发动机排气压力波性能仿真分析

研究汽车排气系统内压力波的形成及其传播过程与发动机工作状况之间的关系,为及时诊断故障,利用GT—POWER软件建立一个四缸四行程的汽油发动机模型。计算结果显示在发动机的一个工作周期内,压力波出现了四个波峰和波谷,分别对应四个汽缸的排气行程;修改发动机的转速,会影响排气压力波的幅值、谐波成分、相位等参数;在排气行程末期,排气压力波在排气管内发生干涉、叠加和负压效应。结果对利用排气压力波进行发动机的综合故障诊断具有重要意义。     

水下航行器摆盘发动机缸内过程数值仿真

摆盘发动机性能的好坏决定于其气缸内工质压力、温度等参数的变化曲线,而这些变化曲线是由气缸内的工质质量、内能所决定的.为此在建立了摆盘发动机配气过程进排气面积数学模型的基础上,建立了缸内工质的内能和质量的微分方程.然后采用龙格-库塔法对微分方程进行了数值求解,得到一个工作循环内内能、质量、温度、压强等热力参数的曲线图,并计算了循环过程各项性能指标.该数值计算可用于判断配气参数、配气阀尺寸等对工作过程、性能指标的影响,从而指出性能改进的方向及潜力大小.     

汽车水泵与发动机冷却液温度过高关系研究

汽车水泵是发动机冷却系统中十分重要的部件,水泵的作用是能够提高冷却液的压力,从而促使冷却液在发动机的冷却水道内不断的进行循环,通过这种方式带走发动机工作过程中产生的热量,进而保持发动机的正常工作温度。     

航空发动机试车台推力仿真技术研究

航空发动机试车准确测量推力问题,航空发动机推力是衡量发动机性能的主要参数.航空发动机试车台推力测量系统复杂,测试环境恶劣,易出现推力测量不准确.为解决上述问题,提出贝叶斯正则化算法的BP神经网络技术和相关性分析建立航空发动机试车台推力模型,进行推力仿真.在发动机试验过程中可实时计算发动机推力,和试车台推力测量系统的测试结果进行对比,判别推力测量系统工作是否正常.仿真结果表明,设计的基于L-M贝叶斯正则化算法的BP神经网仿真模型可准确的计算不同大气条件下和各个工作状态的发动机实际推力.     

液体火箭发动机再生冷却槽黏塑性分析

采用Robinson黏塑性模型,利用渐近积分法结合大型有限元程序Marc完成液体火箭发动机再生冷却槽的热结构耦合分析.经验证,Robinson黏塑性模型能够很好地模拟内壁材料NARloy Z高温时的拉伸特性,且在循环载荷下迟滞回环曲线与试验结果符合良好.冷却槽关键点应力分析明确阐释在不同工作阶段内外壁由于材料属性、工况不同导致的应力制约关系;残余应变分析清晰再现冷却槽“狗窝”失效模式及变形情况,同时为定量计算其损伤累积及剩余寿命提供坚实基础.     

双组元姿控发动机燃烧与传热的数值分析

本文在前人建立的发动机燃烧模型的基础上加入了热传导模型和辐射模型，采用一体化耦合方法对发动机的燃烧和传热进行了分析计算。仿真结果表明，一体化的数值模拟得到了与理论分析相一致的结果，较好的反映了发动机工作状态下内流场和壁面温度的情况，辐射模型的加入。也使我们对小发动机燃烧室内燃气的辐射有了初步的了解。为了更好的对计算结果进行分析，本文还计算了绝热边界条件下发动机的内流场，并将结果与耦合计算情况进行了对比，并估算了发动机外壁面散热对发动机性能的影响。     

卫星推进系统发动机启动过程数值仿真

以一维管道瞬变流理论和特征线方法为基础，建立了仿真系统部件流体动力学模型。考虑燃烧时滞的影响，采用带内插的特征线方法和四阶龙格库塔法，计算了卫星推进系统发动机在启动过程中各参数的变化。仿真结果与卫星推进系统热试车试验结果基本吻合。这表明计算模型较好地描述了发动机启动过程中的水击和管流振荡现象，采用的仿真方法符合精度要求，为卫星推进系统的优化设计提供了参考。     

