Optimal Aircraft Control Surface Layouts for Maneuver and Gust Load Alleviation

The goal of this work is to conduct aeroservoelastic optimization of a high aspect ratio transport wingbox with distributed control surfaces along the trailing edge. The control surfaces are utilized for both quasi-steady maneuver load alleviation (MLA) and unsteady gust load alleviation (GLA). The optimizer dictates the sizing details of the wingbox, the steady and unsteady control surface rotations, and also the control surface layout. Layout design variables specifically dictate which control surfaces to retain, and which to remove. The objective function is to minimize the sum of the actuator weight and the structural weight, with several imposed constraints related to structural failure and actuator saturation. The optimizers preferences with regards to control surface layout for MLA are in strong contrast to GLA-driven designs. The GLA-driven design space also suffers from local minima not evident in the MLA space

Chippe : a system for constraint driven behavioral synthesis

This report describes the Chippe system, gives some background previous work and describes several sample design runs of the system. Also presented are the sources of the design tradeoffs used by Chippe, and overview of the internal design model, and experiences using the system

The effects of end wall profiling on secondary loss is a turbine nozzle row

This thesis presents detailed investigations of the effect of end wall profiling on the secondary loss in a turbine nozzle row. The purpose of this project was to obtain a detailed view of the flow structure downstream of the blade passage, especially of the area adjacent to the end wall, and the influence of the shaped end wall on both flow and loss. The cascade was designed and manufactured by Yan [1999] based on nozzle blades by ALSTOM Energy Ltd. The low speed wind tunnel used by Yan was also used for the experimental work of this thesis, which were carried out in Durham thermodynamic laboratories. Two finer probes were used for the experiments allowing closer to the end wall measurements.».The flow field was investigated at exit from the blade row through two traverse slots. The secondary flow structure was understood and the effect of the profiled end wall was demonstrated by a reduction in the secondary loss and boundary layer thickness. The data collected along with the previous work by Yan could be regarded as an indication of the possible advantages of end wall profiling in a real turbine. The next step would be to carry out similar work on real turbomachinery

Speeding up Convolutional Neural Networks with Low Rank Expansions

The focus of this paper is speeding up the evaluation of convolutional neural networks. While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain. Our methods are architecture agnostic, and can be easily applied to existing CPU and GPU convolutional frameworks for tuneable speedup performance. We demonstrate this with a real world network designed for scene text character recognition, showing a possible 2.5x speedup with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy, still achieving state-of-the-art on standard benchmarks

Design for Additive Manufacturing of Conformal Cooling Channels Using Thermal-Fluid Topology Optimization and Application in Injection Molds

Additive manufacturing allows the fabrication parts and tools of high complexity. This capability challenges traditional guidelines in the design of conformal cooling systems in heat exchangers, injection molds, and other parts and tools. Innovative design methods, such as network-based approaches, lattice structures, and structural topology optimization have been used to generate complex and highly efficient cooling systems; however, methods that incorporate coupled thermal and fluid analysis remain scarce. This paper introduces a coupled thermal-fluid topology optimization algorithm for the design of conformal cooling channels. With this method, the channel position problem is replaced to a material distribution problem. The material distribution directly depends on the effect of flow resistance, heat conduction, as well as forced and natural convection. The problem is formulated based on a coupling of Navier-Stokes equations and convection-diffusion equation. The problem is solved by gradient-based optimization after analytical sensitivity derived using the adjoint method. The algorithm leads a two -dimensional conceptual design having optimal heat transfer and balanced flow. The conceptual design is converted to three-dimensional channels and mapped to a morphological surface conformal to the injected part. The method is applied to design an optimal conformal cooling for a real three dimensional injection mold. The feasibility of the final designs is verified through simulations. The final designs can be exported as both three-dimensional graphic and surface mesh CAD format, bringing the manufacture department the convenience to run the tool path for final fitting

Automatic generation of multi-precision multi-arithmetic CNN accelerators for FPGAs

Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems, we propose Tomato, a framework designed to automate the process of generating efficient CNN accelerators. The generated design is pipelined and each convolution layer uses different arithmetics at various precisions. Using Tomato, we showcase state-of-the-art multi-precision multi-arithmetic networks, including MobileNet-V1, running on FPGAs. To our knowledge, this is the first multi-precision multi-arithmetic auto-generation framework for CNNs. In software, Tomato fine-tunes pretrained networks to use a mixture of short powers-of-2 and fixed-point weights with a minimal loss in classification accuracy. The fine-tuned parameters are combined with the templated hardware designs to automatically produce efficient inference circuits in FPGAs. We demonstrate how our approach significantly reduces model sizes and computation complexities, and permits us to pack a complete ImageNet network onto a single FPGA without accessing off-chip memories for the first time. Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs. To the best of our knowledge, our automatically generated accelerators outperform closest FPGA-based competitors by at least 2-4x for lantency and throughput; the generated accelerator runs ImageNet classification at a rate of more than 3000 frames per second.EPSRC Doctoral Scholarship Peterhouse Graduate Studentshi