Similar Rules Research Articles

Diversified characteristic of carbon nanotube nanoparticles on the entropy minimization for the flow of hybrid nanofluid through a convectively heated surface

AbstractAn analysis of entropy is essential to determine the heat transfer efficiency characteristics of nanofluids in different applications. Implementation of carbon nanotubes (CNTs) that is the combined effect of “single‐wall carbon nanotube” (SWCNT) and “multi‐wall carbon nanotube” (MWCNT) in water shows their effective properties in enhancing the heat transport phenomena. In general, these are useful in different industrial processes for the better shape of the product proposed as a coolant, cancer therapy, solar radiation, etc. Based on special characteristics, the current investigation analyses the flow properties of water‐based CNT cross‐hybrid nanofluid past a convectively heated surface. The heat transport characteristic enriches by the insertion of dissipative heat, thermal radiation, and external heat source/sink. The appropriate choice of similarity rules is useful in transforming the governing designed problem in non‐dimensional form and further, a “spectral quasi‐linearization method (SQLM)” is imposed to solve the set of equations. After getting the result, the process of irreversibility due to various factors is obtained, that is, the analysis of entropy is presented briefly. The physical significance of designed factors is deployed graphically and described in the discussion section. However, the validation with the earlier result is projected to show a good correlation.

Enhanced heat transfer rate on the flow of hybrid nanofluid through a rotating vertical cone: a statistical analysis

The recent study needs the optimal conditions for maximizing heat transfer rate with minimizing energy consumption. This is a basic objective of several industries in their production processes along with in improving the efficiency in electronic gadgets. Therefore, hybrid nanofluid plays an important role in enhancing heat transport phenomena. The current study investigates the improved heat transfer properties of a water-based hybrid nanofluid containing Al2O3 and Cu nanoparticles flowing through a rotating vertical cone embedded in a porous medium. The convective flow is enhanced by a magnetic field and thermal radiation, contributing to the complexity of the flow dynamics. However, the dimensional physical quantities are transformed to non-dimensional parameters by the implementation of suitable similarity rules. Furthermore, the transformed equations are numerically solved using a fourth-order Runge-Kutta shooting method. Henceforth, to get the optimal heat transfer rate, a robust statistical technique called response surface methodology (RSM) and for the validation a hypothetical test is organized by using analysis of variance. The parametric behavior is conducted via graphs and the physical description is presented briefly. Further, the major outcomes of the study are: both the particle concentrations attenuate the axial velocity whereas the fluid temperature enhances significantly. Increasing radiative heat augments the heat transport phenomena and the observation reveals that the case of hybrid nanofluids overrides the case of nanofluids.

FOLK MEDICINE THEN AND NOW - CHARACTERIZATION OF THE HEALING PROCESS IN FOLK MEDICINE IN 19TH AND 20TH CENTURY ON POLISH RURAL AREAS, AND ANALYSIS OF THE CONTEMPORARY APPROACH TO PRACTICES DERIVED FROM FOLK HEALING

