In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems.

For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level. We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.

<div>In this thesis, we propose a model to predict the interfacial delamination in a flexible solar cell. The interface in a multilayer Copper Indium Gallium Diselenide (CIGS) flexible solar cell was studied applying the principles of fracture mechanics to a fixed-arm-peel test. </div><div>The principles of fracture mechanics ( J-integral and cohesive model) were implemented in a finite element software to compare the experimental with the numerical peeling force. A fixed-arm-peel test was used to obtain the peeling force for different peeling angles. This peel force and material properties from the CIGS solar cell were processed in several non-linear equations, so the energy required to start the delamination was obtained.The accuracy of the model was compared by fitting the experimental and numerical peeling force, which had a difference of 0.08 %. It is demonstrated that the peeling process for 90-degree could be replicated in COMSOL® software for a CIGS solar cell.</div>

ABSTRACT Multidisciplinary Design Optimization of an Extreme Aspect Ratio HALE UAV Bryan J. Morrisey Development of High Altitude Long Endurance (HALE) aircraft systems is part of a vision for a low cost communications/surveillance capability. Applications of a multi payload aircraft operating for extended periods at stratospheric altitudes span military and civil genres and support battlefield operations, communications, atmospheric or agricultural monitoring, surveillance, and other disciplines that may currently require satellite-based infrastructure. Presently, several development efforts are underway in this field, including a project sponsored by DARPA that aims at producing an aircraft that can sustain flight for multiple years and act as a pseudo-satellite. Design of this type of air vehicle represents a substantial challenge because of the vast number of engineering disciplines required for analysis, and its residence at the frontier of energy technology. The central goal of this research was the development of a multidisciplinary tool for analysis, design, and optimization of HALE UAVs, facilitating the study of a novel configuration concept. Applying design ideas stemming from a unique WWII-era project, a “pinned wing” HALE aircraft would employ self-supporting wing segments assembled into one overall flying wing. The research effort began with the creation of a multidisciplinary analysis environment comprised of analysis modules, each providing information about a specific discipline. As the modules were created, attempts were made to validate and calibrate the processes against known data, culminating in a validation study of the fully integrated MDA environment. Using the NASA / AeroVironment Helios aircraft as a basis for comparison, the included MDA environment sized a vehicle to within 5% of the actual maximum gross weight for generalized Helios payload and mission data. When wrapped in an optimization routine, the same integrated design environment shows potential for a 17.3% reduction in weight when wing thickness to chord ratio, aspect ratio, wing loading, and power to weight ratio are included as optimizer-controlled design variables. Investigation of applying the sustained day/night mission requirement and improved technology factors to the design shows that there are potential benefits associated with a segmented or pinned wing. As expected, wing structural weight is reduced, but benefits diminish as higher numbers of wing segments are considered. For an aircraft consisting of six wing segments, a maximum of 14.2% reduction in gross weight over an advanced technology optimal baseline is predicted.

Current exercise machines create resistance to motion and dissipate energy as heat. Some companies create ways to harness this energy, but not cost-effectively. The Energy Harvesting from Exercise Machines (EHFEM) project reduces the cost of harnessing the renewable energy. The system architecture includes the elliptical exercise machines outputting power to DC-DC converters, which then connects to the microinverters. All microinverter outputs tie together and then connect to the grid. The control system, placed around the DC-DC converters, quickly detects changes in current, and limits the current to prevent the DC-DC converters and microinverters from entering failure states. An artificial neural network learns to mitigate incohesive microinverter and DC-DC converter actions. The DC-DC converter outputs 36 V DC operating within its specifications, but the microinverter drops input resistance looking for the sharp decrease in power that a solar panel exhibits. Since the DC-DC converter behaves according to Ohm’s Law, the inverter sees no decrease in power until the voltage drops below the microinverter’s minimum input voltage. Once the microinverter turns off, the converter regulates as intended and turns the microinverter back on only to repeat this detrimental cycle. Training the neural network with the back propagation algorithm outputs a value corresponding to the feedback voltage, which increases or decreases the voltage applied from the resistive feedback in the DC-DC converter. In order for the system to react well to changes on the order of tens of microseconds, it must read ADC values and compute the output neuron value quicker than previous control attempts. Measured voltages and currents entering and leaving the DC-DC converter constitute the neural network’s input neurons. Current and voltage sensing circuit designs include low-pass filtering to reduce software noise filtering in the interest of speed. The complete solution slightly reduces the efficiency of the system under a constant load due to additional component power dissipation, while actually increasing it under the expected varying loads.

