基于多目标模拟预算最优分配的飞机维护计划

将多目标模拟预算最优分配算法和偏好规划引入到飞机维护研究中。模型分为优化阶段和决策阶段。在优化阶段中,将影响飞机维护计划的关键因素及优化目标用随机仿真模型表示,将偏好信息引入优化模型,提出了偏好多目标模拟预算最优分配算法,通过该算法获得成对非支配集合。采用多属性决策模型和偏好规划模型从成对非支配解中确定偏好非成对支配解。最后,运用决策准则获得最优方案。仿真表明该方法的有效性。

CFRP加固混凝土梁配布(筋)率优化与造价模拟分析

针对CFRP加固混凝土梁配布(筋)率优化问题,提出了在考虑材料价格的基础上,采用基于应变协调条件和加固混凝土梁正截面受弯承载力的计算方法,利用拉格朗日函数推导出了配布(筋)率优化表达式,并分别运用Excel软件和预算模拟软件进行分析.结果表明:优化配布(筋)率与弹性模量之比、价格之比、抗拉强度之比有关;纤维复合材料的加固量一般在2~5层;当钢筋级别较高、混凝土强度等级较低(C20)时,不宜用碳纤维布加固,这与《混凝土结构加固设计规范》(GB50367-2013)规定较符合.

模拟核算在航空集团公司运用初探

模拟核算作为一种行之有效的管理模式在西南航空公司进行了成功试点 ;试点结果表明 ,模拟核算中的建立责任中心、制定模拟价格、编制模拟预算等一系列基本做法在航空集团公司中具有进一步推广应用的价值。

大数据技术在预算编制与执行中的应用

随着大数据技术的快速发展,其在预算编制与执行中的应用逐渐成为国内外企业管理的热门话题。本文将探讨大数据技术在预算编制与执行中的应用,重点关注数据收集与分析、预测与预算模拟等方面。通过大数据技术,企业能够更准确地了解内外部的各种因素,为预算编制提供可靠依据,同时也可以预测未来的经营业绩,优化预算方案。然而,大数据技术的应用也面临着一些挑战,如数据隐私与安全问题。因此,本文探讨了大数据技术在预算编制与执行中的应用,以期为企业在预算编制与执行中充分发挥大数据技术的优势提供参考。

浅谈仓储成本的预测与决策

介绍了仓储成本预测与决策的概念与作用,阐述了仓储成本预测与决策的基本程序,并就预测方法和预测结果的关键问题,提出了处理和分析环节。

Uncertainty in Dust Budget over East Asia Simulated by WRF/Chem with Six Different Dust Emission Schemes

In this study, the dependence of dust budgets on dust emission schemes is investigated through the simulation of dust storm events, which occurred during 14–25 March 2002, over East Asia, by the Weather Research and Forecasting with Chemistry(WRF/Chem) model coupled with six dust emission schemes. Generally, this model can reasonably reproduce the spatial distribution of surface dust concentration; however, the simulated total dust budget differs significantly with different emission schemes. Moreover, uncertainties in the simulated dust budget vary among regions. It is suggested that the dust emission scheme affects the regional dust budget directly through its impact on the total emitted dust amount; however, the inflow and outflow of dust aerosols simulated by different schemes within a region also depend on the geographical location of the dust emission region. Furthermore, the size distribution of dust particles for a specific dust emission scheme has proven to be important for dust budget calculation due to the dependence of dust deposition amount on dust size distribution.

Simulating Net Carbon Budget of Forest Ecosystems and Its Response to Climate Change in Northeastern China Using Improved FORCCHN

As dominant biomes,forests play an important and indispensable role in adjusting the global carbon balance under climate change.Therefore,there are scientific and political implications in investigating the carbon budget of forest ecosystems and its response to climate change.Here we synthesized the most recent research progresses on the carbon cycle in terrestrial ecosystems,and applied an individual-based forest ecosystem carbon budget model for China(FORCCHN) to simulate the dynamics of the carbon fluxes of forest ecosystems in the northeastern China.The FORCCHN model was further improved and applied through adding variables and modules of precipitation(rainfall and snowfall) interception by tree crown,understory plants and litter.The results showed that the optimized FORCCHN model had a good performance in simulating the carbon budget of forest ecosystems in the northeastern China.From 1981 to 2002,the forests played a positive role in absorbing carbon dioxide.However,the capability of forest carbon sequestration had been gradually declining during the the same period.As for the average spatial distri-bution of net carbon budget,a majority of the regions were carbon sinks.Several scattered areas in the Heilongjiang Province and the Liaoning Province were identified as carbon sources.The net carbon budget was apparently more sensitive to an increase of air temperature than change of precipitation.