Europäische Wissenschaftliche Gesellschaft About the Journal Pee Home r Review Editorial Board For Authors Reviewer Recognition Archive Contact Impressum EWG e.V. Elektronische Zeitschriftbibliotek Logo Bielefeld Academic Search Engine Logo Erfolgreich durch internationale Zusammenarbeit HISTORY OF MEDICINE Cite as: Archiv EuroMedica. 2024. 14; 3. DOI 10.35630/2024/14/3.347 Received 28 May 2024; Accepted 20 June 2024; Published 25 June 2024 FOLK MEDICINE THEN AND NOW - CHARACTERIZATION OF THE HEALING PROCESS IN FOLK MEDICINE IN 19TH AND 20TH CENTURY ON POLISH RURAL AREAS, AND ANALYSIS OF THE CONTEMPORARY APPROACH TO PRACTICES DERIVED FROM FOLK HEALING Maciej Rumian1 email orcid id logo, Wojciech Koziołek2 orcid id logo, Alicja Kosel1 orcid id logo, Maria Antos1 orcid id logo, Angelika Wawryszuk1 orcid id logo, Grzegorz Łyko3 orcid id logo, Weronika Kaminska3 orcid id logo, Adrian Bobrzyk3 orcid id logo 1Collegium Medicum, Jan Kochanowski University, Kielce, Poland 2Jagiellonian University, Medical College, Świętej Anny 12, 31-008 Kraków, Poland 3Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland download article (pdf) rumni1998@interia.pl ABSTRACT Introduction and purpose: Folk medicine is a vast subject, describing various actions drawn from herbal medicine, magic and folk beliefs, as well as conventional medicine, aimed at healing the sick. The purpose of the study was to characterize the main principles and components of the healing process in folk medicine practiced among the rural population in 19th and 20th century Poland, based on available literature. Materials and review methods: The PubMed, Google Scholar databases and available book literature were searched for studies and chapters describing the causes of diseases in 19th and 20th century folk medicine, the activities of rural healers, and the methods of treating illnesses practiced at that time. State of knowledge, results: Folk medicine has been a part of the tradition of polish rural medicine for a long time. Folk medicine included both preventive and curative procedures and interventions. Despite their significant differences and diversity, depending on the region of practice, many healing procedures were based on similar principles and had a lot in common. Even today, folk medicine remains alive and, although sometimes treated with a grain of salt, it still remains a practiced element of the medical profession. Conclusion: The review of historical and current literature conducted by the authors allows us to confidently say that folk medicine was very widespread among the rural population in the 19th and 20th centuries in Poland. Despite many differences, the methods used in folk medicine drew from similar principles and rules, which granted them healing power. Studies conducted by contemporary authors demonstrate that despite the intensive development of science and medicine, methods derived from folk medicine are still in use, although they are not as popular as in past centuries.

Magnetic dissipation on radiative free convection of a conducting hybrid nanofluid within a rotating cone and circular disc

A significant attention is gained on the thermophysical properties corresponding to hybrid nanofluid flow within a rotating cone and circular disc in this present investigation due to its several applications. The enhanced thermal conductivity of the nanofluid is particularly useful in cooling systems, reducing energy consumption in systems, preventing overheating, and improving the overall performance of electronic gadgets, etc. In the present investigation, the free convective flow of conducting fluid for the interaction of magnetic field and thermal radiation encases their greater impact on the flow phenomena. Further, the assumption of dissipative heat i.e. mutual effects of viscous and Joule, energies the flow properties. The complex nonlinear form of the constitutive equations presented in the dimensional form is renovated into the nonlinear ordinary system with a non-dimensional form by introducing suitable similarity rules. The computation of the profiles is obtained numerically with the help of the built-in function bvp4c in MATLAB and depicted graphically. Finally, it is observed that the volume fraction of both the nanoparticles favors enhancing the heat transport phenomena due to the augmented thermal conductivity whereas a dual characteristic is revealed at both the surfaces of the circular disc and the cone.

Probing New Physics in Light of Recent Developments in b → c ℓ v Transitions

Abstract Experimental studies of the observables associated with $b \rightarrow c$ transitions in semileptonic B-meson decays at BaBar, Belle, and LHCb have shown some deviations from the Standard Model predictions, consequently providing a handy tool to probe the possible new physics (NP). In this context, we first revisit the impact of recent measurements of $R({D^{(*)}})$ and $R(\Lambda _c)$ on the parametric space of the NP scenarios. In addition, we include the $R(J/\psi )$ data in the analysis and find that their influence on the best-fit point and the parametric space is mild. Using the recent HFLAV data, after validating the well established sum rule of $R(\Lambda _c)$, we derive a similar sum rule for $R(J/\psi )$. Furthermore, according to the updated data, we modify the correlation among the different observables, giving us their interesting interdependence. Finally, to discriminate the various NP scenarios, we plot the different angular observables and their ratios for $B \rightarrow D^{*} \tau \nu _\tau$ against the transfer momentum square $\left(q^2\right)$, using the $1\sigma$ and $2\sigma$ parametric space of considered NP scenarios. By implementing the collider bounds on NP Wilson coefficients (WCs), we find that the parametric space of some NP WCs is significantly restrained. To see the clear influence of NP on the amplitude of the angular observables, we also calculate their numerical values in different $q^2$ bins and show them through bar plots. We hope that their precise measurements will help to discriminate various NP scenarios.

