Simulation of quasi-dimensional combustion model for predicting diesel engine performance

In order to improve the precision of quasi-dimensional combustion model for predicting diesel engine performance and promote the real time operating performance of the simulation model, a new phase-divided spray mixing model is proposed and the quasi-dimensional combustion model of diesel engine working process is developed. The software MATLAB/Simulink is utilized to build the quasi-dimensional combustion model of diesel engine working process, and the performance for diesel engine is simulated. The simulation results agree with experimental data quite well. The comparisons between them show that the relative error of power and brake specific fuel consumption is less than 2.8% and the relative error of nitric oxide and soot emissions is less than 9.1%. By utilization of this simulation model with personal computer, the average computational time for one diesel engine working process is 36 s, which presents good real time operating performance of the model. At the same time, the influence of parameters in calculation of air entrainment on prediction precision of diesel engine’s simulation model is analyzed.     

On a Simplified Delay Time Model of Reliability of Equipment Subject to Inspection Monitoring

The delay time model developed by Christer to determine the reliability consequences of various inspection periods is shown to be considerably simplified if information is obtained only at the inspection times. It is shown that the average reliability over an inspection period may be approximated by a linear function giving a relative error of the order of 10% and by a cubic resulting in a relative error of less than 1%.     

State estimation with guaranteed performance for switching-type fuzzy neural networks in presence of sensor nonlinearities

This paper investigates the state estimation with guaranteed performance for a class of switching fuzzy neural networks. A switching-type fuzzy neural networks (STFNNs) model is proposed which captures external disturbances, sensor nonlinearities, and mode switching phenomenon of the fuzzy neural networks without the Markovian process assumption. For such a model, a state estimation problem is formulated to achieve the guaranteed performance: the estimation error system is exponentially stable with certain decay rate and a prescribed H_∞ disturbance attenuation level. A novel sufficient condition for this problem is established using the Lyapunov functional method and the average dwell time approach, and the estimator parameters are explicitly given. A numerical example is presented to show the effectiveness of the developed results.     

Multiphysics finite element model for the computation of the electro-mechanical dynamics of a hybrid reluctance actuator

ABSTRACT

In hybrid reluctance actuators, the achievable closed-loop system bandwidth is affected by the eddy currents and hysteresis in the ferromagnetic components and the mechanical resonance modes. Such effects must be accurately predicted to achieve high performance via feedback control. Therefore, a multiphysics electro-mechanical finite element model is proposed in this paper to compute the dynamics of a 2-DoF hybrid reluctance actuator. An electromagnetic simulation is adopted to compute the electromagnetic dynamics and the actuation torque, which is employed as input for a structural dynamic simulation computing the electro-mechanical frequency response function. For model validation, the simulated and measured frequency response plots are compared for two actuators with solid and laminated outer yoke, respectively. In both cases, the model accurately predicts the measurement results, with a maximum relative phase error of 1.7% between the first resonance frequency and 1 kHz and a relative error of 1.5% for the second resonance frequency..     

Some properties of a simple moving average when applied to forecasting a time series

Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.     

The new physiology of vision

α-Nitroso-β-naphthol was successfully employed for the gravimetric determination of Mercury (ii) (as nitrate) and in its separation from copper (ii). The former was percipitated at pH 1·9 and the latter at pH 11·00. The precipitate could be dried to constant weight at 110° C. The composition of the complex was established by analysis to be Hg (C₁₀H₆NO₂)₂ 20 mg. of mercury could be estimated with an error of ± 0·3% in pure solutions. Appreciable amounts of copper could be removed in alkaline medium without co-precipitation of mercury. The error was ±0·5%.     

基于时域误差限的大规模系统自适应模型降阶

为满足解大规模动态系统常微分方程组对精度和速度权衡的要求,提出了一种基于误差限的大规模系统自适应模型降阶方法,其中方法的误差分析基于时域最大误差限,降阶方法基于SVD-Krylov子空间的方法.方法既考虑了算法的复杂性,又保证了算法的精度.通过对典型实例分析,结果表明该方法在给定相对误差限10~(-4)下得出的降阶阶数在不同频率下都能给出很好的近似精度,低频1~10Hz平均相对误差为1.1812×10~(-5),高频1~10GHz平均相对误差为5.6408×10~(-5),即在很宽的频率范围内都能满足精度要求.     

