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The biomedical applications along with nanotechnology field have generated considerable interest because of their unique thermal properties. The present investigation explores the irreversibility processes for the squeezing flow of a polar hybrid nanofluid where blood is considered the conventional liquid and copper (Cu) and Silver (Ag) are introduced as solid nanoparticles. The integration of dissipative heat effects (viscous and Joule) combined with thermal radiation adequately enhances the transport phenomena. Furthermore, the unique rheological significance of blood-based nanofluids is relevant for efficient drug delivery systems, improving circulation, optimizing side effects, etc. The essential study of entropy is used for optimizing the design and efficiency of devices used in various tasks ranging from medical diagnostics to environmental monitoring. The proposed system of dimensional equations is diverting into non-dimensional system by the use of similarity rules, and further, the spectral quasilinearization method is employed to tackle the transformed system. The parametric analysis upon the various flow profiles, rate coefficients, and entropy as well as the Bejan number is depicted through graphs, and the validation shows a good correlation in a particular situation. [ABSTRACT FROM AUTHOR]

Natural resource depletion and increased mining waste pose significant challenges to global sustainability efforts. This study investigates the processability of mining waste during the metal recovery stage to evaluate its potential contribution to anthropogenic circularity. The mining industry, abundant in valuable metals that are crucial for a carbon-neutral economy, plays a pivotal role in this context. We determine the grades of metals by looking at their chemical makeup, and then we use statistical entropy to model how easy it is to process certain waste materials. This provides us with processability measures that range from 0.19 bit to 1.18 bit. Our findings highlight that while some waste contains "abundant" metals, its complexity may diminish its economic value, raising concerns about its environmental impacts and resource availability at the end-of-life stages. Estimating potential revenue involves multiplying processed amounts by commodity prices, revealing a maximum value of 8.73 USD/metric ton for processed waste. This assessment underscores the importance of integrating circular economy principles, aiming to mitigate environmental damage and promote industrial ecology. By advancing our understanding of mining waste management through rigorous scientific inquiry, this study contributes to sustainable resource utilization strategies that are essential for future industrial practices and environmental stewardship. [ABSTRACT FROM AUTHOR]

A major challenging proffered study rely on the inquisitive optimization of entropy together with thermal as well as mass transportation towards the chemically reactive 3-D Prandtl liquid under consideration of applied magnetic field, variable thermal conductivity and also diffusivity, hall current together with ion slip features, viscous dissipation and heat generation. Moreover, in present research study, model such as Cattaneo Christov heat flux was implemented in order to explore the thermal relaxation characteristics. By employing correspondence transformations, the system of modeled equations modified into non-linear ODEs system. The Optimal Homotopy Analysis methodology (OHAM) was adopted to solve the proffered problem. The influential arising constraints demeanor within both velocities (horizontal as well as vertical), temperature and concentration profiles were discussed and also shown graphically. Moreover, outcomes of diverse parameters were also examined and presented graphically within the entropy formation rate and also Bejan number. The effects of implanted factors across the drag force, the rate of thermal as well as mass transportation are accessible in current study via tables and validate the obtained results. Both velocity profiles (horizontal as well as vertical) decreased for increment in Hartman number whilst opposite demeanor seen for other considered constraints. Temperature profile drops for Prandtl number and elastic constraints whereas enhanced for other influential considered parameters whilst concentration profile augmented with Prandtl number. Present problem novelty relies on the modeling of comprehensive system of equations which modified into nonlinear ODEs. In order to obtain the solution numerically, technique such as Optimal Homotopy was opted.

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients. [ABSTRACT FROM AUTHOR]

Abstract Background There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation. Methods In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power. Results The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980–0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM’s gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve. Conclusions Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP4i) are important second-line treatments for patients with type 2 diabetes mellitus (T2DM). Patients taking SGLT2i have favorable cardiovascular outcomes via various mechanisms, including autonomic nervous system (ANS) modulation. This study aimed to use neuroelectrocardiography (neuECG) to test the effects of SGLT2i or DPP4i on the ANS. Methods: Patients with T2DM, who did not reach target hemoglobin (Hb)A1C levels despite metformin treatment, were enrolled. SGLT2i or DPP4i were prescribed randomly unless a compelling indication was present. NeuECG and heart rate were recorded for 10 min before and after a 3-month treatment. The patients were treated according to standard practice and the obtained data for skin sympathetic nerve activity (SKNA) and ANS entropy were analyzed offline. Results: We enrolled 96 patients, of which 49 received SGLT2i and 47 received DPP4i. The baseline parameters were similar between the groups. No adverse event was seen during the study period. In the burst analysis of SKNA at baseline, all parameters were similar. After the 3-month treatment, the firing frequency was higher in SGLT2i group (0.104 ± 0.045 vs 0.083 ± 0.033 burst/min, p < 0.05), with increased long firing duration (7.34 ± 3.66 vs 5.906 ± 2.921, p < 0.05) in 3-s aSKNA scale; the other parameters did not show any significant change. By symbolic entropy, the most complex patterns (Rank 3) were found to be significantly higher in SGLT2i-treated patients than in DDP4i-treated group (0.084 ± 0.028 vs 0.07 ± 0.024, p = 0.01) and the direction of change in Rank 3, after SGLT2i treatment, was opposite to that observed in the DDP4i group (0.012 ± 0.036 vs. -0.005 ± 0.037, p = 0.024). Our findings demonstrated the favorable autonomic modulation by SGLTi and the detrimental effects of DPP4i on ANS. Conclusion: We demonstrated the autonomic modulation by SGLTi and DPP4i using SKNA in patients with DM, which might provide insights into the favorable outcomes of SGLT2i. Furthermore, we refined the analytical methods of neuECG, which uses SKNA to evaluate autonomic function. [ABSTRACT FROM AUTHOR]

