Applications in engineering science最新文献

Transitional behaviour prediction in iron tailings via artificial intelligence 基于人工智能的铁尾矿过渡行为预测

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-27 DOI: 10.1016/j.apples.2026.100302

Ismail Adeniyi OKEWALE, Hendrik GROBLER

The significant contributions of mining of minerals to the development of any nation make the generation of tailings inevitable and therefore, understanding their characteristics is vital. The contribution of engineering granulometric signatures to the aspect of behaviour called transitional mode (non-convergent) is also crucial. This work presents the artificial intelligence based study for the prediction of transitional behaviour in iron tailings considering engineering granulometric indices. This was achieved by conducting laboratory tests on dry compacted DC, wet compacted WC and slurry SL iron tailings and re-analysis of data from previous studies to determine transitional behaviour as well as predicting their behaviour using artificial neural network and adaptive neuro-fuzzy inference system. The iron tailings are poorly graded with strong degree of transitional behaviour with m values ranging from 0.32 to 0.81. The ANN models for DC, WC, SL and combined samples CS have relative similar correlation values and ditto for the ANFIS models. This signifies that the influence of sample preparations is not significant. The ANN model is reliable and could be used to predict the transitional mode of behaviour in iron tailings. However, the ANFIS model is less suitable for the prediction of transitional behaviour in iron tailings. The ANN model has the best performance based on low model errors and highest accuracy in prediction.

矿物开采对任何国家的发展都有重大贡献，因此尾矿的产生是不可避免的，因此，了解它们的特性是至关重要的。工程颗粒特征对称为过渡模式（非收敛）的行为方面的贡献也至关重要。本文提出了一种基于人工智能的考虑工程粒度指标的铁尾矿过渡特性预测方法。这是通过对干压实DC、湿压实WC和浆状SL铁尾矿进行实验室测试，并重新分析先前研究的数据，以确定过渡行为，并使用人工神经网络和自适应神经模糊推理系统预测其行为来实现的。铁尾矿级配差，过渡性强，m值在0.32 ~ 0.81之间。DC、WC、SL和组合样本CS的人工神经网络模型具有相对相似的相关值，而ANFIS模型也具有相似的相关值。这表明样品制备的影响不显著。人工神经网络模型可靠，可用于预测铁尾矿的过渡模式。然而，ANFIS模型不太适合预测铁尾矿的过渡行为。人工神经网络模型具有模型误差小、预测精度高的特点。

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引用次数: 0

A damping-reinforced anti-vibration hammer based on negative-stiffness mechanisms for transmission lines 基于负刚度机构的输电线路阻尼增强减振锤

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-23 DOI: 10.1016/j.apples.2026.100301

Rui Xu , Houjie Huang , Haohao Zhang , Wenping Shi , Wenyuan Zhang

Anti-vibration hammers have been widely installed on the transmission lines to mitigate aeolian vibrations. However, accidents due to aeolian vibration still occur frequently in practical engineering. To improve the vibration control efficiency of transmission lines, this paper proposes a damping-reinforced anti-vibration hammer (DRH) based on negative-stiffness mechanisms. It provides an effective reference direction for the innovative design of anti-vibration hammers. First, a structural scheme is introduced for DRH, along with the derivation of their vibration equations and mechanical impedance. Then, the vibration equations of a transmission line equipped with anti-vibration hammers are derived, and these equations are then solved using the finite difference method. A numerical simulation and a comparative study are conducted to verify the effectiveness of the proposed DRH. The simulation results indicate that the proposed DRH exhibits a remarkable suppression effect on the vibration of the transmission line and has the potential to improve its adaptability to installation positions. A physical prototype of the DRH has been fabricated, and an experimental platform has been established to measure its power dissipation. The results demonstrate that within the 20–60 Hz scanning window, the conductor equipped with the DRH prototype consistently dissipates more power than the same conductor without any damper.

