This paper presents an approach to system-of-systems engineering for product development with the use of ontology. A proposed method for building as well as using ontology to generate and explore system-of-systems design spaces based on identified system-of-system needs is presented. The method is largely built to cover the first levels of related work, where a process for system of systems in the context of product development is introduced. Within this work, it is shown that scenarios for a system-of-systems can be used to identify needs and subsequently the system-of-systems capabilities that fulfils them. The allocation of capabilities to possible constituent systems is used to show the available design space. The proposed method of this paper therefore addresses these initial challenges and provides a framework for approaching the system-of-systems design space creation using ontology. A case study is used to test the method on a fictitious search and rescue scenario based on available resources and information from the Swedish Maritime Administration. The case study shows that a representation of a system-of-systems scenario can be created in an ontology using the method. The ontology provides a representation of the involved entities from the fictitious scenario and their existing relationships. Defined ontology classes containing conditions are used to represent the identified needs for the system-of-systems. The invocation of a description logic reasoner is subsequently used to classify and create an inferred ontology where the available system-of-systems solutions are represented as sub-classes and individuals of the defined classes representing the needs. Finally, several classes representing different possible system-of-systems needs are used to explore the available design space and to identify the most persistent solutions of the case study.

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This work illustrates how a proposed method can be used to create optimization frameworks for early conceptual design studies and to increase overall knowledge at an early design stage. The method is intended to facilitate concept selection in challenging domains that typically involve multidisciplinary design problems with contradictory requirements. The main focus of the work presented here is on the conceptual design of helicopters; however, the method is intended to be applicable to early design studies in other domains as well. In short, statistics about existing helicopters are collected and compiled to provide a basis for various regression analyses. The purpose of this is to unravel relationships in the data and to obtain simple estimation models from statistical regressions that can be used in conjunction with existing formulas and equations to generate an initial helicopter design estimate. Models for each discipline, such as aerodynamics, are then created using the outcomes of the regression analyses and existing equations. Lastly, the method is used to define a multidisciplinary design optimization framework incorporating all the models obtained from the different disciplines. A case study based on search and rescue operations is used to test the proposed framework in order to obtain possible first suggestions for the baseline design of a new general-purpose search and rescue helicopter.

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This paper illustrates how a Singular Value Decomposition (SVD) and regression analyses can be used to create estimation models for aircraft actuator components by use of industrial data. The estimation models are at the end used to show how an electromechanical actuator´s weight and size will evolve with respect to output force. An essential step in the early design of aircraft is to be able to predict the weight and size of a resulting concept. This weight and size typically include contributions of main components such as wing and fuselage. Weight and size estimations at this stage can also range down to components at a sub-system level, for example, the aircraft actuators. The weight and size of an actuator depends on many parameters, and it is desirable to understand any underlying relationship to make qualified estimations of an actuator’s characteristics. However, the knowledge about a design is often limited at an early design stage and the required information is not always available. Consequently, estimations must be made from limited information and desired properties of the actuator. One way to approach this problem is to use SVD. An SVD analysis determines the most influential parameters in a data set and uses these to create an estimation model that only requires a few inputs for estimating the remaining parameters in the data set. An SVD can thereby be used for both identifying the driving parameters in a statistical dataset of existing solutions and to estimate the characteristics of new designs to be developed.