Performance of Gradient-Based Optimizer for Optimum Wind Cube Design

Renewable energy is a safe and limitless energy source that can be utilized for heating,cooling,and other purposes.Wind energy is one of the most important renewable energy sources.Power fluctuation of wind turbines occurs due to variation of wind velocity.A wind cube is used to decrease power fluctuation and increase the wind turbine’s power.The optimum design for a wind cube is the main contribution of this work.The decisive design parameters used to optimize the wind cube are its inner and outer radius,the roughness factor,and the height of the wind turbine hub.A Gradient-Based Optimizer(GBO)is used as a new metaheuristic algorithm in this problem.The objective function of this research includes two parts:the first part is to minimize the probability of generated energy loss,and the second is to minimize the cost of the wind turbine and wind cube.The Gradient-Based Optimizer(GBO)is applied to optimize the variables of two wind turbine types and the design of the wind cube.The metrological data of the Red Sea governorate of Egypt is used as a case study for this analysis.Based on the results,the optimum design of a wind cube is achieved,and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind cube.The energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.

Gradient-based optimization method for producing a contoured beam with single-fed reflector antenna

A gradient-based optimization method for producing a contoured beam by using a single-fed reflector antenna is presented. First, a quick and accurate pattern approximation formula based on physical optics(PO) is adopted to calculate the gradients of the directivity with respect to reflector's nodal displacements. Because the approximation formula is a linear function of nodal displacements, the gradient can be easily derived. Then, the method of the steepest descent is adopted, and an optimization iteration procedure is proposed. The iteration procedure includes two loops: an inner loop and an outer loop. In the inner loop, the gradient and pattern are calculated by matrix operation, which is very fast by using the pre-calculated data in the outer loop. In the outer loop, the ideal terms used in the inner loop to calculate the gradient and pattern are updated, and the real pattern is calculated by the PO method. Due to the high approximation accuracy, when the outer loop is performed once, the inner loop can be performed many times, which will save much time because the integration is replaced by matrix operation. In the end, a contoured beam covering the continental United States(CONUS) is designed, and simulation results show the effectiveness of the proposed algorithm.

GBO algorithm for seismic source parameters inversion

The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer(GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization(MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion.

An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

The Deep Neural Networks(DNN)training process is widely affected by backdoor attacks.The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers.The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger.To overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)algorithm.In the ICNN-GBO model,we are injecting the triggers via a steganography and regularization technique.We are generating triggers using a single-pixel,irregular shape,and different sizes.The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and functionality.When the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target label.The proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M dataset.The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT

The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.

基于Wolfe搜索机制的随机梯度地震反演方法

地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.

A Gradient-Based Optimization Method for the Design of Layered Phononic Band-Gap Materials

Phononic materials with specific band-gap characteristics at desired frequency ranges are in great demand for vibration and noise isolation, elastic wave filters, and acoustic devices. The attenuation coefficient curve depicts both the frequency range of band gap and the attenuation of elastic wave, where the frequency ranges corresponding to the none-zero attenuation coefficients are band gaps. Therefore, the band-gap characteristics can be achieved through maximizing the attenuation coefficient at the corresponding frequency or within the corresponding frequency range. Because the attenuation coefficient curve is not smooth in the frequency domain, the gradient-based optimization methods cannot be directly used in the design optimization of phononic band-gap materials to achieve the maximum attenuation within the desired frequency range. To overcome this difficulty, the objective of maximizing the attenuation coefficient is transformed into maximizing its Cosine, and in this way, the objective function is smoothed and becomes differentiable. Based on this objective function, a novel gradient-based optimization approach is proposed to open the band gap at a prescribed frequency range and to further maximize the attenuation efficiency of the elastic wave at a specific frequency or within a prescribed frequency range. Numerical results demonstrate the effectiveness of the proposed gradient-based optimization method for enhancing the wave attenuation properties.

