Estimating the Purse Seine Net Geometry during a Hauling Operation Using a Data Assimilation Method

Modular Multilevel Converter (MMC) is a mainstream technology in the field of flexible DC transmission engineering. The establishment of electromagnetic transient simulation model of MMC is the basis for theoretical research and engineering application. The Thévenin’s equivalent-based electromagnetic transient model of MMC has high simulation accuracy and less simulation time, which is widely used in the simulation system of MMC with high level number. However, the model is difficult to simulate the internal electromagnetic transient process of the submodule, such as the inability to effectively measure the current stress of the IGBT and the continuity diode when testing and analyzing the equivalent experimental stress, as well as the inability to simulate and analyze the internal faults of the submodule. In order to solve this problem, this paper proposes a highly accurate and efficient electromagnetic transient simulation model of MMC converter based on PSCAD/EMTDC. Specifically, the internal sub-modules of the MMC model are divided into two categories: special sub-modules and general sub-modules. For the special sub-modules, the detailed models are established respectively; for the general sub-modules, the Thévenin’s equivalent model is established. The model has high simulation speed and accuracy, while the interface is generalized and preserves the station-level and valve-level control strategies. The simulation is carried out by PSCAD/ EMTDC, and the experimental results verify the correctness and effectiveness of the proposed modeling method.

Long term variation in transparency in the inner area of Ariake Sea

Copula-based dynamic models for multivariate time series

This paper describes a study finding on the determination of various policy alternatives based on biological, technological, social and economical aspects that may be suitably applied for development of purse seine fishery. This study was conducted in the District of Parigi Moutong, Central Sulawesi from January to April 2006. This study involved 5 purse seines and their owners determined using a cencus sampling technique and 20 purposedly sampled fishermen. The primnary data collected were the yield of captured fish. The policy analyses was done using an Analytical Hierarchi Process ( AHP) with Expert Choice 9.0 software on a personal computer. Results showed that adding purse seines (a ranking value of 0.49) was the most important policy that may be taken in overall effort in a sustainalbe fishery development while maintaining their current number (a values of 0.32) was considered as a moderate choice and removing it was shown to be a contradictory policy

An Adaptive Tracking Control Approach for Nonholonomic Mobile Robot

We apply information theory within an ensemble‐based data assimilation approach and define information matrix in ensemble subspace. The information matrix in ensemble subspace employs a flow‐dependent forecast error covariance and it is of relatively small dimensions (equal to the ensemble size). The information matrix in ensemble subspace can be directly linked to the information matrix typically used in non‐ensemble‐based data assimilation methods, such as the Kalman Filter (KF) and the three‐dimensional variational (3D‐Var) methods, which provides a framework for consistent comparisons of information measures between different data assimilation methods.We evaluate information measures, such as degrees of freedom for signal, within the Maximum Likelihood Ensemble Filter (MLEF) data assimilation approach and compare them with those obtained using the KF approach and the 3D‐Var approach. We assimilate model‐simulated observations and use the Goddard Earth Observing System Single Column Model (GEOS‐5 SCM) as a dynamical forecast model.The experimental results demonstrate that the proposed framework is useful for comparing information measures obtained in different data assimilation approaches. These comparisons indicate that using a flow‐dependent forecast error covariance matrix (e.g. as in the KF and the MLEF experiments) is fundamentally important for adequately describing prior knowledge about the true model state when calculating information measures of assimilated observations. We also demonstrate that data assimilation results obtained using the KF and the MLEF approach (when ensemble size is larger than 10 ensemble members) are superior to the results of the 3D‐Var approach. Copyright © 2007 Royal Meteorological Society

Summary Daqing Oilfield has successfully achieved the largest scale chemical flooding industrial application in the world, the annual oil production by chemical flooding has been more than 10 million tons for 20 years. The reservoir numerical simulation technology has played an important role to ensure better oil recovery effect for chemical flooding. With the continuous expansion of chemical flooding application scale in Daqing Oilfield, the reservoir numerical simulation for chemical flooding has ever encountered a series of new challenges need to be resolved, such as mathematical characterization of oil displacement process for chemical flooding, computation speed and simulation accuracy, etc. This paper focuses on the existing difficulties in the reservoir numerical simulation for chemical flooding, developing a series of novel techniques for chemical flooding numerical simulation suitable for large scale field application of chemical flooding. Firstly, based on the new cognation of chemical flooding theory in recent years, a chemical flooding mathematical model has been developed in order to simulate the complex oil recovery process mechanism of chemical flooding more accurately, the aspect of oil displacement mechanism description, in addition to the mobility control and capillary desaturation processes, but also adds oil displacement model for chemical flooding which includes: polymer elasticity improves microscopic oil displacement efficiency, the multiple molecular weight polymers process mechanism, compound synergistic effect of chemical flooding process with low surfactant concentration, multi-surfactant compound effect, chemical competitive adsorption, reservoir wettability altering, alkali corrosion and scaling. Secondly, based on operator splitting, potential characteristics, and multi-grid method, a fast solution scheme with high stability for complex mathematical model of chemical flooding has been developed, increasing the computation speed to a large extent. Thirdly, a series of numerical simulation parameters measuring experiments have been carried out, obtaining the accurate parameters for chemical flooding numerical simulation. Finally, based on Ensemble Kalman filter algorithm, a data assimilation method has been constructed, increasing numerical simulation history matching accuracy for chemical flooding. The established reservoir numerical simulation novel method for chemical flooding has been successfully applied to chemical flooding projects in Daqing Oilfield, and an application case of the chemical flooding project in Daqing Oilfield is given in this paper, the numerical simulation results for chemical flooding demonstrate that established reservoir numerical simulation technology increases both the computation speed and simulation accuracy.

