A hardware-based multi-disciplinary design optimization method for aeronautical applications

There are many applications in aeronautics where there exist strong couplings between disciplines. One practical example is within the context of Unmanned Aerial Vehicle(UAV) automation where there exists strong coupling between operation constraints, aerodynamics, vehicle dynamics, mission and path planning. UAV path planning can be done either online or offline. The current state of path planning optimisation online UAVs with high performance computation is not at the same level as its ground-based offline optimizer's counterpart, this is mainly due to the volume, power and weight limitations on the UAV; some small UAVs do not have the computational power needed for some optimisation and path planning task. In this paper, we describe an optimisation method which can be applied to Multi-disciplinary Design Optimisation problems and UAV path planning problems. Hardware-based design optimisation techniques are used. The power and physical limitations of UAV, which may not be a problem in PC-based solutions, can be approached by utilizing a Field Programmable Gate Array (FPGA) as an algorithm accelerator. The inevitable latency produced by the iterative process of an Evolutionary Algorithm (EA) is concealed by exploiting the parallelism component within the dataflow paradigm of the EA on an FPGA architecture. Results compare software PC-based solutions and the hardware-based solutions for benchmark mathematical problems as well as a simple real world engineering problem. Results also indicate the practicality of the method which can be used for more complex single and multi objective coupled problems in aeronautical applications.

Investigation of methods for increasing the energy efficiency on unmanned aerial vehicles (UAVS)

In recent years, development of Unmanned Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This thesis presents an investigation of methods for increasing the energy efficiency on UAVs. One method is via the development of a Mission Waypoint Optimisation (MWO) procedure for a small fixed-wing UAV, focusing on improving the onboard fuel economy. MWO deals with a pre-specified set of waypoints by modifying the given waypoints within certain limits to achieve its optimisation objectives of minimising/maximising specific parameters. A simulation model of a UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. This simulation model was separately integrated with a multi-objective Evolutionary Algorithm (MOEA) optimiser and a Sequential Quadratic Programming (SQP) solver to perform single-objective and multi-objective optimisation procedures of a set of real-world waypoints in order to minimise the onboard fuel consumption. The results of both procedures show potential in reducing fuel consumption on a UAV in a ight mission. Additionally, a parallel Hybrid-Electric Propulsion System (HEPS) on a small fixedwing UAV incorporating an Ideal Operating Line (IOL) control strategy was developed. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine was determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

Multi-objective design optimization of morphing UAV aerofoil/wing using hybridised MOGA

The paper investigates two advanced Computational Intelligence Systems (CIS) for a morphing Unmanned Aerial Vehicle (UAV) aerofoil/wing shape design optimisation. The first CIS uses Genetic Algorithm (GA) and the second CIS uses Hybridized GA (HGA) with the concept of Nash-Equilibrium to speed up the optimisation process. During the optimisation, Nash-Game will act as a pre-conditioner. Both CISs; GA and HGA, are based on Pareto optimality and they are coupled to Euler based Computational Fluid Dynamic (CFD) analyser and one type of Computer Aided Design (CAD) system during the optimisation.

Saturated core high-temperature superconducting fault current limiters as an alternative to conventional series reactors in a distribution grid

Series reactors are used in distribution grids to reduce the short-circuit fault level. Some of the disadvantages of the application of these devices are the voltage drop produced across the reactor and the steep front rise of the transient recovery voltage (TRV), which generally exceeds the rating of the associated circuit breaker. Simulations were performed to compare the characteristics of a saturated core High-Temperature Superconducting Fault Current Limiter (HTS FCL) and a series reactor. The design of the HTS FCL was optimized using the evolutionary algorithm. The resulting Pareto frontier curve of optimum solution is presented in this paper. The results show that the steady-state impedance of an HTS FCL is significantly lower than that of a series reactor for the same level of fault current limiting. Tests performed on a prototype 11 kV HTS FCL confirm the theoretical results. The respective transient recovery voltages (TRV) of the HTS FCL and an air core reactor of comparable fault current limiting capability are also determined. The results show that the saturated core HTS FCL has a significantly lower effect on the rate of rise of the circuit breaker TRV as compared to the air core reactor. The simulations results are validated with shortcircuit test results.

