The objective is to reduce the computational complexity of thermal management systems using [[Model Order Reduction - MOR]] techniques, specifically in the context of [[Finite Element Modelling - FEM]] simulations which have a high degree of freedom (DOF) ranging from 10^5 to 10^6. #### Existing Approach Nonlinear Iterative Reduction Algorithm (NIRA) and Nonlinear Iterative Reduction Procedure (NIRP) NIRA (Nonlinear Iterative Reduction Algorithm) and NIRP (Nonlinear Iterative Reduction Procedure) are techniques used in the context of model order reduction (MOR) to simplify complex models while retaining essential nonlinear dynamics. These methods focus on reducing the computational complexity of mathematical models, making them more manageable for real-time simulations and control applications. ### Key Aspects of NIRA/NIRP: 1. **Nonlinear Dynamics**: Unlike traditional linear MOR techniques, NIRA/NIRP are designed to handle nonlinear systems, which are common in many practical engineering problems. 2. **Iterative Process**: Both techniques employ an iterative approach to progressively simplify the model while preserving the critical characteristics and behaviors of the original system. 3. **Algorithmic Efficiency**: NIRA/NIRP aim to achieve significant computational savings by reducing the number of states and parameters in the model, making it faster and less resource-intensive to simulate. 4. **Application in Thermal Management**: In the automotive industry, NIRA/NIRP can be particularly useful for developing reduced-order models of thermal management systems, where accurate real-time performance is crucial. These methods enable the derivation of fast, real-time capable thermal models from full-scale finite element models (FEM), which are typically high-dimensional and computationally expensive. Ref: [[Linear Parameter Varying Systems]] > Reduce computational expense while retaining the model accuracy ![[Pasted image 20240528114701.png]] ### Opportunities 1. **Model Reduction**: AI can be used to create reduced-order models that approximate the behavior of full-scale FEM simulations with significantly fewer computational resources. Machine learning techniques can identify and retain the most critical features of the thermal model ^[https://pubs.acs.org/doi/10.1021/acs.chemrev.3c00708] 2. [[Physics Informed Neural Operators]] for Physics based Model Order Reduction ### References: 1. [Reduced Order Modelling for Battery Thermal Behaviour](https://www.youtube.com/watch?v=rd2Qs5q1HeM) 2. [[Quantum x CFD]] 3. [[Quantum Algorithms x Tensor Networks]]