A statistical model on heat transportation of hybrid magnetic nanoparticles with slip constraints on a heated shrinking sensor device using analysis of variance: Sensitivity analysis

A statistical model finds application across various engineering and scientific contexts, aiding in the analysis and enhancement of heat transfer processes. Its utility extends to supporting scientists and engineers in gaining deeper insights into the impact of diverse factors on heat transfer and optimizing procedures for enhanced efficiency. Notably, the heat transfer efficiency of hybrid nanofluids containing ferromagnetic nanoparticles outperforms that of conventional fluids. In this study, a statistical model is utilized to predict the increased heat transfer rate in a heated shrinking sensor employing hybrid magnetic nanoparticles with slip restrictions. Incorporating radiative heat flux and heat source/sink components contributes novelty to the study. The model is converted into a non-dimensional form using appropriate similarity rules, and the resulting problem is solved computationally using bvp5c, a built-in function in MATLAB. Using advanced mathematical modeling and simulations, this research evaluates system performance in various scenarios and identifies optimal conditions for maximizing heat transfer rates. The study's main outcomes are; that including the thermal radiation and Prandtl number enhances the heat transfer rate, whereas the heat source decreases it. Further, the increased modified Hartmann number and suction parameter raise the shear rate coefficient.

Sensitivity analysis and enhanced EMHD performance of unsteady ferrite-water hybrid nanofluid on a Riga plate with variable magnetization and heat generation

In connection to the various applications on energy conservation, fluid-based electronics; the coupling of electro-magnetic along with heat generation presents their significant approach. Therefore, the current investigation aims at analyzing the role of ferrite-water hybrid nanofluid in an Electro-Magnetohydrodynamics (EMHD) system. However, it utilizes the flow over a Riga plate with the variable properties of magnetization with the inclusion of thermal radiation and heat generation. Elucidating the influence of ferrite nanoparticles, the unsteady nature of the system addressed various properties of nanofluid. The system of equations is handled numerically adopting shooting-based fourth-order Runge-Kutta technique followed by similarity rules those are used to transformed the governing phenomena into ordinary. To maximize the EMHD efficiency, an optimization framework is employed using response surface methodology (RSM). However, to validate the work a hypothetical test is conducted with the help of analysis of variance (ANOVA). Through sensitive analysis, the key factors for the response of heat transfer rate are identified. Further, the measure outcomes of the results are; the inclusion of the ferrite nanoparticles with the increasing EMHD parameter the fluid velocity augments in magnitude. Further, increasing radiation the surface of the sheet became cool and it radiate from the surface region for which the fluid temperature boosts up.

Students’ Attitudes in Mathematics: When the Context Highlights the Importance of Emotions – A Secondary Publication

This article aims to gain a better understanding of how students’ attitudes toward mathematical problem-solving operate. More specifically, it examines the extent to which these attitudes may play a different role depending on the context. To this end, we submitted two very similar golden rule math problems to 94 students in grade 10, once in the mathematics class and once in the French class. Our results are presented in three parts. First, in line with much other research, we highlight the significant gender differences in attitudes, with girls developing more negative and potentially deleterious attitudes to their learning. Second, we find significant links between various dimensions of attitudes (cognitive, affective, and behavioral) and students’ academic achievement in mathematics. Finally, our analyses underline that in the context of the mathematics course, it is mainly anxiety that disturbs the success of the exercise, whereas the resolution of a very similar exercise is favored by positive emotions when it is inserted into a French worksheet. This suggests that students activate certain attitudes depending on the context – and in particular certain affects – which will have an impact on their performance. Such a result thus underlines the importance of emotions in learning and the way in which the context can induce them.

Spectral quasi-linearization approach for the swimming of motile microorganisms on the bio-convection Casson nanofluid flow over a rotating circular disk

The ability of motile microorganisms is used to design of biomedical nanofluidic devices that utilize for targeted drug delivery, to influence heat transfer in nanofluids that are applied to the optimization of cooling system, in biotechnological processes where precise control over fluid flow, etc. However, based upon the several facts, the current investigation lead to propose the heat transfer characteristic of motile microorganisms swimming in a flow of bio-convection nanofluid over a rotating circular disk. The proposed study explores the impacts of dissipative heat generated by the movement of microorganisms, thermal radiation, and chemical reactions. The impact of cross-diffusive property organized by the implementation of Brownian motion and thermophorsis energies the flow phenomena of bio-convection. The set of coupled nonlinear system of non-dimensional equations is obtained by the implementation of similarity rules and further quasi-linearization method is utilized for the solution of this system. The characteristics of diversified parameters are presented through graphs with the variation of the proposed factors within their certain limitations and thee discuss briefly. Further, the important outcomes of the study are presented as; the fluid velocity decelerates due to the enhanced non-Newtonian Casson parameter, whereas the radiating heat for the inclusion of thermal radiation and coupling parameter, that is, Eckert number boosts up the heat transport phenomena.

