Engine Cooling Temperature Prediction Without Roughness Parameters
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Solution Overview
Problem
Existing temperature prediction models for engine cooling systems suffer from slow calculation speeds and low accuracy due to the need to calculate unnecessary parameters and immeasurable factors like cylinder wall roughness, and they struggle with dynamic performance and robustness.
Innovation Solution
A method involving iterative calculations based on a double-layer flat plate model, using functional relationships between combustion gas temperature and operating parameters, without relying on immeasurable parameters like cylinder wall roughness, to predict engine cooling system temperatures accurately and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing temperature prediction models are used, then temperature prediction can be performed, but calculation speed is slow and accuracy is low
Solution Approach 1:
The patent extracts and removes the harmful factor of cylinder wall roughness from the calculation model. By recognizing that this parameter is immeasurable and causes both accuracy issues and computational complexity, the model eliminates it entirely, focusing only on measurable operating parameters like cooling water temperature, engine speed, and load to achieve both speed and accuracy improvements
Solution Approach 2:
The patent changes the parameter set used in temperature prediction from including immeasurable geometric parameters (cylinder wall roughness) to exclusively using measurable operating parameters (cooling water temperature, engine speed, load). This parameter transformation enables real-time calculation while maintaining prediction accuracy through iterative optimization based on actual operating conditions
2Adaptability or versatility
If comprehensive parameters including cylinder wall roughness are used, then model completeness is improved, but measurement difficulty increases
Solution Approach 1:
The patent extracts and removes the harmful factor of cylinder wall roughness from the calculation model. By recognizing that this parameter is immeasurable and causes both accuracy issues and computational complexity, the model eliminates it entirely, focusing only on measurable operating parameters like cooling water temperature, engine speed, and load to achieve both speed and accuracy improvements
Solution Approach 2:
The model uses operating parameters that are already measured and available from the engine's normal operation (cooling water temperature, engine speed, load). These parameters self-serve the temperature prediction function without requiring additional sensors or measurements, making the system both complete and practically measurable
3Measurement precision
If traditional calculation methods are used, then temperature prediction is achieved, but additional immeasurable parameters are required
Solution Approach 1:
The patent extracts and removes the harmful factor of cylinder wall roughness from the calculation model. By recognizing that this parameter is immeasurable and causes both accuracy issues and computational complexity, the model eliminates it entirely, focusing only on measurable operating parameters like cooling water temperature, engine speed, and load to achieve both speed and accuracy improvements
Solution Approach 2:
The patent makes the temperature prediction model universally applicable to different engine conditions by using only operating parameters that vary with engine state (cooling water temperature, engine speed, load). This universal parameter set allows the same model structure to accurately predict temperatures across diverse operating conditions without requiring engine-specific geometric parameters
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 improves calculation efficiency and accuracy by relying solely on initial temperatures and operating parameters, reducing the need for additional parameters, thus enhancing the precision and speed of temperature prediction.
Implementation Method 1
a first heat transfer amount between the cooling water and the inner cylinder wall according to the first temperature of the cooling water and the inner cylinder wall temperature
Implementation Method 2
a third heat transfer amount between the outer cylinder wall and the external environment according to the outer cylinder wall temperature
Data Source
AI summary
A method for predicting a temperature of for an engine cooling system, includes: obtaining a first temperature of the engine cooling system at an initial moment, operating parameters of an engine, and a target moment, in which the engine cooling system comprises at least cooling water, an inner cylinder wall, and an outer cylinder wall; determining a number of unit time steps required from the initial moment to the target moment according to a preset unit time step; and obtaining a target temperature of the engine cooling system at the target moment by performing a set number of iterative calculations according to the first temperature and the operating parameters of the engine.