Engineering reports : open access最新文献

{"title":"A State-of-the-Art Study of Vortex-Excited Vibration Problems in Large-Span Flat Steel Box Girder Bridges","authors":"Sai Gong,&nbsp;Peng Tang,&nbsp;Shucheng Yang,&nbsp;Qiwen Jin,&nbsp;Jiawu Li","doi":"10.1002/eng2.71091","DOIUrl":"https://doi.org/10.1002/eng2.71091","url":null,"abstract":"<p>Windborne vibration of large-span bridges occurs from time to time, especially the abnormal windborne vibration of urban bridges, which often causes public panic. The paper consists of four parts. Specifically, the first part analyzes the wind challenges of large-span bridges, including the formation mechanism of wind-induced vibration disaster of large-span bridges and typical cases, as well as the limitations of the current wind design codes. The second part reviews the analysis models and experimental techniques of vortex-excited vibration (VIV) of large-span bridges, including the development and innovation of the theoretical models and breakthroughs and challenges of the experimental techniques. The third part, which is also the focus of the paper, presents the existing scientific problems and technical bottlenecks of VIV research, including the theoretical modeling level and probabilistic analysis level. In the fourth part, alternative models and new ideas of probabilistic models for the analysis of VIV problems of large-span flat steel box girder bridges are proposed, which hopefully can provide reference for the further solving of the problem of VIV of large-span flat steel box girder bridges.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Improved YOLOv8 Approach for Micro-Motion Trajectory Modeling and Real-Time Athlete Motion State Estimation","authors":"Yu Ao,&nbsp;Wu Guancheng,&nbsp;Chen Xi","doi":"10.1002/eng2.71070","DOIUrl":"https://doi.org/10.1002/eng2.71070","url":null,"abstract":"<p>There are problems of insufficient accuracy in capturing small motion trajectories and delay in real-time motion state estimation in the training of track and field athletes. Based on the improved YOLOv8 framework, this paper introduces 3D convolution to construct a spatiotemporal feature pyramid (ST-FPN) and embeds an optical flow-guided cross-frame feature alignment layer for motion compensation. A dual-branch detection head is designed: the main branch uses the Anchor-Free mechanism to detect the overall contour, and the auxiliary branch locates the joint points through key point heat map regression and uses the attention mechanism to fuse features. An LSTM-Transformer hybrid trajectory prediction model is constructed to encode the motion parameters of historical frames, combine the self-attention mechanism to capture long-range dependencies, and incorporate Kalman filtering for kinematic constraints. Experiments show that the detection accuracy ([email protected]) reaches 89.6%, the average accuracy of joint positioning is 93.75%, the trajectory continuity retention rate is 96.4%, and the end-to-end latency is 25.8 ms on Jetson AGX Xavier, providing real-time data support for scientific training.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Student Performance Prediction Using the Bi-LSTM Technique Focused on the Indian Higher Education Context Using a Synthetic Dataset","authors":"Sudhindra B. Deshpande,&nbsp;Praveen U. Kalkundri,&nbsp;Pratijnya Ajawan,&nbsp;Jemal Abate Jilo,&nbsp;Ravi U. Kalkundri,&nbsp;Raghavendra Jadhav","doi":"10.1002/eng2.71084","DOIUrl":"https://doi.org/10.1002/eng2.71084","url":null,"abstract":"<p>Student performance prediction is a critical task for improving educational outcomes and enabling data-driven decision-making in higher education. This study proposes a context-specific predictive framework for Indian higher education that integrates AISHE institutional indicators with student-level behavioral data through multi-level data fusion, incorporates SHAP-based interpretability to support policy and intervention decisions, and employs a Bi-LSTM + Nadam architecture to achieve strong predictive performance. The framework integrates multi-level heterogeneous data, combining institutional indicators from AISHE with student-level academic, behavioral, and demographic features. To address the lack of unified large-scale datasets, a synthetic data generation approach is employed to simulate multi-institutional environments with temporal student records, supported by a mapping mechanism aligning student and institutional attributes. Experimental results demonstrate that the proposed model achieves 93.1% accuracy, significantly outperforming traditional machine learning approaches by approximately 13%. The model maintains precision of 93.0%, recall of 93.1%, and F1-scores of 93% across all performance categories, confirming robust and balanced classification. Convergence analysis shows that Nadam enables faster and more stable training, while the Bi-LSTM architecture improves performance by 1.9% over unidirectional LSTM by capturing bidirectional temporal dependencies. Feature importance analysis reveals that academic and behavioral factors dominate prediction, with GPA, attendance, and assignment submission contributing approximately 42.7% of the total normalized SHAP importance, while demographic factors have minimal influence, ensuring reduced bias. ROC-AUC analysis confirms excellent discrimination across all categories, with a macro-average AUC of 0.993 (range: 0.989–0.997), fully consistent with the reported accuracy and recall metrics. The proposed framework is suitable for early warning systems, targeted interventions, and academic decision support, making it highly applicable in real-world educational settings.