="htmlview paragraph">This paper presents a systematic approach for achieving the cost reduction of an existing 5.7Tonnne LCV truck frame from design consideration. The methodology involves, identification of the components of the frame for the cost reduction, redesign of existing frame compatible to the cost reduction need, preliminary performance evaluation of the present frame and the cost reduction frame, redesign of the frame for the performance, detailed cost evaluation, prototype manufacturing, laboratory testing, vehicle level testing and final release for the mass production. Chassis designers contributed for major design changes to derive maximum cost reduction without compromising durability or performance deterioration due to bending or torsional stiffness changes from existing frame.</div> <div class="htmlview paragraph">After a comprehensive study, it is identified that deletion of the boxing plate and the replacement of the tubular cross members with channel type cross members will contribute for the potential cost savings of the frame. With these inputs concept design of the cost reduction frame is arrived.</div> <div class="htmlview paragraph">The important performance characteristics of a chassis frame are structural integrity (Strength and Stiffness) and durability. The existing and the cost reduction frames are evaluated based on these performance characteristics.</div> <div class="htmlview paragraph">The final design of the cost reduction frame is arrived based on CAE analysis of the frame for structural integrity, Number of iterative operations got eliminated due to the use of multidisciplinary optimization (MDO) package. Different load cases which focus on strength and stiffness of the frame are considered as an example.</div> <div class="htmlview paragraph">Bending and torsional stiffness of the existing and the cost reduction frames are also measured in the test rig. It is found that bending stiffness of the cost reduction frame is 9.15% more than the existing frame and torsional stiffness of the cost reduction frame is closer to the existing frame. It was also found that the durability of the existing and the cost reduction frame in bending is almost same. The test specifications are arrived based on strain measurements on the existing frame in our torture track.</div> <div class="htmlview paragraph">The above methodology helped us in deriving a estimated cost saving of Rs.311/- by the modified frame design. There are other intangible benefits like savings in labour cost due to elimination of the boxing plate, savings in the processing cost of boxing plate welding etc. The estimated cost savings is likely to be double of the above amount and would result in 3.5% of the Chassis frame aggregate cost.</div>

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

Effect of Variable Selection on Multidisciplinary Design Optimization: a Flight Vehicle Example

Multidisciplinary design optimization of long-range slender guided rockets considering aeroelasticity and subsidiary loads

The optimal design of multidisciplinary systems under uncertainty involves three types of iterative analyses- multidisciplinary analyses, reliability analyses, and optimization. If these processes are carried out in a nested manner in reliability based multidisciplinary design optimization (RBMDO), the computational expense can be enormous. Computationally efficient simultaneous analysis and design (SAND) or All-AtOnce methods have been developed earlier for deterministic MDO. Single loop RBDO methods have also been developed to integrate reliability analysis and optimization in a single loop. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints. The formulation allows for solution with a single design optimization loop by using the KKT optimality conditions of the first-order reliability method (FORM) as constraints in the design optimization along with disciplinary consistency constraints. Alternative solution processes which remove the FORM MPP’s from the decision space and many constraints from the problem are developed. The alternative solution methods are more efficient and more robust than using nonlinear programming to solve the single loop formulation. A numerical example is solved with the two proposed solution algorithms. I. Introduction In the design of multidisciplinary systems it is often necessary to make decisions under uncertainty. This requires that multidisciplinary systems modeling be integrated with uncertainty analysis and optimization. A common example of such an engineering design application would be the design of an aircraft wing under uncertain aerodynamic loading conditions, with multiple structural components with uncertain capacities, such that the entire structural system achieves a prescribed level of reliability. The problem has coupled physics, in that the pressure distribution is dependent upon the deflection, and the deflection is dependent on the pressure distribution. Such designs have been performed using multidisciplinary optimization (MDO). Other examples where MDO may be applied include electronics packaging, in which the disciplines of heat transfer and circuit analysis are coupled, the design of new types of aircraft and integrating them into an existing fleet of aircraft, and even the management of interconnected homeland infrastructures. While MDO has matured with the development of different types of algorithms for various types of problems and design organizations, there has been little work to incorporate reliability analysis into MDO. However, system and component reliabilities are often a major concern in the design of multidisciplinary systems. MDO can be computationally expensive due to the need to achieve consistency across multiple disciplinary analysis codes; each disciplinary analysis code may be computationally expensive to run. Further, the optimal design of multidisciplinary systems under uncertainty involves three types of iterative procedures- multidisciplinary analyses, reliability analyses, and optimization. The integration of these three procedures is referred to in this paper as reliability based multidisciplinary design optimization (RBMDO). If these processes are carried out simultaneously, the computational expense can be enormous. Computational savings can often be achieved with simultaneous analysis and design (SAND) or All-At-Once methods, which allow for the solution of the multidisciplinary analyses and the optimization in one single loop. Single loop RBDO methods allow for the integration of reliability analysis and optimization in a single loop. Applying SAND and single-loop RBDO together may allow for large computational savings when applied to problems in RBMDO. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints based on the SAND method for MDO and by use of the KKT conditions of the first order reliability method (FORM). The resulting formulation may be solved as a standard nonlinear programming problem. While the single loop formulation presented in this paper is a solution of the RBMDO problem, robustness and efficiency are improved through two improved algorithms which can remove the MPP’s from the decision space, thereby significantly reducing the optimizer’s workload.

Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods. The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance. A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.

Design of haptic devices requires trade-off between many conflicting requirements, such as high stiffness, large workspace, small inertia, low actuator force/torque, and a small size of the device. With the traditional design and optimization process, it is difficult to effectively fulfill the system requirements by separately treating the different discipline domains. To solve this problem and to avoid sub-optimization, this work proposes a design methodology, based on Multidisciplinary Design Optimization (MDO) methods and tools, for design optimization of six degree-of-freedom (DOF) haptic devices for medical applications, e.g. simulators for surgeon and dentist training or for remote surgery. The proposed model-based and simulation-driven methodology aims to enable different disciplines and subsystems to be included in the haptic device optimization process by using a robust model architecture that integrates discipline-specific models in an optimization framework and thus enables automation of design activities in the concept and detail design phase. Because of the multi-criteria character of the performance requirements, multi-objective optimization is included as part of the proposed methodology. Because of the high-level requirements on haptic devices for medical applications in combination with a complex structure, models such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), and kinematic models are considered to be integrated in the optimization process and presenting a systems view to the design engineers. An integration tool for MDO is used as framework to manage, integrate, and execute the optimization process. A case study of a 6-DOF haptic device based on a TAU structure is used to illustrate the proposed methodology. With this specific case, a Multi-objective Genetic Algorithm (MOGA) with an initial population based on a pseudo random SOBOL sequence and Monte Carlo samplings is used for the optimization.

Multidisciplinary design optimization (MDO) problems are engineering design problems that require the consideration of the interaction between several design disciplines. Due to the organizational aspects of MDO problems, decomposition algorithms are often the only feasible solution approach. Decomposition algorithms reformulate the MDO problem as a set of independent subproblems, one per discipline, and a coordinating master problem. A popular approach to MDO problems is bilevel decomposition algorithms. These algorithms use nonlinear optimization techniques to solve both the master problem and the subproblems. In this paper, we propose two new bilevel decomposition algorithms and analyze their properties. In particular, we show that the proposed problem formulations are mathematically equivalent to the original problem and that the proposed algorithms converge locally at a superlinear rate. Our computational experiments illustrate the numerical performance of the algorithms.