Prediction of Planing Craft Motion Based on Grey System Theory

In order to minimize the harm caused by the instability of a planing craft, a motion prediction model is essential. This paper analyzed the feasibility of using an MGM（1,N） model in grey system theory to predict planing craft motion and carried out the numerical simulation experiment. According to the characteristics of planing craft motion, a recurrence formula was proposed of the parameter matrix of an MGMfl,N） model. Using this formula, data can be updated in real-time without increasing computational complexity significantly. The results of numerical simulation show that using an MGM（1,N） model to predict planing motion is feasible and useful for prediction. So the method proposed in this study can reflect the planing craft motion mechanism successfully, and has rational and effective functions of forecasting and analyzing trends.

Research on Simulation and Test of the Nonlinear Responses for the Hydraulic Shock Absorber

Basically on the multi-body system dynamics,the virtual prototype of the hydraulic shock absorber for the bench test is developed in the ADAMS environment.Dynamic behaviors of the absorber are studied by both computer simulation and real test.Numerical predictions of dynamic responses are produced by the established virtual prototype of the absorber and compared with experimental results.It has been shown from the comparison that the vibration behaviors of the prototype with hysteretic damping characteristics are considered to be more identical with the bench test results than those of the same prototype with piecewise linear damping properties are.The current virtual prototype of the shock absorber is correct and can be a developing terrace for the optimizing design of the absorber and matching capability of the whole car.

Computational Simulation to Predict the Syngas Composition Produced during Charcoal Gasification

Charcoal gasification could mitigate the energetic problems in the rural zones since these regions have considerable amounts of wood, which is the base of such a fuel available. This paper presents some CFD （computational fluid dynamic） predictions of the experimental results obtained from the fixed bed gasification of charcoal made in a pilot-scale downdraft reactor using air, which was designed and built by the Research Group in Clean Development Mechanisms and Energy Management, from the National University of Colombia. The quality of the syngas obtained from the process was evaluated through the CO and CO2 percentages measured in its composition. The performance at various air flow rates （measured at the system entrance, through an analog flow meter） is evaluated with the help of 11 thermocouples, which give the information to create a temperature profile, and three load cells to measure the solid fuel conversion rate. To simulate the process, the information from temperature profile, charcoal proximate analysis, air flow meter and load cells were taken as inputs and the syngas composition was obtained as the result from the calculation. The domain was defined as 2D with an axis-symmetric description, using quads as mesh elements. The calculation and results were performed in a CFD commercial code widely used for this type of simulations： ANSYS FLUENT. The predictions made by the software were validated with the experimental results obtained in the laboratory.

Identification of Potential Flavonoid Inhibitors of the SARS-CoV-2 Main Protease 6YNQ:A Molecular Docking Study

Objective Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the causative agent for coronavirus disease 2019(COVID-19),is responsible for the recent global pandemic.As there are no effective drugs or vaccines available for SARS-CoV-2,we investigated the potential of flavonoids against SARS-CoV-2 main protease 6YNQ.Methods In silico molecular simulation study against SARS-CoV-2 main protease 6YNQ.Results Among the 21 selected flavonoids,rutin demonstrated the highest binding energy(−8.7 kcal/mol)and displayed perfect binding with the catalytic sites.Conclusions Our study demonstrates the inhibitory potential of flavonoids against SARS-CoV-2 main protease 6YNQ.These computational simulation studies support the hypothesis that flavonoids might be helpful for the treatment of COVID-19.

