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The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs.

Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints.

All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error.

The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm.

The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases.

Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.

As an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.

This study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.

First, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.

The scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

The purpose of this paper is to study the consideration of uncertainty from analysis modules for aircraft conceptual design by implementing uncertainty-based design optimization methods. Reliability-Based Design Optimization (RBDO), Possibility-Based Design Optimization (PBDO) and Robust Design Optimization (RDO) methods were developed to handle uncertainties of design optimization. The RBDO method is found suitable for uncertain parameters when sufficient information is available. On the other hand, the PBDO method is proposed when uncertain parameters have insufficient information. The RDO method can apply to both cases. The RBDO, PBDO and RDO methods were considered with the Multidisciplinary Design Optimization (MDO) method to generate conservative design results when low fidelity analysis tools are used.

Methods combining MDO with RBDO, PBDO and RDO were developed and have been applied to a numerical analysis and an aircraft conceptual design. This research evaluates and compares the characteristics of each method in both cases.

The RBDO result can be improved when the amount of data concerning uncertain parameters is increased. Conversely, increasing information regarding uncertain parameters does not improve the PBDO result. The PBDO provides a conservative result when less information about uncertain parameters is available.

The formulation of RDO is more complex than other methods. If the uncertainty information is increased in aircraft conceptual design case, the accuracy of RBDO will be enhanced.

This research increases the probability of a feasible design when it considers the uncertainty. This result gives more practical optimization results on a conceptual design level for fabrication.

It is RBDO, PBDO and RDO methods combined with MDO that satisfy the target probability when the uncertainties of low fidelity analysis models are considered.

The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer.

The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer.

Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers.

The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

The purpose of this paper is to present an example of pedagogical strategy, called innovation pedagogy, and study whether its learning environments (activating teaching and learning methods, working life orientation and research, development and innovation (RDI) integration, multidisciplinary learning environments, flexible curricula, entrepreneurship and internationalization) can be associated with students’ innovation competences (creativity, critical thinking, initiative, teamwork and networking).

In this case study, the electronic self-assessment questionnaire was distributed to third- and fourth-year bachelor students (n=236) from one Finnish university of applied sciences at the end of the Spring semester in 2017.

Two profiles of students concerning their level of innovation competences can be identified. The level of students’ innovation competences is associated with all the six elements of learning environments. The more students have experience with learning environments of innovation pedagogy, the higher they scored when assessed for their innovation competences.

Because of the case study setting and a limited sample, there are limitations to the generalizability of the findings.

Focusing on different levels of innovation competences of students and approaching their study path in more detail, it could be better understood how to develop more effective education, and thus, respond to the demands of an innovation society. This study extends approaches on research in education and innovation and strengthens the understanding that learning environments should be versatile and include many-sided learning opportunities. It also shows that implementing pedagogical strategy needs lot of work to be revealed in practice.

Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model.

The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated.

The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations.

This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm.

The paper proposed a fast procedure for solving the reliability-based robust design optimization (RBRDO) by modifying the RBRDO formulation and transforming it into a series of RBRDO subproblems. Then for each subproblem, the objective function, constraint function and reliability index are approximated using Taylor series expansion, and their approximate forms depend on the deterministic design vector rather than the random vector and the uncertain estimation in the inner loop of RBRDO can be avoided. In this way, it can greatly reduce the evaluation number of performance function. Lastly, the trust region method is used to manage the above sequential RBRDO subproblems for convergence.

As is known, RBRDO is nested optimization, where the outer loop updates the design vector and the inner loop estimate the uncertainties. When solving the RBRDO, a large evaluation number of performance functions are needed. Aiming at this issue, the paper proposed a fast integrated procedure for solving the RBRDO by reducing the evaluation number for the performance functions. First, it transforms the original RBRDO problem into a series of RBRDO subproblems. In each subproblem, the objective function, constraint function and reliability index caused are approximated using simple explicit functions that solely depend on the deterministic design vector rather than the random vector. In this way, the need for extensive sampling simulation in the inner loop is greatly reduced. As a result, the evaluation number for performance functions is significantly reduced, leading to a substantial reduction in computation cost. The trust region method is then employed to handle the sequential RBRDO subproblems, ensuring convergence to the optimal solutions. Finally, the engineering test and the application are presented to illustrate the effectiveness and efficiency of the proposed methods.

