Chapter 3 - CFD simulation, design, and optimization for biohydrogen systems

Cooling towers are required in building HVAC systems that use water as the cooling condenser fluid. Cooling towers used in this study are of the forced draft, counter flow, indirect/closed evaporative type. This study sought to demonstrate the performance characteristics of a closed system cooling tower by its effectiveness value, Number of Transfer Units (NTU), cooling capacity, and overall heat transfer and mass coefficient of the cooling tower. Experiments were performed on a heat exchanger coil intercrossed with ⅜ inch diameter intersections on parallel lines. Results of the experiment were then compared with the heat and mass transfer correlations taken from previous studies, and also combined with Computational Fluid Dynamics (CFD) simulations to examine the physical processes that occur in the cooling towers. All the experimental results, theoretical calculations and CFD simulations used variations of warm water mass, cold air, and water spray to present a clear description of the performance characteristics of a closed system cooling tower. The results of this study have shown that an increase in the amount of water spray mass flow causes an increase in the effectiveness value, heat transfer and overall mass transfer, as well as the cooling capacity of the cooling tower. The waste heat typically utilizes up to 80% of latent evaporation heat, and 20% of sensible air heat; however, waste heat in the closed system cooling tower utilizes 100% of latent evaporation heat. The mass transfer coefficient rate tends to be stable for a small mass of water spray.

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to ‘losses’ in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

Computational Fluid Dynamics (CFD) is a computational technology that enables researchers to study the dynamics of things that flow. By using CFD, it is possible to build a computational model that represents a system under study. It not only predicts fluid flow behavior, but also the transfer of heat, mass, phase change, chemical reaction, mechanical movement, and stress or deformation of related solid. In this study, hydrodynamics behavior of a laboratorial fluidized bed dryer containing carrot cubes as well as heat transfer in the dryer was simulated using CFD. In addition, to understand the energy utilization the system was optimized using the Taguchi technique. Simulations were planned based on L 9 orthogonal array of Taguchi, and they were conducted at inlet air temperatures 50, 60 and 70 oC, bed depths 3, 6 and 9 cm and carrot cube dimensions 4, 7 and 10 mm. Results show that cube size and bed depth have the maximum and minimum contribution on the energy utilization ratio, respectively. According to the results inlet air temperature 70°C, cube size 4mm and bed depth 9cm were obtained as optimum conditions. Finally, a verification test was performed to confirm the validity of the used statistical method.

In this paper, the surface evaporation of a sessile water droplet on hydrophobic solid substrates was numerically and experimentally investigated. The droplet evaporation is governed by multi-dimensional heat and mass transport and interfacial processes of three (gas, liquid, and solid) phases. A Computational Fluid Dynamics (CFD) simulation was performed to analyze the simultaneous heat and mass transfer in the surface evaporation of a sessile water droplet by considering the Marangoni and/or free convection. The results from the CFD simulation were validated using experimental results for the water droplet evaporation on a hydrophobic-coated silicon substrate. It was found that the droplet evaporation rate is greatly increased by a thermocapillary-driven flow (Marangoni convection) in the droplet with small liquid volumes of less than 2.4 μL on the hydrophobic substrate, while the contribution of the free convection is negligible. The peak mass flux of droplet evaporation was always observed along the three-phase contact line.

Superheated steam is widely employed in various energy systems, particularly in power plants, chemical industries, and other applications where high-temperature and high-pressure steam is essential for efficient energy conversion and process control. In these systems, regulation valves are crucial components that control the flow of steam, adjusting its pressure and temperature to ensure safe and efficient operation. Accurate understanding and prediction of temperature variations within regulation valves are essential for optimizing their performance and improving the overall system efficiency. This study investigates the temperature variations of superheated steam flowing through a regulation valve using computational fluid dynamics (CFD) simulations combined with Proper Orthogonal Decomposition (POD) techniques. The primary goal is to analyze the behavior of steam within the valve and to identify the underlying flow structures that contribute to temperature fluctuations, particularly in the throttling regions where steam undergoes rapid changes in pressure and temperature. The numerical model is first established and validated through experiments conducted at operating conditions of 2 MPa pressure and 574°C steam temperature. These conditions are representative of typical industrial steam systems, allowing for a realistic simulation of the phenomena. The analysis begins with an examination of the internal flow field parameters, including temperature and pressure, to understand the overall fluid dynamics within the valve. Special focus is given to the throttling regions, where significant variations in both temperature and pressure occur due to the Joule-Thomson effect. This phenomenon results in temperature changes as the steam passes through the restriction of the valve, which can have important implications for system performance and energy efficiency. To gain further insights into the complex three-dimensional flow patterns, POD is applied to reduce the dimensionality of the CFD results. This approach involves extracting the most dominant flow modes from the high-dimensional data generated by CFD simulations. The original 3D CFD data is imported into MATLAB, where resampling is performed to prepare the data for POD analysis. Singular Value Decomposition (SVD) is then employed to extract the dominant modes that capture the key flow structures responsible for heat transfer and temperature fluctuations. The POD analysis reveals that the most influential modes are associated with regions of high turbulence intensity and significant temperature gradients, which are critical to the thermal performance of the steam flow through the regulation valve. The application of POD to 3D CFD results represents a novel approach, particularly for complex fluid flow models such as steam flow through regulation valves. This method offers a tool for simplifying the analysis of intricate flow phenomena, enabling the identification of key flow features that influence heat transfer. The insights gained from this study have practical implications for the design and optimization of temperature and pressure regulation valves in energy systems, providing a theoretical foundation for enhancing the efficiency and reliability of these systems.

