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Mechanical surface treatments using an elastic-plastic cold working process can develop residual stresses on the surface of a workpiece. Compressive residual stresses on the surface increase resistance against surface crack propagation, so the overall mechanical performance can be improved by this technique. Compressive residual stresses can be created by different methods such as hammering, rolling, and shot peening. Shot peening is a well-established method to induce compressive residual stresses in the metallic components using cold working, and often ascribed to being beneficial to fatigue life in the aerospace and automobile industries. In this method, the surface is bombarded by high-velocity spherical balls which cause plastic deformation of the substrate, leading to a residual compressive stress after shot peening on the surface of the part. Computational modeling is an appropriate and effective way which can predict the amount of produced residual stresses and plastic deformation to obtain surface roughness after shot peening simulation. Finally, an experimental method to measure the magnitude of the residual stress using a nanoindentation technique was developed. The experimental indentation method was compared to both computational predictions (in aluminum) and with x-ray diffraction measurements of stress (in an alloy steel). The current study validates the relation between the nanoindentation method and numerical simulation for assessing the surface residual stresses resulting from single or multiple shot peening processes.

<div>Hot surface ignition is a safety design concern for serval industries including mining, aviation, automotive, boilers, and maritime applications. Bleed air ducts, exhaust pipes, combustion liners, and machine tools that are operated at elevated temperatures may be a source of ignition that needs to be accounted for during design. An apparatus for the measurements of minimum hot surface ignition temperature (MHSIT) of 3 aviation fluids (Jet-A, Hydraulic Oil (MIL-PRF-5606) and Lubrication Oil (MIL-PRF-23699)) has been developed. This study expands a widely utilized database of values of MHSIT. The study will expand the current range of design parameters including air temperature, crossflow velocity, fluid temperature, global equivalence ratio, injection method, and the effects of pressure. The expanded data are utilized to continue the development of a physics-anchored data dependent system and machine learning model for the estimation of MHSIT.</div><div><br></div><div>The aviation industry, including Rolls Royce, currently use a database of MHSIT values resulting from experiments conducted in 1988 at the Air Force Research Laboratory (AFRL) within the Wright Patterson Air Force Base in Dayton, OH. Over the three decades since these experiments, the range of operating conditions have significantly broadened in most applications including high performance aircraft engines. For example, the cross-stream air velocities (V) have increased by a factor of two (from ~3.4 m/s to ~6.7 m/s). Expanding the known database to document MHSIT for a range of fuel temperatures (TF), air temperatures (TA), pressure (P) and air velocities (V) is of great interest to the aviation industry. MHSIT data for current aviation fluids such as Jet-A and MIL-PRF-23699 (lubrication oil) and their relation to the design parameters have recently been under investigation in a generic experimental apparatus. </div><div><br></div><div>The current work involves utilization of this generic experimental apparatus to further the understanding of MHSIT through the investigation of intermediate air velocities, global equivalence ratios, injection method, and the effects of pressure. This study investigates the effects of air velocity in a greater degree of granularity by utilizing 0.6 m/s increments. This is done to capture the uncertainty seen in MHSIT values above 3.0 m/s. Furthermore, this study also expands the understanding of the effects of injection method on the MHSIT value with the inclusion of spray injected lubrication oil (MIL-PRF-23699) and stream injected Jet-A. The effects of global equivalence ratio are examined for spray injected Jet-A by modulating the aviation fluid injection rate and the crossflow air velocity in tandem. </div><div><br></div><div>During previous experimental campaigns, it was found that MHSIT did not monotonically increase with crossflow air velocity as previously believed. This new finding inspired a set of experiments that found MHSIT in crossflow to have four proposed ignition regimes: conduction, convective cooling, turbulent mixing, and advection. The current study replicates the results from the initial set of experiments at new conditions and to determine the effects of surface temperature on