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetically Controlled Mixed Convection Nanofluid Flow With Slip and Suction: Implications for Thermal System Design","authors":"Mazhar Hussain,&nbsp;Muhammad Mansoor,&nbsp;Iqra Amer,&nbsp;Muhammad Zohaib Hanif,&nbsp;Mubashir Qayyum,&nbsp;Gilbert Chambashi","doi":"10.1002/eng2.71090","DOIUrl":"https://doi.org/10.1002/eng2.71090","url":null,"abstract":"<p>This study numerically investigates mixed convective magnetohydrodynamic nanofluid flow over a nonlinearly stretching sheet with wall suction, velocity slip, and thermal slip. Particular emphasis is placed on the microscopic nanoparticle transport mechanisms of Brownian motion and thermophoresis and their coupling with macroscopic heat and mass transport. The governing partial differential equations, incorporating magnetic, buoyancy, thermal-radiation, and viscous-dissipation effects, are reduced to a coupled system of nonlinear ordinary differential equations via similarity transformations and solved numerically using MATLAB's bvp4c boundary-value solver. The principal novelty lies in the simultaneous treatment of magnetic field, mixed convection, thermal radiation, viscous dissipation, Lewis number, and combined velocity/thermal slip together with suction in a single nonlinearly stretching-sheet configuration, and in the physical interpretation of how these mechanisms jointly govern the momentum, thermal, and concentration boundary layers. Results show that an increasing magnetic field suppresses the Lorentz-force-retarded velocity field while increasing temperature and concentration through reduced convective cooling; enhanced mixed convection accelerates the flow, thinning the thermal and concentration boundary layers; thermal radiation and viscous dissipation raise the temperature profile; thermal slip lowers the temperature profile, while velocity slip and suction respectively increase and uniformly reduce the velocity, temperature, and concentration fields. Brownian motion and thermophoresis are shown to influence the temperature and concentration fields in contrasting ways. These findings offer quantitative guidance for selecting magnetic field strength, slip conditions, and suction rates in the design of nanofluid-based cooling and heating systems.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Discrimination of Acoustic Emission Signal Transmission Modes for Void Damage in Concrete-Filled Steel Tubes","authors":"Peng Tang,&nbsp;Ning Zhang","doi":"10.1002/eng2.70993","DOIUrl":"https://doi.org/10.1002/eng2.70993","url":null,"abstract":"<p>Concrete-filled steel tubes (CFST) have been extensively employed in large-scale construction projects as high-performance composite structural members. Nevertheless, varying degrees of void damage frequently occur in CFST due to construction quality issues and environmental influences, which may potentially result in serious structural failures. This study proposes a method for detecting void damage in steel reinforced concrete using acoustic emission (AE) technology. AE testing was used to detect void damage in CFST, and the effects of different void rates and specimen sizes on AE signal characteristics were studied and explored. By conducting AE experiments and employing the first-wave arrival time parameter for signal analysis, four theoretical transmission modes of steel-concrete interface signals under void damage conditions were successfully identified. The experimental results showed that the void area significantly changed the propagation path of sound waves. By using signal conformity testing parameters to calculate and compare the predicted duration and measured duration of three theoretical modes in the void area, the final agreement between the average experimental duration and the theoretical duration of Path 2 was 87.41%. Considering the heterogeneity of concrete and the errors present in experiments, and that the increasing trend of test duration is consistent with theoretical values, this verifies the effectiveness of acoustic emission technology in damage identification and structural health monitoring.