Diesel-electric trains for the yukon

Computational Fluid Dynamics Simulation of CO2 Methanation in a Fixed-bed Profile Reactor

In order to predict and manage the thermal behavior of microelectronic packages cooled principally by natural convection, a two-way coupling method was developed between a accurate conduction model and a computational fluid dynamics (CFD) simulation. An iterative solution loop was performed by applying as a boundary condition the local convection coefficients obtained from the CFD to the conduction model, and the temperature field at the solid-fluid interface obtained from the solid conduction model to CFD simulation. For the first iteration, the conduction heat transfer was solved by considering an initial guess of a uniform convection coefficient (e.g. from empirical formulas) for each solid-fluid interface. The procedure was repeated until the convergence of the solution was reached. The comparison between the total convection coefficients including the radiation obtained with the CFD iterative procedure and those from the conventional empirical methods showed important differences, thus demonstrating the usefulness of the CFD approach to obtain accurate thermal results. The numerical results were compared to measurements from a test vehicle, carried out under the natural convection Jedec JESD51 standards in a still air chamber. Radiation plays an important role in heat transfer in this setting because of the large temperature difference between the walls of the box and the test vehicle. The junction temperatures obtained with the numerical simulation for different operating powers were in good agreement with the experimental measurements, with an error of less than 1°C, showing that the proposed methodology allows the accurate simulation of the natural heat transfer for microelectronic packages mounted on PCB in the horizontal orientation.

Analysis of the effect of turbulence promoters in hollow fiber membrane distillation modules by computational fluid dynamic (CFD) simulations

Microtomography-based numerical simulations of heat transfer and fluid flow through β-SiC open-cell foams for catalysis

Bench test and computational fluid dynamics (CFD ) simulation of the air pump loss on the 380 hydraulic retarder are completed in this paper . It turns out that the CFD simulation has nice reliability from the contrast of the air pump loss between the test and the simulation . And on this foundation , CFD simulation of two kinds of retarders with different turbulent structures is done . The contrast of the results of the simulation give the effects of different turbulent structures on cutting down the are pump loss .

The removal of toxic gases leaking during excavation in infrastructure projects is crucial for both human health and environmental protection. In this process, Computational Fluid Dynamics (CFD) simulations are highly valuable for modeling and analyzing complex physical processes associated with the transport and dispersion of gases. During infrastructure projects in geothermal regions, various gases may leak from underground sources. These gases may include toxic and/or flammable substances such as hydrogen sulfide (H₂S), carbon dioxide (CO₂), and methane (CH₄). Designing an effective ventilation system is essential for the removal of toxic gases through ventilation. CFD simulations can be utilized to evaluate the design and performance of ventilation systems. Modeling gas leaks from sources encountered during excavation using CFD simulations can help understand how gases move throughout infrastructure projects and their environmental impacts. CFD simulations can assist in optimizing factors such as the placement of ventilation systems, duct design, air flow rates, and filtration efficiency. This optimization ensures the effective removal of toxic gases and minimizes environmental impacts. CFD simulations can also be used to assess the efficiency and safety of ventilation systems designed for the mitigation of toxic gases. Additionally, simulating the operation of ventilation systems during potential emergency situations can help in evaluating associated risks.