在输电线路上广泛安装防振锤以减轻风振。然而，在实际工程中，由风振引起的事故仍时有发生。为了提高输电线路的振动控制效率，提出了一种基于负刚度机理的阻尼增强抗振锤。为抗振锤的创新设计提供了有效的参考方向。首先，介绍了DRH的结构方案，推导了DRH的振动方程和力学阻抗。在此基础上，推导了装有防振锤的输电线路的振动方程，并用有限差分法对其进行了求解。通过数值模拟和对比研究验证了该方法的有效性。仿真结果表明，所提出的DRH对输电线路的振动有明显的抑制效果，并有可能提高其对安装位置的适应性。制作了DRH的物理样机，并建立了测量其功耗的实验平台。结果表明，在20-60 Hz的扫描窗口内，安装DRH原型的导体始终比未安装阻尼器的导体耗散更多的功率。

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引用次数: 0

PCA and CNN-based detection and classification of faults in distribution network with distributed energy resources 基于PCA和cnn的分布式能源配电网故障检测与分类

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-22 DOI: 10.1016/j.apples.2026.100300

Nilesh Chothani , Ishan Desai , Choon Kit chan , Subhav Singh , Deekshant Varshney , Nithesh Naik

Distributed Energy Resources (DERs) have been preferred to fulfil load demand in the Distribution Network (DN) for the past few years because of advantages like power loss reduction, improvement in reliability and voltage regulation. Integrating DERs in the distribution network increases normal as well as fault current, alters existing protection coordination and introduces complex dynamics that necessitate modern fault detection and classification techniques to ensure reliable operation. This article proposes a hybrid approach combining Principal Component Analysis (PCA) and Convolutional Neural Networks (CNN) for Fault Detection and Classification (FDC) in Distribution Networks with DERs. PCA is employed for feature extraction that captures fault-related patterns while mitigating noise, and computational complexity and fault classification with high accuracy is performed by CNN, which leverages its deep learning capabilities. The proposed method is validated using a modified IEEE 9-Bus distribution network, while data was generated through PSCAD/EMTDC software with different cases. The developed Results demonstrate that the hybrid PCA and CNN framework-based scheme achieves superior fault detection sensitivity and classification accuracy compared to other existing methods, with robust performance under different conditions. This approach offers an efficient solution for enhancing the reliability and resilience of modern distribution networks.

近年来，分布式能源（DERs）因其具有降低功率损耗、提高可靠性和电压调节等优点而成为满足配电网（DN）负荷需求的首选。在配电网中集成DERs增加了正常和故障电流，改变了现有的保护协调，并引入了复杂的动力学，需要现代故障检测和分类技术来确保可靠运行。本文提出了一种将主成分分析（PCA）与卷积神经网络（CNN）相结合的配电网络故障检测与分类方法。采用PCA进行特征提取，在降低噪声的同时捕获故障相关模式，CNN利用其深度学习能力进行计算复杂度和高精度的故障分类。采用改进的IEEE 9总线配电网对该方法进行了验证，并通过PSCAD/EMTDC软件生成了不同情况下的数据。研究结果表明，基于PCA和CNN框架的混合故障检测方案在不同条件下均具有较好的鲁棒性，具有较好的故障检测灵敏度和分类精度。该方法为提高现代配电网的可靠性和弹性提供了一种有效的解决方案。

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引用次数: 0

Mechanics and thermodynamics: A link between the two theories 力学和热力学：两个理论之间的联系

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-21 DOI: 10.1016/j.apples.2026.100297

Henri Gouin

In this note, we analyse the relationships that should govern the use of thermodynamics in fluid mechanics in a way that we believe is understandable to mathematicians. We also aim to better define the reasons why mechanics and thermodynamics must be correctly linked by showing that the principle of virtual work expressed using a specific internal energy is perfectly suited to fluid mechanics problems, provided that a well-chosen internal energy is proposed.

在这篇文章中，我们以一种数学家可以理解的方式分析了流体力学中热力学应用的关系。我们还旨在通过展示使用特定内能表示的虚功原理完全适合于流体力学问题，从而更好地定义力学和热力学必须正确联系的原因，只要提出了一个精心选择的内能。

引用次数: 0

Thermal performance and entropy minimization of magnetohydrodynamic flow in a triangular domain with a rotating solid cylinder 旋转固体圆柱体三角形区域磁流体动力流的热性能和熵最小化

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-19 DOI: 10.1016/j.apples.2026.100299