This paper present a novel logic-timing simulator that includes the degradation delay model (DDM) called HALOTIS. DDM obtains high accuracy in glitches treatment and it has been included in HALOTIS simulation engine. Also, it is possible to estimate switching activity using it, and the results show a high accuracy in simulations when these are compared to HSPICE.

A novel grid generation method based on multi-resolution data fusion for 2D shallow water models

Data assimilation is the process of estimating the state of dynamic systems (linear or nonlinear, Gaussian or non-Gaussian) as accurately as possible from noisy observational data. Although the Three Dimensional Variational (3D-VAR) methods, Four Dimensional Variational (4D-VAR) methods and Ensemble Kalman filter (EnKF) methods are widely used and effective for linear and Gaussian dynamics, new methods of data assimilation are required for the general situation, that is, nonlinear non-Gaussian dynamics. General Bayesian recursive estimation theory is reviewed in this thesis. The Bayesian estimation approach provides a rather general and powerful framework for handling nonlinear, non-Gaussian, as well as linear, Gaussian estimation problems. Despite a general solution to the nonlinear estimation problem, there is no closed-form solution in the general case. Therefore, approximate techniques have to be employed. In this thesis, the sequential Monte Carlo (SMC) methods, commonly referred to as the particle filter, is presented to tackle non-linear, non-Gaussian estimation problems. In this thesis, we use the SMC methods only for the nonlinear state estimation problem, however, it can also be used for the nonlinear parameter estimation problem. In order to demonstrate the new methods in the general nonlinear non-Gaussian case, we compare Sequential Monte Carlo (SMC) methods with the Ensemble Kalman Filter (EnKF) by performing data assimilation in nonlinear and non-Gaussian dynamic systems. The models used in this study are referred to as state-space models. The Lorenz 1963 and 1966 models serve as test beds for examining the properties of these assimilation methods when used in highly nonlinear dynamics. The application of Sequential Monte Carlo methods to different fixed parameters in dynamic models is considered. Four different scenarios in the Lorenz 1063 [sic] model and three different scenarios in the Lorenz 1996 model are designed in this study for both the SMC methods and EnKF method with different filter siz

Data assimilation (DA) based on Bayes' theorem helps improve the accuracy of numerical models and simultaneously enables the estimation of unknown parameters used in the numerical model by combining simulation results with observational data. We applied the local ensemble transform Kalman filter (LETKF), a computationally efficient and accurate DA methodology, to a phase-field model of dendritic solidification in a binary alloy. We demonstrated the efficiency of LETKF through numerical experiments (twin experiments) wherein we estimated the unknown state of the solidification and the model parameters from synthetic observation datasets of a growing dendrite morphology. Results of the twin experiments show that using LETKF we could successfully estimate three-dimensional (3D) time evolution of the solute concentration-field in the liquid phase. Further, we could inversely identify multiple model parameters, including interfacial energy between the solid and liquid phases and the solute diffusion coefficient in the liquid phase only from the 3D morphological information of a growing dendrite. We demonstrated that the LETKF-based DA method is a promising methodology for performing accurate phase-field simulations in conjunction with experimental data.

