Assessment of economic, thermal and hydraulic performances a corrugated helical heat exchanger filled with non-Newtonian nanofluid

Improved heat transfer efficiency with considering economic analysis in heating systems is an interesting topic for researchers and scientists in recent years. This research investigates the heat transfer rate (HTR) and flow of non-Newtonian water-Carboxyl methyl cellulose (CMC) based Al2O3 nanofluid in a helical heat exchanger equipped with common and novel turbulators using two-phase model. The requirements for dimensions and cost reduction and also energy saving in thermal systems are the main goal of this study. According to gained results usage of corrugated channel in helical heat exchanger has a considerable influence on thermal and hydraulic performance evaluation criteria (THPEC) index of helical heat exchanger and can improve the THPEC index. Thus, Re = 5000 is obtained as an optimum value, in which the maximum THPEC value is achieved. As it is found in this paper, in case of using novel heat exchanger instead of the basic smooth system, the thermal properties (by considering Nusselt number) increases about 210%, the hydraulic performance (friction factor) reduces about 28%, performance evaluation criteria index increases about 57% and the material consumption (in case of similar THPEC) decreases about 31%. In another word, with considering economic analysis for the basic and novel system which has same efficiencies, the novel one has lower length and consequently 31% lower material

Entropy optimization and heat flux analysis of Maxwell nanofluid configurated by an exponentially stretching surface with velocity slip

Abstract Hybrid nanofluids are extremely important in field of engineering and technology due to their higher heat transportation performance resulting in increased heat transfer rates. In the presence of thermal heat flux, the effect of a slanted MHD with velocity slip condition on a CNTs hybrid nanocomposite across a gradually extending surface is investigated. In present analysis, Maxwell nanofluid is embedded with SWCNT and MWCNT (single and multiple wall carbon nanotubes) nanoparticles. The nanomaterials transformation framework is obtained by employing Xue modified theoretical model. Various factors like dissipation, thermal radiations and Ohmic heat influences are adequately implemented in heat formulation. The physical features of thermodynamical mechanism of irreversibility are explored. The thermodynamics second law is used to produce the entropy optimization formulation. In addition, entropy is utilized to assess the energy aspects of a heat exchanger. Utilizing appropriate parameters, the model nonlinear PDEs are transformed to ODEs. The HAM technique is used to compute the solution of nonlinear ODEs. For both types of CNTs, the variations of entropy rate, Bejan number, velocity and temperature field versus key technical parameters is analyzed. The Nu and C f computational result for both CNTs are examined in tabulated and chart form. Velocity is inversely proportional to magnetic and solid volume nanoparticle parameters. The Br and Rd accelerates NG and Be for both nanocomposites. Additionally, a comparison of the HAM result and the numerical result is validated

Numerical Study of Dry Reforming of Methane in Packed and Fluidized Beds: Effects of Key Operating Parameters

Replacing the conventionally used steam reforming of methane (SRM) with a process that has a smaller carbon footprint, such as dry reforming of methane (DRM), has been found to greatly improve the industry’s utilization of greenhouse gases (GHGs). In this study, we numerically modeled a DRM process in lab-scale packed and fluidized beds using the Eulerian–Lagrangian approach. The simulation results agree well with the available experimental data. Based on these validated models, we investigated the effects of temperature, inlet composition, and contact spatial time on DRM in packed beds. The impacts of the side effects on the DRM process were also examined, particularly the role the methane decomposition reaction plays in coke formation at high temperatures. It was found that the coking amount reached thermodynamic equilibrium after 900 K. Additionally, the conversion rate in the fluidized bed was found to be slightly greater than that in the packed bed under the initial fluidization regime, and less coking was observed in the fluidized bed. The simulation results show that the adopted CFD approach was reliable for modeling complex flow and reaction phenomena at different scales and regimes

CFD modelling of hydrate slurry flow in a pipeline based on Euler-Euler approach

The presence and agglomeration of hydrates particle in oil and gas transportation pipeline can pose a major threat for the flow assurance. Understanding the hydrate-containing flow characteristics is of essence to efficiently manage and transport hydrate slurries. In this work, a 3D computational fluid dynamics model of hydrates slurry flow in pipeline was built using Eulerian-Eulerian multiphase approach. Reynolds averaged numerical simulation based on the Reynolds stress model was used to capture the turbulence. User defined functions of hydrates particle size and shear viscosity models were developed and integrated into the CFD model. The model predictions on pressure gradients at different inlet velocities and hydrates volume fractions were compared with the experimental data. Hydrates deposition characteristics were investigated and the hydrates deposition bed heights were determined for low inlet velocities. This study should provide valuable insight into hydrate-laden flow in pipelines that might help redesign them for better flow assurance

Features of flow and heat transport of MoS2+GO hybrid nanofluid with nonlinear chemical reaction, radiation and energy source around a whirling sphere

The current investigation employs a numerical simulation to demonstrate the impact of hall current on unsteady free convective flow caused by hybrid-nanofluid over a revolving sphere approaching the stagnation point. The prominent characteristics of Lorentz force as a result of magnetic field coupling with hybrid nanofluid is also explored. The process of energy and mass transmission is inspected with nonlinear thermal radiations, non-uniform energy supply, dissipation and nonlinear chemical reaction. In current flow model, a unique class of nanofluid known as the hybrid nanofluid is being used, which contain GO (graphene oxide) and MoS2 (molybdenum disulfide) with water. The angular speed of both the sphere and free stream changes frequently with time. Employing adequate dimensionless variables, the partial-differential patterns strongly non-linear that represent the situational analysis are morphed into non-linear ordinary differential patterns. The analytical outcomes of ordinary differential pattern have been developed via OHAM technique. Utilizing tables and graphs, various aspects of such controllable physical characteristics have been highlighted and explored in depth. For varying values of M,φ,δ,Sc and Kn,the variations in Cfx, Cfz,Nu and Sh in MoS2-GO/H2O are the greatest as contrasted to MoS2/H2O. The results are also compared to those reported existing literature and they are noticed to be in very close agreement

Analysis of Supercritical CO2 Cycle Using Zigzag Channel Pre-Cooler: A Design Optimization Study Based on Deep Neural Network

The role of a pre-cooler is critical to the sCO2-BC as it not only acts as a sink but also controls the conditions at the main compressor’s inlet that are vital to the cycle’s overall performance. Despite their prime importance, studies on the pre-cooler’s design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number (Nu) and friction factor (f). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle’s performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler’s operating regime. Cycle simulation results suggest that the cycle’s performance calculation can be misleading without considering the pre-cooler’s pumping power. Moreover, the optimization study indicates that the compressor’s inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel’s Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle’s efficiency and the pre-cooler’s size