Md. Tusher Mahmud, Fayes Us Shoaib, Sumon Saha

Conjugate magnetohydrodynamic (MHD) mixed convection heat transfer in an isosceles triangular fluid domain with a heat-conducting spinning internal cylinder has been numerically examined in this study. The enclosure is filled with air, and the solid cylinder is made of low-carbon steel. A steady magnetic field is imposed in the domain, resulting in a magnetohydrodynamic effect on the system. The cylinder is placed at the center of the domain and spins in a clockwise or counterclockwise direction, inducing aiding or opposing forced flow within the system. The top surface of the domain is at an elevated temperature, while the right-tilted sidewall is at a reduced temperature, thereby enforcing a natural convection current. This conjugate thermal problem is mathematically modeled using the Navier-Stokes and energy equations, along with appropriate boundary and solid-fluid interface conditions. The numerical solutions are obtained by implementing the Galerkin finite element method. The present model is also validated before carrying out the parametric simulation for this study. The results are enumerated for the broad range of governing parameters, such as Reynolds number (31.62 ≤ Re ≤ 316.23), Richardson number (0.1 ≤ Ri ≤ 10), Grashof number (10³ ≤ Gr ≤ 10⁵), and Hartmann number (0 ≤ Ha ≤ 20) in terms of qualitative and quantitative evaluation of flow and thermal characteristics. The analysis reveals that introducing the MHD effect reduces heat transfer by approximately 5.3 % for both clockwise and counterclockwise rotations of the cylinder. It increases the average fluid temperature for clockwise rotation by up to 4.9 % and decreases it for counterclockwise rotation by approximately 2 %. However, the MHD effect reduces entropy generation as flow intensity increases, thereby reducing the irreversibility caused by fluid friction. Additionally, the clockwise rotation of the cylinder exhibits better heat transfer.

本文对等腰三角形流体域中具有导热旋转内柱的共轭磁流体混合对流换热进行了数值研究。外壳内充满空气，实心圆筒由低碳钢制成。在磁畴中施加稳定磁场，使系统产生磁流体动力学效应。圆柱体位于区域的中心，并以顺时针或逆时针方向旋转，在系统内诱导辅助或相反的强制流动。区域的顶表面温度升高，而右倾斜的侧壁温度降低，从而加强了自然对流。使用Navier-Stokes方程和能量方程，以及适当的边界和固-流界面条件，对该共轭热问题进行了数学建模。采用伽辽金有限元法得到了数值解。在进行本研究的参数化仿真之前，也对模型进行了验证。列举了广泛的控制参数，如雷诺数（31.62≤Re≤316.23）、理查德森数（0.1≤Ri≤10）、Grashof数（103≤Gr≤105）和Hartmann数（0≤Ha≤20）在流动和热特性的定性和定量评价方面的结果。分析表明，引入MHD效应，无论顺时针还是逆时针旋转，都能减少约5.3%的换热。顺时针旋转时，平均流体温度可提高4.9%，逆时针旋转时，平均流体温度可降低约2%。然而，随着流动强度的增加，MHD效应降低了熵产，从而降低了流体摩擦引起的不可逆性。此外，顺时针旋转的气缸表现出更好的传热。

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引用次数: 0

From compressive strength studies to predictive machine learning models: Rubberised concrete containing brick powder 从抗压强度研究到预测机器学习模型：含砖粉的橡胶混凝土

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-17 DOI: 10.1016/j.apples.2026.100298

David Sinkhonde , Derrick Mirindi , Tajebe Bezabih , Frederic Mirindi

Through waste tire rubber recycling and thanks to very simple pozzolanic materials such as brick powder (BP), it has been demonstrated that sustainable construction can be achieved during concrete production. Since concrete is a heterogeneous material with variable and complex behaviour by nature, it is important to incorporate machine learning (ML) models in forecasting its behaviour. Although ML models have been employed for predicting concrete containing BP and/or tire rubber aggregate (TRA), no studies have explored the use of adaptive boosting (AdaBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), gradient boosting regression (GBR), cluster regression, multilayer perceptron (MLP) and Gaussian process (GP) models to forecast the behaviour of rubberised concrete containing BP. In this comprehensive research, the foregoing ML algorithms are employed to forecast the compressive strength of rubberised concrete with BP. The findings illustrate that the GBR model is superior during predictions for the training, validation and testing stages, as evidenced by higher R² values ranging from 0.77 to 0.98. SHarpley Additive exPlanations (SHAP) analysis results reward age as the highest influential variable having an average SHAP value of 3.561, followed by tire rubber aggregate, coarse aggregate and cement. In addition, pronounced model performance differences are observed using the Taylor diagram analysis. The research also establishes a predominantly overfitting behaviour displayed by most folds during k-fold cross-validation. Regularisation of the model is proposed to prevent overfitting by penalising model complexity. The ML algorithms are competent to predict the compressive strength of rubberised concrete with BP well, thereby enabling practitioners and engineers to make versatile decisions regarding concrete mix designs and quality controls.

