Thermal Analysis and Optimization of Heat Exchangers: A Review of Design and Simulation Approaches

Moses Adondua Abah ¹ , Jeremiah Oluwayomi Oladetan² , Michael Oladosu Abimbola ¹

¹ResearchHub Nexus Institute, Nigeria

²Department of Aerospace Engineering, Faculty of Engineering, Lagos State University, Lagos State, Nigeria

Corresponding Author Email: m.abah@fuwukari.edu.ng

DOI : https://doi.org/10.51470/eSL.2025.6.4.53

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Introduction

Throughout the power generating, petrochemical, food processing, HVAC, and transportation sectors, heat exchangers are essential parts that support energy conversion, process heating/cooling, and thermal management [1].  They are essential to decarbonization and resource-efficiency initiatives due to their combined effect on system efficiency and operating costs. The strategic importance of heat-exchange technology has been increased by current industry interest in capturing waste and low-grade heat. Recent surveys have shown significant, deployable energy savings when appropriately sized and combined with bottoming cycles like Organic Rankine Cycles and heat-pump systems [2, 3]. Improved heat-transfer coefficient, surface-area density, and pressure-loss tradeoffs at the device level directly result in lower fuel and emissions at the facility scale; on the other hand, subpar HE performance has quantifiable negative effects on the economy and environment due to increased fuel consumption and maintenance costs. Compact, lightweight, and high-effectiveness designs (micro/compact exchangers, printed/AM geometries) are receiving more and more attention, which is a reflection of both technological capability and industry demand in fields where mass and volumetric constraints are crucial, such as concentrated solar, electric vehicles, and aerospace [4]. 

The main instrument for identifying performance limits, calculating irreversibilities, and directing design trade-offs is thermal analysis, which can take many forms, from lumped analytical techniques to intricate CFD and multiphysics simulations.  Reviewing exergetic optimization shows consistent gains when exergy is used as the objective or constraint in design and control algorithms. In particular, exergy-based evaluations have developed as a way to identify thermodynamic losses and set optimization targets that align energy, economic, and environmental objectives [5]. Analysis is enhanced by optimization: heuristic and metaheuristic methods (such as particle swarms and genetic algorithms) combined with contemporary surrogate and adjoint approaches allow for the simultaneous adjustment of geometry, flow distribution, and operating point while taking into account multiple objective criteria (such as heat duty, pressure drop, cost, and lifecycle emissions) [6, 7]. Simulation-driven optimization makes it possible to transform component-level innovations into system-level sustainability gains when combined with advancements in manufacturing (additive manufacturing enabling complex internal topologies) and materials (nanofluids, high-conductivity coatings) [8]. 

Since they assume simplified flow patterns, uniform properties, and steady one-dimensional heat transfer, traditional HE design tools (LMTD, ε–NTU correlations, and single-tube empirical correlations) are fundamentally limited for meeting modern performance demands. As a result, they are unable to capture complex 3-D effects like flow maldistribution, end-effects, conjugate conduction, phase-change pockets, or localized fouling dynamics [9].  One of the main practical reliability constraints remains fouling and its coupled thermal, hydraulic, and chemical interactions. Particulate, biological, and crystallization fouling mechanisms increase pumping energy and decrease heat transfer, and their prediction necessitates experimental data or high-fidelity models that go beyond classical correlations [10].  According to [11], uncertainty quantification and appropriate experimental validation are necessary to prevent misleading design conclusions in complex geometries. Similarly, while CFD and FEM offer crucial detail, their predictive quality depends on turbulence closure, wall-treatment, meshing strategy, and V&V/UQ practice. 

