Predictability of exergy destruction in helically coiled tube heat exchangers: global, pitch-specific and hierarchical correlations

Purpose This study aims to quantify second-law performance in a vertically oriented helically coiled tube heat exchanger (HCTHEX) and develop predictive correlations for the dimensionless exergy-destruction fraction ϕD=E˙D/E˙in,total across operating conditions and coil pitches, using more than 2,400 CFD simulations. Design/methodology/approach A steady, fluid-to-fluid CFD model was used with water on both shell and coil sides and laminar treatment over the stated Reynolds-number range. Exergy rates at shell/coil inlets and outlets were evaluated over a heat-exchanger control volume to compute E˙Dand ϕD. Predictability was assessed via global regressions including pitch as an explicit predictor and pitch-specific regressions trained separately at each pitch; all models were trained in log space and evaluated using five-fold cross-validation. Findings The global baseline power-law ϕD=A ReshellaRecoilbpc shows statistically significant dependence on pitch and Reynolds numbers (e.g. Dh-based: A=0.2238, a = 0.04885, b = 0.04982 and c = 0.7507), but limited cross-validated accuracy (Dh: RCV,log2=0.1687, RMSECV,log=0.1402). Among advanced surrogates, LogLog–GPR–ARDSE provides the best global performance for both characteristic-length definitions (for Dh: RCV,log2=0.7171, RMSECV,log=0.08181. Pitch-specific modeling indicates the best advanced method depends on pitch: GPR–ARDSE is selected at p = 1.80, 1.85 and 2.00, while bagged trees outperform at p = 1.90and 1.95 under the minimum RMSECV,log⁡ criterion. Research limitations/implications Correlations are calibrated to the simulated geometry family and operating ranges and should not be extrapolated without verification. Practical implications The correlations enable rapid estimation of ϕD from ReshellRecoilp, reducing the need for repeated full exergy accounting during screening and mapping. Originality/value This study benchmarks baseline, advanced and pitch-conditioned predictive models for ϕD in HCTHEXs using a unified CFD–exergy data set and cross-validation.