ExROPPP: Fast, accurate, and spin-pure calculation of the electronically excited states of organic hydrocarbon radicals.

Recent years have seen an explosion of interest in organic radicals due to their promise for highly efficient organic light-emitting diodes and molecular qubits. However, accurately and inexpensively computing their electronic structure has been challenging, especially for excited states, due to the spin-contamination problem. Furthermore, while alternacy or "pseudoparity" rules have guided the interpretation and prediction of the excited states of closed-shell hydrocarbons since the 1950s, similar general rules for hydrocarbon radicals have not to our knowledge been found yet. In this article, we present solutions to both of these challenges. First, we combine the extended configuration interaction singles method with Pariser-Parr-Pople (PPP) theory to obtain a method that we call ExROPPP (Extended Restricted Open-shell PPP) theory. We find that ExROPPP computes spin-pure excited states of hydrocarbon radicals with comparable accuracy to experiment as high-level general multi-configurational quasi-degenerate perturbation theory calculations but at a computational cost that is at least two orders of magnitude lower. We then use ExROPPP to derive widely applicable rules for the spectra of alternant hydrocarbon radicals, which are completely consistent with our computed results. These findings pave the way for highly accurate and efficient computation and prediction of the excited states of organic radicals.

Improving Marketing Strategies Using a Clustering and AR-MBA Methods at Indomaret Kaliurang

Marketing is the main activity carried out by entrepreneurs to maintain the viability of their business, develop the company, and get maximum profit. Companies need to know the right and appropriate marketing strategy so that the products to be sold on the market. Marketing strategy is one of the strategies applied to the retail industry in Indonesia, one of which is Indomaret. The purpose of this research is to increase market competitiveness so that it can survive and develop, and Indomaret in Kaliurang hopes to make a strategy to increase sales by conducting an analysis of consumer segments and product categories so that they can create sales strategies that can increase sales. The method used is clustering to find potential target markets that have similar characteristics and association rules &; Market basket analysis (AR-MBA) is used to find best practices for product marketing. The results of the Cluster Method with Non-Hierarchical Algorithm K-Means are 3 clusters. Based on the analysis conducted, it was found that the results of the AR-MBA method, there could be 6 rules for goods purchased simultaneously. Marketing strategies that can be applied by Indomaret Kaliurang such as promotions can be created by making products that are rarely purchased as bonuses for buying products that are often purchased. In addition, vouchers can be created as a promotional tool to encourage greater consumer purchases. Keywords: AR-MBA, clustering, sales strategy, strategy marketing

Optimizing heat transfer rate and sensitivity analysis of hybrid nanofluid flow over a radiating sheet: Applications in solar-powered charging stations

In a solar collector system, absorbing radiation through solar panel and transforming it into heat, the role of nanofluid is vital. However, the combined effect of CuO and Cu water hybrid nanofluid efficiently carries the fascinated heat away from the solar collector. Further, it helps improve in efficiency of the thermal solar thermal system by enhancing heat transfer and thermal losses. Based upon the advanced properties, the study aims at the behavior of a conducting hybrid nanofluid comprised of CuO and Cu nanoparticles through a nonlinear stretching surface filled with a porous matrix. Essentially, the use of thermal radiation with dissipative heat and convective heat transfer boundary conditions are important in enhancing heat transfer properties. The assemble of diversified similarity rules is useful for the transformation of the governing equations into dimensionless form. Further, numerical technique with the help of built-in function bvp4c in MATLAB is employed for the solution of the designed model equipped with different constraints. A robust statistical analysis i.e. response surface methodology (RSM) combined with analysis of variance (ANOVA) is used to optimize the heat transfer rate. Sensitivity analysis for the associated factors united to the response of heat transfer is obtained and presented briefly. Finally, the parametric behavior is important to describe following the pattern of the graph. Further, the important outcomes are; the enhanced volume fraction of the nanoparticles augments the fluid velocity as well as the temperature significantly. Thermal radiation has influential behavior in enhancing the heat transport phenomenon of hybrid nanofluid.