浅析国际工程成本核算及投标报价方法

在境外不同国别开展市场开拓,因地域、施工环境、法律法规、标准规范等不尽相同,项目成本测算和投标报价没有统一的固定体系。本文结合自身从事多年海外开发工作实践,通过分析“实物量法”和“模拟标后预算清单成本法”在核算项目成本核算中的应用,以及通过分析“清单定额概(预)算法”和“借鉴同类型项目市场中标价法”在投标报价中的应用,确定更适合属地国和企业自身的成本、报价方法,进而达到做实项目成本、做优投标报价、做大企业效益的目的。

Conventional and Predictive Control Algorithms for Controlling Nonlinear Processes Using Multiple-Model Approach

The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear process dynamics relating to gain and time constant variations. The multi-model strategy was implemented on several controllers such as Smith-Predictor using PI （Proportional-lntegral） and GPC （Generalized Predictive Control）. Computer simulations and experiments were conducted on several nonlinear systems and compared to the original form of these controllers. The enhanced approach was tested on controlling the screw speed of an injection molding machine and temperature of a steel cylinder.

Progress in mesoscopic modeling of microstructure evolution in steels

The mesoscopic modeling developed rapidly in the past three decades is a promising tool for predicting and understanding the microstructure evolution at grain scale.In this paper,the recent development of mesoscopic modeling and its application to microstructure evolution in steels is reviewed.Firstly,some representative computational models are briefly introduced,e.g.,the phase field model,the cellular automaton model and the Monte Carlo model.Then,the emphasis is put on the application of mesoscopic modeling of the complex features of microstructure evolution,including solidification,solid-state phase transformation,recrystallization and grain growth.Finally,some issues in the present mesoscopic modeling and its perspective are discussed.

Seeing permeability from images： fast prediction with convolutional neural networks

Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples,(2) computation of permeability via fluid dynamics simulations,(3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny–Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity.

Dynamic simulation based on Jacobian-free Newton-GMRES methods with adaptive preconditioner for power systems

Dynamic simulation is one of the most complex and important computations for power systems researches.Traditional solutions based on normal Newton iterations almost all depend on evaluations of Jacobian matrixes,which increases the programming complexity of and limits the parallelizability of the whole simulation.In this paper,a new adaptive preconditioned Jacobian-free Newton-GMRES(m)method is proposed to be applied to dynamic simulations of power systems.This new method has totally Jacobian-free characteristics,which saves calculations and storages of Jacobian matrixes and features strong parallelizability.Moreover,several speedup strategies are introduced to enhance efficiency and parallelizability of overall computations.Numerical tests are carried out on IEEE standard test systems and results show that in series computing environment,simulations based on the proposed method have comparable speed to those based on classical Newton-Raphson methods.

Numerical Simulation for Fan Broadband Noise Prediction

In order to elucidate the broadband noise of fan,the numerical simulation of fan operating at two different rotational speeds is carried out using the three-dimensional unsteady Reynolds-averaged Navier-Stokes (URANS) equations.The computed results are compared to experiment to estimate its accuracy and are found to show good agreement with experiment.A method is proposed to evaluate the turbulent kinetic energy in the framework of the Spalart-Allmaras one equation turbulence model.From the calculation results,the turbulent kinetic energy is visualized as the turbulence of the flow which leads to generate the broadband noise,and its noise sources are identified.

Investigations on Several Mechanical Problems in Windblown Sand Movement

It is very necessary for investigation on mechanism of windblown sand movement to understand and find out effective measures of preventing and reducing windblown sand, This also deals with some general features and hot spots in the scientific forelands. such as multi-scale problems. interactions among. multi-physical-fields, randomness and nonlinearity as well as complex systems. In recent years, a series of experiments in wind tunnels and theoretical modeling as well as computer simulation have been taken by our research group （the Laboratory of Environmental Mechanics on Windblown Sand Movement in Lanzhou University ）in a cross-disciplinary （mechanics and geography） viewpoint. Several original and essential studies were explored such as the main regularities of charges on sand particles, the mechanisms of electric field in windblown sand flux, the effects induced by the electric field on the flux, the microwave propagations, the evolution process of windblown and flux under mutual couple interations among several physical fields, and the simulation of the main features of Aeolian sand ripples.

A new approach for Bayesian model averaging

Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.