Research on scheduling problems in machining and assembly processes using multiobjective evolutionary algorithms

制度:新 ; 報告番号:甲3420号 ; 学位の種類:博士(工学) ; 授与年月日:2011/7/25 ; 早大学位記番号:新574

Influence of inlet distortion on fan aerodynamic performance

Inlet distortion can lead to loss in efficiency and stability margin of the fan which in return jeopardises flight safety. These aspects driven by inlet distortion are becoming increasingly challenging for the designers of next-generation turbofan engines. With the increase of computational capability and improvements in numerical models, computational fluid dynamics (CFD) is becoming increasingly powerful and favoured by scientific researchers and industrial engineers. Time-accurate, high-fidelity CFD simulations of the compressor behaviour at extreme operational conditions (such as during stall with inlet distortion) has become possible. In the present thesis, CFD is used to determine the effects of inlet distortion on fan aerodynamic stability and stall hysteresis. NASA stage 67 is used for this study. At the very beginning an appropriate numerical strategy was developed and validated with extensive experimental data. A good match was obtained for both the flow field variables at the peak efficiency point and the stall boundary. In this research, two types of inlet distortion were examined. One is the consistent distortion which mimics the setup in experiments with slow throttling; another type is the abrupt distortion (due to sudden maneuver) whose effect is poorly understood. It will be shown that abrupt distortion can result in larger stall margin loss than consistent distortion. Therefore, experimental tests based on consistent distortion tend to be more optimistic in stall margin prediction. Thereafter the stall and recovery process of a transonic fan with both types of inlet distortions were performed. The results showed that the stall process with inlet distortion can be very different from that in uniform inflow. However, distortion has minor effect on the recovery point (corrected mass flow) of the fan and the clean flow region plays the most important role in the recovery process. In the presence of abrupt distortion, it was found that the stall margin of a fan can be influenced by the length of exit duct. This phenomenon was explained using the wave propagation theory. A shorter exit duct reduces the time lag of expansion pressure wave reflected from the nozzle, which upon arriving at fan trailing edge can prevent the fan from stalling. A critical length ratio was proposed which provides useful guidelines on test rig and engine design. A preliminary study of the behaviour of the BLI fan with serpentine intake (S intake) at near stall condition and its stall process was performed. It was found that the distortion pattern upstream of the fan is complex and can be divided into different zones radially. The stall behaviour of the fan is similar to that with a circumferential distortion, but more complex because of the coupling with the swirl distortion near the casing. Although the present work is restricted to NASA stage 67, some of the conclusions gained are general and expected to be valid for modern fan and compressor designs. Finally, during this research it has become apparent that there is a significant lack of open published measured data for fans and compressors operating under inlet distortions, which is mainly due to the difficulties and costs involved in setting up such experimental campaigns. The above indicates that validated CFD codes are going to play an even more important role in development of distortion tolerant fans. The objective of this work is to show the suitability of CFD for the modelling of fan aerodynamic performance and stability with inlet distortion, which can provide an economical alternative strategy to subscale rig tests.Open Acces

3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

On the Iteration Complexity of Smoothed Proximal ALM for Nonconvex Optimization Problem with Convex Constraints

It is well-known that the lower bound of iteration complexity for solving nonconvex unconstrained optimization problems is $\Omega(1/\epsilon^2)$, which can be achieved by standard gradient descent algorithm when the objective function is smooth. This lower bound still holds for nonconvex constrained problems, while it is still unknown whether a first-order method can achieve this lower bound. In this paper, we show that a simple single-loop first-order algorithm called smoothed proximal augmented Lagrangian method (ALM) can achieve such iteration complexity lower bound. The key technical contribution is a strong local error bound for a general convex constrained problem, which is of independent interest

SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode the appearance and motion information before fusing them to identify the target and generate object masks. However, this pipeline is computationally expensive and can lead to suboptimal performance due to the difficulty of fusing the two modalities properly. In this paper, we propose a novel UVOS model called SimulFlow that simultaneously performs feature extraction and target identification, enabling efficient and effective unsupervised video object segmentation. Concretely, we design a novel SimulFlow Attention mechanism to bridege the image and motion by utilizing the flexibility of attention operation, where coarse masks predicted from fused feature at each stage are used to constrain the attention operation within the mask area and exclude the impact of noise. Because of the bidirectional information flow between visual and optical flow features in SimulFlow Attention, no extra hand-designed fusing module is required and we only adopt a light decoder to obtain the final prediction. We evaluate our method on several benchmark datasets and achieve state-of-the-art results. Our proposed approach not only outperforms existing methods but also addresses the computational complexity and fusion difficulties caused by two-stream architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow also obtains competitive results on video salient object detection datasets.Comment: Accepted to ACM MM 202