Influence of We Media Information Dissemination on Public Emotional Cognition and Behavior under Government Responsibility Constraint

In recent years, We Media’s chaotic behavior has emerged one after another. How to properly supervise We Media and effectively manage its violations has become an urgent problem in the process of national governance system and governance capacity building. From the three aspects of opportunity, motivation, and control methods, this paper analyzes the relevant stakeholders and their relationships in the process of We Media information dissemination. It constructs a tripartite evolutionary game model of government, We Media, and public participation, which focuses on the analysis of the equilibrium point of the game model and carries out simulation experiments to explore the influence of government responsibility constraints on the evolution results. The research results show that government regulation plays an important role in restricting We Media’s information release. When the government's willingness to regulate increases, We Media will be punished more if it violates rules. In order to reduce the cost of punishment and other factors, We Media will reduce the willingness to violate the rules. After the occurrence of social hot events, the public is more willing to be guided by positive information from We Media, prompting the government to choose strict supervision strategies, effectively reducing the violations of We Media and achieving the Pareto optimum. According to the research results, this paper puts forward reasonable countermeasures to realize the comprehensive governance pattern of noncompliance of We Media and correct guidance of public emotional cognitive behavior under responsibility constraints. The research results provide theoretical support and decision-making basis for We Media information management and control.

Optimal R&D Investment Strategy of Pollution Abatement and Incentive Mechanism Design under Asymmetric Information

The study examines optimal pollution control R&D investment strategy of firms under asymmetric information and further analyzes the impact of government incentive mechanism on it. We use stochastic optimal control theory to get the exact solution of R&D investment strategy and incentive mechanism. Our analysis reveals that if there is no supervisor, firms choose not to cooperate, but the government can take appropriate incentive compensation to make firms reach Nash equilibrium. If there are supervisors, the optimal strategy of the enterprise is to choose cooperation, and there will be Pareto optimum among the firms. Furthermore, the R&D investment level decreases with increasing environmental uncertainty.

Multi-performance Target Collaborative Optimization Methods for Battery Electric Vehicle

The present studies on battery electric vehicles (BEVs) has mainly focused on the single-objective or weighted multi-objective optimization based on energy management, which can not manifest the coupling relationship among the vehicle performance objectives essentially. To optimize the handling stability, ride comfort and economy of BEV, this paper built the stability dynamics analysis model, ride comfort simulation half-car model and power consumption calculation model of BEV, as well as two-point virtual random excitation model on Level B road and proposed related evaluation indexes, including vehicle handling stability factor, weighted acceleration root-mean-square (RMS) value of vertical vibration at the driver’s seat and power consumption per 100 m at a constant speed. The Pareto optimum principle–based multi-objective evolutionary algorithm (MOEA) of BEV was also designed, which was encoded with real numbers and obtained the target values of all optional schemes via MATLAB/Simulink simulation software. The merits and demerits of alternative schemes could be judged according to the Pareto dominance principle, so that alternative schemes obtained after optimization were realizable. The results of simulation experiment suggest that the proposed algorithm can perform the multi-objective optimization on BEV, and obtain a group of Pareto optimum solutions featured by high handling stability, favorable ride comfort and low energy consumption for the decision-makers.

Multi-Objective Optimization of Turbine Blade Tip With Film Cooling Holes

Tip leakage flow is inevitable due to the tip clearance over rotor blades in turbines. This phenomenondeteriorates blade aerodynamic performance and induces severe thermal damage to the tip surface.Introduction of cooling jets to the tip can effectively controls the tip leakage flow and improves the tip heat transfer. Therefore, this paper aims to optimize film cooling holes on a flat tip of a subsonic cascade and an topology-optimized tip of a transonic cascade. A design variable is a material parameter defined at each grid node along the blade camber line. This idea is based on the topology optimization method. The objective is to minimize blade energy loss and maximize tip heat transfer intensity. A response surface optimization based on the design of experiment (DOE) analysis is employed, and a multi-objective Genetic Algorithm is used to get Pareto optimum solutions. During the DOE process, a CFD method using injection source terms is integrated for numerical simulations to reduce computational costs. Optimized tip film cooling holes are finally re-constructed. The influence of the newly designed tip cooling holes configuration on blade aero-thermal performance is evaluated via CFD simulations using body-fitted mesh. Results show that compared with the uniform arrangement of cooling film holes along the axial direction, all the optimized film cooling holes can improve both blade aerodynamic performance and tip heat transfer performance.

Ions motion optimization algorithm for multiobjective optimization problems

This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature. The main inspiration behind this approach is the force of attraction and repulsion between anions and cations. A storage and leader selection strategy is combined with the single objective Ions Motion Optimization (IMO) approach to estimate the Pareto optimum front for multiobjective optimization. The proposed method is applied to 18 different benchmark test functions to confirm its efficiency in finding optimal solutions. The outcomes are compared with three novel and well-accepted techniques in the literature using five performance parameters quantitatively and obtained Pareto fronts qualitatively. The comparison proves that MOIMO can approximate Pareto optimal solutions with good convergence and coverage with minimum computational time.

Numerical Investigation and Multi-Objective Optimization of Internal EGR and Post-Injection Strategies on the Combustion, Emission and Performance of a Single Cylinder, Heavy-Duty Diesel Engine

This work presents a numerical study that investigates the optimum post-injection strategy and internal exhaust gas recirculation (iEGR) application with intake valve re-opening (2IVO) aiming to optimize the brake specific nitric oxide (bsNO) and brake specific soot (bsSoot) trade-off with reasonable brake specific fuel consumption (BSFC) via 1D engine cycle simulation. For model validation, single and post-injection test results obtained from a heavy-duty single cylinder diesel research engine were used. Then, the model was modified for 2IVO application. Following the simulations performed based on Latin hypercube DoE; BSFC, bsNO and bsSoot response surfaces trained by feedforward neural network were generated as a function of the injection (start of main injection, post-injection quantity, post-injection dwell time) and iEGR (2IVO dwell) parameters. After examining the effect of each parameter on pollutant emission and engine performance, multi-objective pareto optimization was performed to obtain pareto optimum solutions in the BSFC-bsNO-bsSoot space for 8.47 bar brake mean effective pressure (BMEP) load and 1500 rpm speed condition. The results show that iEGR and post-injection can significantly reduce NO and soot emissions, respectively. The soot oxidation capability of post-injection comes out only if it is not too close to the main injection and its efficiency and effective timing are substantially affected by iEGR rate and main injection timing. It could also be inferred that by the combination of iEGR and post-injection, NO and soot could be reduced simultaneously with a reasonable increase in BSFC if start of main injection is phased properly.