Optimal speed of hypersonic cruise flight

A coupling frame of speed gain and maintain was suggested to assess the flight performance of hypersonic cruise vehicles(HCV).The optimal cruise speed was obtained by analyzing the flight performance measured by the ratio of initial boost mass to generalized payload.The performance of HCVs based on rockets and air-breathing ramjets was studied and compared to that of a minimum-energy ballistic trajectory under a certain flight distance.It is concluded that rocket-based HCVs flying at the optimal speed are a very competitive choice at the current stage.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

Development of optimal speed trajectory control strategy for electric vehicles to suppress battery aging

In order to suppress the battery aging of electric vehicles(EVs),a multi-objective optimization function is established to describe the battery aging behavior based on a high-precision battery aging model,and the state–space equation is then constructed to reveal the intrinsic relationship between vehicle speed,acceleration,and battery state-of-charge(SOC).The constructed optimization model is solved by using a sequential quadratic programming(SQP)algorithm,and based on the model predictive control(MPC)theory,the efficient real-time control of vehicle speed is achieved.Simulation results show that the developed strategy extends the battery life by 10.33%compared to the baseline strategy when the traffic flow is not involved.In the case of involving the traffic flow,the optimization results of battery aging improves as the look-ahead time period increases,while the computational burden increases.The results show that the developed strategy reduces the battery aging of the target vehicle by 33.02%compared to the preceding vehicle while meeting the real-time requirement.

Phase transition on speed limit traffic with slope

Through introducing a generalized optimal speed function to consider spatial position, slope grade and variable safe headway, the effect of slope in a single-lane highway on the traffic flow is investigated with the extended optimal speed model. The theoretical analysis and simulation results show that the flux of the whole road with the upgrade （or downgrade） increases linearly with density, saturates at a critical density, then maintains this saturated value in a certain density range and finally decreases with density. The value of saturated flux is equal to the maximum flux of the upgrade （or downgrade） without considering the slight influence of the driver＇s sensitivity. And the fundamental diagrams also depend on sensitivity, slope grade and slope length. The spatiotemporal pattern gives the segregation of different traffic phases caused by the rarefaction wave and the shock wave under a certain initial vehicle number. A comparison between the upgrade and the downgrade indicates that the value of saturated flux of the downgrade is larger than that of the upgrade under the same condition. This result is in accordance with the real traffic.

二级展开式圆柱齿轮减速器的智能CAD软件

简要地介绍了二级展开式圆柱齿轮减速器的智能ＣＡＤ系统──ＴＳＣＡＤ系统的功能、主要结构及程序设计过程，初步探讨了专家系统与ＣＡＤ相结合的问题。

Improved particle swarm optimization based on particles' explorative capability enhancement

Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.

An integrated optimization design of a fishing ship hullform at different speeds

This paper shows how to improve the hydrodynamics performance of a ship by solving a shape optimization design problem at different speeds using the simulation-based design(SBD) technique. The SBD technique is implemented by integrating the advanced CFD codes, the global optimization algorithms and the geometry modification methods, which offers a new way for the hullform optimization design and the configuration innovation. The multiple speed integrated optimization for the hullform design is a challenge. In this paper, an example of the technique application for a fishing ship hullform optimization at different speeds is demonstrated. In this optimization process, the free-form deformation method is applied to automatically modify the geometry of the ship, and the multi-objective particle swarm optimization(MOPSO) algorithm is adopted for exploring the design space. Two objective functions, the total resistances at two different speeds(12 kn and 14 kn) are assessed by the RANS solvers. The optimization results show that the decrease of the total resistance is significant after the optimization at the two speeds, with a reduction of 5.0% and 11.2%, respectively. Finally, dedicated experimental validations for the design model and the optimized model are carried out for the computation and the optimization processes. At the two speeds, the reduction of the total resistance in the model scale is about 6.0% and 11.8% after the optimization. It is a valuable result in view of the small modifications allowed and the good initial performances of the original model. The given practical example demonstrates the feasibility and the superiority of the proposed SBD technique for the multiple speed integrated optimization.

Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete samplebased random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

Design of high-speed and low-power finite-word-length PID controllers

ASIC or FPGA implementation of a finite word-length PID controller requires a double expertise： in control system and hardware design. In this paper, we only focus on the hardware side of the problem. We show how to design configurable fixed-point PIDs to satisfy applications requiring minimal power consumption, or high control-rate, or both together. As multiply operation is the engine of PID, we experienced three algorithms： Booth, modified Booth, and a new recursive multi-bit multiplication algorithm. This later enables the construction of finely grained PID structures with bit-level and unit-time precision. Such a feature permits to tailor the PID to the desired performance and power budget. All PIDs are implemented at register-transfer4evel （RTL） level as technology-independent reusable IP-cores. They are reconfigurable according to two compilemtime constants： set-point word-length and latency. To make PID design easily reproducible, all necessary implementation details are provided and discussed.