the regimes. </div><div><br></div><div>The MHSIT of flammable liquids depends on several factors including leak type (spray or stream), liquid temperature, air temperature, velocity, and pressure. ASTM standardized methods for ignition are limited to stagnant and falling drops downward (autoignition) at atmospheric pressure (ASTM E659, ASTM D8211, and ASTM E1491) and at pressures from 218 to 203 kPa (ASTM G72). Past studies have shown that MHSIT decreases with increasing pressure, but the available databases lack results of extensive experimental investigation. Therefore, such data for pressures between 101 to 203 kPa are missing or inadequate. As such the generic experimental apparatus was modified to produce the 101 to 203 kPa air duct pressure levels representative of a typical turbofan engine. </div><div><br></div><div>Machine learning (ML) and deep learning (DL) have become widely available in recent years. Open-source software packages and languages have made it possible to implement complex ML based data analysis and modeling techniques on a wide range of applications. The application of these techniques can expedite existing models or reduce the amount of physical lab investigation time required. Three data sets were utilized to examine the effectiveness of multiple ML techniques to estimate experimental outcomes and to serve as a substitute for additional lab work. To achieve this complex multi-variant regressions and neural networks were utilized to create estimating models. The first data sets of interest consist of a pool fire experiment that measured the flame spread rate as a function of initial fuel temperature for 8 different fuels, including Jet-A, JP-5, JP-8, HEFA-50, and FT-PK. The second data set consists of hot surface ignition data for 9 fuels including 4 alternative piston engine fuels for which properties were not available. The third data set is the MHSIT data generated by the generic experimental apparatus during the investigations conducted to expand the understanding of minimum hot surface ignition temperatures. When properties were not available multiple imputation by chained equations (MICE) was utilized to estimate fluid properties. Training and testing data sets were split up to 70% and 30% of the respective data set being modeled. ML techniques were implemented to analyze the data and R-squared values as high as 92% were achieved. The limitation of machine learning models is also discussed along with the advantages of physics-based approaches. The current study has furthered the application of ML in combustion through use of the MHSIT database.</div>

Ces travaux s’intègrent dans le projet de recherche SUPERSWITCH dans lequel des solutions alternatives à l’IGBT, utilisées dans les convertisseurs de puissance dans la gamme des tenues en tension 600-1200 V, sont envisagées. Les nouvelles structures du transistor MOS basées sur le principe de Super-Jonction tel que le transistor DT-SJMOSFET et sa terminaison originale, la « Deep Trench Termination » se propose comme alternative aux IGBT. Dans ce contexte, cette thèse se focalise sur la caractérisation de la robustesse de la terminaison DT2 adapté à une diode plane. Après avoir effectué un état de l’art sur les composants de puissances à semi-conducteur unidirectionnels en tension, les terminaisons des composants de puissance et la fiabilité des modules de puissance, un véhicule de test a été conçu en vue de réaliser les différents essais de vieillissement accéléré et suivi électrique. La fiabilité de la terminaison DT2 a été évaluée par des essais expérimentaux et des simulations numériques, dont une méthodologie innovante a été proposée. Au final de nouvelles structures ont été proposées pour limiter les problèmes de délaminage et de charges aux interfaces mis en avant dans notre étude. / This work is a part of the research project SUPERSWITCH in which alternatives solutions to the IGBT, are investigated. This solution was used IGBT in power converters in the 600-1200 V breakdown voltage range. The new MOSFET structures based on the super-junction, such as the DT-SJMOSFET and its "Deep Trench Termination", is proposed as an alternative to IGBT. In this context, this thesis focuses on the robustness characterization of the DT2 termination adapted to a planar diode. After a state of the art on unidirectional voltage power components, the power components termination, and power modules reliability, a test vehicle has been designed in order to carry out different accelerated ageing tests and electrical monitoring. The reliability of DT2 termination was evaluated by experimental tests and numerical simulations. An innovative modeling methodology has been proposed. Finally, new structures have been proposed to limit the delamination failure mechanisms and interface charges problems highlighted in this thesis.

