Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed ...Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.展开更多
目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能...目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能够有效克服传统模拟存在的问题。本文简述了DDDAS提出的历史背景和基本概念,以该系统在农田温室气体排放、农田定量灌溉和河流污染监控中的具体应用为例,进一步阐明了DDDAS在农业和环境科学中应用的思路和方法。并提出DDDAS应用中需要解决的一些具体问题。展开更多
Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including cryst...Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including crystallizing time, flow rate of melt and temperature of glycerine bath, on purity of DDDA and crystallizing rate were investigated. Over 99% (by mole) DDDA was obtained for a feed composition of 96% (by mole). The main factors affecting the separation efficiency are flow rate of melt and temperature of glycerine bath. The crystallizing layer of DDDA was further purified by sweating and blasting. A set of optimized operation data are provided for better understanding the mechanism of heat and mass transfer in FFC, and for further industrial application of DDDA purification process.展开更多
Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-l...Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF （B3DPF） is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies （sensor data management, parameters used in the weight computation, resampling） of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.展开更多
文摘Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.
文摘目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能够有效克服传统模拟存在的问题。本文简述了DDDAS提出的历史背景和基本概念,以该系统在农田温室气体排放、农田定量灌溉和河流污染监控中的具体应用为例,进一步阐明了DDDAS在农业和环境科学中应用的思路和方法。并提出DDDAS应用中需要解决的一些具体问题。
文摘Separation and purification of dodecanedioic acid (DDDA) from its homologous compounds were studied experimentally by falling film crystallization (FFC). The influences of various operation parameters, including crystallizing time, flow rate of melt and temperature of glycerine bath, on purity of DDDA and crystallizing rate were investigated. Over 99% (by mole) DDDA was obtained for a feed composition of 96% (by mole). The main factors affecting the separation efficiency are flow rate of melt and temperature of glycerine bath. The crystallizing layer of DDDA was further purified by sweating and blasting. A set of optimized operation data are provided for better understanding the mechanism of heat and mass transfer in FFC, and for further industrial application of DDDA purification process.
基金supported by the State Basic Scientific Research of National Defense (No. c0420110005)13th Five-Year Key Basic Research Project (No. JCKY2016206B001)the Six talent peaks project in Jiangsu Province (No. XXRJ-004)
文摘Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF （B3DPF） is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies （sensor data management, parameters used in the weight computation, resampling） of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.
