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.