Uncertainty Analysis of Performance Parameters of a Hybrid Thermoelectric Generator Based on Sobol Sequence Sampling
Source abstract
Abstract
Hybrid thermoelectric generators (HTEGs) play a pivotal role in sustainable energy conversion by harnessing waste heat through the Seebeck effect, contributing to global efforts in energy efficiency and environmental sustainability. In practical sustainable energy systems, HTEG output performance is significantly influenced by uncertainties in the operational parameters (such as temperature differences and load resistance), material properties (including Seebeck coefficient and resistance), and structural configurations (like the number of series/parallel thermoelectric components), which impact both efficiency and system stability. This study employs the Sobol-sequence-sampling method to characterize these parameter uncertainties, analyzing their effects on HTEG output power and conversion efficiency using mean values and standard deviations as evaluation metrics. The results show that higher temperature differences enhance output performance but reduce stability, a larger load resistance decreases performance while improving stability, thermoelectric materials with high Seebeck coefficients and low resistance boost efficiency at the expense of stability, increasing series-connected components elevates performance but reduces stability, parallel configurations enhance power output yet decrease efficiency and stability, and greater contact thermal resistances diminish performance while enhancing system robustness. This research provides theoretical guidance for optimizing HTEGs in sustainable energy applications, enabling the development of more reliable, efficient, and eco-friendly thermoelectric systems that balance performance with environmental resilience for long-term sustainable operation.
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Research context
OpenAlex topics: Thermodynamic and Structural Properties of Metals and Alloys · Probabilistic and Robust Engineering Design · Advanced Thermodynamic Systems and Engines
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanics and Transportation Engineering, Northwestern Polytechnical University, Xi’an 710129, China
College of Civil Engineering, Hunan University, Changsha 410082, China
Funding
Key R&D Program of Jiangxi Province
References (45)
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