Gradient-Based Tradeoff Design for Engineering Applications

This research presents a formal method for gradient based tradeoff design including methods that extend to cases with singularities and cases with more performance characteristics then design variables. The goal is to find revised design variables that can achieve the targeted performance characteristics and remove any violations to the constraint functions. The tradeoff design problem is formulation in the framework of the Sequential Quadratic Programming and is solved using the gradient based method. The optimal solution is the search direction, s, which represents the most effective way to reduce the current objective and correct the current violation. In this research the search direction is broken up into two parts, ��1 and ��2. Where ��1 can reduce the objective function or functions without changing the value of the constraints and ��2 is responsible to reduce the constraint violations. Additionally, a scalar factor �� is introduced in the search direction to produce a search direction that can achieve the targeted change in the objective function. This paper presents a new method to calculate alpha to adjust the cost function instead of reducing the penalized objective function. The details of the mathematical formulation are presented and discussed here, along with three design examples. The first demonstrative example is the design of a cubic box, the second is a control problem with three targeted eigenvalues, and the third is the design of an I-beam. The design examples demonstrate and validate the use of single objective approach, the constraint only approach and the multi-objective approach. These examples also show that smaller changes produce better results, an iterative process can achieve more accurate results, additional performance characteristics can be added during the design process, methods for handle cases with linearly dependent constraint functions, and, finally, methods for handling cases with more performance characteristics then design variables. Additionally, the third example includes the use of a finite element method demonstrating that this method can be extended to finite element applications

Computational Augmentation of Model Based System Engineering: Supporting Mechatronic System Model Development with AI Technologies

Efforts in applying computational support for automatic design synthesis and configuration generation as well as efforts to support descriptive and computational model development for system design and verification has been approached with semantic formalisation of modelling languages and of generic structural and functional concepts using meta-models. Modelling the system using descriptive models helps the designer to explicitly document dependencies between properties and parameters of system and external entities. The descriptive models thus produced often do not consider physics based justification for presence and/or absence of relations. It is often the case, the simulation results obtained at later stages requires changing requirements as well as modifying logical (modelling relations between high level functions parameters/properties and parameters/properties of high level entities) and physical architectures (modelling relations between component’s parameters and properties) to accommodate those requirements. The current MBSE (Model Based System Engineering) tools have capabilities to verify construction of models according to predefined model formats i.e. meta-models. However, these tools and current research in augmenting capabilities of these tools lacks the focus on evaluating content inside the models i.e. whether the system modelled by models represents a system that can be physically realized. This work has tried to avail the potential of available AI (Artificial Intelligence) technologies for assisting modelling activities performed for requirement definition and analysis, architecture design and verification phase of system development process by directing designer to tools that can formalise outputs of model development activities. The proposed problem formulation is based on the insight that a system modelled at both conceptual and detailed design level can be represented by logical and mathematical relations between the properties and parameters of internal and external components or functions of the system and domain. Therefore formulation defines concepts used in requirement, logical architecture and physical architecture models using relation between parameters and properties in those models. Concepts, such as operational requirements (or non-functional requirements for particular use case scenario), are defined through the usage of sets and linking value domains of those sets to particular system application domain for which system model is being developed. These relations enables systematic elaboration of requirements into logical and physical architecture models as well as storage and retrieval of existing model knowledge using existing AI tools. A novel framework has been developed to retrieve existing descriptive structure and function models using logical reasoning as well as to retrieve existing simulation models stored in embedding space of auto-encoder neural network. Beside adopting the concepts of semantic formalisation and meta-model based descriptive knowledge retrieval it utilises novel application of unsupervised representation learning capability of neural network auto-encoders to store known physically and technologically feasible designs in low dimensional representation that cluster similar designs therefore inducing similarity or distance metric that can be used to retrieve the known design with similar behaviour as new required behaviour. Framework also enable application of generic and domain specific logical constraints (as other works has done before) and introduces new concept of system application domain to ensures that at every stage of the model development leading to conceptual physical design architecture stays inside the physical constraints as per system usage domain. The instantiated meta-model elements which are classified to a system application domain (SAD) are implicitly constraint by system usage context constraints (e.g. parameter value restriction), similarly known simulation models can also be categorised to different SADs. The proposed framework extends the conventional approach of automated design synthesis which is only based only on decomposition of high level function (summarizing input to output mapping) into basic functions and selecting components to realize those basic functions. "A system is designed with the aim that it can execute its function(s) as per performance requirements of that function(s) in required operational conditions"- By concentrating on this statement it can be seen that conventional approach of functional decomposition and function allocation to known structural components cannot guarantee to yield a working system in required scenarios by ignoring the dependencies between environment or operating conditions and operating modes of prospective designs satisfying high level function. The results obtained from the implementation of domain specific knowledge representation and retrieval (involving mixture of numerical and logical constraints) as well as the results obtained from implementation of neural network auto-encoder for representation and retrieval of domain specific simulation model demonstrates the viability of these technologies to support the proposed framework

Optimising cost and availability estimates at the bidding stage of performance-based contracting

Performance-Based Contracting (PBC), e.g. Contracting for Availability (CfA), has been extensively applied in many industry sectors such as defence, aerospace and railway. Under PBC, complex support activities (e.g. maintenance, training, etc.) are outsourced, under mid to long term contracting arrangements, to maintain certain level of systems’ performance (e.g. availability). However, building robust cost and availability estimates is particularly challenging at the bidding stage because therei is lack of methods and limited availability of data for analysis. Driven by this contextual challenge this PhD aims to develop a process to simulate and optimise cost and availability estimates at the bidding stage of CfA. The research methodology follows a human-centred design approach, focusing on the end-user stakeholders. An interaction with seven manufacturing organisations involved in the bidding process of CfA enabled to identify the state-of-practice and the industry needs, and a review of literature in PBC and cost estimation enabled to identify the research gaps. A simulation model for cost and availability trade-off and estimation (CATECAB) has been developed, to support cost engineers during the bidding preparation. Also, a multi-objective genetic algorithm (EMOGA) has been developed to combine with the CATECAB and build a cost and availability estimation and optimisation model (CAEOCAB). Techniques such as Monte-Carlo simulation, bootstrapping resampling, multi-regression analysis and genetic algorithms have been applied. This model is able to estimate the optimal investment in the attributes that impact the availability of the systems, according to total contract cost, availability and duration targets. The validation of the models is performed by means of four case studies with twenty-one CfA scenarios, in the maritime and air domains. The outcomes indicate a representable accuracy for the estimates produced by the models, which has been considered suitable for the early stages of the bidding process

Evolutionary Spacecraft Design Using a Generalized Component-Resource Model

A new framework is proposed for modeling complex multidisciplinary systems as a collection of components and resource flows between them. The framework is developed for modeling and optimizing conceptual spacecraft designs. Its goal is to remain sufficiently general to address any space mission without modification of the developed model or code. Spacecraft are modeled as a collection of components and the resources that flow between them. New missions can be considered and capabilities added by simply adding components and resources. Constraints can be imposed on a component basis or system-wide, and are based on the flow of the resources within the system. Additionally, the proposed component-resource model and framework can address many complex systems engineering problems beyond spacecraft design by a similar implementation. Design optimization is performed by a genetic algorithm utilizing a variable length genome. This allows the algorithm to represent the variable number of components that could be present in a system design, enabling a more open-ended design capability than previous frameworks of this nature. Systems are evaluated through a user-defined simulation, and results can be presented in any trade space of interest based on the designs' performance in the simulation. We apply the framework to the design of a simple Earth orbiting, data gathering mission, as well as to the design of low Earth orbit active debris removal spacecraft constellations