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70993","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RSM-Based Optimization and Sensitivity Analysis of Radiative MHD Tangent Hyperbolic Nanofluid Flow Over an Exponentially Stretching Porous Sheet With Variable Heat and Mass Fluxes","authors":"Mehari Fentahun Endalew,&nbsp;Xiaoming John Zhang,&nbsp;Habtamu Zegeye Alemu","doi":"10.1002/eng2.71087","DOIUrl":"https://doi.org/10.1002/eng2.71087","url":null,"abstract":"&lt;p&gt;This study investigates the steady, incompressible magnetohydrodynamic (MHD) flow of a tangent hyperbolic nanofluid over an exponentially stretching porous sheet considering variable heat and mass fluxes, thermal radiation, Brownian motion, thermophoresis, porous-medium resistance, and non-Newtonian effects. Similarity transformations are applied to convert the governing partial differential equations into a nonlinear system of ordinary differential equations, which is solved numerically using MATLAB's &lt;span&gt;bvp5c&lt;/span&gt;\u0000solver. The influences of suction/blowing, exponential stretching, magnetic field strength, Prandtl number, Schmidt number, Brownian motion, thermophoresis, thermal radiation, and tangent hyperbolic parameters on the velocity, temperature, and concentration fields are systematically examined. The results indicate that wall blowing increases the surface temperature and nanoparticle concentration, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ theta (0) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ϕ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ phi (0) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, by 130.8% and 98.3%, respectively, compared with suction conditions. Increasing the exponential stretching parameter &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ N $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; reduces &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ theta (0) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ϕ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ phi (0) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; by 73.3% and 62.0%, respectively. Thermophoresis enhances &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ theta (0) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 ","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149084093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Hybrid Deep Learning Approach for Robust Stator–Rotor Fault Identification in DFIGs Using FFT-Based Features and CNN–LSTM–Transformer Models Under Nonstationary Wind Turbulence","authors":"Mehdi Mohammadian Mehr,&nbsp;Mohsen Saniei,&nbsp;Mahmood Joorabian,&nbsp;Hossein Farzin","doi":"10.1002/eng2.71068","DOIUrl":"https://doi.org/10.1002/eng2.71068","url":null,"abstract":"<p>Reliable fault diagnosis in doubly fed induction generators (DFIGs) is essential for the stable and efficient operation of wind energy systems under nonstationary conditions caused by wind turbulence. This paper proposes a hybrid deep learning framework that combines FFT-based frequency-domain feature extraction with a CNN–LSTM–Transformer architecture for detecting and estimating the severity of electrical faults in DFIGs. The study focuses on stator inter-turn short-circuit faults and rotor winding open-circuit fault. A comprehensive dataset was generated using MATLAB/Simulink under diverse operating conditions, including multiple fault severities, realistic load profiles, and single and simultaneous fault scenarios. An overlapping sliding window technique was employed to capture transient fault signatures and enhance sensitivity to early-stage faults. The CNN component extracts discriminative spectral features, the LSTM models temporal dependencies, and the Transformer attention mechanism highlights fault-relevant patterns while reducing the influence of load variations. The proposed framework was evaluated against several deep learning models, including CNN, LSTM, CNN–LSTM, Temporal Convolutional Network (TCN), Transformer, and CNN–Transformer architectures using standard performance metrics. Validation results obtained from independent test datasets indicate that the proposed CNN–LSTM–Transformer model achieves the best performance, with lower prediction errors (MSE ≈ 0.017 ± 0.001) and higher coefficients of determination (<i>R</i>² up to 0.992 ± 0.001), consistently outperforming the comparative models across all diagnostic scenarios. These findings indicate that the proposed framework is a robust and effective solution for early fault detection and predictive maintenance in DFIG-based wind turbine systems.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-Dependent Settlement Model for Driven Piles Using Hybrid Finite Element-Artificial Neural Network and Regression Approaches","authors":"Gbênihon Céleste-Amour Kenoukon,&nbsp;Joseph Ng'ang'a Thuo,&nbsp;Kiplagat Chelelgo,&nbsp;Alphonce Ayado Owayo","doi":"10.1002/eng2.71049","DOIUrl":"https://doi.org/10.1002/eng2.71049","url":null,"abstract":"<p>The prediction of time-dependent pile settlement remains challenging due to the nonlinear behavior of saturated clay soils, consolidation, and pile setup effects. The complexity of this phenomenon is not fully captured by classical elasticity theories. This study develops a hybrid model for time-dependent pile settlement based on the mobilized stiffness of an instrumented field pile. A Finite Element Method (FEM) is combined with an Artificial Neural Network (ANN) to predict load-settlement curves at different times. Advanced performance metrics, such