A comparison of average and discounted cost models for the dynamic lot size inventory problem

It is a common practice in the inventory literature to use average cost models as approximations to the theoretically correct discounted cost models. An average cost model minimizes the average undiscounted cost per period, while a discounted cost model minimizes the total discounted cost over the problem horizon. This paper attempts to answer an important question: How good are the results (the total discounted costs) for the average cost models compared to those for the discounted cost models? This question has been conclusively answered for the simplest inventory model where the demand rate and other parameters are assumed to remain constant in time. This paper addresses this issue for the first time for the case where demand rates are allowed to be nonstationary in time.A discounted cost model has been developed in the paper to carry out this comparison. It is shown that a simple dynamic programming algorithm can be used to find optimal order policies for the discounted cost model.The effect of the varying interest rates and other parameters on the relative performance of the average cost model has been studied by developing an insightful analysis and also by doing a computational study. The results show that, while the average cost model can cost as much as about 26% more than the discounted cost model in extreme cases, this increase is not significant for the parameter values in the range of the common interest.     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Sequential nonlinear estimation with nonaugmented priors

How can the basic compatibility of theory and observations be investigated for nonlinear processes without requiring stochastic characterizations for residual error terms? The present paper proposes a flexible least-cost approach. For each possible estimatex for the sequence of process states, letc _D (x) andx _M(x) denote the costs incurred for deviations away from the prior dynamic specifications and prior measurement specifications, respectively. Define the cost-efficiency frontier to be the greatest lower bound for the set of all possible cost pairs [c _D (x),c _M(x)], conditional on the given observations. State sequence estimatesx that attain the cost-efficiency frontier indicate the possible ways that the actual process could have developed over time in a manner minimally incompatible with the prior dynamic and measurement specifications. An algorithm is developed for the exact sequential updating of the cost-efficient state sequence estimates as the duration of the process increases and additional observations are obtained.     

Thermal behaviour prediction utilizing artificial neural networks for an open office

This study investigates a neural network-based non-linear autoregressive model with external inputs (NNARX), a non-linear autoregressive moving average model with external inputs (NNARMAX), and a non-linear output error model (NNOE) to predict the thermal behaviour of an open-plan office in a modern commercial building. External and internal climate data recorded over one summer, autumn and winter season were used to build and validate the models. The paper illustrates the potential of using these models to predict room temperature and relative humidity for different time scales ahead (30 min or 2 h ahead). The prediction performance is evaluated using the criteria of goodness of fit, coefficient of determination, mean absolute error and mean squared error between predicted model output and real measurements. To obtain an optimal network structure (avoiding overfitting) after training, a pruning algorithm called optimal brain surgeon (OBS) was used to remove unnecessary input signals, weights and hidden neurons. The results demonstrate that all models provide reasonably good predictions but the NNARX and NNARMAX models outperform the NNOE model. These models can all potentially be used for improving the performance of thermal environment control systems.     

Joint demand and capacity management in a restaurant system

This paper studies the demand and capacity management problem in a restaurant system. A queueing-based optimization model with underlying quasi birth-and-death process and state-dependent functions is developed to address the dynamic and nonlinearity difficulties. In particular, our model explicitly captures the demand changes with respect to the system congestion state on a near real-time dynamic basis. With this framework, we empirically examine the relative performance of commonly used strategies for the case of a local restaurant. The study shows that a strategy that balances service quality and cost yields maximum profit. The result indicates that the traditional view of the conflict between service quality and cost can be overcome by using an interdisciplinary perspective of marketing and operations. Both perspectives should be embraced in academic research and industrial practice in capacity planning decisions for services.     

The use of Bayesian forecasting to make process adjustments during transitions

In many manufacturing operations, a system may exhibit dynamic behavior before reaching a steady-state level. This is most frequently associated with a transition in production like a product style change or a grade change. During the transition phase, the output does not respond instantaneously to a change in input. However, there is typically some information about the past transition phase performance available. We develop an adjustment policy for transition periods based on using a Bayesian forecast to incorporate the prior information. We present computational results showing average process improvements under various system and noise disturbance conditions.     

Inventory control under temporal demand heteroscedasticity

This paper studies an inventory control problem when the variance of demand is time-varying and exhibits temporal heteroscedasticity. We use a first-order autoregressive process to characterize the dynamic changes in the level of demand over time and a GARCH(1, 1) structure to describe the changes in the variance of demand. Under these demand settings, we quantify the effect of a temporal heterogeneous variance on inventory performance for a system controlled via an order-up-to-level policy. We show that the effect of temporal heteroscedasticity on the forecasting accuracy can be additively decomposed from the total forecasting error variance. The decomposition is used to derive the absolute and relative cost deviations when the temporal heteroscedasticity is ignored. The relationship of these cost deviations to demand autocorrelation and replenishment leadtime is investigated. Computational results show that ignoring temporal heteroscedasticity can increase firm’s inventory costs by as much as 30% when demand autocorrelation is highly positive.     