通过回收废旧轮胎橡胶，并利用非常简单的火山灰材料，如砖粉（BP），已经证明了在混凝土生产过程中可以实现可持续建筑。由于混凝土本质上是一种具有可变和复杂行为的异质材料，因此在预测其行为时结合机器学习（ML）模型非常重要。虽然ML模型已被用于预测含有BP和/或轮胎橡胶骨料（TRA）的混凝土，但没有研究探索使用自适应增强（AdaBoost）、轻梯度增强机（LightGBM）、极端梯度增强（XGBoost）、梯度增强回归（GBR）、聚类回归、多层感知器（MLP）和高斯过程（GP）模型来预测含有BP的橡胶混凝土的行为。在这项综合研究中，将上述ML算法用于BP预测橡胶混凝土的抗压强度。研究结果表明，GBR模型在训练、验证和测试阶段的预测中具有优越性，R2值在0.77 ~ 0.98之间。SHarpley加性解释（SHAP）分析结果显示，年龄是影响最大的变量，其平均SHAP值为3.561，其次是轮胎橡胶骨料、粗骨料和水泥。此外，使用泰勒图分析可以观察到明显的模型性能差异。该研究还建立了k-fold交叉验证期间大多数褶皱显示的主要过拟合行为。提出了模型的正则化，通过惩罚模型复杂性来防止过拟合。ML算法能够预测BP井橡胶混凝土的抗压强度，从而使从业者和工程师能够在混凝土配合比设计和质量控制方面做出通用决策。

{"title":"From compressive strength studies to predictive machine learning models: Rubberised concrete containing brick powder","authors":"David Sinkhonde , Derrick Mirindi , Tajebe Bezabih , Frederic Mirindi","doi":"10.1016/j.apples.2026.100298","DOIUrl":"10.1016/j.apples.2026.100298","url":null,"abstract":"<div><div>Through waste tire rubber recycling and thanks to very simple pozzolanic materials such as brick powder (BP), it has been demonstrated that sustainable construction can be achieved during concrete production. Since concrete is a heterogeneous material with variable and complex behaviour by nature, it is important to incorporate machine learning (ML) models in forecasting its behaviour. Although ML models have been employed for predicting concrete containing BP and/or tire rubber aggregate (TRA), no studies have explored the use of adaptive boosting (AdaBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), gradient boosting regression (GBR), cluster regression, multilayer perceptron (MLP) and Gaussian process (GP) models to forecast the behaviour of rubberised concrete containing BP. In this comprehensive research, the foregoing ML algorithms are employed to forecast the compressive strength of rubberised concrete with BP. The findings illustrate that the GBR model is superior during predictions for the training, validation and testing stages, as evidenced by higher R<sup>2</sup> values ranging from 0.77 to 0.98. SHarpley Additive exPlanations (SHAP) analysis results reward age as the highest influential variable having an average SHAP value of 3.561, followed by tire rubber aggregate, coarse aggregate and cement. In addition, pronounced model performance differences are observed using the Taylor diagram analysis. The research also establishes a predominantly overfitting behaviour displayed by most folds during k-fold cross-validation. Regularisation of the model is proposed to prevent overfitting by penalising model complexity. The ML algorithms are competent to predict the compressive strength of rubberised concrete with BP well, thereby enabling practitioners and engineers to make versatile decisions regarding concrete mix designs and quality controls.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100298"},"PeriodicalIF":2.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Further remarks on isotropic extension of anisotropic constitutive functions via structural tensors 再论各向异性本构函数经结构张量的各向同性扩展

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-17 DOI: 10.1016/j.apples.2026.100295

Chi-Sing Man , Joe D. Goddard

For the original method of isotropic extension of anisotropic constitutive function via structural tensors to work, a necessary condition is that the symmetry group

G

of the anisotropic solid can be characterized as the intersection of stabilizers of specific tensors (called structural tensors) under the action of O(3) on the tensor spaces in question. Here we strengthen the method by replacing “structural tensor” in the characterization of

G

with “set of structural tensors”, thereby broadening the range of subgroups of O(3) covered when the structural tensors are restricted to be of order not higher than two. Mathematical theorems are proved to support our strengthened method of isotropic extension via structural tensors, and a procedure is formalized for using the method to find a representation formula for the anisotropic constitutive function when the structural tensors involved are restricted to be of order not higher than two. As illustration, we consider the constitutive function in anisotropic nonlinear Cauchy elasticity and examine the cases where the anisotropic symmetry group does not satisfy the characterization required by the original method.

