Constructal Research Articles

Geometric Evaluation of an Oscillating Water Column Wave Energy Converter Device Using Representative Regular Waves of the Sea State Found in Tramandaí, Brazil

Aiming to contribute to studies related to the generation of electrical energy from renewable sources, this study carried out a geometric investigation of an oscillating water column (OWC) wave energy converter (WEC) device. The structure of this device consists of a hydropneumatic chamber and an air duct, where a turbine is coupled to an electrical energy generator. When waves hit the device, the air inside it is pressurized and depressurized, causing the air to flow through the duct, activating the turbine. In this sense, the present study used the constructal design method to evaluate the influence of the ratio between the height and length of the hydropneumatic chamber (H1/L) on the mean available hydropneumatic power (PH(RMS)). Fluent software was used to perform numerical simulations of representative regular waves from the sea state in the municipality of Tramandaí, southern Brazil, impacting the OWC. Thus, it was possible to identify the geometry that maximized the performance of the OWC WEC, with (H1/L)O=0.3430, yielding PH(RMS)=56.66 W. In contrast, the worst geometry was obtained with H1/L=0.1985, where PH(RMS)=28.19 W. Therefore, the best case is 101% more efficient than the worst one.

Numerical and geometric investigation of pool boiling heat transfer in cavities with isothermal rectangular corrugations

This numerical work presents a geometrical investigation of a corrugated isothermal surface placed in a two-dimensional cavity subjected to unsteady, turbulent pool boiling flows. The main purposes are maximizing the heat transfer rate between the isothermal surface and the surrounding water flow, and the volume of vapor generated into the cavity. The geometric investigation followed the constructal design method, being the ratio Hi/Li (i = 1, 2 or 3) of the corrugations varied for three different numbers of corrugations: N = 1, 2, and 3, keeping constant the corrugations area. The volume of fluid (VOF) and Lee's evaporation-condensation models are used to estimate the volume fractions of water vapor/liquid and interfacial mass transfer. The unsteady Reynolds Averaged Navier Stokes (URANS) continuity, momentum and conservation of energy equations, and volume fraction transport equation, are solved using the finite volume method (FVM) available in software Ansys FLUENT. For closure of turbulence, the k - ɛ model is adopted. For validation of the model, the heat flux and convection heat transfer coefficient obtained for a pool boiling bared surface case are compared with Rohsenow's correlations, and differences lower than 7.0 % are reached. Results indicated a strong influence of the ratio Hi/Li and number of corrugations (N) in the heat transfer rate per unit depth (qs) and dimensionless volume of vapor (Vdim) generated into the cavity. The highest intrusion of the corrugations led to the generation of few large and many small scales, benefiting the thermal performance, regardless of the performance indicator employed. The optimal configuration, N = 3 and H3/L3 = 2.0 improved 49 % and 188 % the Vdim and qs compared with the worst corrugated case, showing the importance of the geometry of the corrugation in this problem.

A Method for Predicting the Impact of Labor Education at Institutions of Higher Education Based on a Two-Stage Clustering Algorithm

In order to develop more accurate teaching plans and methods based on students’ characteristics and needs, and improve the effectiveness of labor education, a two-stage clustering algorithm based method for predicting the effectiveness of labor education in universities is studied. A two-stage clustering algorithm-based method for predicting the effectiveness of labor education in universities has been proposed. Through in-depth analysis of labor education data in universities, principal component analysis (PCA) was used to select 16 most representative variables from the original features. Subsequently, self-organizing map (SOM) neural network was used for preliminary clustering, and the optimal number of clusters was determined to be 8. Furthermore, the K-means clustering algorithm performs fine clustering based on the initial centers provided by SOM, significantly improving the stability and accuracy of clustering. At the same time, processing the outliers in the clustering results has reduced their impact on the clustering effect. Finally, the processed data was input into an improved XGBoost prediction model, which achieved a true positive rate (TPR) of over 85% on the ROC curve when predicting the effectiveness of labor education in universities. While maintaining a low false positive rate (FPR), the model outperformed other comparison methods significantly. This achievement not only validates the effectiveness of the proposed method in feature selection, clustering optimization, and prediction model construction, but also confirms its ability to accurately predict the labor education effectiveness of students in different grades, providing strong support for higher education institutions to optimize teaching plans and improve educational outcomes.

