Concentric Tube Heat Exchanger Benchmark Dataset: Experimental Measurements, CFD Configuration, and PINN Surrogate

This dataset accompanies the manuscript "PINN surrogate for concentric tube heat exchangers: experimental validation and operational envelope mapping" submitted to the Journal of the Brazilian Society of Mechanical Sciences and Engineering (2026). It provides synchronized experimental measurements, CFD-derived results, and Physics-Informed Neural Network (PINN) predictions for a laboratory-scale concentric tube heat exchanger (TecQuipment TD360a) operating with water in both circuits. Eight experimental cases were measured, combining four cold-side mass flow rates (0.050, 0.033, 0.017, 0.008 kg/s) with parallel and counterflow configurations, while keeping the hot-side flow rate constant at 0.050 kg/s. The dataset includes:- Experimental temperature and flow-rate measurements with calibrated instrumentation- Moffat-propagated uncertainties for heat-transfer rates and effectiveness- Energy balance analysis for the eight cases- CFD validation results from ANSYS CFX with SST k-omega turbulence model and mesh-independence study- Trained PINN surrogate (Jupyter notebook) with full reproducibility through fixed random seeds- PINN predictions for the eight experimental cases- Comparative analysis tables (Exp, CFD, PINN) The PINN surrogate, with architecture [5, 32, 32, 2] and 1,314 parameters, was trained against epsilon-NTU theoretical targets with energy-conservation and effectiveness-consistency constraints. It reproduces experimental outlet temperatures with mean absolute percentage errors of 0.98% (hot stream) and 3.00% (cold stream), and reduces computational cost by approximately one order of magnitude relative to CFD. The dataset is intended to support reproducibility of the published results and to serve as a reference benchmark for subsequent methodological studies on surrogate modeling of concentric tube heat exchangers.