各向异性本构函数经结构张量进行各向同性扩展的原始方法，其必要条件是各向异性实体的对称群G可以表征为特定张量（称为结构张量）在O(3)作用下的稳定器的交集。本文通过将G的表征中的“结构张量”替换为“结构张量集”来加强该方法，从而扩大了当结构张量被限制为不高于2阶时所覆盖的O(3)子群的范围。证明了一些数学定理支持我们的结构张量各向同性扩展的强化方法，并形式化了用该方法求各向异性本构函数在结构张量限制为不高于2阶时的表示公式。作为说明，我们考虑了各向异性非线性柯西弹性中的本构函数，并考察了各向异性对称群不满足原方法所要求的表征的情况。

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引用次数: 0

Research on thermo-mechanical coupled damage of high-temperature concrete based on close-packed model 基于密实模型的高温混凝土热-力耦合损伤研究

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-14 DOI: 10.1016/j.apples.2026.100296

Yi-Da Zhao, Xiao-Hui Liu, Zheng-Lin Lan, Zhong-Wei Yao

This study employs ABAQUS numerical simulations to compare a model incorporating aggregates with a homogeneous model without aggregates. This comparison reveals the mechanical properties and micro-damage characteristics of concrete after exposure to high temperatures. The study systematically elucidates the intrinsic degradation mechanisms of concrete under thermo-mechanical coupling, spanning from macro to micro scales. It also highlights the advantages of the proposed close-packed model. The findings indicate that both peak stress and elastic modulus exhibit nonlinear reductions as temperature increases in high-temperature concrete. Stress-strain variation patterns demonstrate similarities across both models. The close-packed model effectively represents the mesoscale damage characteristics of high-temperature concrete. High temperatures significantly lower the stress threshold for damage initiation, and the damage evolution gradually slows down. The damage transitions from localized expansion at the aggregate-matrix interface to a globally diffuse expansion. Furthermore, the close-packed model effectively captures the settlement and packing characteristics of aggregates during the actual pouring process, addressing homogeneous models and random aggregate models that overlook physical processes.

本研究采用ABAQUS数值模拟比较了含骨料模型和不含骨料的均匀模型。通过对比，揭示了高温下混凝土的力学性能和微损伤特征。本研究系统地阐明了混凝土在热-力耦合作用下从宏观到微观的内在降解机制。它还突出了拟议的密集模式的优势。研究结果表明，高温混凝土的峰值应力和弹性模量均随温度升高而非线性降低。应力-应变变化模式在两种模型中表现出相似性。密排模型有效地表征了高温混凝土的中尺度损伤特征。高温显著降低了损伤起始应力阈值，损伤演化逐渐减缓。损伤由聚集-基体界面局部扩展过渡到全局扩散扩展。此外，密实充填模型有效地捕捉了实际浇注过程中骨料的沉降和充填特征，解决了忽略物理过程的均匀模型和随机骨料模型。

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引用次数: 0

Predictive assessment of delamination characteristics in E-glass/epoxy composites using sequential back-propagation neural networks 序贯反向传播神经网络对e -玻璃/环氧复合材料分层特性的预测评估

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-06 DOI: 10.1016/j.apples.2026.100292

Saman Jajemizadeh, Mazaher Salamat-Talab, Amir Hossein Rabiee

This study focuses on the detection and quantification of delaminations in E-glass/epoxy composite samples. A comprehensive investigation involving 603 distinct numerical tests was conducted, each varying in terms of the intensity, number, and spatial arrangement of damage traversing the sample's length and thickness. The primary objective was to extract the first five natural frequencies from these samples. To enable damage prediction, we devised a sequential back-propagation artificial neural network. This network was trained utilizing the initial five natural frequencies as input data. Importantly, the output of each network in the sequence was fed as input to the subsequent network. The results underscored the network's efficacy and robustness in predicting damage severity, count, and precise locations along both the sample's length and thickness. Furthermore, an exploration of the influence of delaminations on the natural frequency values revealed a coherent and meaningful correlation. Notably, variations in the natural frequency values demonstrated a consistent relationship with damage attributes, encompassing both intensity and spatial distribution (across the length and thickness of the sample). This study thus establishes a sound foundation for employing sequential neural networks in the accurate assessment of delamination characteristics within composite structures, while also shedding light on the interconnectedness of damage features with alterations in natural frequency behavior.