Maximal heat transfer density from cross‐flow heat exchanger with tapered fins using constructal design method

AbstractTapered fins are widely used in heat sinks cooled by forced convection. In this study, maximal forced convective heat transfer from a set of tapered fins in cross‐flow is investigated based on the constructal design method. The tapering of the fins is done for a three‐fin base‐to‐tip ratio (taper ratio). The first taper ratio is TR = 0.5 (fin base < fin tip), the second taper ratio is TR = 1 (fin base = fin tip, straight fin), and the third taper ratio is TR = 2 (fin base > fin tip). In all these cases, the fin length is constant. The fins are heated at constant surface temperature and they are cooled by cross‐flow. A constant pressure difference pushes the cross‐flow toward the fins. The Bejan number ranges from 105 to 107. The forced convective heat transfer density is maximized from the fins for the three taper ratios, and a comparison between them is carried out. The maximization is conducted by numerical and scale analysis. In the numerical analysis, the pressure‐driven flow equations (continuity, momentum, and energy) are solved by means of the finite volume method. In the scale analysis, two extremes are considered. The first extreme is for TR < 1, and the second extreme is for TR > 1. These two extremes are intersected to find the maximal forced convective heat transfer density. The results obtained from numerical and scale analysis confirmed that the maximal forced convective heat transfer density occurs for straight fins (TR = 1) in the whole range of the Bejan number. The heat transfer density from straight fins (TR = 1) is higher than that of tapered fins (TR = 2) by 45.2%, and it is higher than that of tapered fins (TR = 0.5) by 52.7% at Be = 107.

Optimizing solar collector through constructal design of Y-shaped networks with power-law nanofluid flow

Solar energy is a plentiful and renewable resource; therefore, its use through clean energy technologies is necessary to reduce the consumption of fossil fuels and their consequent impact on the environment. Solar thermal energy can be harnessed to collect thermal energy through solar collectors for domestic or industrial use. In this work, a Y-shaped duct network was developed for a solar collector with a disc-shaped body. The shape and size of the network were defined using the constructal design method to harvest greater thermal energy from exposure to irradiance. For this purpose, the working fluid was a nanofluid composed of nanoparticles and a base fluid with non-Newtonian behavior, as described by the power-law model. The properties of the nanofluid were described using theoretical models that depend on the volume fraction of the nanoparticles in the base fluid. The aspect ratio of each element in the network was defined under the condition of minimum thermal resistance. For the shear-thinning nanofluid with a volume fraction of 0.04, the thermal resistance was reduced by 5.5% with an aspect ratio 5.8% lower than that of the pure base fluid. The minimum thermal resistance of the Y-shaped network arrangement occurs for shear-thinning fluids, whereas thermal resistance increases for shear-thickening fluids. This effect can be observed at different levels of branching and in pumping power. Results show that increasing the volume fraction of the nanofluid and base fluid with shear-thinning behavior aids in achieving minimal thermal resistance more easily with a smaller network. The above offers new design options for devices optimized to improve solar harvesting and thermal management, through shape, structure and rheological characteristics.

Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.

The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study. This study was a retrospective cohort study. The study population was 1855 participants who underwent consecutive physical examinations at the First Affiliated Hospital of Soochow University between 2018 and 2022.The dataset included medical history, physical examination, and biochemical index test results. The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. The performance of these models is evaluated by an area under the receiver operating characteristic (ROC) curves (AUC), calibration curves and decision curve analysis (DCA). To interpret the best-performing model, the Shapley Additive exPlanation (SHAP) Plots was used in this study. The training/validation dataset consists of 1,855 individuals from the First Affiliated Hospital of Soochow University, yielded significant variables following selection by the Boruta algorithm and logistic multivariate regression analysis. These significant variables included systolic blood pressure (SBP), fatty liver, waist circumference (WC) and serum creatinine (Scr). The XGBoost model outperformed the other models, demonstrating an AUC of 0.7391 in the validation set. The XGBoost model was composed of SBP, fatty liver, WC and Scr may assist doctors with the early identification of IFG in middle-aged and elderly people.

Obstructed Branching Networks: A Constructal Approach in Fluid Flow Investigation

Tree flow networks are common in both natural and manufactured systems. The organization of the flow hierarchy passes through the dimensional evolution of the form that is linked to the function. Thus, the objective of comparing bifurcated tube networks obtained by the constructal design method, where part of the structure is obstructed, aims to understand the effects on fluid flow and the prediction of evolutionary deviations in its function. This study compares designs of 3D tree networks with various homothety reduction factors for sizes, having tubes obstructed in some locals of the network. In this computational fluid dynamics study, the geometric constraint applied to these networks is the equal total volume of tubes at each branch level. The evaluation is based on the flow resistance of the networks. This study shows, among other things, that the performance of tree designs is highly dependent on geometric characteristics and the branching level where the obstructions are applied. The effect of the number and position of tubes obstructed in the network, as well as the alignment of the tubes across the network branching levels, on the asymmetry of fluid flow through the network is also studied. It is recommended that the results presented be considered when designing networks for engineering systems.