Information and communication systems enter an increasing number of areas of daily lives. Our reliance and dependence on the functioning of such systems is rapidly growing together with the costs and the impact of system failures. At the same time the complexity of hardware and software systems extends to new limits as modern hardware architectures become more and more parallel, dynamic and heterogenous. These trends demand for a closer integration of formal methods and system engineering to show the correctness of complex systems within the design phase of large projects. The goal of this thesis is to introduce a formal holistic approach for modeling, analysis and synthesis of parallel systems that potentially addresses complex system behavior at any layer of the hardware/software stack. Due to the complexity of modern hardware and software systems, we aim to have a hierarchical modeling framework that allows to specify the behavior of a parallel system at various levels of abstraction and that facilitates designing complex systems in an iterative refinement procedure, in which more detailed behavior is added successively to the system description. In this context, the major challenge is to provide modeling formalisms that are expressive enough to address all of the above issues and are at the same time amenable to the application of formal methods for proving that the system behavior conforms to its specification. In particular, we are interested in specification formalisms that allow to apply formal verification techniques such that the underlying model checking problems are still decidable within reasonable time and space bounds. The presented work relies on an exogenous modeling approach that allows a clear separation of coordination and computation and provides an operational semantic model where formal methods such as model checking are well suited and applicable. The channel-based exogenous coordination language Reo is used as modeling formalism as it supports hierarchical modeling in an iterative top-down refinement procedure. It facilitates reusability, exchangeability, and heterogeneity of components and forms the basis to apply formal verification methods. At the same time Reo has a clear formal semantics based on automata, which serve as foundation to apply formal methods such as model checking. In this thesis new modeling languages are presented that allow specifying complex systems in terms of Reo and automata models which yield the basis for a holistic approach on modeling, verification and synthesis of parallel systems. The second main contribution of this thesis are tailored branching-time and alternating time temporal logics as well as corresponding model checking algorithms. The thesis includes results on the theoretical complexity of the underlying model checking problems as well as practical results. For the latter the presented approach has been implemented in the symbolic verification tool set Vereofy. The implementation within Vereofy and evaluation of the branching-time and alternating-time model checker is the third main contribution of this thesis.

<p>Uncertainty analysis is of great use both for calculating outputs that are more akin to real<br> flight, and for optimization to more robust shapes. However, implementation of uncertainty<br> has been a longstanding challenge in the field of aerodynamics due to the computational cost<br> of simulations. Geometric uncertainty in particular is often left unexplored in favor of uncer-<br> tainties in freestream parameters, turbulence models, or computational error. Therefore, this<br> work proposes a method of geometric uncertainty analysis for aerodynamic shapes that miti-<br> gates the barriers to its feasible computation. The process takes a two- or three-dimensional<br> shape and utilizes a combination of multifidelity meshes and Gaussian process regression<br> (GPR) surrogates in a multifidelity Monte Carlo (MFMC) algorithm. Multifidelity meshes<br> allow for finer sampling with a given budget, making the surrogates more accurate. GPR<br> surrogates are made practical to use by parameterizing major factors in geometric uncer-<br> tainty with only four variables in 2-D and five in 3-D. In both cases, two parameters control<br> the heights of steps that occur on the top and bottom of airfoils where leading and trailing<br> edge devices are attached. Two more parameters control the height and length of waves<br> that can occur in an ideally smooth shape during manufacturing. A fifth parameter controls<br> the depth of span-wise skin buckling waves along a 3-D wing. Parameters are defined to<br> be uniformly distributed with a maximum size of 0.4 mm and 0.15 mm for steps and waves<br> to remain within common manufacturing tolerances. The analysis chain is demonstrated<br> with two test cases. The first, the RAE2822 airfoil, uses transonic freestream parameters<br> set by the ADODG Benchmark Case 2. The results show a mean drag of nearly 10 counts<br> above the deterministic case with fixed lift, and a 2 count increase for a fixed angle of attack<br> version of the case. Each case also has small variations in lift and angle of attack of about<br> 0.5 counts and 0.08◦, respectively. Variances for each of the three tracked outputs show that<br> more variability is possible, and even likely. The ONERA M6 transonic wing, popular due<br> to the extensive experimental data available for computational validation, is the second test<br> case. Variation is found to be less substantial here, with a mean drag increase of 0.5 counts,<br> and a mean lift increase of 0.1 counts. Furthermore, the MFMC algorithm enables accurate<br> results with only a few hours of wall time in addition to GPR training. </p>