By integrating developments in theory, computing, experimentation, and practical application, this review aims to present a thorough synthesis of the state-of-the-art in thermal analysis and heat exchanger optimization [12].  In order to provide a basis for the more complex talks that follow, the scope covers basic topics such as heat exchanger classifications, controlling heat transfer and fluid flow principles, and commonly used performance measurements. It goes beyond that to critically assess the analytical, numerical, and experimental methods used in thermal analysis, focusing on their accuracy, applicability, and limitations at various scales and operating circumstances [13]. Furthermore, the review examines optimization techniques from advanced heuristic and multi-objective methods, including thermo-economic and exergy-based approaches, to traditional parametric studies, emphasizing the growing significance of striking a balance between technical efficiency and cost and environmental considerations [14].  In order to guide both research and industrial practice, this effort aims to identify existing capabilities and gaps in design and simulation methodologies. In reinventing the design and functionality of heat exchangers, it seeks to evaluate the contribution of cutting-edge enablers like nanofluids, additive manufacturing, and artificial intelligence, as well as contemporary computing tools like CFD and digital twins.  The review aims to improve knowledge of how next-generation approaches might improve efficiency, sustainability, and dependability by incorporating results from recent investigations.  Finally, it offers a research and innovation path that is in line with global energy efficiency and decarbonization goals.

Fundamentals of Heat Exchangers

Heat exchangers are indispensable devices in energy systems, chemical processing, power generation, refrigeration, and sustainable technologies because they enable efficient thermal energy transfer between fluids at different temperatures. A robust understanding of their classifications, governing principles, performance evaluation metrics, and design challenges provides the foundation for their thermal analysis and optimization [15]. This section reviews these fundamental aspects to establish the basis for advanced simulation and optimization methods. These fundamental aspects are discussed below;

Classification of Heat Exchangers

Heat exchangers are categorized based on their design, flow pattern, and method of heat transmission. One of the most popular types is the shell-and-tube heat exchanger (STHE), which is renowned for its resilience and versatility in high-temperature and high-pressure applications. One fluid circulates inside the tubes while the other circulates outside of a bundle of tubes encased in a cylindrical shell [16]. Power plants, petrochemical facilities, and oil refineries are the main locations for STHEs.  Robustness and scalability are among their benefits, while fouling and high space needs are drawbacks [17]. 

The plate heat exchanger (PHE), which uses corrugated metal plates stacked together to improve turbulence and heat transfer efficiency, is another significant category.  PHEs are ideal for food processing, pharmaceuticals, and HVAC applications due to their small size, high thermal effectiveness, and ease of maintenance [18].  However, because of sealing problems, its use in high-pressure or high-temperature environments is still restricted. 

Compact heat exchangers (CHEs) can achieve high effectiveness in tiny spaces because of their high surface-area-to-volume ratios.  They are frequently employed in cooling applications for microelectronics, automobiles, and aircraft.  Although they are susceptible to fouling and flow maldistribution, microchannel heat exchangers are an advanced subset that maximize heat transmission by using very small hydraulic diameters [19].  Aerospace, electronics, and high-flux applications frequently use compact and microchannel exchangers (plate-fin, tube-fin, and microchannels), which are distinguished by their high surface-area density and small hydraulic diameters. These exchangers provide high heat transfer coefficients but frequently have higher pressure drops and manufacturing costs [20]. 

The same surface alternately stores and releases heat to the fluid streams in regenerative heat exchangers, which sets them apart from recuperative types.  Applications where great temperature efficiency is crucial include gas turbines and cryogenic procedures [21]. Furthermore, new designs that suit the demands of hydrogen liquefaction and compact energy systems, including printed circuit heat exchangers (PCHEs) and additively built exchangers, offer improved design flexibility and durability [22].

Governing Principles of Heat Transfer and Fluid Flow

The performance of a heat exchanger is determined by the interplay between conduction, convection, and fluid flow characteristics. Fundamentally, heat transfer occurs by conduction through the separating wall and convection between the fluids and the wall surfaces. The local heat flux can be expressed as:

ℎ is the convective heat transfer coefficient, Tf the fluid temperature, and and T𝑠the surface temperature. These resistances are aggregated into the overall heat transfer coefficient (U), leading to the governing design equation:         ​

Where:

A is the heat transfer area and ΔTlm is the log-mean temperature difference [23].

