Physical Model Research Articles

Investigation of Improved Energy Dissipation in Stepped Spillways Applying Bubble Image Velocimetry

This study investigates skimming flow regimes, two-phase air–water flow conditions, and simple measures to improve energy dissipation in stepped spillways. Experiments were conducted using two different scale physical models, 1:50 and 1:17, within separate rectangular flumes to define scale effects. Flow patterns were analyzed using the Bubble Image Velocimetry (BIV) technique, which tracks air bubbles. The introduction of splitters resulted in a 7% increase in relative energy dissipation. Additionally, the length of inception was reduced to Li/ks = 10, thereby decreasing the potential for subsequent cavitation. Beyond the BIV experiments, two experiments were conducted on the large-scale model using Acoustic Doppler Velocimetry (ADV), with and without splitters, to examine the impact of splitters on the velocity profile above the crest. In the experiment with splitters, the vertical velocity vector (v) contributed to turbulence by changing direction, thereby reducing average velocities both in front of and behind the ogee crest. This led to a reduction in energy on the downstream side of the spillway. Although the small-scale model appears unsuitable for studying two-phase flow, the change in relative energy dissipation from the baseline to the splitter configuration was practically identical for both scale models, thereby supporting the findings of the large-scale model.

Matching and Rewriting Rules in Object-Oriented Databases

Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through rewriting rules, those are scarcely or partially adopted in graph query languages. Their inability to do so has a major impact on the subsequent way the information is structured, as it might then appear more natural to provide major constraints over the data representation to fix the way the information should be represented. On the other hand, recent works are starting to move towards the opposite direction, as the provision of a truly general semistructured model (GSM) allows to both represent all the available data formats (Network-Based, Relational, and Semistructured) as well as support a holistic query language expressing all major queries in such languages. In this paper, we show that the usage of GSM enables the definition of a general rewriting mechanism which can be expressed in current graph query languages only at the cost of adhering the query to the specificity of the underlying data representation. We formalise the proposed query language in terms declarative graph rewriting mechanisms described as a set of production rules L→R while both providing restriction to the characterisation of L, and extending it to support structural graph nesting operations, useful to aggregate similar information around an entry-point of interest. We further achieve our declarative requirements by determining the order in which the data should be rewritten and multiple rules should be applied while ensuring the application of such updates on the GSM database is persisted in subsequent rewriting calls. We discuss how GSM, by fully supporting index-based data representation, allows for a better physical model implementation leveraging the benefits of columnar database storage. Preliminary benchmarks show the scalability of this proposed implementation in comparison with state-of-the-art implementations.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

Untrained single scanning phase retrieval neural network for surface deformation measurement of large-aperture antennas

Research on data-driven Fourier phase retrieval neural networks is well-established. However, in many practical applications, such as phase retrieval-based antenna surface deformation measurements in astronomy, obtaining a sufficient number of ground truth images for training is unlikely. In this paper, we propose a purely physics-driven neural network model for retrieving the aperture-field phase of large antennas. This is accomplished through the integration of the physical model describing the interaction between the aperture-field and the far-field with the neural network. Our simulations and experiments demonstrate accurate reconstruction of the aperture-field phase using only a single far-field intensity image. Furthermore, we present a solution tailored for scenarios with low signal-to-noise ratio.

TNSP: A framework supporting symmetry and fermion tensors for tensor network state methods

Recent advancements have established tensor network states (TNS) as formidable tools for exploring the complex realm of strongly-correlated many-particle systems in both one and two dimensions. To tackle the challenges presented by strongly-correlated fermion systems, various fermion tensor network states (f-TNS) methodologies have been developed. However, implementing f-TNS methods poses substantial challenges due to their particularly complex nature, making development efforts significantly difficult. This complexity is further exacerbated by the lack of underlying software packages that facilitate the development of f-TNS. Previously, we developed TNSPackage, a software package designed for TNS methods [1]. Initially, this package was only capable of handling spin and boson models. To confront the challenges presented by f-TNS, TNSPackage has undergone significant enhancements in its latest version, incorporating support for both symmetry and fermion tensors. This updated version provides a uniform interface for the consistent management of tensors across boson, fermion, and various symmetry types, maintaining its user-friendly and versatile nature. This greatly facilitates the development of programs based on f-TNS. The new TNSP framework consists of two principal components: a low-level tensor package named TAT, which supports sophisticated tensor operations, and a high-level interface package called tetragono that is built upon TAT. The tetragono package is designed to significantly simplify the development of complex physical models on square lattices. The TNSPackage framework enables users to implement a wide range of physical models with greater ease, without the need to pay close attention to the underlying implementation details.

