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Internship (USA) Exploring Acoustic Field Gradients in Large Damped Cavities Using Advanced Methods Beyond SEA

Introduction:

Accurately predicting sound energy distribution in large, damped acoustic cavities is essential for fields such as vehicle NVH or  aerospace. Traditionally, Statistical Energy Analysis (SEA) is the go-to method for modeling these systems. SEA works well for far-field approximations and high-frequency ranges, but it has limitations in capturing direct and reverberant field gradients, especially near sources where energy is not uniformly distributed. In such cases, SEA assumes homogenity in energy distribution, leading to inaccurate results when spatial gradients are significant.

Wave 6, already include features to capture direct fields and spatial energy gradients, which SEAM currently lacks. This project aims to address that gap by enhencing SEAM solver to handle these scenarios effectively.

This internship focuses on developing and refining computational methods to address the limitations of SEA, particularly in large and damped cavities where spatial energy gradients exist. The intern will explore alternative approaches such as the Radiative Energy Transfer Method (RETM), Energy Intensity Boundary Element Method (EIBEM), and Energy Boundary Element Analysis (EBEA) to more accurately predict acoustic energy fields and sound transmission.

Objective:

The goal of the internship is to develop a more accurate model for capturing spatial energy gradients in large, damped acoustic cavities, particularly when traditional SEA fails. The intern will explore and validate alternative methods, with the aim of improving acoustic simulations for complex systems in SEAM solver.

Internship Goals:

Literature Review and Background Research: Perform a detailed review of SEA and its limitations in predicting energy field gradients in large acoustic cavities. Investigate alternative methods like RETM, EIBEM, and EBEA, focusing on their advantages and drawbacks to account for non-uniform energy distribution and near-field effects. Understand material properties and boundary conditions (e.g., reflection, absorption) that impact sound energy distribution. Key Methods to Explore: Statistical Energy Analysis (SEA):

SEA provides quick, far-field approximations for structural radiation through direct and indirect paths (mass law, reverberant field). It is worth noting that the accuracy of this approach may diminish near the source, as it is highly sensitive to boundary conditions and geometry. The intern will review SEA's limitations and identify specific conditions where it becomes ineffective.

Energy Boundary Element Analysis (EBEA):

EBEA also has a lot of  potential in solving this limitation in SEA. It uses boundary conditions related to acoustic power radiated from structural surfaces, enabling high-frequency sound radiation predictions in unbound, undamped environments. The intern will apply EBEA to large, damped cavities and validate the results against analytical solutions. See for instance the work done by Wang et al 2004.

Radiative Energy Transfer Method (RETM):

RETM offers ray-based approximations to predict reverberant field gradients by assuming diffuse reflection and accounting for directivity. It models energy balance by meshing cavity walls and replacing radiating structures with equivalent sources. If time allow, The intern will investigate RETM's applicability in complex acoustic scenarios and compare it to SEA in accuracy and computational cost. See for instance the work done by  Le Bot et al , 2007.

Energy Intensity Boundary Element Method (EIBEM):

EIBEM models the energy field using fictive boundary sources and interior sources. This method can handle complex boundary conditions such as rigid, absorbing, and SEA-coupled surfaces. If time allows, the intern will incorporate EIBEM into existing SEA equations and assess how well it models spatial gradients in cavities with complex boundary interactions.

Model Validation and Testing: Apply each developed model to a set of benchmark problems involving large acoustic cavities with complex boundary conditions and material properties. Compare simulation results with analytical solutions and experimental data to validate accuracy. Ensure computational efficiency and stability, making the models suitable for practical applications. Applications and Case Studies: Investigate sound transmission and energy distribution in large acoustic environments such as vehicle cabins. Use the developed models to study the effects of varying material properties, boundary conditions, and cavity geometry on sound energy distribution. Provide insights into optimizing acoustic cavity designs to achieve better sound insulation and transmission control.

Expected Outcomes:

By the end of the internship, the intern will have developed working computational models that can accurately predict acoustic energy gradients in large, damped cavities. These models will go beyond SEA’s capabilities and will be validated through comparison with experimental and numerical data. The intern will also produce a final report and working code that can be integrated into SEAM solver.

Duration:

This internship will last for a period of 12 to 16 weeks, with an option for extension based on project progress and intern performance.

Prerequisites:

A background in numerical methods and vibro-acoustics Familiarity with computational modeling and numerical methods (e.g., C++, Fortran, MATLAB or python). Knowledge of acoustic theory, including sound transmission, reflection, and boundary conditions.

Supervision and Mentorship:

The intern will work under the supervision of the lead acoustic solver manager and the NVH development team. Regular progress meetings will be held to ensure the intern receives continuous feedback and guidance throughout the project.

 

Altair is an equal opportunity employer. Our backgrounds are diverse, and every member of our global team is critical to our success. Altair's history demonstrates a belief that empowering each individual authentic voice reinforces a culture that thrives because of the uniqueness among our team.  

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