随机激励下四自由度车辆-道路耦合系统动力分析

采用四自由度车辆模型,以 Gauss平稳随机过程模拟路面的不平整度,编制程序得到不同路面等级下的不平整度序列;并将车辆和道路看作一个相互作用的整体系统,建立了车辆 道路耦合系统的动力平衡方程.在对车辆施加随机激励时,为了简化分析过程,避开以往研究中使用随机振动理论求解动轮胎力的复杂性,将得到的路面不平整度序列,直接以向量的形式输入到所建立的动力平衡方程中.基于增量形式的Newmark-β法开发了一个MATLAB程序对该方程进行求解.并对所提出的理论模型进行了试验验证,证明了模型的可靠性.随后,通过一个实例,分析了车速变化、路面等级变化对车辆动荷载系数和车体垂向加速度的影响.最后,对不同路基刚度对车辆振动特性的影响规律进行了探讨.     

Using ARMA models to forecast workpiece roundness error in a turning operation

The paper describes the methodology for developing autoregressive moving average (ARMA) models to represent the workpiece roundness error in the machine taper turning process. The method employs a two stage approach in the determination of the AR and MA parameters of the ARMA model. It first calculates the parameters of the equivalent autoregressive model of the process, and then derives the AR and MA parameters of the ARMA model. Akaike's Information Criterion (AIC) is used to find the appropriate orders m and n of the AR and MA polynomials respectively. Recursive algorithms are developed for the on-line implementation on a laboratory turning machine. Evaluation of the effectiveness of using ARMA models in error forecasting is made using three time series obtained from the experimental machine. Analysis shows that ARMA(3,2) with forgetting factor of 0.95 gives acceptable results for this lathe turning machine.     

Mathematical modelling tools for the optimisation of direct smelting processes

Direct smelting operations involve the strong interaction of a wide range of complex physico-chemical processes. Moreover, for such processes to be efficient, these interactions have to be optimised to yield the desired set of chemical reactions and exchanges of heat and mass amongst the variety of gaseous, liquid and solid phases. This paper focuses upon a consideration of Computational Fluid Dynamics (CFD) based models developed to represent the HIsmelt^® direct smelting process. The models are extremely sophisticated and at every stage have challenged the limits of CFD technology, as well as the adequacy of constitutive sub-models to represent the chemical reaction/combustion phenomena. From an unprecedented synergy amongst process metallurgists, experimental scientists and CFD modellers, sophisticated, comprehensive and well-validated models of the process have evolved. The paper highlights some of the key state-of-the-art CFD techniques developed for the models and the role of specially designed experiments in parameter estimation and validation against plant measurements.     

组合GM(1,1)幂模型及其应用

GM(1,1)幂模型是灰色Verhulst模型的推广.由于初始条件选取影响GM(1,1)幂模型的精度,将平均相对误差函数分别看成是幂指数、发展系数、灰作用量的函数,利用蚁群算法进行参数辨识,从而建立多个单项GM(1,1)幂模型.利用这些单项模型建立了线性组合GM(1,1)幂模型,组合权系数利用最大相对误差最小化原则采用粒子群算法确定.实例表明,组合GM(1,1)幂模型的建模精度高于传统GM(1,1)幂模型,同时也说明方法是有效的和可行的,具有重要的理论意义.     

A model of parameter adaptive law with time varying function for robot control

In this article, a model of adaptive control law for controlling robot manipulators using the Lyapunov based theory of guaranteed the stability of uncertain a system is derived. The novelty of obtained result is that the adaptive control algorithm is developed using a parameter estimation rule depending on manipulator kinematic, dynamic parameters and tracking error. This study is supported by a computer simulation and tracking performance has been improved.     

模糊概率神经网络水质评价模型及其应用

鉴于水质类型和分级标准存在模糊性,将模糊数学中的相对隶属度理论和概率神经网络和相结合,构建了模糊概率神经网络水质评价模型(FPNN).阐明了该模型的构建方法,提出了基于指标相对隶属度矩阵插值构建学习样本的方法,并将该模型应用于实际水质评价.通过与综合评判法、属性识别法和BP网络法的比较,验证了该模型操作简便,评价结果客观可靠.