Reduced Order Model for Fluid Thermal Property Extraction
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Solution Overview
Problem
Existing thermal sensors face challenges in accurately determining thermal properties like thermal conductivity and volumetric heat capacity due to non-monotonic relationships between direct measured quantities and desired properties, which analytical models and equivalent electrical circuit thermal models fail to address effectively.
Innovation Solution
The use of a calibrated reduced order model (ROM) with a thermal sensor, where a first sensor element heats the fluid and a second sensor element measures temperature, applying a parametric reduced order model to extract thermal properties by fitting measured temperature data, specifically using Proper Orthogonal Decomposition (POD) and harmonic excitation, reduces computational complexity and enhances accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical models or equivalent electrical circuit thermal models are used to determine thermal properties, then the measurement process is straightforward, but the accuracy is insufficient due to non-monotonic relationships between measured quantities and desired properties
Solution Approach 1:
The patent transforms the thermal sensing problem by changing the mathematical representation from traditional analytical models to reduced order models (ROMs). This parameter change in the modeling approach enables accurate inversion of non-monotonic relationships while maintaining computational efficiency. The ROMs are created by reducing the dimensionality of finite element models, preserving the essential thermal behavior while enabling accurate property determination from temperature measurements.
2Measurement precision
If high order models (FEM models) are used to achieve high accuracy, then measurement precision is improved, but computational effort and processing time increase significantly
Solution Approach 1:
The patent segments the computational model into two distinct parts: an offline phase where high-fidelity finite element models are created and reduced to ROMs, and an online phase where the pre-reduced models are used for rapid property determination. This segmentation allows the computationally intensive model reduction to be performed once, while subsequent measurements benefit from fast ROM-based calculations, resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent performs preliminary model reduction by creating reduced order models from high-fidelity FEM models before actual thermal measurements are taken. This preliminary action pre-processes the complex thermal system into a simplified representation that retains accuracy for property determination while enabling rapid computation during actual measurements, thus preparing the system in advance to avoid computational bottlenecks during operation.
3Productivity
If reduced order models are used to reduce computational complexity, then processing speed is improved, but accuracy may be compromised due to model simplification
Solution Approach 1:
The patent implements a feedback mechanism where the reduced order model predictions are compared against actual temperature measurements, and the model parameters are adjusted to minimize the difference. This feedback loop ensures that even though the ROM is a simplified representation, it accurately captures the thermal behavior of the specific sensor-fluid system by learning from actual measurement data, thus maintaining precision while benefiting from computational speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a precise and efficient method for determining thermal properties, such as thermal conductivity and volumetric heat capacity, by compensating for effects that analytical models cannot model, using lower-dimensional models that can be computed quickly, even in embedded systems.
Implementation Method 1
a first sensor element that is heated to provide heat to the fluid under investigation
Implementation Method 2
a second sensor element that can sense the temperature of the fluid under investigation
Data Source
AI summary
A Process of determining at least one thermal property of a fluid under investigation with a thermal sensor. The thermal sensor has at least a first sensor element that is heated to provide heat to the fluid under investigation. The first or a second sensor that can sense the temperature of the fluid under investigation, wherein the process is characterized by the following steps: a) Providing a calibrated reduced order model which is calibrated with one or more thermal properties of at least a second and a third fluid; b) (Applying an amount of heat to the fluid under investigation by the first sensor element and) measuring the temperature Tsens at the first and/or second sensor element said fluid under investigation; and c) Extracting one or more thermal property of the fluid under investigation by applying the said temperature Tsens to said calibrated reduced order model.