Fluid flow strongly influences heat transfer and pressure drop. Dimensionless groups such as the Reynolds, Prandtl, and Nusselt numbers characterize convective regimes. Turbulent flow enhances mixing and heat transfer coefficients, though at the expense of increased pumping power. Conversely, laminar flow is associated with lower heat transfer rates but reduced friction losses. In two-phase exchangers, additional complexities such as boiling, condensation, and flow maldistribution arise. Boiling introduces nucleation dynamics, while condensation involves filmwise or dropwise mechanisms that significantly affect performance. For compact and microchannel exchangers, conjugate heat transfer becomes particularly important, requiring coupled numerical solutions [24, 25].

Performance Evaluation Metrics

Heat exchanger performance evaluation is based on defined criteria that combine hydraulic and thermal factors.  The ratio of actual heat transfer to the maximum heat transfer feasible under specific input circumstances is known as the effectiveness (ε), and it is a commonly used metric.  Based on this, the ε–NTU technique (NTU = UA/Cmin) links effectiveness to the number of transfer units. This approach is beneficial for comparative examination of setups and is particularly useful when outlet temperatures are unknown [26].  A crucial factor that includes convective resistances, wall conductive resistance, and fouling resistance is the overall heat transfer coefficient (U).  The precise calculation of U is essential for both design and performance assessment, and empirical correlations are commonly employed for intricate geometries [27]. 

The main metrics used to evaluate hydraulic performance are pressure drop (ΔP) and related pumping power. There are trade-offs between thermal and hydraulic performance since increased turbulence increases ΔP even while it enhances heat transmission. In microchannels and tiny exchangers, this equilibrium is particularly important. Economically speaking, thermo-economic measures like cost per unit heat duty and exergy-based efficiency are being used more and more to make sure that enhancements in thermal performance are in line with sustainability and lifespan cost goals [28].

Key Challenges in Design

Even with their extensive use, heat exchanger design and operation nevertheless face a number of difficulties.  One of the most important is fouling, which results in higher pumping requirements, decreased efficiency, and increased heat resistance.  The fluid chemistry and operating circumstances have an impact on the fouling mechanisms of crystallization, particle deposition, corrosion, and biofouling [29].  Fouling is one of the biggest causes of maintenance expenses, yet there are currently few predictive fouling models available.  Another issue is thermal strains, particularly in shell-and-tube exchangers that are exposed to significant temperature variations.  Design changes like floating heads or expansion joints are necessary because of the possibility of fatigue and leakage caused by the different expansion between tubes and shells [30]. 

Trade-offs between cost and performance present another challenge.  Despite their excellent performance, compact exchangers are difficult to clean and expensive to produce.  On the other hand, traditional designs could be less effective but less expensive to construct and maintain.  A continuous optimization problem is striking a balance between manufacturing, maintenance, and lifecycle costs and thermal effectiveness [31]. Lastly, when new laboratory-scale designs like exchangers improved by nanofluid or made by additive manufacturing are implemented in industry, scale-up and validation problems occur.  Predicted performance is frequently decreased by manufacturing tolerance variability, unexpected flow maldistribution, and actual fouling conditions, highlighting the significance of combined experimental and numerical validation [32].

Thermal Analysis Approaches

The accurate thermal analysis of heat exchangers is critical for predicting performance, guiding design improvements, and ensuring long-term operational reliability. Conventional design techniques alone are insufficient to capture the complexity of modern systems, especially under variable operating conditions, multiphase flows, and compact geometries [34]. To address these challenges, researchers and engineers have developed a range of analytical, numerical, and experimental approaches, each with unique strengths and limitations.