本研究的重点是e -玻璃/环氧复合材料样品中分层的检测和定量。对603个不同的数值试验进行了全面的研究，每个数值试验在强度、数量和穿越试样长度和厚度的损伤空间排列方面都有所不同。主要目的是从这些样本中提取前五个固有频率。为了实现损伤预测，我们设计了一个顺序反向传播人工神经网络。该网络使用初始的五个固有频率作为输入数据进行训练。重要的是，序列中每个网络的输出作为输入馈送到后续网络。结果强调了该网络在预测损伤严重程度、数量和沿样本长度和厚度的精确位置方面的有效性和稳健性。此外，探索分层对固有频率值的影响揭示了连贯和有意义的相关性。值得注意的是，固有频率值的变化表现出与损伤属性的一致关系，包括强度和空间分布（跨越样本的长度和厚度）。因此，本研究为采用序列神经网络准确评估复合材料结构内部的分层特征奠定了良好的基础，同时也揭示了损伤特征与固有频率行为变化之间的相互联系。

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引用次数: 0

Data driven performance prediction and optimization model for a nano-lubricated multi-pad active journal bearing using modified Krieger–Dougherty viscosity and couple stress models 基于改进Krieger-Dougherty黏度和耦合应力模型的纳米润滑多垫主动滑动轴承性能预测与优化模型

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2026-01-05 DOI: 10.1016/j.apples.2026.100293

Girish Hariharan, Nitesh Kumar, Shiva Kumar, Shivani S, Subraya Krishna Bhat, Meghana Kundala Navada, Ganesha Aroor

High demand for optimum bearing operation with suppressed vibration amplitudes and enhanced stability has led to the advent of active/controllable fluid film bearings. Integration of active bearing technology in turbomachinery applications facilitates reduced energy losses and supports resource-efficient, sustainable operation. In the present study, a novel form of active journal bearing with multiple adjustable elements is designed to modify the hydrodynamic behavior of rotor bearing systems. Along with pad adjustments, improved bearing performance will be attained due to the presence of Titanium dioxide nanoparticle additives in oil. Theoretical modelling is performed using modified Krieger-Dougherty viscosity method to predict the relative viscosities of nano-Titanium dioxide oil by considering the volume fraction and aggregate size of nanoparticles. A variable viscosity method is utilized to evaluate the steady state characteristics in a multi-pad adjustable bearing operated with nano-Titanium dioxide lubricant. Using Response Surface Methodology approach, process parameters are mapped to the output parameters to identify the operating zone of the adjustable fluid film bearing and nature of response variation. Further, appropriate weights are assigned to three output parameters to identify the optimum combination of input parameters to attain an improvement in bearing performance characteristics. In the normal operating zone, pad adjustments in negative radial and tilt adjustments play a significant role in influencing the peak bearing pressures and loa capacity.

对最佳轴承操作的高要求，抑制振动幅度和增强稳定性，导致主动/可控流体膜轴承的出现。在涡轮机械应用中集成主动轴承技术有助于减少能量损失，并支持资源高效，可持续的运行。为了改善转子轴承系统的流体动力特性，设计了一种新型的多可调元件主动滑动轴承。随着垫的调整，由于在油中存在二氧化钛纳米颗粒添加剂，轴承性能将得到改善。采用改进的Krieger-Dougherty黏度法，考虑纳米颗粒的体积分数和团聚度，对纳米二氧化钛油的相对黏度进行了理论建模。采用变粘度法研究了纳米二氧化钛润滑下多垫可调轴承的稳态特性。采用响应面法，将工艺参数映射到输出参数，确定可调油膜轴承的工作区域和响应变化的性质。此外，对三个输出参数赋予适当的权重，以确定输入参数的最佳组合，从而提高轴承性能特性。在正常工况区，垫块负径向调整和倾斜调整对峰值承载压力和承载能力有显著影响。

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引用次数: 0