Physics-informed geostatistical approach (PIGA) for real-time fluvial flood modeling

This study presents the Physics-Informed Geostatistical Approach (PIGA), a novel methodology aimed at improving flood prediction accuracy in riverine systems during hurricane events. PIGA integrates reduced spatial correlations derived from fluvial hydrodynamic models with a reduced geostatistical approach (RGA) based on monitoring data to provide real-time estimations of water surface elevations (WSE) across river channels and reaches. Focusing on the Savannah River basin, an area prone to riverine flooding during hurricane seasons, we developed a 1D-2D HEC-RAS model as the physical model and compared its performance with PIGA for various hurricane events, including Hurricane Matthew, Irma, Dorian, and Idalia. Our findings reveal that PIGA consistently outperforms the HEC-RAS model in predicting WSE, particularly in areas with limited bathymetry data. Statistical analyses demonstrate significant improvements in prediction accuracy achieved by PIGA, emphasizing its potential as a reliable tool for flood prediction in data-deficient regions. Additionally, we explore the transferability of reduced spatial correlations across different hurricane events. Our results indicate that spatial correlations derived from one hurricane event can effectively predict WSE for other events, with Hurricane Irma’s spatial correlations proving to be the most versatile for our study site. However, we also observe variability in spatial correlations among different hurricane events, highlighting the importance of selecting the optimal alternative spatial reference for prediction. Furthermore, we highlight PIGA’s computational efficiency, with simulations completed in significantly less time compared to traditional physical models, rendering its potential to provide real-time prediction of WSE during flooding events.

The impact of refrigerant leakage on the dynamic operating performance of R600a refrigerator systems

Refrigerant leakage in refrigeration and freezing systems reduces efficiency and even results in reliable risks like insufficient cooling and component damage. This paper establishes a dynamic model of a dual-temperature zone refrigerator that combines physical and control models. It investigates the impact of refrigerant micro-leakage on the system’s dynamic operational performance, studies the effects of different leakage areas and compressor speeds on leakage performance, and identifies the critical leakage area (Ac) and critical compressor speed (Rc) based on the temperature variation patterns within the cabinets. Furthermore, this paper analyzes the coupled influence of the compressor speed and leakage area on the cabinet’s temperature. The findings reveal that after leakage occurs, the first noticeable deviations are in system pressure and the amount of refrigerant in the accumulator. Once the refrigerant in the accumulator fully vaporizes, the temperature in the cabinets is affected. Throughout the leakage process, both the system pressure and the heat exchange capacity of the evaporator decrease. At a compressor speed of 2000 rpm, the critical leakage area is 0.013 mm2, and the maximum refrigerant leakage that allows the refrigerator cabinet to cool to the shutdown temperature is 67.6 %. If the leakage area is larger than Ac, it reduces the time for alternating cooling between the freezer and refrigeration cabinets, resulting in a rapid rise in the comprehensive weighted average temperature deviation (CATD). For smaller leakage areas (<0.015 mm2), Rc exists. Operating the compressor at Rc can significantly reduce CATD. For larger leakage areas (≥0.015 mm2), Rc does not exist, increasing compressor speed has a lesser impact on CATD. The research results provide guidance for establishing a refrigerant leakage detection system and improving its accuracy, enhancing the operational reliability of refrigeration systems.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

Topography-based and vectorized algorithm for extracting physical quantities in 3D-SPH form and its application in debris-flow entrainment modeling

Extraction of physical quantities such as flow depth and velocities, is one of the major purposes of geophysical flow numerical modeling and critical for estimating consequent impact forces and sediment entrainment. It is simple in nature for mesh-based models but presenting challenges in three-dimensional smoothed particle hydrodynamics (SPH) schemes. The difficulties lie in the substantial number of particles and their uneven spatial-temporal distribution, particularly over complex topography. Inspired by our previous surface cell (SC) -based approach, we propose a novel topography-based and vectorized algorithm that significantly enhances the ability to extract physical quantities over complex topography. In the proposed algorithm, geomorphologic characteristics are mathematically represented by topographical normal vectors. The correlations of physical quantities with distinct coordinate descriptions are established through the vectorization concept, ultimately leading to effective extraction of physical quantities in SPH form over complex topography. This algorithm provides an important tool to incorporate topography-linked physical models within discretized frameworks. To validate its effectiveness, we employed the algorithm to integrate the debris-flow entrainment law with our previous HBP-SPH model, utilizing the 2010 Yohutagawa debris-flow event in Japan as a case study. The results demonstrate a good agreement between the numerical simulation and on-site observation. Discussion regarding the applicability and limitation of the algorithm concludes the paper.