汽车发动机缸内工作循环过程仿真

为得到汽车发动机缸内混合气体压力、温度等参数的变化曲线,达到优化发动机性能指标的目的;对汽车发动机缸内四个阶段的工作过程进行分析,建立相应的数学模型,利用四阶龙格库塔法求解微分方程;选用直喷式四冲程GW2.8TC型柴油机作为仿真对象,数值仿真结果表明缸内工作过程参数变化曲线与该气缸实际情况是一致的,所建立的数学模型是正确的;得到的汽车发动机在工作循环过程中温度、压强及体积随曲轴转角变化曲线,可用来判断配气相位、压缩比等结构参数对发动机性能指标的影响,优化发动机性能.     

非接触压磁式扭矩传感器

开发研制出1种用于测量汽车发动机扭矩的新型装置，由1个非接触压磁式传感器和1个信号处理器组成。装置响应快，能对各汽缸在每次循环中的平均扭矩进行实时测量。     

基于SolidWorks Simulation的连杆杆身仿真优化

随着汽车技术的日新月异,增压技术的普及,发动机汽缸最大爆发压力越来越大,连杆组的工作环境越来越恶劣,发动机研发工程师们在降低连杆重量的同时也必须保证其可靠性。计算机模拟仿真技术的运用,能帮助工程师们精确、高效地工作,设计的产品结构趋于合理。发动机内连杆将活塞的往复运动转化为曲轴的旋转运动,连杆在发动机正常运转时承载较大的拉、压载荷。本文     

基于简化ASM1模型的污水处理厂曝气系统优化控制

本文建立了基于ASM1模型的曝气系统简化数学模型,在此基础上提出了以曝气能量消耗最小为目标函数的曝气系统优化控制问题。采用联立配置法进行优化问题的求解,把非线性微分代数方程组的DAE系统转化为非线性代数方程组,将动态优化问题转化为非线性规划问题,最后调用IPOPT解法器求解。在动态入水的条件下进行曝气池的优化控制仿真,其结果显示比传统定值PID控制可节约近40%的能耗。     

Direct identification of continuous time delay systems from step responses

In this paper, a simple yet robust method is proposed for identification of linear continuous time delay processes from step responses. New linear regression equations are directly derived from the process differential equation. The regression parameters are then estimated without iterations, and an explicit relationship between the regression parameters and those in the process are given. Due to use of the process output integrals in the regression equations, the resulting parameter estimation is very robust in the face of large measurement noise in the output. The proposed method is detailed for a second-order plus dead-time model with one zero, which can approximate most practical industrial processes, covering monotonic or oscillatory dynamics of minimum-phase or non-minimum-phase processes. Such a model can be obtained without any iteration. The effectiveness of the identification method has been demonstrated through simulation.     

Fuzzy Optimal Control for Bilinear Stochastic Systems with Fuzzy Parameters

In this paper we consider the control problem for a class of partially observed bilinear stochastic systems with fuzzy parameters. Using Takagi–Sugeno fuzzy model, the problem is described by three sets of fuzzy stochastic differential equations: one for the state process, one for the observed process and one for the controller which is assumed to be driven by the observed process. With this formulation, the original stochastic control problem can be treated as a deterministic identification problem in which the controller parameters and the corresponding membership functions are the unknowns. Using a suitable performance index, we have developed a set of necessary conditions for determining the parameters of the controller and the corresponding membership functions. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed fuzzy control scheme.     

Modeling biological gradient formation: combining partial differential equations and Petri nets

Both Petri nets and differential equations are important modeling tools for biological processes. In this paper we demonstrate how these two modeling techniques can be combined to describe biological gradient formation. Parameters derived from partial differential equation describing the process of gradient formation are incorporated in an abstract Petri net model. The quantitative aspects of the resulting model are validated through a case study of gradient formation in the fruit fly.     