Cost-efficient virtual machine management in data centers

Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.

Triangle Evolution-A Hybrid Heuristic for Global Optimization

Abstract This paper presents a hybrid heuristic{triangle evolution (TE) for global optimization. It is a real coded evolutionary algorithm. As in di®erential evolution (DE), TE targets each individual in current population and attempts to replace it by a new better individual. However, the way of generating new individuals is di®erent. TE generates new individuals in a Nelder- Mead way, while the simplices used in TE is 1 or 2 dimensional. The proposed algorithm is very easy to use and e±cient for global optimization problems with continuous variables. Moreover, it requires only one (explicit) control parameter. Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems.

Imperfection Insensitive Thin Shells

The buckling of axially compressed cylindrical shells and externally pressurized spherical shells is extremely sensitive to even very small geometric imperfections. In practice this issue is addressed by either using overly conservative knockdown factors, while keeping perfect axial or spherical symmetry, or adding closely and equally spaced stiffeners on shell surface. The influence of imperfection-sensitivity is mitigated, but the shells designed from these approaches are either too heavy or very expensive and are still sensitive to imperfections. Despite their drawbacks, these approaches have been used for more than half a century.

This thesis proposes a novel method to design imperfection-insensitive cylindrical shells subject to axial compression. Instead of following the classical paths, focused on axially symmetric or high-order rotationally symmetric cross-sections, the method in this thesis adopts optimal symmetry-breaking wavy cross-sections (wavy shells). The avoidance of imperfection sensitivity is achieved by searching with an evolutionary algorithm for smooth cross-sectional shapes that maximize the minimum among the buckling loads of geometrically perfect and imperfect wavy shells. It is found that the shells designed through this approach can achieve higher critical stresses and knockdown factors than any previously known monocoque cylindrical shells. It is also found that these shells have superior mass efficiency to almost all previously reported stiffened shells.

Experimental studies on a design of composite wavy shell obtained through the proposed method are presented in this thesis. A method of making composite wavy shells and a photogrametry technique of measuring full-field geometric imperfections have been developed. Numerical predictions based on the measured geometric imperfections match remarkably well with the experiments. Experimental results confirm that the wavy shells are not sensitive to imperfections and can carry axial compression with superior mass efficiency.

An efficient computational method for the buckling analysis of corrugated and stiffened cylindrical shells subject to axial compression has been developed in this thesis. This method modifies the traditional Bloch wave method based on the stiffness matrix method of rotationally periodic structures. A highly efficient algorithm has been developed to implement the modified Bloch wave method. This method is applied in buckling analyses of a series of corrugated composite cylindrical shells and a large-scale orthogonally stiffened aluminum cylindrical shell. Numerical examples show that the modified Bloch wave method can achieve very high accuracy and require much less computational time than linear and nonlinear analyses of detailed full finite element models.

This thesis presents parametric studies on a series of externally pressurized pseudo-spherical shells, i.e., polyhedral shells, including icosahedron, geodesic shells, and triambic icosahedra. Several optimization methods have been developed to further improve the performance of pseudo-spherical shells under external pressure. It has been shown that the buckling pressures of the shell designs obtained from the optimizations are much higher than the spherical shells and not sensitive to imperfections.