Background: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation. Methods: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power. Results: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980–0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM's gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve. Conclusions: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients. [ABSTRACT FROM AUTHOR]

Several engineering systems currently use microchannel heat sinks. In order to increase the performance of these devices, optimisation according to the first and second law of thermodynamics is employed. One way to achieve the goal is to modify the geometry of the cross-section, as is done in this paper for square ducts, having the walls at a uniform temperature which is higher than that of the bulk fluid at the inlet. The effects of both the thermal entry region of the duct and the heat generation due to viscous dissipation are considered. The resulting Graetz–Brinkman problem is solved numerically to obtain the velocity and temperature fields. It is demonstrated that non-negligible viscous heating eventually causes the heat flux to reverse (from fluid to walls), and that, only after this condition is achieved, can the flow become fully developed, which makes the entry region the only useful stretch for real-life applications. The length after which the direction of the heat flux reverses due to viscous heating in the fluid is obtained as a function of the Brinkman number and of the smoothing radius. Optimisation with performance evaluation criteria and entropy generation minimisation was carried out separately, and the results were combined into a single objective function. A comparison with published models highlights how neglecting the entry region and viscous heating yields misleading results. It turns out that smoothing the corners is always profitable in the case of the constrained heated perimeter or area of the cross-section but seldom when the characteristic length or the hydraulic diameter is fixed. With few exceptions, viscous heating amplifies the trends experienced for zero-Brinkman flows. The results are in non-dimensional form, yet they have been obtained starting from plausible dimensional values and are applicable to real-life devices. [ABSTRACT FROM AUTHOR]

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction‐based self‐supervised anomaly detection model to overcome these challenges. It has a dual‐subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy‐based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method. [ABSTRACT FROM AUTHOR]

The work's goal is to learn more about how a magnetic field, Brownian motion, and thermophoresis diffusion influence convective heat transfer in a micropolar-nanofluid flow's laminar boundary layer. Near a vertically moving, permeable plate, the complex fluid is subjected to MHD. The MATLAB application bvp4c was utilized to simplify the governing nonlinear and coupled equations for the micropolar-nanofluid, leading to the solution of the ensuing ordinary differential equations (ODEs). Graphs have been used to analyze the effect of different relevant active factors on the flow field and temperature. The results demonstrate that the micro-rotation of the nanoparticles taken into account and in suspension becomes significant for the complex fluid in the presence of the magnetic field. Analysis of the generation entropy shows that the surface is a significant source of irreversibility. There is no discernible effect of micropolarity on the relationship between Brownian and thermophoresis numbers and entropy generation.

Nanofluids are utilized in cancer therapy to boost therapeutic effectiveness and prevent adverse reactions. These nanoparticles are delivered to the cancerous tissues under the influence of radiation through the blood vessels. In the current study, the propagation of nanoparticles within the blood in a divergent/convergent vertical channel with flexible boundaries is elaborated computationally. The base fluid (Carreau fluid model) is speculated to be blood, whereas nanofluid is believed to be an iron oxide–blood mixture. Because of its shear thinning or shear thickening features, the Carreau fluid model more precisely depicts the rheological characteristics of blood. The arterial section is considered a convergent or divergent channel based on its topological configuration (non-uniform cross section). An iron oxide (Fe2O3{\rm{F}}{{\rm{e}}}_{2}{{\rm{O}}}_{3}) nanoparticle is injected into the blood (base fluid). To eliminate the viscous effect in the region of the artery wall, a slip boundary condition is applied. An analysis of the transport phenomena is preferred using the melting heat transfer phenomena, which can work in melting plaques or fats at the vessel walls. The effects of thermal radiation, which is advantageous in cancer therapy, biomedical imaging, hyperthermia, and tumor therapy, are incorporated in heat transport mechanisms. The governing equation for the flow model with realistic boundary conditions is numerically tickled using the RK45 mechanism. The findings reveal that the flow dynamism and thermal behavior are significantly influenced by melting effects. Higher Re\mathrm{Re} can produce spots in which the track of the wall shear stress fluctuates. The melting effects can produce agitation and increase the flow through viscous head losses, causing melting of the blockage. The maximum heat transfer of 5%5 \% is achieved with We{\rm{We}} when the volume friction is kept at 1%1 \% . With higher estimation of inertial forces Re\mathrm{Re}\hspace{1em}and same volume friction, the skin drag coefficient augmented to 34%34 \% . The overall temperature is greater for the divergent flow scenario.