as the A₂₀, Scatter, and Agreement indices, confirm the robustness of the model. A regression-based methodology is subsequently used to derive the mobilized stiffness of the pile-soil system from the initial portion of the curves, revealing a logarithmic trend. A formulation is proposed by extending the elastic shaft stiffness of Randolph and Wroth's solution with a time-dependent amplification factor accounting for pile installation, geometric and scale effects. The rate of stiffness evolution is related to soil compressibility parameters, including void ratio, modified swelling and compression indices. Validation through laboratory model pile tests and comparison with existing analytical frameworks shows that the proposed model can reliably capture the time-dependent settlement and stiffness evolution within the working-load range.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning-Based Study on the Perception of Community Park Landscapes: An Empirical Analysis from Social Media Reviews","authors":"Hongqi Zhou,&nbsp;Jiahui Zhou,&nbsp;Yiduo Ha","doi":"10.1002/eng2.71009","DOIUrl":"https://doi.org/10.1002/eng2.71009","url":null,"abstract":"<p>This study employs machine learning methods, utilizing social media reviews as a data source, to explore the perceptual characteristics and dynamic changes of urban residents towards community park landscapes. Focusing on 25 community parks in Shanghai, 32,733 review texts from the Dianping platform spanning 2016–2025 were collected. After cleaning and word segmentation, a landscape perception corpus was constructed. The Latent Dirichlet Allocation (LDA) model was used for topic modeling of the unstructured text, identifying four core topics of residents' landscape perception: Comfort (40.49%), Landscape Attractiveness (24.04%), Recreational Experience (20.45%), and Cultural Aesthetics (15.01%). The study found that residents' perception of community parks exhibits a hierarchical structure dominated by environmental comfort, supplemented by landscape attractiveness and recreational experience, with cultural aesthetics being relatively weaker. Temporal analysis indicates that the comfort topic remained stable long-term, while landscape attractiveness and recreational experience showed periodic fluctuations influenced by seasonal landscapes, facility updates, and social events. From the perspective of resident perception, the study proposes that community parks should prioritize environmental comfort and accessibility, enhance distinctive landscapes and multifunctional activity spaces, and promote the integration of cultural connotations with daily usage scenarios. This paper validates the feasibility of using social media data and machine learning methods in landscape perception research, providing empirical evidence for the refined design and human-centered renewal of community parks.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient-Based Optimization Modeling of Total Entropy and Heat Transfer in Exothermic Blood Analog Gold Nanoparticle Suspension","authors":"Mojeed T. Akolade,&nbsp;Mathew F. Oyedotun,&nbsp;Michael O. Oni,&nbsp;Yusuf O. Tijani,&nbsp;Adeshina T. Adeosun,&nbsp;T. Sathiyaraj,&nbsp;Abiodun O. Ajibade,&nbsp;Mohamed M. Awad","doi":"10.1002/eng2.71011","DOIUrl":"https://doi.org/10.1002/eng2.71011","url":null,"abstract":"<p>The Casson-micropolar fluid model herein represents the microstructure (yield stress) and micro-rotational dynamics of engineered nanoparticles in blood-analog suspensions. We present the mathematical model for a Casson (blood)-gold nanoparticle suspension and perform gradient-based objective optimization to (i) maximize the convective heat-transfer coefficient and (ii) minimize total entropy generation. The computational framework of non-Newtonian exothermic Casson-micropolar fluid with variable viscosity and thermal conductivity is implemented via the spectral method, while the optimization scheme employs the multi-start Nelder–Mead methodology to simulate the decision variables by random restarts with local convergence and a derivative-free simplex method. Fluid velocity decreases with higher nanoparticle volume fractions but increases with the Frank-Kamenetskii parameter, particularly near the convective slippery wall, and the thermodynamic irreversibility escalates with the Frank-Kamenetskii parameter near the thermally insulated wall, whereas increasing the nanoparticle volume fraction and thermal conductivity effectively minimizes entropy generation. The least contribution of the variable viscosity, maximum values of thermal conductivity, Kamenetskii parameter, higher convective heating, continuous injection of nanoparticle volume fraction, and the avoidance of thermal radiation have the greatest impact on minimizing the disorderliness in the considered geometry. Maximizing the system heat transfer, the dominant contribution of nanoparticle fraction, thermal radiation, convective heat transfer, and variability of the thermal conductivity is required.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.71011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}