A Systematic Investigation of the Kinetic Models Applied to the Transport Behaviors of Aromatic Solvents in Unfilled Hydrogenated Nitrile Rubber/Ethylene Propylene Diene Monomer Composites.

Kinetic models of solvent transport behaviors are widely used in rubber-solvent systems, and some key points are still worthy of attention. In this work, the Korsmeyer-Peppas and Peppas-Sahlin models were chosen to fit the transport behaviors of three aromatic solvents, benzene, toluene and p-xylene, in the hydrogenated nitrile rubber (HNBR)/ethylene propylene diene monomer (EPDM)-based vulcanizates. The different effects of the various selected transport times (ti) used for fitting on the results of the mathematical models were compared. Moreover, a method to obtain the n parameter for the Korsmeyer-Peppas model and the m parameter for the Peppas-Sahlin model at ti = 0 was discussed. It was found that the differences in values of ti greatly influenced the impact on the fitting results of all the parameters for the two models. In addition, the n parameter for the Korsmeyer-Peppas model along with the m parameter for the Peppas-Sahlin model, which can characterize the transport mechanism, showed differing applicability. But the n and m parameters at ti = 0 obtained by linear fitting showed similar rules with some differences in values. These discussions give important guidance for the application of kinetic transport models in rubber-solvent systems.

Artificial neural network approach to simulate the impact of concentration in optimizing heat transfer rate on water-based hybrid nanofluid under slip conditions: A regression analysis

The recent era of science depends upon the efficient performance of the heat transfer rate in several engineering applications for which the role of nanofluids have a greater impact. Therefore, the impact of particle concentration in optimizing heat transfer analysis of water-based hybrid nanofluid is conducted via an artificial neural network. The combined effect of oxide nanoparticles such as MgO and TiO2 in water performs their effective role with various factors involved in the flow phenomena. The magnetized hybrid nanofluid flow over an expanding surface filled with porous material shows its noble behavior on the flow properties. Further, it is not usual to omit the effectiveness of the dissipative heat impact. Therefore, the role of viscous, Joule, and Darcy dissipation is incorporated in the energy profile for which the profiles became coupled. The designed nonlinear model invokes suitable similarity rules that give rise to a system of nonlinear ordinary equations in non-dimensional form. Afterwards, the traditional numerical approach is beneficial to conduct the simulation of the flow profiles. The optimization of the heat transfer rate analysis is obtained by the implementation of an artificial neural network (ANN) for the response of Nusselt number using various factors. The regression analysis for the embedding data shows the effectiveness of the proposed approach in this discussion. The proposed study describes the important outcomes as; an increasing slip produces a thinning in the bounding surface thickness for the case of pure fluid comparing to the nanofluid and hybrid nanofluid. Further, the data set used for the hybrid nanofluid shows an efficient fitting than the case of nanofluid.

Sleep deprivation drives brain-wide changes in cholinergic presynapse abundance in Drosophila melanogaster

Sleep is an evolutionarily conserved state that supports brain functions, including synaptic plasticity, in species across the animal kingdom. Here, we examine the neuroanatomical and cell-type distribution of presynaptic scaling in the fly brain after sleep loss. We previously found that sleep loss drives accumulation of the active zone scaffolding protein Bruchpilot (BRP) within cholinergic Kenyon cells of the Drosophila melanogaster mushroom body (MB), but not in other classes of MB neurons. To test whether similar cell type-specific trends in plasticity occur broadly across the brain, we used a flp-based genetic reporter to label presynaptic BRP in cholinergic, dopaminergic, GABAergic, or glutamatergic neurons. We then collected whole-brain confocal image stacks of BRP intensity to systematically quantify BRP, a marker of presynapse abundance, across 37 neuropil regions of the central fly brain. Our results indicate that sleep loss, either by overnight (12-h) mechanical stimulation or chronic sleep disruption in insomniac mutants, broadly elevates cholinergic synapse abundance across the brain, while synapse abundance in neurons that produce other neurotransmitters undergoes weaker, if any, changes. Extending sleep deprivation to 24 h drives brain-wide upscaling in glutamatergic, but not other, synapses. Finally, overnight male-male social pairings induce increased BRP in excitatory synapses despite male-female pairings eliciting more waking activity, suggesting experience-specific plasticity. Within neurotransmitter class and waking context, BRP changes are similar across the 37 neuropil domains, indicating that similar synaptic scaling rules may apply across the brain during acute sleep loss and that sleep need may broadly alter excitatory-inhibitory balance in the central brain.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Quantifying the environmental synergistic effect of cooling-air purification-carbon sequestration from urban forest in China