Dynamic multi-objective differential evolution algorithm based on the information of evolution progress

The multi-objective differential evolution(MODE)algorithm is an effective method to solve multi-objective optimization problems.However,in the absence of any information of evolution progress,the optimization strategy of the MODE algorithm still appears as an open problem.In this paper,a dynamic multi-objective differential evolution algorithm,based on the information of evolution progress(DMODE-IEP),is developed to improve the optimization performance.The main contributions of DMODE-IEP are as follows.First,the information of evolution progress,using the fitness values,is proposed to describe the evolution progress of MODE.Second,the dynamic adjustment mechanisms of evolution parameter values,mutation strategies and selection parameter value based on the information of evolution progress,are designed to balance the global exploration ability and the local exploitation ability.Third,the convergence of DMODE-IEP is proved using the probability theory.Finally,the testing results on the standard multi-objective optimization problem and the wastewater treatment process verify that the optimization effect of DMODE-IEP algorithm is superior to the other compared state-of-the-art multi-objective optimization algorithms,including the quality of the solutions,and the optimization speed of the algorithm.

Ecological cruising control of connected electric vehicle:A deep reinforcement learning approach

Ecological cruising control methods of vehicles have been extensively studied to further cut down energy consumption by optimizing vehicles’speed profiles.However,most controllers cannot be put into practical application because of future terrain data requirements and excessive computational demand.In this paper,an eco-cruising strategy with real-time capability utilizing deep reinforcement learning is proposed for electric vehicles(EVs)propelled by in-wheel motors.The deep deterministic policy gradient algorithm is leveraged to continuously regulate the motor torque in response to road elevation changes.By comparing the proposed strategy to the energy economy benchmark optimized with dynamic programming(DP),and traditional constant speed(CS)strategy,its learning ability,optimality,and generalization performance are verified.The simulation results show that without a priori knowledge about the future trip,the proposed strategy provides 3.8%energy saving compared with the CS strategy.It also yields a smaller gap than the globally optimal solution of DP.By testing on other driving cycles,the trained strategy reveals good generalization performance and impressive computational efficiency(about 2 ms per simulation step),making it practical and implementable.Additionally,the model-free characteristic of the proposed strategy makes it applicable for EVs with different powertrain topologies.

An efficient method for collision-free and jerk-constrained trajectory generation with sparse desired way-points for a flying robot

In this paper,an efficient approach is developed to plan jerk-constrained smooth trajectories passing through desired way-points with allowable accuracy for an autonomous quadrotor.First,based on the B-spline model,we introduce the sequential convex programming(SCP)into the flexible path planning method to satisfy the way-points constraints.By approximating the quadrotor and obstacles as spheres and directed planes,the length-optimal and collision-free path planning problem is formulated as a nonconvex optimization problem and solved by SCP.The initial C^(3) continuous path curve for optimization is constructed efficiently based on a proposed strategy of generating new way-points near the obstacles without the need to calculate the embedding distance between the curve and the obstacles.On this basis,the time-optimal speed planning problem is addressed in two steps.In the first step,the forward-backward approach is introduced to solve the problem under the chord error,velocity,and acceleration constraints by changing the variables and deforming the constraints.Then by relaxing the constraints appropriately,the problem under jerk constraints is formulated into a linear programming(LP)problem.The feasibility of the proposed approach is verified through simulations and an indoor navigation experiment.The proposed approach can generate a C^(3) continuous collision-free trajectory that passes through the sparse desired way-points with allowable accuracy while guaranteeing the chord error,velocity,acceleration,and jerk constraints.When the number of sample points is 3000,the proposed speed planning method reduces the calculation time by 40%compared to an existing method.

Analysis and optimization of current sensing circuit for deep sub-micron SRAM

A quantitative yield analysis of a traditional current sensing circuit considering the random dopant fluctuation effect is presented. It investigates the impact of transistor size, falling time of control signal CS and threshold voltage of critical transistors on failure probability of current sensing circuit. On this basis, we present a final optimization to improve the reliability of current sense amplifier. Under 90 nm process, simulation shows that failure probability of current sensing circuit can be reduced by 80% after optimization compared with the normal situation and the delay time only increases marginally.