Assimilation of Lagrangian data in oceanography

Within the framework of Global Ocean Data Assimilation Experiment (GODAE), an increasing amount of data are available. A crucial issue for oceanographers is to exploit at best these observations, in order to improve models, climatology, forecasts, etc. Thanks to the international program Argo and to more localized experiments, a new type of data is now available: positions of floats drifting at depth in the ocean. Unlike other data, mainly Eulerian, these ones are Lagrangian: the measuring instrument move in the flow. I will briefly described methods about 4D-Var assimilation of Lagrangian data in the OPAVAR ocean model, and show some results assessing the complementarity with temperature data. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the approximation of empirical data for service system simulations

The difficulties associated with parameter estimation for phase-type approximations of empirical data distributions in queue modelling are well known. While significant progress has been achieved in improving such approximations, difficulties in parameter estimation still limit the extent to which queue modelling is applied in practice. This paper presents a simplified technique for approximating empirical data in service system simulations, based on the specialised Cox phase-type distribution. When utilised in simulation modelling of a service system, the specialised Cox distribution is shown to provide improved approximations to the combined waiting and service time distribution without the need for complex parameter estimation techniques. This approach should enable much greater flexibility in the application of queue modelling to service systems.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Numerical simulations of traffic data via fluid dynamic approach

In this paper we introduce a simulation algorithm based on fluid dynamic models to reproduce the behavior of traffic in a portion of the urban network in Rome. Numerical results, obtained comparing experimental data with numerical solutions, show the effectiveness of our approximation.     

TheSELENA teaching/research system for automated design of remote data processing networks

Translated fromIssledovaniya po Prikladnoi Matematike, No. 19, 1992, pp. 14–23.     

The graphical data representation subsystem of theSelena teaching/research system for automated design of remote data processing networks

Translated fromIssledovaniya po Prikladnoi Matematike, No. 19, 1992, pp. 9–14.     

Mesh-Free Interpolant Observables for Continuous Data Assimilation

This paper is dedicated to the expansion of the framework of general interpolant observables introduced by Azouani, Olson, and Titi for continuous data assimilation of nonlinear partial differential equations. The main feature of this expanded framework is its mesh-free aspect, which allows the observational data itself to dictate the subdivision of the domain via partition of unity in the spirit of the so-called Partition of Unity Method by Babuska and Melenk.As an application of this framework...     

Mathematical modelling of a hydraulic accumulator for hydraulic hybrid drives

ABSTRACT

Hydraulic accumulators are used as energy storages in a wide area of applications. In particular, in automotive hybrid drive-trains, this type of energy storage is an interesting alternative to today’s common strategies like chemical batteries or flywheels. This article deals with the mathematical modelling of a hydraulic accumulator for passenger vehicles, which comprises a carbon fibre reinforced plastic (CFRP) body and aluminium piston. The thermodynamical behaviour of the oil and gas as well as the interaction with the CFRP body is investigated in detail. Starting from a complex model, two models of reduced complexity are derived. The validation of these models with measurement data from a test drive with a prototype series hydraulic hybrid drive-train proves their high accuracy.     

Interacting with local and remote data repositories using the stashR package

The stashR package (a Set of Tools for Administering Shared Repositories) for R implements a basic versioned key-value style database where character string keys are associated with data values. Using the S4 classes ‘localDB’ and ‘remoteDB’, and associated methods, versioned key-value databases can be either created locally on the user’s computer or accessed remotely via the Internet. The stashR package can enhance reproducible research by providing a ‘localDB’ database format for the caching of computations which can subsequently be stored on the Internet. To reproduce a particular computation, a reader can access the ‘remoteDB’ database and obtain the associated R objects. This work was supported in part by the Johns Hopkins Training Program in the Epidemiology and Biostatistics of Aging (NIA T32 AG00247) and the Faculty Innovation Fund, Johns Hopkins Bloomberg School of Public Health.     

基于局地和非局地观测算子的GPS 掩星资料后向映射四维变分同化研究

本文通过在区域暴雨预报模式(AREM) 的后向映射四维变分同化系统(AREM-B4DVar) 中引入GPS 掩星折射率局地和非局地两种观测算子, 使得该系统具备了同化全球定位系统(GPS) 掩星折射率资料的能力, 并采用台湾地区与美国联合执行的气象、电离层和气候星座观测系统计划(COSMIC计划) 探测得到的GPS 掩星折射率资料和常规探空资料, 对2007 年7 月4 日至5 日发生在我国江淮流域的暴雨个例进行了同化预报试验. 结果表明, 在同化系统中采用局地和非局地两种观测算子, 加入GPS 掩星折射率资料后, 均可以提高观测资料附近初值的分析质量, 从而在改进24 小时的降水预报中起到正效果; 基于非局地观测算子的掩星折射率资料同化可以通过大气非局地的约束, 进一步改进基于局地观测算子掩星折射率资料同化的不足; 在常规资料的基础上加入掩星折射率资料, 可以使同化系统进一步改进初值分析质量和24 小时预报效果, 尤其能更好地发挥非局地观测算子的作用.     