Analytical Methods

Closed-form formulas and assumptions are used in the classical analysis of HXs.  In order to connect heat transfer to stream heat capacity ratios and efficacy, engineers employ the ε–NTU approach and the LMTD method, which treat the exchanger as a black box with specified inlet/outlet temperatures.  Quick estimations are provided by these methods, which come from lumped-parameter models of HXs [35].  The following simplifying assumptions must be met, though: uniform flow distribution, steady-state operation, no axial conduction in walls, and constant fluid characteristics.  For example, the ε–NTU method requires known inlet conditions on one side, but the LMTD approach overlooks bypass flows and assumes logarithmic temperature profiles. These traditional techniques (LMTD, ε–NTU), as [36] point out, “are generally based on certain assumptions and conditions, such as constant physical properties, steady-state operation, no axial conduction in walls, and uniform flow distribution.” 

For example, the ε–NTU method requires known inlet conditions on one side, but the LMTD approach overlooks bypass flows and assumes logarithmic temperature profiles.  [37]. highlight that these traditional techniques (LMTD, ε–NTU) “generally rely on certain assumptions and conditions, such as constant physical properties, steady-state operation, negligible wall heat conduction, [and] uniform distribution of flow properties” [37].  When working with non-ideal flows, phase changes, or changeable qualities, these presumptions may reduce accuracy.  Frequently, experimental correction factors and empirical correlations (such as efficacy charts for different configurations and fouling variables) are added to analytical models.  Changing tube diameters, lengths, or baffle spacing can have an impact on performance. Parametric studies can be performed manually or with the use of basic scripts. These techniques, however, may find it difficult to record intricate geometry or multiphase flows.  For preliminary design estimates and for comparing more sophisticated models, analytical formulas are still crucial.

Numerical Methods

Beyond analytical simplifications, numerical simulation offers an in-depth understanding of heat exchanger behavior.  The governing conservation equations (mass, momentum, and energy) are discretized in either finite volume or finite element form using computational fluid dynamics (CFD), which is commonly used to model heat transfer and HX flow [14, 15].  The fluid domains (shell side and tube/fin side) are separated into tiny control volumes or elements in a CFD model.  A computer solves the fluid mechanics equations iteratively by converting the integral and differential terms into algebraic equations [16]. 

By resolving local temperature and velocity fields, this makes it is possible to anticipate precise distributions of pressure drops, heat transfer coefficients, and regions vulnerable to hotspots or maldistribution.  Fin geometry, baffles, and irregular flow can all be captured using sophisticated meshes [17].  While the Finite Volume Method (FVM) is widely utilized for fluid flow, the Finite Element Method (FEM) can also be employed for solid heat conduction and structural stress analysis in exchanger walls.  All things considered, numerical models help to reconcile the disparity between simplistic theory and experiment.  But they need to be set up and validated carefully; according to [18], numerical techniques like CFD “often require significant computational resources,” and in order to capture boundary layers, grid resolution needs to be improved.  Open-source solvers like OpenFOAM or commercial CFD algorithms like ANSYS Fluent, COMSOL Multiphysics, and STAR-CCM+ are commonly used.

Experimental Methods

Thermal analysis and model validation still require experimental testing.  Temperature and pressure sensors are used to monitor performance in laboratory-scale HXs (or representative test sections) under regulated flow conditions.  Micro-thermocouples, heat flux meters, and flow visualization (such as particle image velocimetry) can all supply local data.  CFD predictions are validated, empirical correlations are calibrated, and unmodeled effects (such as small leaks or unexpected flow patterns) are found using experimental data.  Marzouk et al. emphasize that “experimental validation” in practical applications should be used in conjunction with numerical and theoretical studies [18].  To correctly track temperature and flow rate, modern experimental setups frequently incorporate automated data capture and sophisticated sensors (thermocouples, pressure transducers, and heat flux probes).  In conclusion, experiments serve as a foundation for analysis since they validate designs, uncover intricate phenomena (such as the onset of fouling or flow maldistribution), and supply ground truth for analytical and numerical models.

