A MODIFIED METHOD OF SOLVING THE PRESSURE–RATE DECONVOLUTION PROBLEM FOR IDENTIFICATION OF WELLBORE–RESERVOIR SYSTEM

In the paper, the method suggested in [5] for solving the pressure–rate deconvo- lution problem was modified with implementation for the synthetic (quasi-real) oil and gas data. Modification of the method is based on using the additional a priori information on the function v(t) = tg(t) in the logarithmic scale. On the initial time interval, the function is concave and its final interval is monotone. Here, g(t) is the solution of the basis equation (1). To take into account these properties in the Tikhonov algorithm, the penalty function method is used. It allowed one to increase the precision of the numerical solution and to improve quality of identification of the wellbore–reservoir system. Numerical experiments are provided.

A constraint method for supersaturation in a variational data assimilation system

The reproducibility of precipitation in the early stages of forecasts, often called a spin-down or spin-up problem, has been a significant issue in numerical weather prediction. This problem is caused by moisture imbalance in the analysis data, and in the case of the Japan Meteorological Agency’s (JMA’s) mesoscale data assimilation system JNoVA, we found that the imbalance stems from the existence of unrealistic supersaturated states in the minimal solution of the cost function in JNoVA. Based on the theory of constrained optimization problems, we implemented an exterior penalty function method for the mixing ratio within JNoVA to suppress unrealistic supersaturated states. The advantage of this method is the simplicity of its theory and implementation. The results of twin data assimilation cycle experiments conducted for the Heavy Rain Event of July 2018 over Japan show that—with the new method—unrealistic supersaturated states are reduced successfully, negative temperature bias to the observations is alleviated, and a sharper distribution of the mixing ratio is obtained. These changes help to initiate the development of convection at the proper location and improve the fractions skill score (FSS) of precipitation in the early stages of the forecast. From these results, we conclude that the initial shock caused by moisture imbalance is mitigated by implementing the penalty function method, and the new moisture balance has a positive impact on the reproducibility of precipitation in the early stages of forecasts.

Penalty function method for the minimal time crisis problem

In this note, we propose a new method to approximate the minimal time crisis problem using an auxiliary control and a penalty function, and show its convergence to a solution to the original problem. The interest of this approach is illustrated on numerical examples for which optimal trajectories can leave and enter the crisis set tangentially.

Ascent Trajectory Optimization for Air-Breathing Hypersonic Vehicles Based on IGS-MPSP

This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.