Internship: Graduate-level Research in Industrial

vor 3 Wochen

Berlin, Deutschland ZIB Vollzeit

Are you a master or PhD-student and interested in an internship in Berlin during summer this year?

Then the Graduate-Level Research in Industrial Projects for Students (GRIPS) might be the right project for you

Located at the Zuse-Institute Berlin (Research Campus MODAL), we offer graduate students in mathematics and related disciplines the opportunity to work on industry-sponsored research problems. (See list of projects below.)

The program will run from June 24 - August 16, 2024.

G-RIPS is a joint activity of ZIB (Research Campus MODAL) and IPAM (Institute for Pure & Applied Mathematics (at) UCLA).

Tasks

Students from the U.S. and Europe will work for 8 weeks on cross-cultural teams on three research problems designed by the industrial sponsor. The projects will be of serious interest to the sponsor and will offer a stimulating challenge to students; most will involve both analytic and computational work.

**Requirements**:
**Please submit the following documents**:
1. motivation letter (please state which project you are interested in),
2. resume / short CV,
3. an academic record or transcript (can be unofficial).

**Benefits**:
Costs for traveling and accommodation in Berlin are covered.

This year, we offer the following three projects.

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- **Project 1**: Flow instabilities in wind energy systems

**Sponsor**: I2DAMO (Intelligent Integrated Data Analysis and Mathematical Optimization) GmbH

**Project Description**:
**Motivation**: Integrating renewable energy sources,
such as wind power, is critical for sustainable energy production. Wind
farms comprising multiple turbines face challenges related to wake
interactions that can significantly impact overall efficiency. Flow
instabilities, e.g., the Kelvin-Helmholtz instability (KHI), a
hydrodynamic phenomenon that occurs when two fluids of different
densities flow past each other with different velocities, may play a
crucial role in shaping wind farm wakes. The instability of wind flows
around wind turbines may form vortices and thus reduce the efficiency of
turbines. Understanding and mitigating these instabilities is essential
for optimizing wind farm layouts and enhancing energy extraction.

**Methodology**:

- Using CFD Simulations to model the flow around a single turbine and a

multi-turbine wind farm via solving the Navier-Stokes equations
- Analyzing the wake characteristics, including the formation and

evolution of vortices via identifying the locations where flow
instabilities occur and their influence on the wind farm power output.
- Analyze the effects on turbulence levels, wake dynamics, and downstream turbine performance.
- Determining the optimal turbine spacing and layout to minimize

instability modes-induced losses and maximize wind farm efficiency,
e.g., adjusting the distance between turbines
- Identifying the stability condition of the flow around the turbine to prevent the vortex structures (instability)

**Expected Outcomes**: The project could lead to the development of new design guidelines for wind turbines and wind farm layout by mitigating instability modes-induced losses and optimizing turbine layout, thus improving the overall performance and economic viability of wind energy systems. By minimizing wake-induced
losses and increasing wind farm efficiency, the project can reduce the environmental impact of wind energy production.

**Requirements for the Applicants**:

- Strong programming skills in C/C++ or Rust for numerical calculations, data analysis, and plotting
- Good knowledge of computational tools, such as CFD software or mathematical modeling and numerical methods (e.g., FEM or VOF)
- Background in fluid dynamics, aerodynamics, or a related field
- Excellent communication skills for reporting findings in a written document and oral presentation
- Ability to work independently and as part of a team to achieve

project goals, an interest in wind energy research, and a passion for
learning new concepts

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- **Project 2**: Machine learning for optimizing numerical settings of finite-element based optics simulations

**Sponsor**: JCMwave GmbH

the evaluation of data enables decision-making and quantification processes. In addition to model-based simulations of measurement processes, artificial intelligence (AI) methods are becoming increasingly important here. The MODAL NanoLab is developing methods for efficient, error-controlled and self-adaptive simulation of
light-matter interactions in optical nanostructures.Efficient tuning of accuracy settings of numerical simulations is of crucial importance for the fidelity and interpretability of the corresponding simulation results. For finite-element based simulations, these set

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