Constructal Design Applied to an Overtopping Wave Energy Converter Locate on Paraná Coast in Brazil

This research aims a numerical and geometrical investigation about an Overtopping Device Wave Energy Converter (OTD-WEC). Constructal Design method was employed to perform a geometric evaluation of this OTD-WEC submitted to ocean waves with similar characteristics of the coastal region of Paraná State – Pontal do Paraná city in southern Brazil, located 41 km from the coast and 30 m water deep. It was analyzed two different significant wave heights, HS1 = 1.25 m and HS2 = 1.50 m. In each set, it were simulated 10 cases with different ramp angles, totaling 20 numerical simulations. The main goal was to evaluate the influence of the ramp angle on the water mass flow rate entering the reservoir. Therefore, through the Construtal Design, the degree of freedom (DOF) H1/L1 (ratio between the height and length of the ramp) was varied, considering a constant area of the ramp. For the numerical analysis, a two-dimensional computational model was employed using the commercial software Ansys Fluent, which is based on the Finite Volume Method (FVM). The multiphase Volume of Fluid (VOF) model was applied to tackle with the water-air interaction. The results showed that the best geometric configurations happened for those cases with smaller ramp angles. In addition, a reconfiguration of the ramp angles, keeping the area fixed, allowed a significant improvement in the OTD-WEC available power.

Constructal Design Applied to an Overtopping Wave Energy Converter Locate on Paraná Coast in Brazil

This research aims a numerical and geometrical investigation about an Overtopping Device Wave Energy Converter (OTD-WEC). Constructal Design method was employed to perform a geometric evaluation of this OTD-WEC submitted to ocean waves with similar characteristics of the coastal region of Paraná State – Pontal do Paraná city in southern Brazil, located 41 km from the coast and 30 m water deep. It was analyzed two different significant wave heights, HS1 = 1.25 m and HS2 = 1.50 m. In each set, it were simulated 10 cases with different ramp angles, totaling 20 numerical simulations. The main goal was to evaluate the influence of the ramp angle on the water mass flow rate entering the reservoir. Therefore, through the Construtal Design, the degree of freedom (DOF) H1/L1 (ratio between the height and length of the ramp) was varied, considering a constant area of the ramp. For the numerical analysis, a two-dimensional computational model was employed using the commercial software Ansys Fluent, which is based on the Finite Volume Method (FVM). The multiphase Volume of Fluid (VOF) model was applied to tackle with the water-air interaction. The results showed that the best geometric configurations happened for those cases with smaller ramp angles. In addition, a reconfiguration of the ramp angles, keeping the area fixed, allowed a significant improvement in the OTD-WEC available power.

Causal Modeling and Thermodynamics: Towards a new convergence of the two fields

In 1824, Nicolas Léonard Sadi Carnot paid for the publication of his first book. Unfortunately it sparked little interest, and the young engineer never published another. In quick succession, Carnot served in the military, suffered from scarlet fever, mania, and cholera, and passed away in obscurity at age 36. Two centuries have elapsed since Carnot published his only book. Recognition came later. In particular, Carnot's reasoning inspired scientists to formulate the first and second laws of Thermodynamics. The new science that has emerged around these physical laws is nothing short of breathtaking. Yet, with success and growth, critical attention and skepticism have followed. In 1924, Louis de Broglie lauded the first law of Thermodynamics, while remaining more reserved to wards the second. The first law builds on a long history rooted in Causal Modeling, while the second does less so. Today, physicists such as Adrian Bejan continue praising Thermodynamics but contend that some formulations of the second law may have attracted broken science. The present article revisits this history in an attempt to cut through some of the fog. As an outcome of this re-evaluation, the article outlines a new convergence of Thermodynamics and Causal Modeling.

Constructal design for a composite structure with an “X”-shaped high conductivity channel and a “T”-shaped fin

Based on constructal theory, a novel composite heat dissipation structure model with an “X”-shaped high conductivity channel and an exteriorly connected “T”-shaped fin within a square heat-generation body is developed. The optimal constructs with minimum maximum temperature difference are obtained through multi-variable optimization. The impacts of the ratio of high-to-low thermal conductivity and area proportion of the high conductivity channels on the optimal constructs are investigated. The research indicates that enhancing the conductivity ratio and area proportion can result in a notable reduction in the maximum temperature of square heat-generation body. For instance, when the ratio of high-to-low thermal conductivity is raised from 100 to 600, the quintic minimum dimensionless maximum temperature difference experiences a reduction of 48.08 %. Compared with one-degree-of-freedom, three-degree-of-freedom and five-degree-of-freedom optimizations, two-degree-of-freedom and four-degree-of-freedom optimizations can significantly reduce the dimensionless maximum temperature difference in the square heat-generation body. The minimum dimensionless maximum temperature difference after five-degree-of-freedom optimization is 9.44 % lower than that after one-degree-of-freedom optimization. Furthermore, when comparing the composite heat dissipation structure model made up of “arrow”-shaped high conductivity channel and exteriorly connected “T”-shaped fin, the composite heat dissipation structure model herein exhibits a significant reduction in hot spot temperature.