Optimization of Heat Exchangers

Optimization of heat exchangers is essential for enhancing thermal performance while minimizing cost, energy consumption, and environmental impact. Traditional approaches have relied on parametric studies and empirical correlations, but these often fall short in addressing the nonlinear and multi-objective nature of exchanger design [22]. With advances in computational tools, modern optimization methods such as genetic algorithms, particle swarm optimization, and multi-objective frameworks have enabled more accurate and efficient solutions. Increasingly, thermo-economic and sustainability considerations are also being integrated, ensuring designs meet both performance and environmental goals [23].

Traditional Optimization Techniques

In the past, parametric and empirical approaches were used for optimization in HX design.  By methodically altering geometrical or operational factors (such as tube diameter, length, spacing, flow velocity, etc.) and evaluating the effects on performance metrics like heat duty and pressure drop, engineers employed parametric research.  Quick evaluations appropriate for early-stage design were offered by analytical techniques, particularly those based on ε–NTU or LMTD formulations [25].  Design decisions were frequently guided by empirical correlations obtained from experimental data, which related Nusselt and friction factors to Reynolds and Prandtl numbers for particular configurations. 

Although these equations and charts facilitate quick trade-off analysis, they are only applicable in situations that are comparable to the ones in which they were created [26].  Even if they are straightforward and computationally light, typical parametric approaches frequently falter when faced with intricate constraints, multiple goals, or the introduction of novel HX geometries.  However, these methods are still useful today for quick feasibility and preliminary design. Before using more complex computational techniques, parametric optimization, for example, might identify key design factors and constrain design spaces [27].

Advanced Optimization Techniques

Advanced optimization approaches have gained popularity as computing power and algorithmic advancements have increased.  Genetic algorithms (GAs), particle swarm optimization (PSO), and simulated annealing are examples of metaheuristic algorithms that provide strong, adaptable frameworks for negotiating extremely nonlinear, multi-objective HX design spaces [28].  To efficiently manage trade-offs between thermal duty and pumping power, researchers have employed GAs to improve baffle spacing, tube arrangements, or plate layouts in shell-and-tube and plate heat exchangers.  Similar to minichannel HXs, PSO has been used to track Pareto-optimal fronts that balance pressure drop and heat transfer [29]. According to [30], hybrid approaches improve convergence time while preserving design quality by fusing global search with local refinement or surrogate models. 

When computational fluid dynamics (CFD) and advanced optimization are combined, a significant breakthrough occurs.  For precise performance predictions, these frameworks use automated loops that iteratively adjust geometry or operating factors by calling CFD solvers.  Despite being computationally demanding, these closed-loop optimizations produce extremely sophisticated designs that are suited to intricate flows, such as turbulence effects and maldistributions [31].  By using machine learning to approximate CFD outputs, alternative surrogate modeling techniques save computing costs.  Rapid design space exploration is made possible by surrogate-based optimizations, which are especially helpful for high-dimensional parameter sets or real-time design tools [1].  This lesson revolves around multi-objective optimization, wherein HX design must concurrently handle conflicting objectives, maximizing heat transmission, minimizing pressure drop, lowering material costs, and satisfying space restrictions.  These trade-offs are shown by Pareto front approaches, which facilitate well-informed design decision-making.

Thermo-Economic Optimization

Economic and environmental factors are increasingly being incorporated into HX optimization in addition to performance-based criteria. While incorporating thermodynamic efficiency and sustainability measures like exergy or carbon emissions, thermo-economic optimization assesses design options according to lifecycle costs, which include capital investment, maintenance, energy consumption, and downtime costs [2].  By identifying the irreversibility sources inside HX operation, energy analysis provides a roadmap for focused enhancements.  In order to provide economically optimal designs that are also thermodynamically efficient, optimization procedures may specify objective functions that minimize exergy destruction and operating costs [3]. 

