JT90 thermal barrier coated vanes

The technology of plasma sprayed thermal barrier coatings applied to turbine vane platforms in modern high temperature commercial engines was advanced to the point of demonstrated feasibility for application to commercial aircraft engines. The three thermal barrier coatings refined under this program are zirconia stabilized with twenty-one percent magnesia (21% MSZ), six percent yttria (6% YSZ), and twenty percent yttria (20% YSZ). Improvement in thermal cyclic endurance by a factor of 40 times was demonstrated in rig tests. A cooling system evolved during the program which featured air impingement cooling for the vane platforms rather than film cooling. The impingement cooling system, in combination with the thermal barrier coatings, reduced platform cooling air requirements by 44% relative to the current film cooling system. Improved durability and reduced cooling air requirements were demonstrated in rig and engine endurance tests. Two engine tests were conducted, one of 1000 cycles and the other of 1500 cycles. All three coatings applied to vanes fabricated with the final cooling system configuration completed the final 1500 cycle engine endurance test. Results of this test clearly demonstrated the durability of the 6% YSZ coating which was in very good condition after the test. The 21% MSZ and 20% YSZ coatings had numerous occurrences of significant spalling in the test

Modeling The Microrelief Structure of Ti6Al4V Titanium Alloy Surface After Exposure to Femtosecond Laser Pulses

A method of mathematical modeling of the ordered surface relief of titanium alloy Ti6Al4V after femtosecond laser treatment is proposed, which allowed obtaining the informative signs of the self-organized surface irregularities, taking into account the stochastic and cyclic nature of this process. An algorithm has been developed, and a package of computer programs has been created based on the proposed mathematical model. These methods make it possible to analyze the zone-spatial two-dimensional structure of the cyclic relief of the modified surface. They are also the basis for creating the specialized software for the automated profilometric diagnostic systems

First order algorithms in variational image processing

Variational methods in imaging are nowadays developing towards a quite universal and flexible tool, allowing for highly successful approaches on tasks like denoising, deblurring, inpainting, segmentation, super-resolution, disparity, and optical flow estimation. The overall structure of such approaches is of the form ${\cal D}(Ku) + \alpha {\cal R} (u) \rightarrow \min_u$ ; where the functional ${\cal D}$ is a data fidelity term also depending on some input data $f$ and measuring the deviation of $Ku$ from such and ${\cal R}$ is a regularization functional. Moreover $K$ is a (often linear) forward operator modeling the dependence of data on an underlying image, and $\alpha$ is a positive regularization parameter. While ${\cal D}$ is often smooth and (strictly) convex, the current practice almost exclusively uses nonsmooth regularization functionals. The majority of successful techniques is using nonsmooth and convex functionals like the total variation and generalizations thereof or $\ell_1$-norms of coefficients arising from scalar products with some frame system. The efficient solution of such variational problems in imaging demands for appropriate algorithms. Taking into account the specific structure as a sum of two very different terms to be minimized, splitting algorithms are a quite canonical choice. Consequently this field has revived the interest in techniques like operator splittings or augmented Lagrangians. Here we shall provide an overview of methods currently developed and recent results as well as some computational studies providing a comparison of different methods and also illustrating their success in applications.Comment: 60 pages, 33 figure

Contributions of Continuous Max-Flow Theory to Medical Image Processing

Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration. To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience. The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem

ret2spec: Speculative Execution Using Return Stack Buffers

Speculative execution is an optimization technique that has been part of CPUs for over a decade. It predicts the outcome and target of branch instructions to avoid stalling the execution pipeline. However, until recently, the security implications of speculative code execution have not been studied. In this paper, we investigate a special type of branch predictor that is responsible for predicting return addresses. To the best of our knowledge, we are the first to study return address predictors and their consequences for the security of modern software. In our work, we show how return stack buffers (RSBs), the core unit of return address predictors, can be used to trigger misspeculations. Based on this knowledge, we propose two new attack variants using RSBs that give attackers similar capabilities as the documented Spectre attacks. We show how local attackers can gain arbitrary speculative code execution across processes, e.g., to leak passwords another user enters on a shared system. Our evaluation showed that the recent Spectre countermeasures deployed in operating systems can also cover such RSB-based cross-process attacks. Yet we then demonstrate that attackers can trigger misspeculation in JIT environments in order to leak arbitrary memory content of browser processes. Reading outside the sandboxed memory region with JIT-compiled code is still possible with 80\% accuracy on average.Comment: Updating to the cam-ready version and adding reference to the original pape