Field equations for incompressible non-viscous fluids using artificial intelligence

In this article, we introduce new field equations for incompressible non-viscous fluids, which can be treated similarly to Maxwell’s electromagnetic equations based on artificial intelligence algorithms. Lagrangian and Hamiltonian formulations are used to arrive at field equations that are solved using convolutional neural networks. Four linear differential equations, which describe the two fields, namely, the dynamic pressure and the vortex fields, are derived, and these can be used in place of Euler’s equation. The only assumption while deriving this equation is that the dynamic pressure and vortex fields obey the superposition principle. The important finding to be noted is that Euler’s fluid equations can be converted into field equations analogous to Maxwell’s electromagnetic equations. We solve the flow problem for laminar flow past a cylinder, sphere, and cone in two dimensions similar to the conduction in a uniform electric field and arrive at closed-form expressions. These closed-form expressions, which are obtained for the potentials of fluid flow, are similar to the streamline potential functions in the case of fluid dynamics.

     

Serving many at once: How a database approach can create unity in dynamical ecosystem modelling

Simulation modelling in ecology is a field that is becoming increasingly compartmentalized. Here we propose a Database Approach To Modelling (DATM) to create unity in dynamical ecosystem modelling with differential equations. In this approach the storage of ecological knowledge is independent of the language and platform in which the model will be run. To create an instance of the model, the information in the database is translated and augmented with the language and platform specifics. This process is automated so that a new instance can be created each time the database is updated. We describe the approach using the simple Lotka–Volterra model and the complex ecosystem model for shallow lakes PCLake, which we automatically implement in the frameworks OSIRIS, GRIND for MATLAB, ACSL, R, DUFLOW and DELWAQ. A clear advantage of working in a database is the overview it provides. The simplicity of the approach only adds to its elegance.     

Kernels, regularization and differential equations

Many common machine learning methods such as support vector machines or Gaussian process inference make use of positive definite kernels, reproducing kernel Hilbert spaces, Gaussian processes, and regularization operators. In this work these objects are presented in a general, unifying framework and interrelations are highlighted.With this in mind we then show how linear stochastic differential equation models can be incorporated naturally into the kernel framework. And vice versa, many kernel machines can be interpreted in terms of differential equations. We focus especially on ordinary differential equations, also known as dynamical systems, and it is shown that standard kernel inference algorithms are equivalent to Kalman filter methods based on such models.In order not to cloud qualitative insights with heavy mathematical machinery, we restrict ourselves to finite domains, implying that differential equations are treated via their corresponding finite difference equations.     

Construction of the optimal outcome function for a time-optimal problem on the basis of a symmetry set

The set of the symmetry is introduced into consideration, allowing to construct the optimal outcome function for one class of time-optimal problems. Formulas for a finding of this set on a plane are offered. The results find application at studying of geometry of nonconvex sets, in the theory of optimal control and in the theory of positional differential games at studying nonsmooth features of sets of attainability, stable bridges. Besides results can be useful to experts on the equations of Hamilton-Jacobi types, working within the limits of various concepts of the generalized solutions of these equations.     

Analytical scrutiny of nonlinear equation of hypocycloid motion by AGM

The curve produced by fixed point on the circumference of a small circle of specified radius rolling around the inside of a large circle with bigger radius than the other one is named hypocycloid. In the present paper, a semi-hypocycloid motion of a cylinder has been simulated by extracting the governing nonlinear differential equation of this especial motion, and then, the obtained equation has been solved completely by a simple and innovative approach which we have named it Akbari–Ganji’s method (AGM). On the basis of comparisons which have been made between the gained solutions by AGM, numerical method (Runge–Kutta 4th) and VIM, it is possible to indicate that AGM can be successfully applied for various differential equations. In this paper, a nonlinear vibrational equation has been solved by AGM, and afterward, the application of AGM will be shown in our own specified problem. It is noteworthy that this method has some valuable advantages, for instance in this approach, it is not necessary to utilize dimensionless parameters in order to simplify equation. So there is no need to convert the variables to new ones that heightens the complexity of the problem. Moreover by utilizing AGM, the shortage of boundary condition(s) for solving differential equation will be terminated by using derivatives of main differential equation(s). The results reveal that this method is very effective, simple, reliable and can be applied for many other nonlinear problems.