Mechanical Engineering
Numerical and Theoretical Investigation of Thermal Performance in Double Pipe Heat Exchanger Using Nanofluids with Integrated CFD and ANN Methods
Recent research has focused significantly on the rising energy demand and advancements in heat exchangers, which are effective in heat transfer. This study examines the counter-flow Double Pipe Heat Exchanger (DPHE) under various flow, geometric, and thermal conditions using water-based nanofluids via Computational Fluid Dynamics (CFD). The objective of this research is to investigate the heat transfer performance in DPHEs, employing the effectiveness-NTU method for theoretical validation. Additionally, this study incorporates widely used water-based nanofluids with Al2O3, CuO, and hexagonal Boron Nitride (h-BN) nanoparticles at varying volume fractions to improve heat transfer. Several cases are designed to assess and compare thermal performance in heat exchangers using CFD. An Artificial Neural Network (ANN) model is developed in Python, integrating MATLAB to estimate thermal performance using the Overall Heat Transfer Coefficient (U). The findings revealed that h-BN nanofluids demonstrated outstanding heat transfer performance in different nanoparticles used in this study. The h-BN showed superior thermal performance compared to water and the other water-based Al2O3 and CuO nanofluids. Comparisons with theoretical calculations validated the precision of the CFD simulation outcomes. Under the same conditions, it was observed that Al2O3 increased U values by up to 20%, CuO by up to 15%, and h-BN by up to two times compared to water samples. The effectiveness method is in good agreement with CFD results. The constructed ANN model predicts heat transfer performance with an accuracy of 98.3% when compared with numerical and theoretical results.
Key Performance Metrics
Here are the core findings from the study, demonstrating the tangible benefits of integrating advanced methods and nanofluids for thermal performance.
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ANN Prediction Accuracy
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h-BN Heat Transfer Increase (vs. Water)
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CFD/NTU Relative Error
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Al₂O₃ U Value Increase
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This research integrates Computational Fluid Dynamics (CFD) for detailed fluid flow and heat transfer analysis, the effectiveness-NTU method for theoretical validation, and Artificial Neural Networks (ANN) for performance prediction. The combination provides a robust framework for assessing thermal performance.
ANN Model Development Workflow
Data Collection (207,360 points)
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Data Splitting (80% Train, 20% Test)
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Model Architecture (Sequential, 4 Hidden Layers, 14 Neurons)
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Activation Function (ReLU)
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Optimizer (RMSprop)
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Loss Function (Mean Square Error)
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Training (1000 Epochs, 12 Batch Size)
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Performance Evaluation (R² Score)
| Nanofluid Type | U Value (W/m²K) | Key Benefit |
|---|---|---|
| Water (Pure) | 667.1 | Baseline Performance |
| Al₂O₃ (10%) | 745.6 | Up to 20% U value increase vs. water |
| CuO (10%) | 717.6 | Up to 15% U value increase vs. water |
| h-BN (1%) | 1279.3 | Up to 2X U value increase vs. water; Superior thermal performance |
The study reveals significant enhancements in heat transfer performance when using h-BN nanofluids, alongside strong validation across numerical, theoretical, and AI models.
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ANN Prediction Accuracy for Heat Transfer (U)
DPHE Thermal Performance Across Nanofluids (Case 1 Simulation)
Simulation results for Case 1 demonstrate the distinct temperature distributions and overall heat transfer coefficient (U) values achieved with different nanofluids. The visual representations clearly illustrate the enhanced thermal boundary layer thickness and improved heat transfer efficiency with h-BN compared to Al₂O₃, CuO, and pure water.
- h-BN nanofluids exhibit significantly superior thermal performance, leading to the thinnest thermal boundary layer and highest U values.
- Al₂O₃ and CuO nanofluids also improve U values compared to pure water, but to a lesser extent than h-BN.
- The CFD simulations are validated against theoretical effectiveness-NTU method, showing relative errors consistently below 2%, confirming model reliability.
Advanced ROI Calculator
Our AI-powered heat exchanger optimization can significantly reduce operational energy costs and improve system efficiency. Estimate your potential annual savings by adjusting the parameters below.
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Your AI Implementation Roadmap
A structured approach to integrating AI for thermal performance, from data to deployment.
Data Ingestion & Preprocessing
Gathering historical operational data from your existing heat exchanger systems and preparing it for AI model training.
CFD Model Integration & Calibration
Integrating high-fidelity CFD models with your specific geometric and flow conditions for accurate baseline and nanofluid performance simulations.
ANN Model Training & Validation
Training the Artificial Neural Network on comprehensive datasets, including CFD and theoretical results, to achieve high-accuracy predictive capabilities.
Nanofluid Selection & Optimization
Utilizing the AI model to identify optimal nanofluid concentrations and flow conditions for maximum heat transfer enhancement and minimal pressure drop.
System Deployment & Monitoring
Implementing the optimized parameters in your DPHE systems and continuously monitoring performance for sustained efficiency gains and predictive maintenance.
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