A wide range of real-world applications have proven the importance of non-Newtonian fluids near a wedge, including the oil and gas industry, the aerospace sector. This study elucidates the dynamics of a Carreau nanofluid around a wedge by combining entropy analysis with periodic magnetohydrodynamics (MHD) and activation energy. The dimensional partial differential equations (PDEs) that describe the fluid flow system undergo non-similar transformations, forming nondimensional PDEs. The numerical solution to these PDEs is obtained by applying quasilinearization followed by the implicit finite difference approach. In the case of n = 0.5 (power-law index), when We (Weissenberg number) improves from 0 to 4, surface friction [Formula: see text] upsurges by approximately 23% and declines by around 41% in the case of n = 1.5. The mass transport intensity of liquid oxygen is about 18% higher than liquid nitrogen’s. Increasing the wedge angle results in a significant increase in fluid velocity.

The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain. [ABSTRACT FROM AUTHOR]

In this paper, a novel numerical model of direct methanol fuel cells (DMFC) is proposed aiming to quantitatively analyze the effect of methanol, water, and oxygen mass transfer on the performance and to further improve power and reduce energy losses. The model was validated against experimental data. The model predicts the values of variation of reactant concentrations in each layer and the effect of parameters is analyzed. The results show that overall performance is better at low methanol concentration (1–2 mol/dm3). A rise in a certain temperature (65 K) increases maximum power density by 13.6%, ECOP by 43.9%, voltage efficiency by 13.6%, thermal efficiency by 14.2% and real efficiency by 19.1%, while reducing entropy yield by 19.3%. Moreover, the average of optimization results based on multi-objective particle swarm algorithm (MOPSO) indicated that maximum power density and its corresponding ECOP , and thermal efficiency were increased by 13.47%, 0.03%, and 6.21%, respectively. [Display omitted] • A novel numerical model of DMFC considering mass transfer is proposed. • The entropy production into overpotential losses and methanol crossover loss is investigated. • The model was validated against experimental data. • The model predicts the values of variation of reactant concentrations in each layer and the leakage current density. • The MOPSO algorithm and TOPSIS method are utilized to optimize the power density, ECOP, and thermal efficiency. [ABSTRACT FROM AUTHOR]

Using hybrid nanoparticles to improve thermal processes is a critical method with implications for a wide range of interventions used in many industries. The Eyring–Powell nanofluid is a promising alternative in chemical engineering, where other non-Newtonian liquids are limited. Along with the nanoparticles of iron oxide, the base liquid contains both single- and multi-walled carbon nanotubes (SWCNT and MWCNT, respectively). Unlike other forms of carbon, nanotubes are excellent electrical conductors. In addition, they have exceptional tensile strength and thermal conductivity thanks to their nanostructure and the strong bonding between carbon atoms. The nonsimilar transformations convert the governing PDEs into nondimensional forms. The transformed equations are subjected to the Quasilinearization technique. Further, the implicit finite difference approach leads to discretising the linearized equations. Incorporating CNTs-Fe3O4 ternary hybrid nanofluid leads to a higher heat transfer rate than mono and hybrid nanofluids. The fluid's velocity diminishes, while the liquid's temperature enhances with enhancing values of ternary nanofluid. The drag coefficient enhances about 16%, 20%, and 15% from Eyring–Powell nanofluid to Newtonian fluid for mano nanofluid, hybrid nanofluid, and ternary nanofluid, respectively. Increased temperature difference ratio enhances the heat transfer, which diminishes the Entropy generation and improves the Bejan number. [ABSTRACT FROM AUTHOR]

To enhance ammonia's flammability, blending methane with ammonia is a viable strategy to improve the laminar burning velocity of ammonia combustion. We conducted three-dimensional numerical simulations to investigate the thermal performance, the 2nd thermodynamic law efficiency, and NO emissions of a micro-combustor fuelled by ammonia/methane. Three key parameters are identified and examined. They include: 1) the inlet volume flow rate, 2) the CH4 mole blended ratio, 3) the N 2 dilution rate. It is found that increasing the inlet volume rate give rise to an increase of the combustor outer wall temperature, and so the thermodynamic second-law efficiency, but a reduction of NO emissions. For example, when the inlet volume flow rate is set to 14.4 mL/s, the wall temperature and 2nd law efficiency are increased by 36 % and 21 % respectively, but the NO emission is reduced by 15 %, in comparison with those in the presence of 7.2 mL/s inlet flow. While an increase of the CH 4 blended ratio (molar fraction) is shown to have limited impact on the thermal performances. This variation of such blended ratio is found to be associated with a notable 22 % reduction of NO emissions. Additionally, injecting N 2 as a dilution gas is shown to be not beneficial to the thermal performance. However, NO emissions are reduced. When the N 2 dilution rate is set to 0.6, the wall temperature is found to be reduced by 173 K. However, 47 % more NO reduction is observed in comparison with those in the presence of the N_2 dilution rate being set to 0.3. • 3D numerical investigation of a micro-combustor fuelled by methane/ammonia is conducted. • Entropy generation analyses of the ammonia/methane-fuelled micro-combustor are performed. • Examining the effect of blending methane with ammonia on thermodynamic performances is conducted. • NO emissions from the ammonia/methane-fuelled micro-combustor are evaluated. • A reduced chemical reaction mechanism of ammonia-methane combustion is proposed. [ABSTRACT FROM AUTHOR]