Urban forest is considered nature-based solution for mitigating the adverse impacts of climate change. However, large-scale quantification of urban forest synergistic effect is still limited. This study integrated multi-source remote sensing data, machine learning, and geospatial methods to quantify the synergistic effect (i.e., spatial interaction) of urban forest on urban heat island, PM2.5 concentration, and carbon stock and its driving mechanism in China. Results showed that urban forest explained 60–71% of the synergistic effect, which was greater than on the individual environmental factors, but decreased by 15.62% from 2000 to 2020. Moreover, urban forest synergistic effect exhibited an obvious spatial-temporal patterns of north-south heterogeneity and was dominated by anthropogenic factors. We further identified the “evolutionary gene” of urban forest, i.e., the similarity rule in the synergistic effect among cities during their development process. This knowledge contributes to the sustainable resource development of climate change adaptation strategies.

Quantifying the relationship between mobility patterns and socioeconomic status of dockless bike-sharing users

Cycling is among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of cyclists with different income have rarely been studied due to the limitation of data availability. Newly emergent dockless bike-sharing platforms that record detailed trip information provide us a unique opportunity. Attributing to its better usage flexibility and accessibility, dockless bike-sharing is booming over the past few years worldwide and reviving the cycling fashion in cities. In this work, by exploiting massive platform-collected trip records in four diversified Chinese cities, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among the users with different income, indicating that human beings all follow similar physical rules. However, collective mobility patterns, in terms of average range and diversity of visitation, and commuting direction, exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract a high volume of cycling activities are quite different over groups, and locations with either a lower or higher income level have a relatively low user ratio. Cyclists from lower income groups are inclined to visit less flourishing locations across all four cities, and have a higher fraction to commute toward the city center in larger cities and away from the city center in smaller cities. Middle income groups of cyclists generally have a higher visitation diversity except in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and toward the transition to a more inclusive and sustainable transportation.

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

Heat transfer enhancement with additively manufactured rough surfaces: Insights from large-eddy simulations

To study the impact of additively manufactured (AM) roughness on fluid flow and heat transfer, we performed a series of high-fidelity large-eddy simulations on turbulent heat transfer over a three-dimensional AM rough surface with varying bulk Reynolds number and average roughness height values. We considered rough surfaces created using AM techniques at Siemens based on Nickel Alloy IN939 material with four different mean roughness heights, ks= 1.594, 1.992, 2.630, and 3.984 mm, and the simulations were performed at five bulk Reynolds numbers of 1000, 3000, 6000, 11 700, and 18 000. The temperature was treated as a passive scalar with a Prandtl number of 0.71. To better understand the effect of wall roughness on the momentum and heat transfer mechanism, mean temperature and velocity profiles as well as heat fluxes are presented. The wall-normal Reynolds stress, ⟨ux′ur′⟩, and heat flux, ⟨ur′Θ′⟩, decrease for larger wall roughness heights, Ra, and their respective magnitudes remain very similar for different Ra. A similarity rule for friction factor and heat transfer is used to correlate and interpret the numerical results and compare them with previously existing results, both theoretical and experimental. The assessment of the thermal performance factor illuminates the improvement in heat transfer with the existing surface roughness. By studying the probability density functions of the instantaneous Stanton number, the recirculation zones, which are the result of an adverse pressure gradient, were found to have a profound effect on heat transfer. This is important as it leads to the wall-scaled mean temperature profiles being of larger magnitude than the mean velocity profiles both inside and outside the roughness layer. This means that the temperature wall roughness function, ΔΘ+, differs from the momentum wall roughness function, ΔU+.