The integration of lava flow forecasting models with satellite remote sensing techniques marks a significant advancement in quantitative hazard assessment for effusive volcanic eruptions. Within the framework of the DT-Geo project, we are developing a lava flow workflow that harnesses High-Performance Computing (HPC) capabilities, aiming to improve hazard assessment through ensemble-based and data assimilation methods. At the core of the workflow is the VLAVA code, which simulates the lava flow propagation, with temperature-dependent viscosity over a complex topography, erupting from one or more vents. The simulation runs for a given time period (order of one or more days), after which the simulated deposit is compared to the observed  lava flow field and, eventually, the observations are assimilated into the model for a further simulation. The measured data include changes of the eruption source parameters and/or the extension and temperature of the lava flow field. These are derived from direct observations on the field or by remote sensing from airborne, drones or satellites (e.g.: Pléiades, EOS-ASTER, SEVIRI, MODIS, VIIRS, Landsat, Sentinel, etc.). Data assimilation is conducted using PDAF, a dedicated software offering various approaches, including ensemble-based Kalman filters, nonlinear filters, and variational methods. The model output provides the potentially impacted area by lava flows, including thickness and temperature distribution, for both a single scenario (utilized for estimating the impact of a lava flow) and an ensemble of weighted scenarios (for generating probabilistic hazard maps). We present the overarching concept of the workflow and share preliminary results obtained for historical eruptions of Mount Etna.

Estimates of the meridional mass transport of Antarctic Bottom Water, calculated using the coupled ocean- atmosphere model called “Earth System Model” in conjunction with the original data assimilation method are presented. We used the data of the latitudinal CTD sections of temperature and salinity of the WOCE international experiment in 1991-1995 for assimilation. Estimates of the current velocities of Antarctic Bottom Water with the assimilation of observational data are given. We used the author’s data assimilation method, which was previously referred as the generalized Kalman Filter (GKF) method. In the particular case it coincides with the classical Kalman method (EnKF). We also analyze the estimates of the mass transport by the standard dynamic scheme. It is shown that model calculations with data assimilation are qualitatively the same and are quantitatively close to the estimates of geostrophic flow transport based on the dynamic method.

In order to improve the simulation accuracy, it is effective to use a data assimilation technique which is capable of reproducing more realistic simulated states by incorporating observational data into simulation models. One of the simplest ones among data assimilation techniques is a Newtonian relaxation‐type nudging method which has been widely used in mesoscale meteorological models. In this study, we proposed a data assimilation method using a vibration equation which can incorporate turbulence winds toward target mean winds while maintaining small‐scale turbulent fluctuations as a different approach from the conventional nudging method. First, we conducted test simulations in which nudging is applied in a basic turbulent boundary layer (TBL) flow toward a target one. It is shown that the basic TBL flow can be reasonably nudged toward the target one while maintaining the turbulent fluctuations well when prescribing the natural frequency in the vibration equation smaller than the spectral peak frequency in the TBL flow. Then, we applied the proposed nudging method by incorporating data obtained from meteorological observations located in the actual city of Kyoto. The mean wind velocity profiles were reasonably nudged toward the target observed profile and the turbulence statistics were also favorably maintained. It is concluded that the data assimilation method using the vibration equation successfully nudges toward the target mean winds while maintaining small‐scale turbulent fluctuations well.

Land use simulations are critical in predicting the impact of land use change (LUC) on the Earth. Various assumptions and policies influence land use structure and are a key factor in decisions made by policymakers. Meanwhile, the spatial autocorrelation effect between land use types has rarely been considered in existing land use spatial simulation models, and the simulation accuracy needs to be further improved. Thus, in this study, the driving mechanisms of LUC are analyzed. The quantity demand and spatial distribution of land use are predicted under natural development (ND), economic development (ED), ecological protection (EP), and sustainability development (SD) scenarios in Zhengzhou based on the coupled Multi-Objective Programming (MOP) model and the Patch-generating Land Use Simulation model (PLUS) considering Spatial Autocorrelation (PLUS-SA). We conclude the following. (1) The land use type in Zhengzhou was mainly cultivated land, and 83.85% of the land for urban expansion was cultivated land from 2000 to 2020. The reduction in forest from 2010 to 2020 was less than that from 2000 to 2010 due to the implementation of the policy in which farmland is transformed back into forests. (2) The accuracy of PLUS-SA was better than that of the traditional PLUS and Future Land Use Simulation (FLUS) models, and its Kappa coefficient, overall accuracy, and FOM were 0.91, 0.95, and 0.29, respectively. (3) Natural factors (temperature, precipitation, and DEM) contributed significantly to the expansion of cultivated land, and the increase in forest, grass, and construction land was greatly affected by socioeconomic factors (population, GDP, and proximity to town). (4) The land use structure will be more in line with the current requirements for sustainable urban development in the SD scenario, and the economic and ecological benefits will increase by 0.75 × 104 billion CNY and 1.71 billion CNY, respectively, in 2035 compared with those in 2020. The PLUS-SA model we proposed had higher simulation accuracy in Zhengzhou Compared with the traditional PLUS and FLUS models, and our research framework can provide a basis for decision-makers to formulate sustainable land use development policies to achieve high-quality and sustainable urban development.