A critical review of turbulator effects on shell-and-tube heat exchanger performance based on CFD studies
Bibliographic record
Abstract
Shell-and-tube heat exchangers (STHEs) are widely recognized as one of the most reliable and versatile thermal devices in power generation, chemical processing, and energy systems. Their robust design and adaptability explain their extensive use, yet the growing need for higher efficiency, compactness, and reduced operating costs continues to drive efforts for performance improvement. Among various enhancement techniques, passive methods such as turbulators are particularly attractive. By introducing structural modifications that disturb fluid flow, turbulators intensify turbulence, enhance mixing, and reduce stagnant zones, which results in stronger heat transfer without the requirement for external energy. However, these benefits are usually accompanied by increased pressure drops (ΔP), making optimization essential. Computational fluid dynamics (CFD) has become the primary tool for exploring these design trade-offs. Unlike analytical or experimental methods, CFD provides detailed insight into velocity fields, temperature distributions, and flow structures at a fraction of the cost and time. Its flexibility makes it indispensable for studying complex turbulator geometries, such as twisted tubes, dimpled and corrugated surfaces, helical and modified baffles, and hybrid configurations. Nevertheless, the predictive power of CFD depends heavily on turbulence modeling, grid quality, and solver strategies, which require careful selection to ensure accuracy and stability. This review offers the first focused and critical synthesis of CFD-based studies on turbulators in STHEs. It evaluates the ability of different designs to enhance hydrothermal performance, while also highlighting the hydraulic penalties that accompany them. Beyond geometric modifications, it examines how artificial intelligence (AI) is being coupled with CFD to accelerate optimization, reduce computational cost, and improve predictive accuracy. By systematically comparing achievements and limitations across the literature, this review provides not only a consolidated reference for engineers but also a roadmap for advancing next-generation STHEs that are compact, energy-efficient, and better aligned with future industrial needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".