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With rapid development of society and economy, the issue of water shortage has presently been more and more serious in China. Optimal water and land resources allocation, involving many aspects such as society, economy, ecology etc., is a rational approach to solve this problem. In this study, a substantially improved model, i.e., multi-objective optimal allocation, is established for coordinating the usage of water and land resources. The model was developed on the basis of Immune Genetic Algorithms (IGA), and it mainly includes three objectives and seven constraints. The results of case study show that there is no water shortage in the predicting year of 2020 in Dongtai City, Jiangsu Province by using the optimal allocation of water and land resources. The new optimal allocation proposed in this study has a positive influence to promote the economic and social harmonious development and the natural environment protection for coastal areas of China.

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In this paper, firstly, discontinuous Galerkin method for improved Stokes equation is proposed. We derive a discontinuous Galerkin (DG) finite element formulation for the improved Stokes equations. Special case of the generalized solution equation for linear and stationary improved Stokes equations retrogresses into generalized solution equation for classical Stokes equation. It is proved that the classical solution and the generalized solution is consistent for the improved Stokes equations, existence and uniqueness of generalized solution for the improved Stokes equations are also proved.

As modern engineering design usually involves dependence of one discipline on another, multidisciplinary system analysis (MDSA) plays an important role in the multidisciplinary simulation and design optimization on coupled systems. The paper proposes an MDSA method based on minimal feedback variables (MDSA MF) to enhance the solving efficiency. There are two phases in the method. In phase 1, design structural matrix (DSM) is introduced to represent a coupled system, and each off-diagonal element is denoted by a coupling variable set; then an optimal sequence model is built to obtain a reordered DSM with minimal number of feedback variables. In phase 2, the feedback in the reordered DSM is broken, so that the coupled system is transformed into one directed acyclic graph; then, regarding the inputs depending on the broken feedback as independent variables, a least-squares problem is constructed to minimize the residuals of these independents and corresponding outputs to zero, which means the multidisciplinary consistence is achieved. Besides, the MDSA MF method is implemented in a multidisciplinary platform called FlowComputer. Several examples of coupled systems are modeled and solved in the platform using several MDSA methods. The results demonstrate that the proposed method could enhance the solving efficiency of coupled systems.