Real-time simulation and the integration of digital twins necessitate the development of computationally efficient surrogate models for contemporary food drying systems, in which complex physical processes must interact dynamically with virtual representations. Traditional modeling approaches for food drying are subject to notable limitations: empirical models often lack physical fidelity, whereas high-fidelity computational fluid dynamics (CFD) simulations require the resolution of tightly coupled heat and mass transfer processes among vapor, liquid water, and air phases within unsaturated porous media, rendering them computationally prohibitive for real-time applications. This work proposes a region-aware graph neural network (GNN) surrogate model for food drying simulation by leveraging mesh-based and region-specific representations of the drying process. The proposed GNN architecture, inspired by MeshGraphNet with specialized masked message passing, handles the heterogeneous domains and sharp discontinuities characteristic of coupled heat and mass transfer in food drying. The GNN model was trained on a limited dataset comprising 28 CFD simulations of hot-air drying of a potato slice under varying ambient conditions, generated using a CFD framework validated against numerical results from the literature and laboratory-scale experimental data. Despite this small training set, the model delivers predictive performance for operating conditions beyond those seen during training. Single-step predictions achieved RMSE values of 0.17 °C for temperature ( T ), 0.26% for relative humidity ( RH ), and 0.0028 for liquid saturation ( S w ). Comparative analysis with standard message passing revealed a threefold reduction in interfacial absolute error (0.003 vs. 0.008) and elimination of nonphysical field propagation across domain boundaries. The model maintained performance on the training domain edges, with moisture ratio curves achieving R 2 > 0.97 for 10-step rollouts (R 2 = 0.989 interior domain; R 2 = 0.979 edge domain) while preserving the characteristic S-shaped drying curves in longer autonomous simulations (R 2 = 0.784 and 0.873 for full rollouts of the interior and edge-of-domain test cases, respectively). This framework is applicable to real-time process control, design optimization, and digital twin implementations in food manufacturing. • Integrated a region-aware GNN into a digital twin for potato drying. • Achieved high accuracy with minimal data in heat and mass transfer prediction. • Region-aware message passing ensured physical consistency across domains. • Model adapted to changes in temperature and geometry with low prediction error. • Fast predictions enable real-time control and food process optimization

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

Today, many wells in heterogeneous carbonate reservoirs throughout the Middle East and Caspian Sea area are being completed with long intervals and complex completion equipment, such as limited entry liners and inflow control devices (ICDs). Accurate modeling and understanding of fluid placement during a stimulation operation through such long complex completions can be quite challenging. In this paper, an integrated approach of modeling, experiments and computational fluid dynamics (CFD) simulations will be presented. A computational tool based on a transient one-dimensional (1D) approach has been developed in-house to model stimulation through such completions. In order to validate the 1D approach for reliable fluid displacement predictions, annulus fluid displacement experiments in perforated liners supported by CFD simulations have been conducted. The experimental setup consists of an outer Lucite pipe representing the well-bore geometry and an inner aluminum pipe with multiple perforations of variable diameter. The setup mimics the real field application in a 1:1 scale with respect to the pipe and perforation diameters while the test section is limited to 27 ft. The experimental procedure is as follows: fluid is pumped into the annulus through the perforations until the annulus is completely filled,the fluid tank is switched "on-the-fly" to the tank with the displacing fluid resulting in the displacement of the annulus fluid by the displacing fluid through the perforations. The experiments and the CFD simulations indicate that even with a single open perforation inside the 27-ft test section, the three-dimensional (3D) flow field around the perforation rapidly transitions to uniform annular flow within a few feet uniformly displacing the annulus fluid. The model also provides insight into various well injection processes in sandstone and carbonate formation, such as scale squeezes, solvent treatments, and HF acid treatments. Introduction The length of production and injection intervals in a single well continues to increase in order to contact more reservoir rock with a single wellbore penetration and improve project economics. Multiple, thick stacked reservoirs are accessed by vertical and deviated wells while thinner reservoirs are being exploited with longer and longer horizontal wells1–3. To keep up with drilling capability, completion technology has advanced considerably in the past few years. Horizontal wells are "compartmentalized" using mechanical and/or swellable packers. Within a given compartment, injection and production can be controlled by use of inflow control devices (ICDs), limited entry (LE) liners, and/or sliding sleeves. The liners also make it easier to enter the completion interval with logging tools, wire line, and coiled tubing. While various tools are available to evaluate long-term, steady-state production and injection into such wells, modeling of short-term, transient injection into such completions is very challenging due to the long lateral lengths, limited entry points from the liner to each compartment, and the presence of an open annulus with occasional packer isolations. This is especially important for long wells in carbonate formations as all wells require acid stimulation upon initial completion, and occasionally throughout the well life.