Constructal design of a hybrid heat sink with rectangular microchannel and porous fin in a 3D electronic device with artificial neural-network, NSGA-II and different decision-making methods

Constructal theory is used to build a hybrid heat-sink model with rectangular microchannel and porous fin in a 3D electronic device. Firstly, constructal design is conducted with objective of minimizing linear weighted sum of maximum temperature-difference and pump power consumption with fixed total volumes of 3D electronic device, porous fin and microchannel. The optimal microchannel aspect ratio and elemental number are gained. Secondly, multi-objective-optimization is conducted based on NSGA-II, which uses the “gamultiobj” function of Matlab's artificial neural-network toolbox. The findings indicate that when microchannel aspect ratio and elemental number are 0.29 and 38, respectively, complex function achieves its double minimum of 0.810, which is decreased by 19.0% compared to its original value. Therefore, optimizing microchannel aspect ratio and elemental number simultaneously contributes to improving the comprehensive performance of 3D electronic device. Based on LINMAP, TOPSIS and Shannon Entropy decision-making strategies, the multi-objective solution set is compared. The third decision-making method has the smallest deviation index. The corresponding optimal microchannel aspect ratio and elemental number are 0.35 and 32, respectively, which can be used as the optional structure design scheme. The research findings can serve as a new theoretical reference for designing hybrid cooling system in a 3D electronic device.

3D transient numerical analysis of PCM dendritic cylindrical heat sinks designed by constructal theory

The phase change material (PCM) heat sink represents an important cooling tool for many applications. The presence of a heat sink in devices requires additional space to occupy it. This led to modeling this space to obtain the highest thermal performance with the smallest size. Among the modern modeling methods is constructal theory. This work presents a numerical modeling of the performance of cylindrical phase change material heat sinks with branched heat transfer fluid tubes in the manner of arterial and venous according to constructal theory. The aim of numerical simulations is to demonstrate the effect of growth in design by increasing the number of branches on thermal performance and melting time and the extent to which this is reflected in pressure drops. Several branching configurations are studied for comparison purpose. The governing continuity, momentum and energy equations were solved using the computational fluid dynamics method. Three-dimensional transient simulations of the melting process of phase change materials were performed using an enthalpy and porosity model. Four combinations of constructal dendritic tubes were considered. The results show that the proposed dendritic PCM heat sink significantly reduces the melting time of PCM compared to the traditional heat sink consisting of a shell and single tube up to 69.1 %. The drop in heat transfer fluid temperature at the outlet decreased by up to 51.7 %. The results proved also the heat transfer enhancement is governed with pressure drop increase because of changing branches configuration. Finally, this paper demonstrated the ability of constructal theory to provide promising solutions for cylindrical heat sinks of phase change materials in various applications.

Recent Progress in Aqueous Zinc-ion Batteries at High Zinc Utilization.

Aqueous zinc ion batteries (AZIBs) are promising candidates for next-generation energy storage systems due to their low cost, high safety, and environmental friendliness. As the critical component, Zn metal with high theoretical capacity (5855 mAh cm-3), low redox potential (-0.763 V vs standard hydrogen electrode), and low cost has been widely applied in AZIBs. However, the low Zn utilization rate (ZUR) of Zn metal anode caused by the dendrite growth, hydrogen evolution, corrosion, and passivation require excess Zn installation in current AZIBs, thus leading to increased unnecessary battery weight and decreased energy density. Herein, approaches to the historical progress toward high ZUR AZIBs through the perspective of electrolyte optimization, anode protection, and substrate construction are comprehensively summarized, and an in-depth understanding of ZUR is highlighted. Specifically, the main challenges and failure mechanisms of Zn anode are analyzed. Then, the persisting issues and promising solutions in the reaction interface, aqueous electrolyte, and Zn anode are emphasized. Finally, the design of 100% ZUR AZIBs free of Zn metal is presented in detail. This review aims to provide a better understanding and fundamental guidelines on the high ZUR AZIBs design, which can shed light on research directions for realizing high energy density AZIBs.

