Numerical Simulation of Supersonic Gas Jet Flows and Acoustics Fields

The source of the jet noise is generated by rocket exhaust plume during rocket engine testing. A domain decomposition approach is applied to the jet noise prediction in this paper. The aerodynamic noise coupling is based on the splitting into acoustic sources generation and sound propagation in separate physical domains. Large Eddy Simulation (LES) is used to simulate the supersonic jet flow. Based on the simulation results of the flow-fields, the jet noise distribution of the sound pressure level is obtained by applying the Ffowcs Williams-Hawkings (FW-H) acoustics equation and Fourier transform. The calculation results show that the complex structures of expansion waves, compression waves and the turbulent boundary layer could occur due to the strong interaction between the gas jet and the ambient air. In addition, the jet core region, the shock cell and the sound pressure level of the gas jet increase with the nozzle size increasing. Importantly, the numerical simulation results of the far-field sound are in good agreement with the experimental measurements in directivity.

An Automatic Model Transformation Methodology Based on Semantic and Syntactic Comparisons and the Granularity Issue Involved

Model transformation, as a pivotal aspect of Modeldriven engineering, attracts more and more attentions both from researchers and practitioners. Many domains (enterprise engineering, software engineering, knowledge engineering, etc.) use model transformation principles and practices to serve to their domain specific problems; furthermore, model transformation could also be used to fulfill the gap between different domains: by sharing and exchanging knowledge. Since model transformation has been widely used, there comes new requirement on it: effectively and efficiently define the transformation process and reduce manual effort that involved in. This paper presents an automatic model transformation methodology based on semantic and syntactic comparisons, and focuses particularly on granularity issue that existed in transformation process. Comparing to the traditional model transformation methodologies, this methodology serves to a general purpose: crossdomain methodology. Semantic and syntactic checking measurements are combined into a refined transformation process, which solves the granularity issue. Moreover, semantic and syntactic comparisons are supported by software tool; manual effort is replaced in this way.

Horizontal Directivity of Pipa Radiation

Pipa is one of the most important Chinese traditional plucked instruments, but its directivity has never been measured systematically. In western, directivity of loudness for western instruments is deeply researched through analysis of sound pressure level, whereas the directivity of timbre is seldom studied. In this paper, a new method for directivity of timbre was proposed, and horizontal directivity patterns of loudness and timbre of Pipa were measured. Directivity of Pipa radiation was measured in an anechoic room. The sound of Pipa played by a musician was recorded simultaneously by 32 microphones with Pipa in the center. The measuring results were examined through listening test. According to the measurement of Pipa directivity radiation, we put forward the best localization of Pipa in the Chinese traditional orchestra and the optimal recording region.

A Multi-objective Fuzzy Optimization Method of Resource Input Based on Genetic Algorithm

With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people-s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory. And then, a fitness function is constructed for genetic algorithm. Finally, a numerical example is presented to show that the method compared with traditional single-objective optimization method is more practical and efficient.