Relational optimising process for a heat exchanger of an air conditioning system
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Solution Overview
Problem
Existing heat recovery systems in ventilation systems face challenges in optimizing external dimensions and achieving economic efficiency, with current regulations aiming for general minimum requirements that may not lead to positive results in all individual cases, and the efficiency is influenced by pressure losses and temperature transfer rates.
Innovation Solution
A computer-implemented relational optimization method for heat exchangers that uses a one-dimensional optimization approach with the depth of the heat exchanger as the variable, calculating the heat recovery coefficient and determining the optimal temperature transfer rate by analyzing the heat flow capacity ratio and dimensionless heat exchanger number, while considering economic and ecological factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the heat recovery coefficient is increased to improve thermal efficiency, then the temperature transfer rate improves, but the pressure losses increase leading to higher auxiliary energy consumption
Solution Approach 1:
The patent applies parameter changes by systematically varying the depth of the heat exchanger as a design parameter to find the optimal balance between temperature transfer rate and pressure losses. The relational optimization method adjusts geometric parameters (depth) to achieve the desired trade-off between thermal efficiency and energy consumption for auxiliary fans.
Solution Approach 2:
The patent employs partial action by implementing one-dimensional optimization that varies only the depth parameter while keeping other dimensions constant. This partial optimization approach allows for finding a satisfactory compromise solution without the complexity of full multidimensional optimization, addressing the contradiction between temperature transfer and pressure losses through selective parameter adjustment.
2Productivity
If the external dimensions of the heat exchanger are increased to improve heat recovery efficiency, then the heat transfer surface area increases, but the device complexity and cost increase
Solution Approach 1:
The patent applies segmentation by separating the optimization problem into independent dimensions - specifically isolating the depth parameter as the sole variable for optimization while keeping width and height fixed. This segmentation simplifies the complex multidimensional optimization problem into a manageable one-dimensional relational optimization, reducing device complexity while maintaining heat recovery efficiency.
Solution Approach 2:
The patent uses dimensionality change by transitioning from a multidimensional optimization problem (involving width, height, and depth) to a one-dimensional optimization by varying only the depth parameter. This dimensional reduction simplifies the optimization process and makes it computationally tractable while still achieving improved heat recovery efficiency through the depth parameter alone.
3Reliability
If general minimum requirements are imposed on heat recovery systems according to regulations, then the baseline efficiency is improved, but individual optimal solutions for specific cases are not achieved
Solution Approach 1:
The patent applies dynamics by implementing an adaptive optimization approach that adjusts the heat exchanger depth parameter based on specific project conditions and requirements. Rather than applying fixed minimum requirements, the relational optimization method dynamically determines the optimal depth for each individual case, allowing the system to adapt to varying conditions while maintaining or exceeding baseline efficiency standards.
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 method allows for the determination of a discrete economic optimum for heat recovery efficiency, optimizing the heat recovery rate and reducing energy waste by identifying the optimal temperature transfer rate and construction depth, which can lead to significant economic and ecological benefits.
Implementation Method 1
heat exchange elements are known to guide a first heat exchange fluid (e.g., air) along the surface of the fins and a second heat exchange fluid (e.g., water) within the tubes
Implementation Method 2
The basic geometries (fin spacing, vertical and horizontal tube spacing, etc.) of these heat exchangers are optimized, for example, according to the required heat output
Data Source
Figure 1
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AI summary
A relational optimization method for a heat exchanger in an air handling unit is proposed. Starting with a specific design for an arbitrary heat recovery system, the method aims to find a discrete optimum efficiency under given project-specific conditions. This is achieved, for example, by varying a dimensionless parameter with a corresponding temperature change and comparing the required effort to the potential benefit of the heat recovery. The optimization is performed by varying at least two dimensions of the heat exchanger. Furthermore, an optimization system for carrying out relational optimization is proposed, comprising a database or query interface for retrieving design data for at least one heat exchanger, a computation unit, and an output unit for presenting optimization results to the user.