人口模型变分同化方法的理论分析

研究人口的阻滞增长模型 ,采用资料变分同化方法及最优控制的技巧 ,从理论上对模型中的最大人口容量和固有增长率进行最优确定     

Antithetic-variate splitting for steady-sate simulations

Obtaining precise estimates of parameters of infinite-horizon or steady-state simulations can be expensive because of the need to discard initial outputs to mitigate the effects of initial conditions. We consider splitting independent replications at the point of output truncation into dependent replications to reduce point estimator variance and/or simulation cost.     

Validation for turbulent mixing simulations

We compare simulation studies of acceleration driven mixing (the Rayleigh-Taylor instability) to laboratory experiments. Good agreement is obtained for variables describing the large scale aspects of the mixing process. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation

Data assimilation refers to the methodology of combining dynamical models and observed data with the objective of improving state estimation. Most data assimilation algorithms are viewed as approximations of the Bayesian posterior (filtering distribution) on the signal given the observations. Some of these approximations are controlled, such as particle filters which may be refined to produce the true filtering distribution in the large particle number limit, and some are uncontrolled, such as ensemble Kalman filter methods which do not recover the true filtering distribution in the large ensemble limit. Other data assimilation algorithms, such as cycled 3DVAR methods, may be thought of as controlled estimators of the state, in the small observational noise scenario, but are also uncontrolled in general in relation to the true filtering distribution. For particle filters and ensemble Kalman filters it is of practical importance to understand how and why data assimilation methods can be effective when used with a fixed small number of particles, since for many large-scale applications it is not practical to deploy algorithms close to the large particle limit asymptotic. In this paper, the authors address this question for particle filters and, in particular, study their accuracy (in the small noise limit) and ergodicity (for noisy signal and observation) without appealing to the large particle number limit. The authors first overview the accuracy and minorization properties for the true filtering distribution, working in the setting of conditional Gaussianity for the dynamics-observation model. They then show that these properties are inherited by optimal particle filters for any fixed number of particles, and use the minorization to establish ergodicity of the filters. For completeness we also prove large particle number consistency results for the optimal particle filters, by writing the update equations for the underlying distributions as recursions. In addition to looking at the optimal particle filter with standard resampling, they derive all the above results for (what they term) the Gaussianized optimal particle filter and show that the theoretical properties are favorable for this method, when compared to the standard optimal particle filter.     

Adaptive accuracy control for Car-Parrinello simulations

Summary. The Car-Parrinello (CP) approach to ab initio molecular dynamics serves as an approximation to time-dependent Born-Oppenheimer (BO) calculations. It replaces the explicit minimization of the energy functional by a fictitious Newtonian dynamics and therefore introduces an artificial mass parameter which controls the electronic motion. A recent theoretical investigation shows that the CP-error, i.e., the deviation of the CP–solution from the BO-solution decreases like asymptotically. Since the computational effort increases like , the choice of has to find a compromise between efficiency and accuracy. The asymptotical result is used in this paper to construct an easily implemented algorithm which automatically controls : the parameter is repeatedly adapted during the simulation by choosing as large as possible while pushing an error measure below a user-given tolerance. The performance and reliability of the algorithm is illustrated by a typical example. Received March 10, 1998     

Preconditioners for hydropower plant flow simulations

We describe the main features of a numerical simulator currently under development for studying water physico-chemical properties during the flooding of hydroelectric plants reservoirs. The work is sponsored by the Brazilian Electric Energy National Agency (ANEEL) and conducted in a joint work of Brazilian universities researchers, with Furnas Centrais Elétricas S. A., the leading Brazilian power utility company. An overview of the simulator requirements is given. The software modules and engineering solutions are briefly discussed, including the finite element based transport module. Some results are presented and discussed. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive numerical components for PDE-based simulations

Numerical simulations based on nonlinear partial differential equations (PDEs) using Newton-based methods require the solution of large, sparse linear systems of equations at each nonlinear iteration. Typically in large-scale parallel simulations such linear systems are solved by using preconditioned Krylov methods. In many cases, especially in time-dependent problems, the attributes of the linear systems can change throughout the stimulation, potentially leading to varying times for solving the linear systems during different nonlinear iterations. We present an approach to characterizing the nonlinear and linear system solution and using the resulting application performance information to dynamically select linear solver methods, with the goal of reducing the total time to solution. We discuss the effect of these adaptive heuristics on fluid dynamics and radiation transport codes. We also introduce general component infrastructure to support dynamic algorithm selection and adaptation in applications involving the solution of nonlinear PDEs. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Optimal operation for a river fishway

In this paper we present an optimal control problem related to the optimal management of a vertical slot fishway. We introduce the mathematical statement of the environmental problem, we derive an optimality condition for characterizing its optimal solutions, and we give an optimization algorithm for their numerical computation. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

不可微预报系统的广义变分同化方法及数值试验

讨论了不可微预报系统中的广义变分同化方法．对于不可微预报系统,由于不可微性,系统不存在切线性系统,而切线性系统的不存在,使得无法用通常的途径导出伴随系统A·D2引进不可微系统的弱形式后,可以不考虑切线性系统,而直接导出伴随系统．主要就3种形式的问题展开了讨论,第1种为低维系统,第2种情形为高维系统整体观测资料,第3种情形为高维系统局部观测资料．可以称此方法为结合反问题思想的广义变分同化方法．