Fouling-related maintenance, replacement frequency, and energy consumption over time are additional dimensions added by life-cycle cost modeling.  For instance, despite a higher initial expenditure, a slightly larger HX may result in reduced overall expenses by reducing fouling rate and cleaning frequency [4].  Sustainability objectives are especially well-aligned with optimization frameworks that include performance, lifecycle cost, and energy measurements.  These are becoming more significant in the design of heat exchangers for low-carbon industrial applications, renewable energy systems, and energy-efficient buildings.

 Emerging Trends and Future Directions

Heat exchangers are central to nearly every sector of energy and process engineering, and ongoing technological transitions are reshaping their design and operation. As industries strive toward higher efficiency, lower emissions, and greater integration with digital tools, several emerging trends are defining the future landscape of thermal systems.

Heat exchangers are essential components of renewable energy systems because they allow for efficient thermal coupling in biomass, geothermal, and solar thermal systems.  For example, by utilizing ground thermal inertia, earth-air heat exchangers (EAHEs) coupled with heat pumps improve building energy efficiency [8].  Similarly, exchangers that deliver extremely dependable heat transfer under difficult circumstances, such as fluids containing minerals and high pressures, are required for geothermal power generation [9] 

Heat transfer performance is improved by nanofluids, which are suspensions of nanoparticles (such as metal oxides or carbon-based) that increase the base fluids’ effective thermal conductivity.  A thorough analysis of nanofluid applications in heat exchangers is provided by [10], who demonstrates notable improvements in both laminar and turbulent regimes.  This perspective is expanded by [11], who investigate nanofluid integrations across a variety of heat-transfer devices, such as plate-fin exchangers and solar collectors.  HX shapes that were previously unattainable with traditional manufacturing are made possible by additive manufacturing (AM).  Compact designs with excellent heat exchange efficiency are made possible by structures like gyroid surfaces, lattice frameworks, turbulence promoters, and microchannels. An authoritative assessment of AM-enabled HX designs is given by [12], who emphasize the significance of manufacturing restrictions and surface roughness. 

Predictive maintenance, simulation, and HX design are increasingly being driven by artificial intelligence (AI) and machine learning (ML).  For example, form optimization with CFD-driven agents is made possible by reinforcement learning techniques [13].  In addition to speeding up design cycles, these AI methods improve the identification of ideal configurations that balance pressure drop and thermal performance something that would be impossible to do by hand.  Thanks to advancements in edge computing and sensor technologies, the idea of the “digital twin,” which combines virtual and physical models, is developing quickly. These digital twins make it possible to continuously monitor and diagnose HX systems in real time [14].  For example, dynamic process management and early fouling and thermal inefficiency identification are made possible by local, low-latency analytics.  Proactive maintenance and adaptive operation are encouraged by this trend, which is consistent with Industry 4.0 ideas.

 Challenges and Research Gaps

Despite major advances in design, analysis, and optimization, heat exchangers (HXs) continue to face several unresolved challenges that constrain their efficiency, durability, and scalability. These limitations are evident across computational modeling, materials performance, optimization frameworks, and data-driven integration. Addressing these gaps is critical for achieving next-generation HX systems that are both highly efficient and environmentally sustainable [20].

The precision and reliability of CFD and numerical forecasts for intricate HX geometries and flow regimes are a major drawback.  Although flow maldistribution, local heat-transfer augmentation, and turbulent mixing can be resolved by high-fidelity CFD, mesh resolution, turbulence closure, wall treatments, and boundary-condition realism all have a significant impact on the prediction quality [21].  HX studies continue to differ in their use of systematic verification and validation (V&V) techniques, such as benchmarks, uncertainty quantification, and rigorous experimental comparisons; without V&V, purported benefits from designs or numerical optimizations run the danger of not being practicable. 

When CFD is integrated into HX design loops, this issue, which has been acknowledged by the larger CFD community for decades, continues to be crucial [22, 23].  Due to their high cost, high-resolution 3D simulations and conjugate heat-transfer models are not suitable for recurrent evaluations within multi-objective searches or population-based optimizers (GA, PSO).  Although reduced-order models (data-driven or physics-informed) and surrogate (meta-model) techniques show promise, they need to be tested across the whole operational envelope and trained on representative data to prevent inaccurate predictions.  Although the usage of surrogates and ML surrogates for HX design is increasing, the literature also reports instances in which surrogate accuracy decreases when extrapolating outside the training cases [24]. 

