Thermohydraulic and data-driven–probabilistic modelling of a turbulator-enhanced hybrid solar–gas water heater for global technoeconomic and environmental performance prediction
Thermohydraulic and data-driven–probabilistic modelling of a turbulator-enhanced hybrid solar–gas water heater for global technoeconomic and environmental performance prediction
Flat-plate solar collectors (FPSCs) are widely deployable for low-carbon domestic hot-water generation, yet their adoption is limited by modest thermal efficiency and intermittent performance under variable solar irradiance. This study adopts a comprehensive, multilayered approach to address these limitations, introducing a novel turbulator-enhanced hybrid solar–gas water-heating system that is investigated through both experimental studies and numerical simulations. Helical-fin and conical turbulators generate dual-swirling flows, substantially enhancing convective heat transfer and increasing thermal efficiency by up to 79% over conventional FPSCs. Coupling the collector with a domestic gas boiler ensures continuous operation under variable irradiance, improving reliability and resilience in practical settings. As a key novelty, a probabilistic, data-driven framework combining symbolic regression, Sobol sensitivity analysis, and Monte Carlo uncertainty quantification is applied to evaluate global technoeconomic and environmental performance. The analysis identifies solar irradiance and fluid mass flow rate as dominant performance drivers, while quantifying the impact of irradiance variability on levelized cost of energy and CO 2 mitigation, an aspect rarely addressed in prior studies, providing actionable insights for design under uncertainty. Across five Sunbelt regions worldwide, the hybrid system reduces energy payback time by 58.8%, lowers levelized cost of energy by 37.3%, and avoids up to 3,826 kg CO 2 eq yr −1 compared with gas-only systems. These results establish a robust, scalable pathway for designing resilient, uncertainty-aware, low-carbon domestic heating solutions that combine high efficiency, operational reliability, and substantial environmental benefits.
Economic and environmental analysis, Flat plate solar collector, Hybrid solar-gas heating system, Machine learning, Uncertainty quantification
Zaboli, Mohammad
df96d8b0-758c-4ef1-957b-ffa3330038a4
Saedodin, Seyfolah
c022c56e-3d99-4c7a-a2b7-49257c432899
Ajarostaghi, Seyed Soheil Mousavi
c46b9bbd-89be-4c2d-aeed-cd7c5947f4be
Karimi, Nader
620646d6-27c9-4e1e-948f-f23e4a1e773a
11 February 2026
Zaboli, Mohammad
df96d8b0-758c-4ef1-957b-ffa3330038a4
Saedodin, Seyfolah
c022c56e-3d99-4c7a-a2b7-49257c432899
Ajarostaghi, Seyed Soheil Mousavi
c46b9bbd-89be-4c2d-aeed-cd7c5947f4be
Karimi, Nader
620646d6-27c9-4e1e-948f-f23e4a1e773a
Zaboli, Mohammad, Saedodin, Seyfolah, Ajarostaghi, Seyed Soheil Mousavi and Karimi, Nader
(2026)
Thermohydraulic and data-driven–probabilistic modelling of a turbulator-enhanced hybrid solar–gas water heater for global technoeconomic and environmental performance prediction.
Energy Conversion and Management, 353, [121170].
(doi:10.1016/j.enconman.2026.121170).
Abstract
Flat-plate solar collectors (FPSCs) are widely deployable for low-carbon domestic hot-water generation, yet their adoption is limited by modest thermal efficiency and intermittent performance under variable solar irradiance. This study adopts a comprehensive, multilayered approach to address these limitations, introducing a novel turbulator-enhanced hybrid solar–gas water-heating system that is investigated through both experimental studies and numerical simulations. Helical-fin and conical turbulators generate dual-swirling flows, substantially enhancing convective heat transfer and increasing thermal efficiency by up to 79% over conventional FPSCs. Coupling the collector with a domestic gas boiler ensures continuous operation under variable irradiance, improving reliability and resilience in practical settings. As a key novelty, a probabilistic, data-driven framework combining symbolic regression, Sobol sensitivity analysis, and Monte Carlo uncertainty quantification is applied to evaluate global technoeconomic and environmental performance. The analysis identifies solar irradiance and fluid mass flow rate as dominant performance drivers, while quantifying the impact of irradiance variability on levelized cost of energy and CO 2 mitigation, an aspect rarely addressed in prior studies, providing actionable insights for design under uncertainty. Across five Sunbelt regions worldwide, the hybrid system reduces energy payback time by 58.8%, lowers levelized cost of energy by 37.3%, and avoids up to 3,826 kg CO 2 eq yr −1 compared with gas-only systems. These results establish a robust, scalable pathway for designing resilient, uncertainty-aware, low-carbon domestic heating solutions that combine high efficiency, operational reliability, and substantial environmental benefits.
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Revised_Manuscript_-_M_Zaboli_et_al._2026
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Restricted to Repository staff only until 11 February 2027.
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Revised Manuscript - M Zaboli et al. 2026
Restricted to Repository staff only
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Accepted/In Press date: 31 January 2026
e-pub ahead of print date: 11 February 2026
Published date: 11 February 2026
Keywords:
Economic and environmental analysis, Flat plate solar collector, Hybrid solar-gas heating system, Machine learning, Uncertainty quantification
Identifiers
Local EPrints ID: 510127
URI: http://eprints.soton.ac.uk/id/eprint/510127
ISSN: 0196-8904
PURE UUID: 327af83f-2030-4cd3-858f-50110eac3429
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Date deposited: 18 Mar 2026 17:31
Last modified: 21 Mar 2026 03:37
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Contributors
Author:
Mohammad Zaboli
Author:
Seyfolah Saedodin
Author:
Seyed Soheil Mousavi Ajarostaghi
Author:
Nader Karimi
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