一种空间飞行器编队重组的轨道优化方法

现有的空间飞行器编队重组的轨道规划方法在求解能量最优策略时,都预先给定了变轨花费的时间,但没有说明给定的时间是怎么选择的。将空间飞行器主从编队重组的轨道规划视为一个多目标优化问题,提出了一种小生境进化算法。该方法通过使用特定的染色体表示方法和进化算子,能有效的搜索到飞行器编队重组轨道规划问题的时间-能量前沿,并引入等值分享法保证优秀个体具有较大的选中概率和前沿的多样性。该方法能同时提供多种变轨方案,编队飞行的任务制定者从而可以根据实际应用情况选择最合适的方案。仿真结果表明了该方法的正确性。

带燃料均衡的空间多飞行器编队重构的轨道规划方法

航天任务需求的多样化对空间多飞行器编队重构的轨道规划问题不仅提出了燃料或时间最优的要求,还提出了燃料和时间最优以及燃料均衡的要求。将带燃料均衡的多飞行器编队重构的轨道规划建模为一个多目标优化问题,通过将进化计算与问题领域的知识相结合,提出了一种基于小生境进化算法的最优轨道规划方法。该方法能从变轨时间、燃料消耗和燃料消耗方差三方面分别评价一个变轨方案的最优性,并且一次规划能够提供多个Pareto最优变轨方案。仿真结果证明了该方法的正确性和有效性,还揭示了编队重构轨道规划问题的三个优化目标之间的关系,对于制定任务计划具有重要的参考价值.

基于进化计算的航天器轨道机动优化方法

轨道机动是航天器执行空间任务的基础，对轨道机动进行优化设计非常重要。 近年来，小推力发动机技术不断成熟，由于小推力发动机具有高比冲、低成本的优点，逐渐被用于轨道机动系统中。小推力轨道机动与常规轨道机动的不同在于小推力情况下，航天器变轨时间长，推力作用时间长，这使小推力轨道机动的优化设计极为困难。因此，小推力轨道机动优化成为航天器轨道机动优化领域的难点和热点，吸引了大批学者的关注和研究。本文对基于进化算法的小推力轨道转移时间-能量优化方法进行了研究。 由于进化算法属于一种参数优化方法，不能直接用于求解泛函形式表示的轨道转移优化问题。因此，本文引入并改进了一种基于Lyapunov反馈控制律的小推力转移轨道设计方法，使用该方法将小推力轨道转移最优控制问题转换成适合进化算法求解的多目标优化问题。 为了求解转换后的多目标优化问题，提出了一种 支配混合多目标进化算法。该算法使用基于 支配概念的选择算子，在保持群体多样性的同时，避免了许多多目标进化算法存在的退化现象。同时，为了改进算法局部搜索能力，将局部搜索方法与算法结合，构造出串行混合算法结构。 数值实验证明，本文提出的方法能够有效求解小推力轨道转移时间-能量优化问题。

Structure-activity relationships for antiplasmodial activity among aminoquinolines

The extended gravitational index G(Q) and quantum-chemical descriptors were calculated for the relationship analysis of aminoquinolines. An evolutionary algorithm was described for variable selection and building QSAR models. And the quasi-newton neural networks were employed with better results.

模糊免疫非线性PID控制的优化设计

在深入分析生物免疫系统中T细胞对B细胞辅助调节作用的基础上,提出了免疫反馈原理。针对非最小相位极点系统控制的难点,借鉴免疫反馈原理,结合积分控制的规律,提出了一种模糊免疫非线性PID控制方法。由于该方法中的参数确定比较复杂,利用免疫进化算法进行参数优化设计,实现了控制参数的合理设计。仿真结果表明,该方法在非最小相位极点系统控制中可行且有效,优于PID控制方法,具有更好的响应特性和抗干扰性能。

理性遗传算法及其在多机器人运动协调中的应用

面对传统遗传算法在解决一些复杂问题时所存在的收敛慢或早熟等困难 ,基于仿人理性决策原则 ,提出一种具有更丰富进化含义的进化算法——理性遗传算法 .其通过遗传信息的反馈或理性规则的建立来指导遗传操作的进行 ,从而将种群内部知识与经验的继承和学习更有效地结合在遗传算法之中 .相对于传统遗传算法 ,较好地解决了多机器人确知环境下协调运动规划问题 .理论分析和仿真实验结果都是令人鼓舞的 .