Study on the combustion characteristics and reaction mechanism of aluminum/alcohol-based nanofluid fuel

Enhancing the volumetric energy density of fuels is a crucial approach in the exploration of efficient energy sources. As a commonly used high-energy additive, Aluminum nanoparticles (Al NPs) holds broad application prospects. This study delves into the effects of Al NPs on the combustion characteristics of methanol and ethanol fuels through in-depth analysis of single droplet combustion and spray combustion experiments involving alcohol-based nanofluid fuels. The experiments revealed that adding Al NPs significantly enhances fuel combustion efficiency, shortening both the combustion life and ignition delay time. Spray combustion tests demonstrated that adding 2 wt% Al NPs increased the maximum and average flame propagation speeds of ethanol fuel spray by 48.93 % and 47.94 %, respectively, while the maximum explosion pressure rose from 0.66MPa to 0.83MPa, with a reduced time to reach peak explosion pressure by 106ms. Based on the SEM and XRD characterization results of the combustion residues and the analysis of flame morphology, a physical model of the alcohol-based nanofluid fuel spray combustion flame was established. Numerical simulations of the gas-phase kinetics model showed that the generation rates of radicals such as H, O, and OH were significantly increased with the addition of Al NPs, intensifying the combustion reaction. The findings provide a theoretical basis for future fuel design and combustion performance optimization.

Phyformer: A degradation physics-informed self-data driven approach to machinery prognostics

Machinery degradation prognostics methods have been suffering the “dilemma” that both physics and data-driven methods have their own limitations. On one hand, constructing accurate close-form mathematical physics models are prohibitively difficult for a complex system. On the other hand, for pure data driven models such as deep learning models, due to the lack of physics guidance, their extrapolation capability decays over time and even yield unexplainable predictions that violate common cognition. Therefore, relaxing the pursuit for the perfect and accurate degradation model, and instead coupling general degradation model with deep learning model, fully leveraging the advantages from both physics-based prognostics and data-driven prognostics, is a promising way to solve the “dilemma”. Driven by this motivation, this paper proposes Phyformer, a general degradation physics-informed self-data-driven method for machinery prognostics. A backbone deep learning model based on auto-correlation and Transformer architecture is developed for time series data prediction. Multiple local physical models that are constructed with data in sliding time windows are embedded into the backbone model in the form of loss function. Only the historical data of the machine itself are used to extrapolate the future, which is significant especially for high-end equipment since their run-to-fail data are scarce. Then the predicted data are mapped to degradation stage by an unsupervised clustering model Gath-Geva adaptively without the requirement of knowing the number of degradation stages in advance. Due to the above nature, Phyformer is high flexibility, easy to be deployed in different applications, working condition-independent and simple. Two typical prognostics tasks are studied, which use direct and indirect condition monitoring data as input, respectively. Comparisons with other state-of-the-art machinery prognostics models show the advances of Phyformer.

Physics-informed transfer learning model for fatigue life prediction of IN718 alloy

To address the challenges posed by inadequate data and data utilization in multiple scenarios of fatigue loading, a Physics-informed Transfer Learning (PITL) model has been developed to predict the fatigue life of IN718 superalloy. Strain-controlled low-cycle fatigue tests were carried out at 400 °C with three distinct strain ratios, which were subsequently segmented for individual transfer learning tests. PITL models with significant engineering value were built by integrating transfer learning methodologies rooted in TrAdaBoost with a physics-based model that hinges on the principles of equivalent strain theory. The findings suggest that PITL models exhibit improved accuracy and greater robustness compared to both transfer learning and physics models.

Identification of Individuals of Two Takin Subspecies Using Biological and Ecological Criteria in Eastern Himalayas of China.