Fouling, corrosion, and thermal fatigue are some of the most ancient and yet most expensive practical issues on the materials and operational side.  Since deposition is dependent on surface chemistry, local hydrodynamics, and transient operating histories, fouling layers increase pressure drop and decrease efficient heat transfer. As a result, current first-principles models do a poor job of predicting their kinetics.  Unless specific alloys or coatings are employed, corrosion and scaling are severe in geothermal, biomass, and marine applications where chemically hostile fluids reduce HX lifetime.  Advanced alloys and surface treatments can help with these issues, but their long-term field performance, cost, and manufacturing feasibility are usually unknown.  One significant translational gap is the discrepancy between laboratory material research and industry service conditions [25]. 

Another area of research that needs attention is the robustness and scalability of optimization techniques.  Numerous sophisticated optimizers, such as adjoint methods, genetic algorithms, and topology optimization, have shown significant improvements at the component or lab size, especially when paired with additive manufacturing to achieve intricate shapes.  It is difficult to apply these techniques to big industrial HX networks, nevertheless, because of the hundreds of interacting units, maintenance requirements, control coupling, and unpredictable loads.  To ensure that optimized designs are resilient to actual operational variability, scalability necessitates the use of hybrid deterministic-stochastic solvers, hierarchical optimization techniques, and uncertainty quantification integration. Research comparing and combining optimization methodologies is ongoing, but there are still a few examples that apply to industry [26]. 

There are risks and opportunities associated with the data-driven modeling frontier.  AI and machine learning (ML) can speed up design (surrogates), make predictive maintenance possible, and identify trends in sensor streams. However, ML techniques rely significantly on representativeness, coverage, and quality of data.  Models trained on industrial datasets may perform poorly in off-design or degraded states because these datasets are frequently proprietary, skewed toward nominal operation, and contain gaps and sensor drift. Furthermore, extrapolating ML predictions without inherent physical limitations may result in non-physical outcomes or violations of conservation rules.  Standardized open datasets, physics-aware machine learning (ML) (hybrid models), strong uncertainty quantification, and open validation procedures that contrast ML outputs with independent experiments and high-fidelity simulation are all necessary to address these problems [27]. 

Lastly, lifespan, health, and environmental factors shouldn’t be neglected.  Although improved coatings, additively created materials, and novel heat-transfer media (such as nanofluids) can enhance thermal performance, their complete life-cycle impacts including manufacturing, nanoparticle release, recycling, and disposal are not well understood.  According to recent assessments, unless specifically evaluated using life-cycle analysis (LCA) and exergy-economic frameworks, possible toxicity, environmental persistence, and energy/capital costs could counteract advantages in operating efficiency.  Early optimization that incorporates LCA and energy factors lowers the possibility of thermally superior but environmentally or financially unsound solutions [28].

Conclusion

Heat exchangers continue to be one of the most important parts of contemporary energy and process systems, supporting everything from advanced manufacturing and renewable energy to power generation and chemical processing.  The state-of-the-art in heat exchanger thermal analysis, optimization, and simulation has been emphasized in this review, demonstrating both notable advancements and enduring difficulties. According to this assessment, practical issues, including fouling, corrosion, and cost-performance trade-offs, are still unresolved, despite the fact that the concepts of classification, controlling principles, and performance measurements are well defined.  To put it briefly, the integration of computational intelligence, innovative materials, and sustainability will determine the future of heat exchangers and ensure systems that are not only thermally efficient but also economically and environmentally viable.

Acknowledgement

 We thank all the researchers who contributed to the success of this research work.

Conflict of Interest

The authors declared that there are no conflicts of interest.

Funding

No funding was received for this research work.

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