The concept of nonlinear thermal convection is taking place for the process of cooling/heating in some thermal industries like solar collectors, combustion, and reactor safety. The significance of linear and nonlinear thermal convection is implemented in the present mathematical modeling to handle nonlinear density-temperature caused by viscous dissipation and flow through a porous medium. Further, the inclined magnetic field is implemented to analyze the flow characteristic at various inclination angles which will be helpful in glass manufacturing, geophysics, crude oil purification, and paper production. Furthermore, entropy generation analysis is made for the stagnation point flow of viscous fluid over a shrinking Riga sheet. Using boundary layer assumptions, the present model made up of fluid motion and energy equations is formed and converted to a system of nonlinear differential equations. Numerical results are collected using the MATLAB bvp4c solver and these results were utilized to study important parameters on the entropy generation and heat transport of fluid flow. The presence of nonlinear thermal convection will intensify the impact of major parameters and the least entropy generated for the inclination angle of the magnetic field. Also, entropy enhanced significantly with the Eckert number and modified Hartmann number. In addition, the surface drag is enhanced by 12%-18 % and the thermal transmission rate is diminished by 4%-7% in the case of nonlinear thermal convection compared to the linear case. The findings of this study are more important to optimize heat transfer and irreversibility in the applications of the automotive industry, ceramics, paints, food packaging, fabrics, pharmaceuticals, and cancer treatment.

Natural resource depletion and increased mining waste pose significant challenges to global sustainability efforts. This study investigates the processability of mining waste during the metal recovery stage to evaluate its potential contribution to anthropogenic circularity. The mining industry, abundant in valuable metals that are crucial for a carbon-neutral economy, plays a pivotal role in this context. We determine the grades of metals by looking at their chemical makeup, and then we use statistical entropy to model how easy it is to process certain waste materials. This provides us with processability measures that range from 0.19 bit to 1.18 bit. Our findings highlight that while some waste contains “abundant” metals, its complexity may diminish its economic value, raising concerns about its environmental impacts and resource availability at the end-of-life stages. Estimating potential revenue involves multiplying processed amounts by commodity prices, revealing a maximum value of 8.73 USD/metric ton for processed waste. This assessment underscores the importance of integrating circular economy principles, aiming to mitigate environmental damage and promote industrial ecology. By advancing our understanding of mining waste management through rigorous scientific inquiry, this study contributes to sustainable resource utilization strategies that are essential for future industrial practices and environmental stewardship.

Emergency water supply is one of the most important lifeline projects for post-disaster survival support. Multi-stage pump can be the core power source for fluid transportation. A stable and reliable operation is crucial to rescue and support at disaster sites. In actual operation, the unexpected shutdown of emergency water supply multi-stage pumps can cause drastic changes in the performance parameters, such as the impeller rotating speed, flow rate, and pressure. The internal flow field in multi-stage pumps can pose a serious threat to the water supply safety. This study aims to investigate the transient response to the internal flow field during the hydraulic transition of multi-stage pump shutdown. A speed prediction was established to numerically calculate the internal flow field of the stage pump, according to the rotation balance equation of the impeller. The transient effects were analyzed from the dynamic characteristics of the impeller rotating speed, outlet flow rate, torque and the flow structures inside each stage of the impeller during the unexpected shutdown. The results indicate that the multi-stage pump shared four conditions during unplanned shutdown, pumping, braking, reversing and runaway. The rotational speed of the impeller showed a trend of first decreasing in the positive direction, and then increasing in the reverse direction, while finally stabilizing around the runaway speed of -4 210 r/min. The flow rate of the pump showed a trend of first positive decrease followed by a reverse increase, then stable reverse decrease and finally stayed around the runaway flow rate of -14.32 kg/s. The torque showed a trend of positive decrease followed by a positive increase, then stable positive decrease and finally stayed near zero. The continuous changes in the magnitude and direction of flow and speed inside the pump were attributed to the flow separation and backflow inside the impeller channel, accompanied by spatiotemporal evolution, such as the formation, development and fragmentation of vortices. The entropy output value was closely related to the unstable flow structure inside the multi-stage pump, while the loss caused by turbulent dissipation played a dominant role. The turbulent dissipation entropy production accounted for about 65.2% after reaching the runaway condition. Energy loss mainly occurred inside the impeller flow channel. There was a significant loss of the internal flow field in the braking condition. There was the most variation in the pressure fluctuation at the monitoring points in the first stage flow channel of the multi-stage pump. The amplitude of the pressure fluctuation coefficient showed a decreasing trend with the increase of the stage number. The dominant frequency of the pressure pulsation amplitude was positively correlated to the rotating speed of the multi-stage pump, corresponding to the blade passage frequency. The amplitude of dominant pressure pulsation frequencies gradually increased along the direction of fluid flow. There was the most significant change in the first stage flow channel during the unexpected shutdown of the multistage pump, which was accompanied by the most complex flow structure. The frequency characteristics of pressure pulsation can reflect the differential changes in flow instability during unexpected shutdowns. The research results can provide theoretical guidance for the safe and stable operation of emergency water supply systems. [ABSTRACT FROM AUTHOR]

