Qualitative Fault Detection and Hazard Analysis Based on Signed Directed Graphs for Large-Scale Complex Systems

Nowadays in modern industries, the scale and complexity of process systems are increased continuously. These systems are subject to low productivity, system faults or even hazards because of various conditions such as mis-operation, equipment quality change, externa

Integrated design optimization methods for optimal sensor placement and cooling system architecture design for electro-thermal systems

Dynamic thermal management plays a very important role in the design and development of electro-thermal systems as these become more active and complex in terms of their functionalities. In highly power dense electronic systems, the heat is concentrated over small spatial domains. Thermal energy dissipation in any electrified system increases the temperature and might cause component failure, degradation of heat sensitive materials, thermal burnouts and failure of active devices. So thermal management needs to be done both accurately (by thermal monitoring using sensors) and efficiently (by applying fluid-based cooling techniques). In this work, two important aspects of dynamic thermal management of a highly dense power electronic system have been investigated. The first aspect is the problem of optimal temperature sensor placement for accurate thermal monitoring aimed toward achieving thermally-aware electrified systems. Strategic placement of temperature sensors can improve the accuracy of real-time temperature distribution estimates. Enhanced temperature estimation supports increased power throughput and density because Power Electronic Systems (PESs) can be operated in a less conservative manner while still preventing thermal failure. This work presents new methods for temperature sensor placement for 2- and 3-dimensional PESs that 1) improve computational efficiency (by orders of magnitude in at least one case), 2) support use of more accurate evaluation metrics, and 3) are scalable to high-dimension sensor placement problems. These new methods are tested via sensor placement studies based on a 2-kW, 60Hz, single-phase, Flying Capacitor Multi-Level (FCML) prototype inverter. Information-based metrics are derived from a reduced-order Resistance-Capacitance (RC) lumped parameter thermal model. Other more general metrics and system models are possible through application of a new continuous relaxation strategy introduced here for placement representation. A new linear Programming (LP) formulation is presented that is compatible with a particular type of information-based metric. This LP strategy is demonstrated to support the efficient solution of finely-discretized large-scale placement problems. The optimal sensor locations obtained from these methods were tested via physical experiments. The new methods and results presented here may aid the development of thermally-aware PESs with significantly enhanced capabilities. The second aspect is to design optimal fluid-based thermal management architectures through enumerative methods that help operate the system efficiently within its operating temperature limits using the minimum feasible coolant flow level. Expert intuition based on physics knowledge and vast experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This work also presents a design framework supporting comprehensive exploration of a class of single-phase fluid-based cooling architectures. The candidate cooling system architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow distribution problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components having variable sets of heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4,051 architectures. In addition, cooling system architectures capable of steady-state operation under a given loading are identified. Optimization of the cooling system design has been done subject to a representative mission, consisting of multiple time-varying loads. Work presented in this thesis clearly shows that the transient effects of heat loads are expected to have important impacts on design decisions when compared to steady-state operating conditions

Hierarchical power management in vehicle systems

This dissertation presents a hierarchical model predictive control (MPC) framework for energy management onboard vehicle systems. High performance vehicle systems such as commercial and military aircraft, on- and off-road vehicles, and ships present a unique control challenge, where maximizing performance requires optimizing the generation, storage, distribution, and utilization of energy throughout the entire system and over the duration of operation. The proposed hierarchical approach decomposes control of the vehicle among multiple controllers operating at each level of the hierarchy. Each controller has a model of a corresponding portion of the system for predicting future behavior based on current and future control decisions and known disturbances. To capture the energy storage and power flow throughout the vehicle, a graph-based modeling framework is proposed, where vertices represent capacitive elements that store energy and edges represent paths for power flow between these capacitive elements. For systems with a general nonlinear form of power flow, closed-loop stability is established through local subsystem analysis based on passivity. The ability to assess system-wide stability from local subsystem analysis follows from the particular structure of the interconnections between each subsystem, their corresponding controller, and neighboring subsystems. For systems with a linear form of power flow, robust feasibility of state and actuator constraints is achieved using a constraint tightening approach when formulating each MPC controller. Finally, the hierarchical control framework is applied to an example thermal fluid system that represents the fuel thermal management system of an aircraft. Simulation and experimental results clearly demonstrate the benefits of the proposed hierarchical control approach and the practical applicability to real physical systems with nonlinear dynamics, unknown disturbances, and actuator delays

A framework for the control of electro-thermal aircraft power systems

This dissertation presents a hierarchical controller framework that utilizes model predictive controllers at multiple time scales in order to manage the operation of aircraft power systems. With current and next generation aircraft replacing traditional power systems with electrically powered components, the coupling between an aircraft engine, electrical system, and thermal management system is becoming increasingly more complex. This presents a unique control problem that requires coordination between the generation, distribution, and consumption of power on board an aircraft, while also maintaining performance guarantees for various systems. The proposed hierarchical control framework splits decision making into multiple levels with each level having a unique update rate. At upper levels, controllers are designed with prediction horizons that can estimate plant performance one hour into the future. Using this extended prediction horizon, the upper level controllers generate references to pass down the hierarchy to lower level controllers. At the lower levels, controllers focus on tracking references from high level controllers while also mitigating high-frequency disturbances. The combination of slow update, long prediction horizon controllers with fast update, short prediction horizon controllers enables the hierarchical control framework to achieve excellent performance and disturbance rejection. A candidate aircraft power system is developed in MATLAB/Simulink using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a baseline logic and PI controller. Controllers are evaluated on figures of merit including specific fuel consumption, thermal endurance, and remaining thermal capacitance at the end of a mission. Results show that the proposed control approach is capable of making thermally-conscious electrical system decisions to help reduce the amount of waste heat generated by the aircraft in order to achieve mission success