The paper proposes a fast procedure of solving the RBRDO can greatly reduce the evaluation number of performance function within the RBRDO and the computation cost can be saved greatly, which makes it suitable for engineering applications.

The standard deviation of the original objective function of the RBRDO is replaced by the mean and the reliability index of the original objective function, which are further approximated by using Taylor series expansion and their approximate forms depend on the deterministic design vector rather than the random vector. Moreover, the constraint functions are also approximated by using Taylor series expansion. In this way, the uncertainty estimation of the performance functions (i.e. the mean of the objective function, the constraint functions) and the reliability index of the objective function are avoided within the inner loop of the RBRDO.

The purpose of this paper is to improve the framework of classical collaborative optimization (CCO) so as to solve the multi-disciplinary optimization problems with parametric and parameter-free variables, and therefore an improved collaborative optimization (ICO) is proposed.

To clarify the relation of design variables, the optimization problem is classified into three general case. For each case, the respective treatment is suggested for coupled or uncoupled variables in the framework of the ICO.

The decoupling treatment suggested in the ICO framework not only avoids the iteration divergence and thus optimization failure, but increases the optimal design space to some extent. The method is validated by optimizing an aircraft assembly and a high-speed train assembly.

The two practical examples proves that the present ICO succeeds in solving the problem that the CCO failed to, also gives the optimal results better than those from the sequential optimization method.

Collaboration between universities and industry is increasingly perceived as a vehicle to enhance innovation. Educational institutions are encouraged to build partnerships and multidisciplinary projects based around real-world open problems. Projects need to benefit student learning, not only the organisations looking for innovations. The context of this study is a multidisciplinary innovation project, as experienced by the students of an University of Applied Sciences in Finland. The purpose of this paper is to unfold students’ conceptions of the learning experience, to help teachers and curriculum designers to organise optimal conditions and processes, and support competence development. The research question was: How do students in higher professional education experience their learning in a multidisciplinary innovation project?

The study took a phenomenographic approach. The data were collected in the form of weekly diaries, maintained by the cultural management and social services students (n=74) in a mandatory multidisciplinary innovation project in professional higher education in Finland. The diary data were analysed using thematic inductive analysis.

The results of the study revealed that students’ understood the learning experience in relation to solvable conflicts and unusual situations they experienced during the project, while becoming aware of and claiming their collaborative agency and internalising phases of an innovation process. The competences as learning outcomes that students could name as developed related to content knowledge, different personal characteristics, social skills, emerging leadership skills, creativity, future orientation, social skills, technical, crafting and testing skills and innovation implementation-related skills, such as marketing, sales and entrepreneurship planning skills. However, future orientation and implementation planning skills showed more weakly than other variables in the data.

The findings suggest that curriculum design should enable networked, student-led and teacher supported pedagogical innovation processes that involve a whole path from future thinking and idea development through prototyping to implementation planning of the novel solution. Teachers promote deep comprehension of the innovation process, monitor and ease the pain of conflict if it threatens motivation, offer assessment tools and help in recognising gaps in individual competences and development needs, promote more future-oriented, concrete and implementable outcomes, and facilitate in bridging from innovation towards entrepreneurship planning.

The multidisciplinary innovation project described in this study provides a pedagogical way to connect higher education to the practises of society. These results provide encouraging findings for organising multidisciplinary project activities between education and working life. The paper, therefore, has significant value for teachers and entrepreneurship educators in designing curriculum and facilitating projects. The study promotes the dissemination of innovation development programmes in between education and work organisations also in other than technical and commercial fields.

In this paper, a turbine blade was optimized by multidisciplinary design optimization (MDO) method. This turbine blade optimization is based on the optimization frame software iSIGHT, in which four disciplines (aerodynamics, thermal dynamics, structural mechanics and structural dynamics) have been integrated. Two commercial discipline analysis soft wares, NUMECA and ANSYS, are coupled in the platform iSIGHT. The three dimensional (3‐D) model of a blade was firstly parameterized. And then a set of parameters are chosen to optimize the blade to obtain the better overall properties. The result shows that the overall performances of the turbine blade have been improved remarkably after it is optimized by using the MDO method.