Project 163: Application of Simulated Data Assimilation Techniques in Small-Scale Models

Contact Information:

Assoc. Prof. Helin Gong

Project Description and Objectives:

This project is focused on the application of data assimilation techniques within small-scale engineering models. The primary objective is to enhance students' understanding of how data assimilation can improve model predictions in engineering contexts, such as fluid dynamics or structural analysis. Students will be tasked with integrating simulated observational data into existing computational models, thereby improving their accuracy and reliability. The project aims to demonstrate the practical importance of data assimilation in engineering and to provide students with hands-on experience in this emerging field.

Eligibility Requirements:

Students majoring in Engineering, Applied Mathematics, Computer Science or related disciplines.

Basic understanding of numerical methods and computational modeling.

Proficiency in programming (preferably Python or MATLAB).

Strong analytical skills and attention to detail.

Main Tasks:

Study and understand the basic principles of data assimilation.

Develop or modify small-scale computational models.

Integrate simulated data into these models.

Analyze the impact of data assimilation on model accuracy.

Document and present the research findings.

Website:

Lab: N/A

School: https://speit.sjtu.edu.cn/