The present study focuses on the experimental investigation and numerical study using Large Eddy Simulation (LES) for an air-impinging jet. The investigation is carried out for slot jet impingement ...on the heated cylinder with an adiabatic flat plate beneath the heated cylinder. The parametric investigation is conducted for parameters such as Reynolds number (Re), jet-to-cylinder spacing (h/S), surface curvature (S/D), and plate length (P/D). They are varied as Re = 10,000–25,000; h/S = 4–12, S/D = 0.072–0.108 and P/D = 0.5–2. The Nusselt number distribution over a cylinder is experimentally obtained, and to understand the impact of parameters such as Re, h/S, S/D, and P/D on it, the numerical results are extracted from LES. The Nusselt number improves with increasing Re and decreasing h/S however their impacts are different. The major influence of Re and h/S is noticeable from θ = 0°–180° and θ = 0°–90°; however, the impact of h/S is negligible from θ = 90°–180°. The Nusselt number gets enhanced with increasing S/D due to more air entrainment, which produces higher Reynolds stress and turbulence kinetic energy. The increasing plate length (P/D) shifts flow separation at a lower angular position (θ), thereby increasing the recirculation zone and reducing the attached flow region. The recirculation zone contributes to heat transfer improvement in that region. Hence, a longer plate length P/D = 2 is an ideal selection compared to P/D = 0.5 and 1 as it better facilitates the positioning of cylindrical food products with optimum heat transfer. Further, correlations are proposed for stagnation and average Nusselt numbers in terms of Re, h/S, S/D and P/D.
Effective removal of heat from the heat-generating porous bed, particularly in a confined space, is essential due to its implications on thermal management and system safety. The safety aspect ...becomes particularly important during post-accident heat removal from decay heat-generating debris in nuclear reactors, as well as thermal management of self-igniting coal stockpiles. In spite of detailed research on thermal convection through porous media, only a handful of studies have considered mixed convective heat transport involving heat-generating porous bed along with external fluid injection. The situation, however, demands an in-depth analysis due to the associated practical implications. The present work addresses one cooling method providing side injection of cold fluid and assuming a typical conical heat-generating porous bed located centrally within a fluid-filled cylindrical enclosure. The study is carried out in a general way to suit other applications. The dimensionless governing equations, along with the boundary conditions in a two-dimensional coordinate system, are solved numerically assuming laminar and incompressible flow along with the Boussinesq approximation. The analysis is carried utilizing the local thermal non-equilibrium model within the porous bed. Injection of cold fluid can markedly affect the convective heat transport rate. Porous media permeability considerably influences the flow mechanism. The major findings of this study can be very useful in improving the management of thermal energy removal from self-igniting coal stockpiles, grain storages, porous debris, etc. Heat transport intensification from heat-generating porous bed is analyzed by injecting coolant through the sidewall and considering liquid water as the working medium. The impacts of pertinent parameters on the thermal convection characteristics are illustrated using the average Nusselt number and energy flux vectors.
•Formulated a machine learning framework for predicting combustion instabilities.•A transfer learning approach demonstrated using deep neural networks.•A simple Rijke tube apparatus chosen as a ...source domain for transfer learning.•The target domain for transfer learning is a more complex premixed combustor.•Transfer learning results in better predictions with less training data requirement.
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The intermittent nature of operation and unpredictable availability of renewable sources of energy (e.g., wind and solar) would require the combustors in fossil-fuel power plants, sharing the same grid, to operate with large turn-down ratios. This brings in new challenges of suppressing high-amplitude pressure oscillations (e.g., thermoacoustic instabilities (TAI)) in combustors. These pressure oscillations are usually self-sustained, as they occur within a feedback loop, and may induce severe thermomechanical stresses in structural components of combustors, which often lead to performance degradation and even system failures. Thus, prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. From this perspective, it is important to identify operating conditions which can potentially lead to thermoacoustic instabilities. In this regard, data-driven approaches have shown considerable success in predicting the instability map as a function of operating conditions. However, often the available data are limited to learn such a relationship efficiently in a data-driven approach for a practical combustion system. In this work, a proof-of-concept demonstration of transfer learning is provided, whereby a deep neural network trained on relatively inexpensive experiments in an electrically heated Rijke tube has been adapted to predict the unstable operating conditions for a swirl-stabilized lean-premixed laboratory scaled combustor, for which data are expensive to obtain. The operating spaces and underlying flow physics of these two combustion systems are different, and hence this work presents a strong case of using transfer learning as a potential data-driven solution for transferring knowledge across domains. The results show that the knowledge transfer from the electrically heated Rijke tube apparatus helps in formulating an accurate data-driven surrogate model for predicting the unstable operating conditions in the swirl-stabilized combustor, even though the available data are significantly less for the latter.
In the field of thermal engineering, one of the biggest concerns is the cooling of heat producing systems. For this purpose, today’s world is encouraging to use such cooling systems which are free ...from any active components (passive systems) for its high reliability and compact size. For this reason, to establish cooling by transferring heat from one place (source) to another (sink) passive system like natural circulation loop (NCL) is highly used. Fluid flow dynamics of the NCL is changing with the increase in heater power which is used as the source for the simulation. We found steady flow dynamics for the comparatively low power of heat, and with the rise in the power first, we saw the oscillatory flow dynamics and then found flow reversal characteristics. This paper presents a novel strategy for the early prediction of flow reversal phenomenon in NCL using symbolic analysis of time series data. This time series data is found from the numerical simulation, and for the proper study, we are considering data after the initial transient part is overcome. Total time series data is transformed into a symbol string by partitioning into a finite number of specified symbolised groups. The state probability vector is calculated based on the number of occurrences of each symbol group. Present work is a single-phase study, and according to our geometry, we can provide a maximum 800 W heater power to stay in the single-phase. Therefore, for the early prediction of flow reversal in NCL, state probability vector evaluated at 800 W heater power which is the most undesirable state (chaotic data), and this is considered as the reference vector. The difference of the reference state vector from the current state vector is used as a parameter for early detection of flow reversal. It can be observed from the results that this difference changes significantly when the system is sufficiently away from the flow reversal.
Natural convection in enclosures driven by heat-generating porous media has diverse applications in fields like geothermal, chemical, thermal and nuclear energy. The present article focuses on heat ...transfer and entropy generation characteristics of a heat-generating porous bed, placed centrally within a fluid-filled cylindrical enclosure. Pressure drop and heat transfer in the porous bed are modelled using the Darcy–Brinkmann–Forchheimer approximation and the local thermal non-equilibrium model, respectively. Energy flux vectors have been utilised for visualising convective energy transfer within the enclosure. The study of a wide range of Rayleigh number (
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) and Darcy number (
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) reveals that heat transfer in the porous region can be classified into conduction-dominated and convection-dominated regimes. This is supplemented with an entropy generation analysis in order to identify and characterise the irreversibilities associated with the phenomenon. It is observed that entropy generation characteristics of the enclosure closely follow the above-mentioned regime demarcation. Numerical computations for the present study have been conducted using ANSYS FLUENT 14.5. The solid energy equation is solved as a user-defined scalar equation, while data related to energy flux vectors and entropy generation are obtained using user-defined functions.
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