AbstractThis study aims to numerically investigate entropy optimization for a time-independent tangent hyperbolic fluid flowing past a porous sheet that is stretching linearly. The fluid motion is generated by stretching surfaces and buoyancy forces. The analysis includes momentum, energy, and mass transport associated with mixed convection, thermal radiation, velocity, thermal, and concentration slip factors. Heat dissipation is also considered in energy transport through the viscous dissipation effect. The mathematical formulation leads to a set of non-linearly coupled partial differential equations. To obtain a similarity solution, similarity variables are introduced. The numerical solution of the leading differential equations is obtained using the fourth-order Runge-Kutta method with the shooting technique. The graphical results are presented to show the physical significance of the relevant parameters. The main objective of entropy optimization is achieved by increasing the magnitude of the Darcy dissipation parameter. Entropy generation is directly proportional to the Brinkmann number, Reynolds number, and radiation parameter. The second law of thermodynamics is fulfilled by introducing slip conditions over the porous medium. The use of a tangent hyperbolic fluid over a porous medium has practical applications in various porous geometries, such as oil/gas reservoir simulation, enhanced oil recovery, carbon dioxide sequestration, and water soil infiltration. Therefore, optimizing entropy generation in these mechanical systems is crucial. [ABSTRACT FROM AUTHOR]

We are concerned with the isothermal limit of entropy solutions in L∞, containing the vacuum states, of the Euler equations for isentropic gas dynamics. We prove that the entropy solutions in L∞ of the isentropic Euler equations converge strongly to the corresponding entropy solutions of the isothermal Euler equations, when the adiabatic exponent γ→1. This is achieved by combining careful entropy analysis and refined kinetic formulation with compensated compactness argument to obtain the required uniform estimates for the limit. The entropy analysis involves careful estimates for the relation between the corresponding entropy pairs for the isentropic and isothermal Euler equations when the adiabatic exponent γ→1. The kinetic formulation for the entropy solutions of the isentropic Euler equations with the uniformly bounded initial data is refined, so that the total variation of the dissipation measures in the formulation is locally uniformly bounded with respect to γ>1. The explicit asymptotic analysis of the Riemann solutions containing the vacuum states is also presented. [ABSTRACT FROM AUTHOR]

A wide range of real-world applications have proven the importance of non-Newtonian fluids near a wedge, including the oil and gas industry, the aerospace sector. This study elucidates the dynamics of a Carreau nanofluid around a wedge by combining entropy analysis with periodic magnetohydrodynamics (MHD) and activation energy. The dimensional partial differential equations (PDEs) that describe the fluid flow system undergo non-similar transformations, forming nondimensional PDEs. The numerical solution to these PDEs is obtained by applying quasilinearization followed by the implicit finite difference approach. In the case of n = 0.5 (power-law index), when We (Weissenberg number) improves from 0 to 4, surface friction (Re1/2Cf) upsurges by approximately 23% and declines by around 41% in the case of n = 1.5. The mass transport intensity of liquid oxygen is about 18% higher than liquid nitrogen’s. Increasing the wedge angle results in a significant increase in fluid velocity. [ABSTRACT FROM AUTHOR]

The goal of this paper is to learn more about how a magnetic field, Brownian motion, and thermophoresis diffusion influence convective heat transfer in a micropolar-nanofluid flow's laminar boundary layer. Near a vertically moving, permeable plate, the complex fluid is subjected to MHD. The MATLAB application bvp4c was utilized to simplify the governing nonlinear and coupled equations for the micropolar-nanofluid, leading to the solution of the ensuing ordinary differential equations (ODEs). Graphs have been used to analyze the effect of different relevant active factors on the flow field and temperature. The results demonstrate that the micro-rotation of the nanoparticles taken into account and in suspension becomes significant for the complex fluid in the presence of the magnetic field. Analysis of the generation entropy shows that the surface is a significant source of irreversibility. There is no discernible effect of micropolarity on the relationship between Brownian and thermophoresis numbers and entropy generation. [ABSTRACT FROM AUTHOR]

The entropy analysis of viscoelastic fluid obeying the simplified Phan-Thien-Tanner (SPTT) model with variable thermophysical properties are obtained for laminar, steady state and fully developed Couette-Poiseuille flow. The homotopy perturbation method (HPM) allows us to solve nonlinear momentum and energy differential equations. The Reynold's model is used to describe the temperature dependency of thermophysical properties. Results indicate that the increase of the group parameter (Br/Ω) and the Brinkman number (Br) which show the power of viscous dissipation effect; increases the entropy generation while increasing fluid elasticity (εDe2) decreases the generated entropy. Increasing the Reynolds variational parameter (α) which control the level of temperature dependence of physical properties attenuate entropy generation when moving plate and applied pressure gradient have the opposite direction and decreases entropy generation when moving plate and applied pressure gradient have the same direction or both plates are at rest. Also, increasing elasticity reduces the difference between variable and constant thermophysical properties cases. These results may give guidelines for cost optimization in industrial processes.