Limited background data are available on the Mishmi takin (Budorcas taxicolor taxicolor) and Bhutan takin (Budorcas taxicolor whitei) subspecies in the Eastern Himalayas of China because of the lack of systematic field investigations and research. Therefore, mature-animal ecological methods were used to evaluate these takin subspecies' phenotypic characteristics, distribution range, activity rhythm, and population size. From 2013 to 2022, 214 camera traps were installed for wild ungulate monitoring and investigation in all human-accessible areas of the Eastern Himalayas, resulting in 4837 distinguishable takin photographs. The external morphological characteristics were described and compared using visual data. Artificial image correction and related technologies were used to establish physical image models based on the differences between subspecies. MaxEnt niche and random encounter models obtained distribution ranges and population densities. Mishmi takins have a distribution area of 17,314 km2, population density of 0.1729 ± 0.0134 takins/km2, and population size of 2995 ± 232. Bhutan takins have a distribution area of 25,006 km2, population density of 0.1359 ± 0.0264 takins/km2, and population size of 3398 ± 660. Long-term monitoring data confirmed that the vertical migration within the mountain ecosystems is influenced by climate. Mishmi takins are active at 500-4500 m, whereas Bhutan takins are active at 1500-4500 m. The two subspecies were active at >3500 m from May to October yearly (rainy season). In addition, surveying combined with model simulation shows that the Yarlung Zangbo River is not an obstacle to migration. This study provides basic data that contribute to animal diversity knowledge in biodiversity hotspots of the Eastern Himalayas and detailed information and references for species identification, distribution range, and population characteristics.

Developing a predictive method based on the vibration behavior of a naval ship hull model using hybrid fuzzy meta-heuristic algorithms

Ships are complex structures composed of various components with exclusive dynamic behaviors and natural frequencies, so assessing their vibration behavior is essential. Some situations in practical applications could alter the ship's dynamic characteristics and cause significant changes in its vibration behavior. Since the ship's mass is one of the most important dynamic parameters in determining vibration behavior, local mass change can lead to changes in its dynamic characteristics and must be considered. This study aims to develop a method to predict the effect of the location and magnitude of mass change on the ship hull's vibration behavior. It would be feasible to enhance the dynamic behavior and reduce undesirable noises by locating the mass change on the ship hull. In this regard, experimental and numerical modal analysis is performed on a scaled model of a naval ship hull. The baseline FE model is used to calculate the variation in frequencies of the model caused by different local mass change scenarios. Using these measurements a fuzzy system is generated and optimized by Genetic and Particle Swarm Optimization algorithms. Finally, the efficiency of the fuzzy-PSO is validated by different mass change scenarios foreseen on the physical model of the ship hull.

Tuning effect in time-lapse seismic inversion for CO2 plume monitoring at Sleipner field

Over 20 million tons of CO2‌ have been injected into the sandy Utsira formation of the Sleipner field in the North Sea basin. The thin layering of sands, CO2-saturated sands, and intra-formation thin shales cause interference effects in the seismic response of the monitor surveys. Initial analysis of the amplitude change suggests over 60 % change in the relative acoustic impedance of the reservoir between the 1996 and 2010 surveys. This study follows a multi-stage inversion scheme applied to time-lapse seismic monitoring of the Sleipner field. At each stage, the errors and uncertainties caused by noise and tuning are deeply analyzed. Time-shift estimation from seismic data shows spurious features caused by tuning, specifically using the window-based methods. The time-strain inversion builds a low-frequency initial model for the subsequent model-based inversion but is contaminated by remnants of noise and interference. The synthetic wedge modelling and analysis provides the origins and severity of the tuning error in time-shift and time-strain estimations at the Sleipner field. The model-based inversion removes noise and injects high-frequency components into the results, improving the outcome of time-strain inversion. However, it fails to fully eliminate the tuning imprints and leaves strong traces of error on the results. Afterward, the time-lapse Bayesian seismic inversion slightly adjusts the outcome and shows how deep the influence of interference effect on the time-lapse inversion is. In addition, the complementary discussion on rock physical models in estimating the saturation changes highlights how the tuning error can lead to flawed quantitative interpretation.

Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model

Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Convective heat transfer and drag coefficients of human body in multiple crowd densities and configurations in semi-outdoor scenarios

This study numerically assessed the impact of human crowd density and outdoor wind conditions (average velocity, its profile, and direction) on the convective heat transfer and drag coefficients (hc and Cd). Five different configurations of standing computer-simulated persons (CSPs) were tested in a semi-outdoor environment. A single isolated CSP, nine CSPs in a block array (with three representative crowd densities), and eighteen randomly allocated CSPs were used. The results indicated a significant impact of crowd density on the overall and local hc values. As the density increases, the body's obstruction against wind increases, resulting in lower heat loss. Newly proposed formulas for hc as a function of the average wind velocity (UAVE.) are (7.56 × UAVE.0.65), (8.02 × UAVE.0.64), and (8.26 × UAVE.0.63) for the high, medium, and low crowd densities, respectively. This reveals an overestimation of hc when an isolated human body is used. The hc values of the upper segments were the most affected by a 22 % reduction in the predicted hc. Moreover, when the crowd density increased, local hc and Cd decreased simultaneously, particularly in the chest, pelvis, and thigh segments. Oblique wind angles (60° and 150°) resulted in the highest hc and Cd values compared to other angles. The chest and pelvis were most affected by shifting the wind direction, indicating the dominance of these segments in concurrently controlling the thermal and drag performances. These results provide valuable insights into the optimization of human thermal and physical comfort models.

Developing a diagnostic system: combining ultrasound and electrical impedance tomography for enhanced urinary tract analysis

The study aims to develop a reconstruction and measurement system for data analysis using ultrasound transmission tomography (UST) and electrical impedance tomography (EIT), which can be implemented in wearable solutions for non-invasive monitoring and diagnosis of functional urinary tract disorders. Functional disorders can be associated with many urinary system defects, constipation, and central nervous system defects (spina bifida, cerebral palsy). The lack of non-invasive diagnostic methods for a functional and comprehensive analysis of the state of the urinary tract reduces the likelihood of a correct diagnosis and effective treatment. Furthermore, constructing a physical model of a tomograph is problematic due to the complexity of acoustic phenomena necessary for modeling in the case of a heterogeneous environment of propagation of waves in small, limited spaces. Nevertheless, the radial propagation model used in UST is sufficient for effectively detecting disturbances in the interior of objects and is used in medical solutions. The innovation of the solution lies in the simultaneous assessment of urinary tract function based on the analysis of images obtained using EIT and UST. As a non-invasive diagnostic, UST allows for visualization of the body's internal structures (including muscles, blood vessels, and internal organs). However, UST-based diagnostic systems of the urinary tract are not present in world markets. Moreover, building such a system requires solving many problems: limited research fields, dependence on the body structure, difficulties in imaging bone structures, and areas filled with gases.

Heat transfer analysis of nanofluid flow with entropy generation in a corrugated heat exchanger channel partially filled with porous medium

AbstractHeat exchanger research is mainly exploited to develop and optimize new engineering systems with high thermal efficiency. Passive methods based on nanofluids, fins, wavy walls, and the porous medium are the most attractive ways to achieve this goal. This investigation focuses on heat transfer and entropy production in a nanofluid laminar flow inside a plate corrugated channel (PCC). The channel geometry comprises three sections, partially filled with a porous layer located at the intermediate corrugate channel section. The physical modeling is based on the laminar, two‐dimensional Darcy–Brinkman–Forchheimer formulation for nanofluid flow and the local thermal equilibrium model for the heat equation, including the viscous dissipation term. Numerical solutions were obtained using ANSYS Fluent software based on the finite volume technique and the appropriate meshed geometries. The numerical results are validated with theoretical, numerical, and experimental studies. The simulations are performed for CuO–water nanofluid and AISI 304 porous medium. The coupled effects of porous layer thickness (δ), Reynolds number (Re), and nanoparticle fraction (φ) on velocity, streamlines, isotherm contours, Nusselt number (Nu), and entropy generation (S) are analyzed and illustrated. The simulation results demonstrate that heat transfer enhancement in clear PCC can be achieved using a porous layer insert. For the porous thickness range of [0.1–0.6], the corresponding range of average Nusselt number increase is [35.7%–176.9%], and the average entropy generation is [105.4%–771.9%]. The effect of the Reynolds number is more important in a porous duct than in a clear one. For δ = 0.4 and φ = 5%, the increase of Re in the range of [200–500] induces an increase in average Nusselt number in the range of [80.9%–108.4%] and average entropy in [222.9%–309.1%] comparatively to clear PCC. The effect of φ is practically the same for porous and clear channels. For φ = 5%, the increase on average Nu is about 9%, and entropy generation is 5%. Accordingly, important improvements in heat transfer in PCC can be achieved through the combined effect of flow Reynolds number and porous layer thickness.