<div class="section abstract"><div class="htmlview paragraph">Conventional charging systems for electric and plug-in hybrid vehicles currently use cables to connect to the grid. This methodology creates several disadvantages, including tampering, risk, depreciation and non-value added user efforts. Loose or faulty cables may also create a safety issue. Wireless charging for electric vehicles delivers both a simple, reliable and safe charging process. The system enhances consumer adoption and promotes the integration of electric vehicles into the automotive market. Increased access to the grid enables a higher level of flexibility for storage management, increasing battery longevity.</div><div class="htmlview paragraph">The power class of 3.7kW or less is an optimal choice for global standardization and implementation, due to the readily available power installations for potential customers throughout the world. One of the key features for wireless battery chargers are the inexpensive system costs, reduced content and light weight, easing vehicle integration.</div><div class="htmlview paragraph">This paper demonstrates a wireless charging design with minimal component content. It includes a car pickup coil with 300 mm side length and low volume and mass 1.5 dm<sup>3</sup> power interface electronics. After an overview of its hardware requirements, power transfer and efficiency benefits are presented, providing the anticipated horizontal and lateral deviations.</div><div class="htmlview paragraph">An intense magnetic field is required to transfer the target power at low volumes between the transfer units. This field heats up any metal object over the transfer coil, similar to an induction oven. Consequently, the system should be powered down whenever a metal object is detected in this area. A Foreign Object Detection (FOD) design has been developed to continuously monitor the critical high field area. Device testing results are also provided.</div><div class="htmlview paragraph">Field characteristics are verified alongside the vehicle, ensuring system safety for living beings; compliant with all applicable standards reference limits which is more challenging than the basic limits [<span class="xref">13</span>].</div></div>

In order to reduce the cost of oceanographic exploration, a new underwater vehicle is designed to sail the required distance with the lowest energy consumed. Since the new underwater vehicle is a complicated multidisciplinary system, it is firstly decomposed into four smaller disciplines and then a multidisciplinary design optimization (MDO) problem is built based on these disciplines. The Multidisciplinary Feasible (MDF) architecture is adopted as the solution strategy to this optimization problem considering that it is easily implemented and a multidisciplinary feasible solution is always guaranteed throughout the optimization process. To solve this optimization problem efficiently, the coupled adjoint method is firstly introduced to improve the efficiency of gradient calculation and then a discipline-merging method is proposed to further enhance the computational efficiency. After this, the discipline-merging method is verified against the finite difference method in two aspects of solution accuracy and computational costs and the results show it is an effective and high efficient gradient calculation method. Finally, the multidisciplinary design optimization of the new underwater vehicle is performed efficiently under the MDF architecture with the discipline-merging method to calculate gradients.

In order to design a vehicle body with high strength and high stiffness, a multi-disciplinary design process should include careful consideration of multi-disciplinary design constraints to properly account for vehicle static stiffness (bending/torsional), durability, Noise/Vibration/Harshness (NVH), crash worthiness, light weight vehicle structure during the early stage of vehicle design process. With this approach, fast development of new vehicle body structures can be achieved with minimal number of iterations to match the conflicting design goals from each discipline. In the current research, a multi-disciplinary design optimization (MDO) based on a meta model is developed and refined to apply for the design of body structure. In an effort to apply the MDO for vehicle body structure, 4 phase procedures were established in the current research. In Phase I, a base model is created. In Phase II, an effect analysis is carried out. In Phase III, a meta model is created. Finally in Phase IV, using the optimization algorithm, the meta model created in Phase III is eventually refined through the process of optimization. In this research, static stiffness (bending / torsional), dynamic stiffness (1st torsion mode) were used for constrained conditions and the mass minimization was the object function for optimization.

<div class="section abstract"><div class="htmlview paragraph">Electric vehicles (EVs) are a promising and proven technology for achieving sustainable mobility with zero carbon emissions, very low noise pollution, and reducing the dependency on fossil fuels.</div><div class="htmlview paragraph">Global EV sales have been increasing by ~110 % since 2015, with a significant rise in 2021 (~6.75 mils EV registered) mainly led by China, the US, and Europe, amplifying the EV market share to 8.3% compared to 4.2% in 2020.</div><div class="htmlview paragraph">Future developments aimed at designing better batteries and charging technologies that reduce charging time, reduce initial battery cost, and increased flexibility. In India, EVs are emerging significantly due to stringent Carbon di Oxide (CO2) reduction drives, increasing crude oil prices, and the availability of cheaper renewable energy.</div><div class="htmlview paragraph">Leveraging government promotional policies, evolving the entire ecosystem, globally advantageous manufacturing costs, and competitive engineering skills form the perfect blend for India. To produce world-class EVs to complement the “Make in India” drive and make India the leading EV engineering and manufacturing hub for global markets.</div><div class="htmlview paragraph">This paper reviews India’s EV market potential with a key focus on the EV ecosystem, including EV commodities infrastructure, supplier plans, and government initiatives to enhance EVs in India with statistical data and reports from research papers.</div></div>

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.