The current study focuses on utilizing an advanced statistical concept for optimizing rate of heat transfer in a micropolar nanofluid flow within a squeezing channel. The study employs RSM to plan experiments and analyze the role of distinct constraints on HT performance. Moreover, the inclusion of dissipative heat due to the interaction of applied magnetic field along with thermal radiation enriches the study. The proposed designed model is transformed to its non-dimensional form using appropriate similarity rules and then numerical simulation is presented to solve the set of formulated problem. However, the irreversibility process of the system is assessing by incorporating the entropy analysis. The validation along with the characteristic of the contributing factor is presented via the solution of the present design. The outcomes reveals that enhanced volume fraction generally increases viscosity of the fluid which resulted in a strong retardation in the fluid velocity. However, the entropy rate augments with an increasing Br. The results provide the optimal conditions for HTR for both of the nanofluids, which is relevant for applications in drug delivery systems. The advancement in optimizing HTR using RSM and the regression analysis using ANOVA (analysis of variance) may have potential implications for biomedical engineering.

This research explores the three-dimensional characteristics of nanofluid dynamics within curved ducts, in contrast to earlier studies that mainly focus on two-dimensional flow. By using this ground-breaking method, we can capture a more accurate depiction of fluid behavior that complies with the intricate duct design. In this study, we investigate the three dimensional flow and entropic analysis of peristaltic nanofluid flows in a flexible curved duct, comparing the effects of silver and copper nanoparticles. To obtain accurate results, we assume physical constraints such as long wavelength and low Reynolds number and used a perturbation technique through NDSolve commands for finding exact solutions of the obtained differential equations. A comprehensive error analysis is provided through residual error table and figures to estimate a suitable range of the physical factors. Our findings indicate that the velocity of the nanofluid is directly proportional to the elasticity of the walls, while the mass per unit volume inversely affects velocity. We show that reducing the aspect ratio of the duct rectangular section can decrease entropy generation by raising magnitudes of damping force exerted by to the flexible walls of the enclosure. Additionally, using a larger height of the channel than the breadth can reduce stream boluses. The practical implications of this study extend beyond turbines and endoscopy to biomedical processes such as drug delivery and microfluidic systems.

A comprehensive study has been made on the unsteady combined convective flow of Williamson ternary hybrid nanofluid over a rotating sphere with multiple slips and entropy generation (EG). The carbon nanotubes with single (SWCNT) and multiwalled (MWCNT) are dispersed in the base fluid along with the titanium dioxide nanoparticles. The relevant coupled nonlinear partial differential equations (PDEs) are formulated using boundary layer approximations. The nonsimilar transformations convert the governing PDEs into nondimensional forms. The transformed equations are subjected to the Quasilinearization technique for linearization. Further, the implicit finite difference approach leads to discretizing the linearized equations. Incorporating CNTs-TiO2 ternary hybrid nanofluid leads to a higher heat transfer rate than single and two components nanofluids with the same volume fraction of 6%. The SWCNT-MWCNT Williamson hybrid nanofluid improves the energy transport rate by approximately 8% compared to the SWCNT Williamson nanofluid. The Williamson ternary hybrid nanofluid improves heat transfer strength by approximately 34% compared to the Williamson fluid. The comparison of the Newtonian ternary hybrid nanofluid (W = 0) with the Williamson ternary hybrid nanofluid (W = 0.5) reveals that the EG is minimum, and the Bejan number is more for the Williamson fluid than that for the Newtonian fluid. A fall in the EG is found near the sphere's surface for higher values of temperature difference ratio and velocity slip. [ABSTRACT FROM AUTHOR]

Several engineering systems currently use microchannel heat sinks. In order to increase the performance of these devices, optimisation according to the first and second law of thermodynamics is employed. One way to achieve the goal is to modify the geometry of the cross-section, as is done in this paper for square ducts, having the walls at a uniform temperature which is higher than that of the bulk fluid at the inlet. The effects of both the thermal entry region of the duct and the heat generation due to viscous dissipation are considered. The resulting Graetz–Brinkman problem is solved numerically to obtain the velocity and temperature fields. It is demonstrated that non-negligible viscous heating eventually causes the heat flux to reverse (from fluid to walls), and that, only after this condition is achieved, can the flow become fully developed, which makes the entry region the only useful stretch for real-life applications. The length after which the direction of the heat flux reverses due to viscous heating in the fluid is obtained as a function of the Brinkman number and of the smoothing radius. Optimisation with performance evaluation criteria and entropy generation minimisation was carried out separately, and the results were combined into a single objective function. A comparison with published models highlights how neglecting the entry region and viscous heating yields misleading results. It turns out that smoothing the corners is always profitable in the case of the constrained heated perimeter or area of the cross-section but seldom when the characteristic length or the hydraulic diameter is fixed. With few exceptions, viscous heating amplifies the trends experienced for zero-Brinkman flows. The results are in non-dimensional form, yet they have been obtained starting from plausible dimensional values and are applicable to real-life devices.

