Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests.

Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests.

An ascent guidance algorithm for the energy management of solid rockets.

Efficient energy management for the solid rocket is very important to exhaust excess energy and to meet the various terminal constraints. Based on the online planning of the velocity capability curve, a Double-arcs Energy Management (DAEM) guidance algorithm is proposed in this paper. In the DAEM, the velocity capability curve is designed with two tangential arcs. By using the elegant geometric characteristic of the arcs, the closed-loop guidance problem is transformed as a problem of solving a system of nonlinear equations about the parameters (radius and center position) of the two arcs, and the nonlinear equations can be solved in real time. The main advantages of this algorithm are that the height increment constraint, which is difficult to be satisfied in traditional energy management methods, can be satisfied theoretically, and the convergence of the solution of the DAEM is quadratic. The effectiveness and advantages of the proposed DAEM algorithm are demonstrated by simulations and comparisons with other energy management algorithms.