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<div class="section abstract"><div class="htmlview paragraph">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. A combination of a new Design of Experiment (DoE) method for sampling points, Neural Networks as metamodel/Response Surface Model (RSM), and a Genetic Algorithm (GA) for global optimization performed on the RSM enables very high flexibility. Key features of the presented MBDO methodology are as follows: A new fully online, adaptive approach working in iterative loops combined with successive refinements of the RSM; Two novel boundary treatment approaches for handling arbitrarily complex constraints; A novel approach to automatically adapt the number of neurons of the Neural Network to the system complexity; An innovative uncertainty-based DoE method to maximize information gain for each new sample point; Comprehensive additional sampling strategies. Detailed benchmarks to popular DoE methods and MBDO approaches from the literature are conducted. The benchmarks show comparable to slightly better performance to current state-of-the-art SoO MBDO approaches with the significant benefit that a global RSM is obtained, providing valuable insight into the system behavior. Compared to state-of-the-art MoO MBDO approaches, the benchmark highlights a considerably better performance in terms of the needed number of samples (i.e., simulations or experiments), significantly fewer resources required, and high accuracy approximation of the Pareto front.</div></div>

<div class="section abstract"><div class="htmlview paragraph">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. Previous reviews primarily focused on a selection of these modules, whereas this novel review equally covers and structures the modules DoE, RSM, and MoO and their combination to MBDO approaches. Many examples of these modules and MBDO implementations and their interrelationship, strengths, and limitations are discussed in detail and supplemented with many exemplary methods, e.g., from engine development. Methods from previous reviews are collected and updated with recent approaches, e.g., including new machine learning methods used in this context. Moreover, this study presents a holistic, extended classification approach to structure any MBDO method. The classification, which is based on the existence, structure, and interactions of the modules DoE, RSM, and MoO, is applied to various MBDO approaches from the literature. One recent MBDO focus of research is the development of online adaptive approaches as these allow to use valuable information obtained during the optimization process to guide the DoE or MoO. Therefore, the online adaptivity, feedback loops, and strengths and limitations of MBDO approaches are a novel focus area of this review. Recommendations and requirements for future “Fully Online MBDO” approaches with enhanced adaptability and generalizability are derived.</div></div>