The control and regulation of thermal fields is of great significance in solving various thermal management problems in human life. Benefitting from the emerging space transformation technique and thermal meta-material, thermal meta-structures with unique thermal control capabilities have been rapidly developed in recent years. However, the exploration of the functional diversity of thermal meta-materials and structures is still inadequate; most related works are still limited to the single-field control effect and lack sensitivity to external environment changes. For the designed functional structures, observation and analysis of energy fluctuations and irreversible heat loss during the regulation process of the diffusive thermal field are also scare. Therefore, in this current work, we design a thermal meta-regulator (based on the space transformation theory) that is capable of differently distributing thermal energy according to the heat input direction and switching field control pattern with the change of ambient temperature. In addition to the common indicator of temperature, we also introduce the local entropy production rate and the total entropy production in the thermo-dynamic category to carry out entropy analysis of the energy processes involved in the thermal meta-regulator, making a multi-angle evaluation of the structural performance. Furthermore, we use the statistical response surface method to explore the comprehensive/interaction effect of multiple influencing factors on the thermal meta-regulator; the derived regression equations can be used to accurately predict the structural effects under different design schemes and temperature conditions. Our work further enriches the diversity and flexibility of thermal field manipulation manners and the demonstrated functions are also expected to be realized in other physical fields. [ABSTRACT FROM AUTHOR]

This paper theoretically examined the inherent irreversibility in hydromagnetic mixed convection of a radiating adjustable viscosity nanofluid between two concentric inclined cylindrical pipes. Thermodynamics' first and second laws are incorporated into the two-phase nanofluid flow model problem to explore the repercussions of thermophoresis, Brownian motion, inclination angle, Joule heating, buoyancy forces, viscous dissipation, thermal radiation and entropy generation rate on the overall flow structure with temperature and nanoparticles concentration distribution. The nonlinear model equations of differential types are obtained and numerically addressed through shooting quadrature in conjunction with the Runge–Kutta–Fehlberg integration scheme. Relevant outcomes are graphically represented and discussed. The findings indicate that a rise in the inclination angle lessens the buoyancy effects and diminishes the entropy generation rate in the annular region of the concentric pipes. Within the annulus, the irreversibility due to heat and mass transfer dominates the entropy generation rate. In contrast, an upsurge in magnetic field intensity decreases the entropy generation rate and the Bejan number. [ABSTRACT FROM AUTHOR]

The adverse effects of global warming and climate change require critical measures for marine refrigeration technology because of its impact on GHG emissions. A novel transcritical CO2 two-stage compression/ejector refrigeration cycle for shipboard cold chamber is proposed in this research. A comparative analysis was conducted between the basic transcritical CO2 two-stage compression cycle and the cycle equipped with a two-phase ejector considering the COP. Meanwhile, the refrigeration cycle was analyzed using entropy analysis to elucidate the distribution of irreversible losses in each component of the two-stage compression/ejector refrigeration cycle, and the effects of system parameters such as the evaporating and gas cooler outlet temperatures as well as the intermediate and discharge pressures on the cycle were investigated. The results showed that the ejector had the largest power capability loss, accounting for 26.95 % of the overall system, which is followed by the low pressure compressor with 26.06%. The COP of basic and ejector system significantly increase as the gas cooler outlet temperature and intermediate pressure decrease as well as the evaporating temperature increases. Furthermore, the entropy production of the system components decreases gradually with increasing evaporating temperature, with the greatest reduction in the ejector. In addition, the entropy production of the two-phase ejector remains constant with the increase of the high pressure side discharge pressure and gas cooler outlet temperature. [ABSTRACT FROM AUTHOR]

A mathematical investigation of a thermodynamical system linked with energy management and its impact on the environment, especially climate change, is presented in this study. In this regard, a numerical investigation of the flow and heat transfer of hydromagnetic third-grade liquid through a porous medium. The permeability of the medium and electrical conductivity of the fluid are assumed to be temperature functions. The appropriate mathematical formulations for momentum, energy, and entropy equations are presented in both dimensional and dimensionless forms. We obtained the numerical solutions using the spectral version of the Chebyshev collocation method and compared the result with the shooting Runge–Kutta method. Numerical results for velocity, temperature, entropy, and Bejan profiles are communicated through tables and graphs with adequate physical interpretation. The thermal stability of the thermo-fluid system that guarantees the prevention of spontaneous fluid heating that fuels climate change is also included in the analysis. [ABSTRACT FROM AUTHOR]

