Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

Compositional Model Conversion

This dissertation presents an initial work towards the development of a technique to convert compositional models from one modelling paradigm to another, by means of a pair of equivalent compositional modelling domain theories. The mapping between model fragments of the two domain theories is not necessarily in a one-to-one manner. It might be the case that a model fragment in one domain theory covers parts of several model fragments in the other domain theory. This is one of the major conversion problems that this technique will focus on. The compositional modelling of ecological systems is used as a testing domain for the implemented conversion technique. For this work, system dynamics and object-oriented representations are the two modelling paradigms adopted. The major intention of this conversion application, implemented in the C++ programming language, is to convert a system dynamics model, composed through a compositional modelling technique, to an object-oriented model. The resulting object-oriented model is expected to reflect the same scenario, but with a different representation, compared to the model produced within the system dynamics modelling paradigm

Simulation Approach Selection in Reservoir Management

Rapid evolution of technologies in petroleum industry in last decades has significantly improved our abilities in hydrocarbon reservoirs development. The number and complexity of tasks to be solved by reservoir engineers are gradually increasing, while the cost of field development projects is rising. In this conditions, optimal decision-making in reservoir management becomes critical since it might result in either significant benefit or financial loss to a production company. Although a significant improvement was made in project risk management to control project costs in the case of unfavorable outcome, reservoir evaluation still plays the important role and affect entire reservoir management and production process. Since the work of petroleum engineers actively involves reservoir simulation and target search for optimal solution of the particular reservoir assessment problems, selection of the most appropriate simulation approach in a timely manner is important. Successful search for suitable solution to a particular reservoir engineering problem is always a non-trivial task since it involves analysis and processing of large amounts of data and requires professional expertise in the subject area. In this work we proposed an expert system, what provide flexible framework for the proper simulation approach selection and involves thorough data analysis, multiple constraints handling, expert knowledge utilization, and intelligent output requirements implementation. This expert system utilizes linguistic method of the pattern recognition theory for knowledge base design and inference engine implementation, what significantly simplifies procedures of the system design and provides it with tuning flexibility. This thesis elaborates on major aspects of the expert system design in close relation to data processing and recommended solution finding methods. To validate the expert system’s applicability, several tests were designed based on the synthetic Brugge field case and real petroleum reservoir data. These tests demonstrate functionality of the major expert system elements and advantages of selected implementation methods. Based on obtained results we can conclude successful development of the expert system for appropriate simulation approach selection

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

Simulation product fidelity: a qualitative & quantitative system engineering approach

La modélisation informatique et la simulation sont des activités de plus en plus répandues lors de la conception de systèmes complexes et critiques tels que ceux embarqués dans les avions. Une proposition pour la conception et réalisation d'abstractions compatibles avec les objectifs de simulation est présentée basés sur la théorie de l'informatique, le contrôle et le système des concepts d'ingénierie. Il adresse deux problèmes fondamentaux de fidélité dans la simulation, c'est-à-dire, pour une spécification du système et quelques propriétés d'intérêt, comment extraire des abstractions pour définir une architecture de produit de simulation et jusqu'où quel point le comportement du modèle de simulation représente la spécification du système. Une notion générale de cette fidélité de la simulation, tant architecturale et comportementale, est expliquée dans les notions du cadre expérimental et discuté dans le contexte des abstractions de modélisation et des relations d'inclusion. Une approche semi-formelle basée sur l'ontologie pour construire et définir l'architecture de produit de simulation est proposée et démontrée sur une étude d'échelle industrielle. Une approche formelle basée sur le jeu théorique et méthode formelle est proposée pour différentes classes de modèles des systèmes et des simulations avec un développement d'outils de prototype et cas des études. Les problèmes dans la recherche et implémentation de ce cadre de fidélité sont discutées particulièrement dans un contexte industriel.In using Modeling and Simulation for the system Verification & Validation activities, often the difficulty is finding and implementing consistent abstractions to model the system being simulated with respect to the simulation requirements. A proposition for the unified design and implementation of modeling abstractions consistent with the simulation objectives based on the computer science, control and system engineering concepts is presented. It addresses two fundamental problems of fidelity in simulation, namely, for a given system specification and some properties of interest, how to extract modeling abstractions to define a simulation product architecture and how far does the behaviour of the simulation model represents the system specification. A general notion of this simulation fidelity, both architectural and behavioural, in system verification and validation is explained in the established notions of the experimental frame and discussed in the context of modeling abstractions and inclusion relations. A semi-formal ontology based domain model approach to build and define the simulation product architecture is proposed with a real industrial scale study. A formal approach based on game theoretic quantitative system refinement notions is proposed for different class of system and simulation models with a prototype tool development and case studies. Challenges in research and implementation of this formal and semi-formal fidelity framework especially in an industrial context are discussed