The intermittent nature of renewable energy, variations in energy demand, and fluctuations in oil and gas prices have all contributed to variable demand for power generation from coal-burning power plants. The varying demand leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system. The transients can cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. The data-driven model based on artificial neural networks (ANN) is developed for the first time to estimate properties of the steam generator components during cycling operations of a power plant. This approach utilizes data from the Coal Creek Station power plant located in North Dakota, USA collected over 10 years with a 1-hour resolution. Cycling characteristics of the plant are identified using a time-series of gross power. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

Axial flux permanent magnet (AFPM) machines have recently attracted significant attention due to several reasons, such as their specific form factor, potentially higher torque density and lower losses, feasibility of increasing the number of poles, and facilitating innovative machine structures for emerging applications. One such machine design, which has promising, high efficiency particularly at higher speeds, is of the coreless AFPM type and has been studied in the dissertation together with more conventional AFPM topologies that employ a ferromagnetic core. A challenge in designing coreless AFPM machines is estimating the eddy current losses. This work proposes a new hybrid analytical and numerical finite element (FE) based method for calculating ac eddy current losses in windings and demonstrates its applicability for axial flux electric machines. The method takes into account 3D field effects in order to achieve accurate results and yet greatly reduce computational efforts. It is also shown that hybrid methods based on 2D FE models, which require semi-empirical correction factors, may over-estimate the eddy current losses. The new 3D FE-based method is advantageous as it employs minimum simplifications and considers the end turns in the eddy current path, the magnetic flux density variation along the effective length of coils, and the field fringing and leakage, which ultimately increases the accuracy of simulations. After exemplifying the practice and benefits of employing a combined design of experiments and response surface methodology for the comparative design of coreless and conventional AFPM machines with cores, an innovative approach is proposed for integrated design, prototyping, and testing efforts. It is shown that extensive sensitivity analysis can be utilized to systematically study the manufacturing tolerances and identify whether the causes for out of specification performance are detectable. The electromagnetic flux path in AFPM machines is substantially 3D and cannot be satisfactorily analyzed through simplified 2D simulations, requiring laborious 3D models for performance prediction. The use of computationally expensive 3D models becomes even more challenging for optimal design studies, in which case, thousands of candidate design evaluations are required, making the conventional approaches impractical. In this dissertation a new two-level surrogate assisted differential evolution multi-objective optimization algorithm (SAMODE) is developed in order to optimally and accurately design the electric machine with a minimum number of expensive 3D design evaluations. The developed surrogate assisted optimization algorithm is used to comparatively and systematically design several AFPM machines. The studies include exploring the effects of pole count on the machine performance and cost limits, and the systematic comparison of optimally designed single-sided and double-sided AFPM machines. For the case studies, the new optimization algorithm reduced the required number of FEA design evaluations from thousands to less than two hundred. The new methods, developed and presented in the dissertation, maybe directly applicable or extended to a wide class of electrical machines and in particular to those of the PM-excited synchronous type. The benefits of the new eddy current loss calculation and of the optimization method are mostly relevant and significant for electrical machines with a rather complicated magnetic flux path, such is the case of axial flux and of transvers flux topologies, which are a main subject of current research in the field worldwide.

Aus der Einleitung: "Durch die Verwendung von Kraft-Wärme-Kopplung (KWK) kann sowohl die erzeugte elektrische als auch die anfallende thermische Energie genutzt und somit der Nutzungsgrad der eingesetzten Primärenergie deutlich gesteigert werden. Dieses Konzept wird sowohl in Blockheizkraftwerken (BHKW) wie auch in Range Extendern (RE) verwendet (Ferrari et al. 2012). Die bisher geringe Reichweite von Elektrofahrzeugen wird durch die zusätzlich vom RE bereitgestellte elektrische Energie erweitert. ..."

Decentralised power systems with a high portion of power generated from renewable energy sources and cogeneration units (CHP) are emerging worldwide. Optimising the energy usage of such systems is a difficult task as the stochastic fluctuations of generation from renewable sources, the coupling of electrical and thermal power generation by CHP and the time dependence of necessary storage devices require new approaches. Evolutionary algorithms are able to solve the optimisation task of the energy management. They use the principles of erroneous replication and cumulative selection that can be observed in biological processes, too. Very often recombination is included in the optimisation process. Using these quite simple principles the algorithm is able to explore difficult, large and high dimensional solution spaces. It will converge to the optimal solution in most of the cases quite fast, compared to other types of optimisation algorithms. At the example of an one dimensional replicator it is derived that the convergence speed in optimising convex functions increases by several orders of magnitude even after a few cycles compared to Monte-Carlo-simulation. For several types of equipment models are developed in this work. The cost to operate a given power system for a given time span is chosen as objective function. There is a variety of parameters (more than 15) that can be set in the algorithm. With quite extensive investigations it could be shown that the product of number of replicators and the number of calculated cycles has the most important influence on the quality of the solution but the calculation time is also proportional to this number. If there are reasonable values chosen for the remaining parameters the algorithm will find appropriate solutions in adequate time in most of the cases. Although a pure evolutionary algorithm will converge to a solution the convergence speed can be greatly enhanced by extending it to a hybrid algorithm. Grouping the replicators of the first cycle in suggestive regions of the solution space by an intelligent initialisation algorithm and repairing bad solutions by introducing a Lamarckian repair algorithm makes the optimisation converge fast to good optima. The algorithm was tested using data of several existing energy systems of different structure. To optimise the energy usage in a power system with 15 different types of units the required computation time is in the range of 15 minutes. The results of this work show that extended hybrid evolutionary algorithms are suitable for integrated optimisation of energy usage in combined local energy systems. They reach better results with the same or less effort than many other optimisation methods. The developed method of optimisation of energy usage can be applied in energy systems of small and large size and complexity as optimisation computations of energy systems on the island of Cape Clear, at FH Offenburg and in the Allgäu demonstrate.

Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.