This study presents an entropy analysis of the losses in different sections of a central air conditioner. The methodology for determining the losses using entropy analysis is presented in this work. The temperature and humidity of the outside and recirculating air were measured, and losses due to mixing of air flows, cooling in the evaporator section, and heating in the condenser section were calculated. A comparative analysis of the results was performed, and the distribution diagrams of the component losses were presented. Conclusions regarding a loss-reduction method were proposed. [ABSTRACT FROM AUTHOR]

LiDAR is a useful technology for gathering point cloud data from its environment and has been adapted to many applications. We use a cost-efficient LiDAR system attached to a moving object to estimate the location of the moving object using referenced linear structures. In the stationary state, the accuracy of extracting linear structures is low given the low-cost LiDAR. We propose a merging scheme for the LiDAR data frames to improve the accuracy by using the movement of the moving object. The proposed scheme tries to find the optimal window size by means of an entropy analysis. The optimal window size is determined by finding the minimum point between the entropy indicator of the ideal result and the entropy indicator of the actual result of each window size. The proposed indicator can describe the accuracy of the entire path of the moving object at each window size using a simple single value. The experimental results show that the proposed scheme can improve the linear structure extraction accuracy. [ABSTRACT FROM AUTHOR]

A mathematical model is presented to study the cilia beating modulated radiated ternary nanofluid flow between two cilia‐carpeted walls propagating with an applicable phase difference to show the asymmetric nature of the pumping. To regulate further the cilia‐based pumping, electroosmosis, and magnetohydrodynamics mechanisms are considered in this model. Ternary nanofluid is considered with TiO2, SiO2, and Al2O3 nanoparticles dispersed in pure blood. The momentum slip condition is employed to derive the model solution. Thermal radiation and buoyancy effects are also taken into consideration. Mathematica NDSolve is utilized to simulate the numerical results for the velocity, temperature, and concentration profiles under the influence of the emerging parameters. Moreover, entropy generation and variation in Nusselt and Bejan numbers are computed for better thermal analysis. A comparative thermal analysis for unary, binary, and ternary nanofluids is also made. The results indicate that the trihybrid nanofluid offers an 8.5% increase in the heat transfer rate as compared to the conventional fluid. Furthermore, the Brownian motion is responsible for the enhancement in fluid temperature, while electric double layer thickness helps in increasing heat transfer irreversibility around the channel lower wall. It is found that the total entropy of the system at the channel upper and lower space is minimized in the case of ternary fluid rather than conventional fluid. The promising results of the present model reflect huge applications in biomedical engineering, particularly in designing thermal cilia‐based microfluidic devices. [ABSTRACT FROM AUTHOR]

In China, as an important source of renewable energy, the standard biogas production module (SBPM) plays a key role and accounts for the majority of biogas production. Given its greater environmental pollution, it is particularly important to assess its quantitative sustainability status for optimizing system design and policy-making. This paper conducted the sustainability assessments of the open-loop standard biogas production module (OL-SBPM) and closed-loop standard biogas production module (CL-SBPM). Compared with the OL-SBPM system, the CL-SBPM system integrates a power generation subsystem. To evaluate two types of systems quantitatively, energy, emergy and entropy methods have been utilized in this study. The results illustrated that only partial indicators are more sustainable in the CL-SBP system rather than all indicators by comparing with the OL-SBPM system, which is contrary to the prevailing view. Taking the information entropy as an example, the equivalent information entropy flow values of the OL-SBPM and CL-SBPM systems are 2.04 × 1025 bits and 1.47 × 1025 bits, respectively, which also demonstrate that the OL-SBPM system has more useful information resulting in higher degree of sustainability. Therefore, these two systems need to be selected according to the existing conditions, rather than the subjective ideation. [ABSTRACT FROM AUTHOR]

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices. • Uncovers diverse urban water use patterns using time series clustering. • Reveals seasonal shifts and drivers of water consumption. • Quantifies complexity of water use with entropy analysis. • Informs targeted conservation strategies for sustainable water management. [ABSTRACT FROM AUTHOR]

Real time anomaly detection is important to performance and efficiency in many areas. This paper offers a complex method for detecting abnormal telecommunication traffic. The proposed method includes components based on entropy analysis, signature analysis and machine-learning detection of anomalies using fractal and recurrence analysis. Datasets containing realizations of normal traffic and realizations of attacks of different types were used as input data. Traffic containing the attack is considered to be abnormal. The multifractal and recurrence methods are briefly described. The results of multifractal and recurrence analysis of data-set traffic showed significant differences between normal and attacked realizations. Based on the obtained results, machine-learning algorithms are proposed. The classifiers random forest and neural network were used and showed good classification accuracy. The methods signature and entropy analysis are briefly described, and the results of their application showed a high degree of anomaly detection. The complex anomaly detection method combines entropy analysis, signature analysis, and machine-learning using the multifractal and recurrence analysis. The proposed complex method is tested on virtual network and is compared pervious methods. Simulation results showed that the proposed complex method has the highest percentage of detected attacks, the lowest percentage of undetected attacks and lost data.