Gradient-based Kriging approximate model and its application research to optimization design

In the process of multidisciplinary design optimization, there exits the calculation complexity problem due to frequently calling high fidelity system analysis models. The high fidelity system analysis models can be surrogated by approximate models. The sensitivity analysis and numerical noise filtering can be done easily by coupling approximate models to optimization. Approximate models can reduce the number of executions of the problem's simulation code during optimization, so the solution efficiency of the multidisciplinary design optimization problem can be improved. Most optimization methods are based on gradient. The gradients of the objective and constrain functions are gained easily. The gra- dient-based Kriging (GBK) approximate model can be constructed by using system response value and its gradients. The gradients can greatly improve prediction precision of system response. The hybrid optimization method is constructed by coupling GBK approximate models to gradient-based optimiza- tion methods. An aircraft aerodynamics shape optimization design example indicates that the methods of this paper can achieve good feasibility and validity.

Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

A review of closed-loop reservoir management

The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

Seamless morphing trailing edge flaps for UAS-S45 using high-fidelity aerodynamic optimization

The seamless trailing edge morphing flap is investigated using a high-fidelity steady-state aerodynamic shape optimization to determine its optimum configuration for different flight conditions,including climb,cruise,and gliding descent.A comparative study is also conducted between a wing equipped with morphing flap and a wing with conventional hinged flap.The optimization is performed by specifying a certain objective function and the flight performance goal for each flight condition.Increasing the climb rate,extending the flight range and endurance in cruise,and decreasing the descend rate,are the flight performance goals covered in this study.Various optimum configurations were found for the morphing wing by determining the optimum morphing flap deflection for each flight condition,based on its objective function,each of which performed better than that of the baseline wing.It was shown that by using optimum configuration for the morphing wing in climb condition,the required power could be reduced by up to 3.8%and climb rate increases by 6.13%.The comparative study also revealed that the morphing wing enhances aerodynamic efficiency by up to 17.8%and extends the laminar flow.Finally,the optimum configuration for the gliding descent brought about a 43%reduction in the descent rate.

The Time-Scaling Transformation Technique for Optimal Control Problems with Time-Varying Time-Delay Switched Systems

In this paper,we consider a class of optimal control problems where the dynamical systems are time-delay switched systems with the delay being a function of time.By applying the control parameterization method,the control heights and switching times become decision variables that need to be optimized.It is well-known that,for this type problem,the variable switching times cannot be optimized directly.To work around this problem,we introduce a time-scaling transformation technique so that the original system is transformed an equivalent system,which is defined on a new time horizon with fixed switching times.Based on the relationship between the original time scale and the new time scale,we derive the gradients of the objective and constraint functions with respect to the control heights and durations.Then,the new problem can be solved by gradient-based optimization approach.To demonstrate the effectiveness of the time-scaling transformation technique,two example problems are solved.

Adjoint-based robust optimization design of laminar flow wing under flight condition uncertainties

It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions.In order to seek a more robust natural laminar flow control effect,it is necessary to develop an effective optimization design method.Meanwhile,attention must be given to the impact of crossflow(CF)instability brought on by the sweep angle.This paper constructs a robust optimization design framework based on discrete adjoint methods and non-intrusive polynomial chaos.Transition prediction is implemented by coupled Reynolds-Averaged Navier-Stokes(RANS)and simplified e^(N)method,which can consider both Tollmien-Schlichting(TS)wave and crossflow vortex instability.We have performed gradient enhancement processing on the general Polynomial Chaos Expansion(PCE),which is advantageous to reduce the computational cost of single uncertainty propagation.This processing takes advantage of the gradient information obtained by solving the coupled adjoint equations considering transition.The statistical moment gradient solution used for the robust optimization design also uses the derivatives of coupled adjoint equations.The framework is applied to the robust design of a 25°swept wing with infinite span in transonic flow.The uncertainty quantification and sensitivity analysis on the baseline wing shows that the uncertainty quantification method in this paper has high accuracy,and qualitatively reveals the factors that dominate in different flow field regions.By the robust optimization design,the mean and standard deviation of the drag coefficient can be reduced by 29%and 45%,respectively,and compared with the deterministic optimization design results,there is less possibility of forming shock waves under flight condition uncertainties.Robust optimization results illustrate the trade-off between the transition delay and the wave drag reduction.