Yes / This paper introduces a novel method referred to as Enhanced Sequence Diagram (ESD) to support rigorous functional modelling of complex multidisciplinary systems. The ESD concept integrates an exchanges based functional requirements reasoning based on a coherent graphical schema, integrated with the system operational analysis based on a sequence diagram. The effectiveness of the method to support generic function modelling of complex multidisciplinary systems at the early conceptual design stages is discussed in conjunction with an electric vehicle powertrain example, followed by an assessment of potential impact for broader application of the method in the industry.

Yes / This paper introduces an Enhanced Sequence Diagram (ESD) as the basis for a structured framework for the functional analysis of complex multidisciplinary systems. The ESD extends the conventional sequence diagrams (SD) by introducing a rigorous functional flow-based modelling schemata to provide an enhanced basis for model-based functional requirements and architecture analysis in the early systems design stages. The proposed ESD heuristics include the representation of transactional and transformative functions required to deliver the use case sequence, and fork and join nodes to facilitate analysis of combining and bifurcating operations on flows. A case study of a personal mobility device is used to illustrate the deployment of the ESD methodology in relation to three common product development scenarios: (i) reverse engineering, (ii) the introduction of a specific technology to an existent system; and (iii) the introduction of a new feature as user-centric innovation for an existing system, at a logical design level, without reference to any solution. The case study analysis provides further insights into the effectiveness of the ESD to support function modelling and functional requirements capture, and architecture development. The significance of this paper is that it establishes a rigorous ESD-based functional analysis methodology to guide the practitioner with its deployment, facilitating its impact to both the engineering design and systems engineering communities, as well as the design practice in the industry.

Doctor of Philosophy / Department of Electrical and Computer Engineering / James E. DeVault / Prakash Krishnaswami / Most state-of-the-art systems are multidisciplinary in nature and encompass a wide range of components from domains such as electronics, mechanics, hydraulics, etc. Design considerations and design parameters of the system can come from any or a combination of these domains. The traditional optimization approach for multidisciplinary systems utilizes sequential optimization, wherein each subsystem is optimized in isolation in a predetermined order, assuming that the designs of the other subsystems remain fixed. This often leads to system designs that are suboptimal. In recent years emphasis has been placed on development of an integrated scheme for analysis and design of multidisciplinary systems. An important aspect is the software architecture required to support such a scheme. This dissertation presents MIXEDMODELS (Multidisciplinary Integrated eXtensible Engine for Driving Metamodeling, Optimization and DEsign of Large-scale Systems) - a unified analysis and design tool for multidisciplinary systems that is based on a procedural, symbolic-numeric architecture. This architecture offers great modeling flexibility at the component level, allowing any engineer to add components in his/her domain of expertise to the platform in a modular fashion. The symbolic engine in the MIXEDMODELS platform synthesizes the system governing equations as a unified set of nonlinear differential-algebraic equations (DAEs). These equations are differentiated with respect to design variables to obtain an additional set of DAEs that describe the sensitivity coefficients of the system state variables. This combined set of DAEs is solved numerically to obtain the solution for the state variables and the state sensitivity coefficients of the system. Finally, knowing the system performance functions, their design sensitivity coefficients can be calculated by using the values of the state variables and state sensitivity coefficients obtained from the DAEs. For ease in error control and software implementation, sensitivity analysis formulation described in this work uses direct differentiation approach as opposed to the adjoint variable approach. The MIXEDMODELS capabilities are demonstrated through several numerical examples and the results indicate that the MIXEDMODELS formulation and architecture is effective in terms of accuracy, modeling convenience, computational efficiency, and the ability to simulate the behavior of a general class of multidisciplinary systems.

Cette étude s’intéresse à la conception systémique intégrant simultanément les questions de dimensionnement et de gestion optimale de l'énergie. Le système étudié concerne un procédé de pompage intégrant un processus de dessalement d’eaux saumâtres alimenté par des sources de puissance hybrides renouvelable incluant un minimum de stockage électrochimique. Ce cas d’étude appartient à une classe typique de systèmes autonomes alimentés par des sources intermittentes dont profil de puissance a une forme "donnée" : « selon les conditions climatiques (ensoleillement, vent), avec un minimum de stockage d’électricité, la puissance offerte doit être convertie ou stockée hydrauliquement sous peine d’être gaspillée ». L'influence des conditions d'environnement et la robustesse du processus d’optimisation est enfin aussi discutée dans cette thèse. Deux types de modèles mathématiques, dynamiques et quasi-statiques, sont mis en œuvre pour décrire l'ensemble du dispositif. Le système est tout d’abord modélisé dynamiquement par Bond Graphs. Pour une simulation plus rapide, plus adaptée à l’optimisation globale du système, un modèle quasi-statique est créé pour être simulé dans l'environnement Matlab. Pour de tels dispositifs, étant donné une certaine puissance offerte au fil du vent et du soleil, trouver le point optimal de fonctionnement à chaque période consiste en un partage de puissance entre les sous systèmes de pompage et de traitement de l’eau : ce processus est plutôt complexe compte tenu des non linéarités (courbes rendement – puissance) et de la présence de nombreuses contraintes relatives aux limitations de puissance des pompes, aux conditions de niveau des réservoirs, ainsi qu’aux limitations de pression et de débit dans les processus hydrauliques (pompes osmoseur). Nous montrerons qu’il n’est pas si trivial de choisir une fonction objectif qui assure simultanément la performance et la robuste du système vis-à-vis des conditions d’environnement : une fonction objectif robuste quel que soit le profil de puissance des sources est ainsi proposée pour mettre en œuvre une gestion optimale de l’énergie. Le problème d’optimisation étant posé sous forme standard, consistant en la maximisation d’une fonction objectif sous contraintes, des approches d’optimisation efficaces par programmation non linéaire sont employées. La question du dimensionnement et son couplage à la gestion énergétique est finalement étudiée. En particulier, l’intérêt de la modularité des systèmes, considérant plusieurs pompes connectées en parallèle pour la même fonction, est investigué. / This study focuses on systemic design, integrating simultaneously issues of sizing and optimal energy management. The system under study consists of a pumping process including a brackish water desalination system fed by hybrid renewable power sources with minimum electrochemical storage. Such a device belongs to the class of “autonomous systems” supplied by intermittent sources whose power profile has a “given” waveform: “with minimum electrical storage, power has to be converted, stored in water tanks, or wasted following climatic (sun, wind) conditions”. Influence of environment conditions and robustness of the optimization process is then also discussed in this thesis. Both dynamic and quasi static models are implemented for representing the whole system. The device is firstly modeled dynamically by Bond Graph methodology. For faster simulations, which are more suitable for system optimization, a quasi static model is created to be simulated in the Matlab environment. For such systems, given a certain source power, finding optimal operation point at each period consists of a power sharing between all pumping devices: it is a complex process with huge nonlinearities (efficiency vs power curves) and with many constraints as for the limitation of pump powers, tank level conditions, or pressure and flow limitations in hydraulic network and pumping devices. It is not so trivial to define an objective function which ensures system performance and robustness versus environment conditions: a convenient objective function, whatever the input power profile, is then proposed to implement the optimal management. The optimization problem being mathematically expressed, consisting of objective function maximization under constraints, efficient optimization methods by non linear programming are implemented. The issue of sizing and its coupling with system management efficiency is finally studied. In particular, the interest of modular operation with several pumps connected in parallel is also concerned in this research.