Towards discovery: an end-to-end system for uncovering novel biomedical relations.

Biomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively addressing this task holds the potential to improve knowledge discovery by automating the extraction of relations from research manuscripts. The first track in the BioCreative VIII competition extended the scope of this challenge by introducing the detection of novel relations within the literature. This paper describes that our participation system initially focused on jointly extracting and classifying novel relations between biomedical entities. We then describe our subsequent advancement to an end-to-end model. Specifically, we enhanced our initial system by incorporating it into a cascading pipeline that includes a tagger and linker module. This integration enables the comprehensive extraction of relations and classification of their novelty directly from raw text. Our experiments yielded promising results, and our tagger module managed to attain state-of-the-art named entity recognition performance, with a micro F1-score of 90.24, while our end-to-end system achieved a competitive novelty F1-score of 24.59. The code to run our system is publicly available at https://github.com/ieeta-pt/BioNExt. Database URL: https://github.com/ieeta-pt/BioNExt.

Target Selection of CAR-T Therapy in Acute Myeloid Leukemia--Review

Chimeric antigen receptor (CAR) T cell therapy, one of the most promising tumor treatments, combines the targeted recognition of antigen and antibody with the killing effect of T cells. CAR-T has shown a strong therapeutic effect in lymphoid tumors and been applied in clinical practice. However, in the treatment of acute myeloid leukemia (AML), no effective and specific target like CD19 in lymphoid tumors has been found. Therefore, the key research direction is to try multiple probabilities and use optimization strategies to enhance efficacy and reduce toxicity. This review introduces the latest research progress of AML targets in CAR-T therapy in recent years, analyzes the related problems that need to be solved at present, and summarizes the optimization construction strategies mentioned in the research. Hope it can provide reference for related research and clinical application of related product.

Comprehensive indicators optimization construction and lithology measurement while drilling based on drilling parameters

Comprehensive indicators optimization construction and lithology measurement while drilling based on drilling parameters

LSTM network optimization and task network construction based on heuristic algorithm

This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.

Thermodynamic design and evolution of unmanned aerial vehicles

This paper reviews the recent evolution of unmanned aerial vehicles (UAVs) through the lens of thermodynamic design. Emphasis is placed on using constructal theory in order to understand the relationship between different degrees of freedom within these complex dynamic systems. Size scale, the way in which UAVs are categorized based on weight and dimensions and one of the most significant factors in the design of any UAV, is analyzed in terms of its relationship to energy consumption. Specifically, constructal theory is used to predict the minimum size at which any UAV will need to be tethered in order to support its energy requirements.

Impacts of climate and land use change on terrestrial carbon storage: A multi-scenario case study in the Yellow River Basin (1992–2050)

Currently, socioeconomic development and climate change pose new challenges to the assessment and management of terrestrial carbon storage (CS). Accurate prediction of future changes in land use and CS under different climate scenarios is of great significance for regional land use decision-making and carbon management. Taking the Yellow River Basin (YRB) in China as the study area, this study proposed a framework integrating the land use harmonization2 (LUH2) dataset, the patch-generating land use simulation (PLUS) model, and the integrated valuation of ecosystem services and trade-offs (InVEST) model. Under this framework, we systematically analyzed the spatiotemporal evolution characteristics of land use and their impact on CS in the YRB from 1992 to 2050. The results showed that (1) CS was highest in forestland and lowest in construction land, with a spatial distribution of high in the south and low in the north. From 1992 to 2020, construction land, forestland, and grassland increased while cropland decreased, reducing the total CS by 74.04 Tg. (2) From 2020 to 2050, under SSP1–2.6 scenario, forestland increased by 158.87 %; under SSP2–4.5 scenario, unused land decreased by 65.55 %; and under SSP5–8.5 scenario, construction land increased by 13.88 %. By 2050, SSP1–2.6 scenario exhibited the highest CS (8105.25 Tg), followed by SSP2–4.5 scenario (7363.61 Tg), and SSP5–8.5 scenario was the lowest (7315.86 Tg). (3) Forestland and construction land were the most critical factors affecting the CS. Shaanxi and Shanxi had the largest CS in all scenarios, and Qinghai had a huge carbon sink potential under SSP1–2.6 scenario. Scenario modeling demonstrated that future climate and land-use changes would have significant impacts on terrestrial CS in the YRB, and green development pathways could strongly contribute to meeting the dual‑carbon target. Overall, this study provides a scientific basis for promoting low-carbon development, land-use optimization, and ecological civilization construction in YRB, China.