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To cope with increasing, challenging requirements and shorter development cycles, more complex, often nonlinear, systems with high interactions have to be optimized in many fields of research, such as the energy sector. As this often goes beyond the classical parameter studies-based approach, systematic optimization approaches offer a key solution. In the context of the development of energy converters, like engines, such techniques are applied to enhance efficiency and enable optimal use of energy. This review provides a comprehensive overview of the field of optimization approaches, more precisely referred to as Metamodel-Based Design Optimization (MBDO). The MBDO approaches essentially comprise three main modules: the Design of Experiment (DoE), the Response Surface Modeling (RSM), and the Multiobjective Optimization (MoO), in varying compositions.

Further advancing key technologies requires the optimization of increasingly complex systems with strongly interacting parameters—like efficiency optimization in engine development for optimizing the use of energy. Systematic optimization approaches based on metamodels, so-called Metamodel-Based Design Optimization (MBDO), present one key solution to these demanding problems. Recent advanced methods either focus on Single-Objective Optimization (SoO) on local metamodels with online adaptivity or Multiobjective Optimization (MoO) on global metamodels with only limited adaptivity. In the scope of this work, a fully online adaptive (“in the loop”) optimization approach, capable of both SoO and MoO, is developed which automatically approximates the global system response and determines the (Pareto) optimum.