A Case of Cervical Schwannoma that Was Initially Suspected as a Cystic Lymph Node Metastasis Secondary to Papillary Thyroid Carcinoma

浜松医科大学医学部耳鼻咽喉科・頭頸部外科学教室　峯田周幸教授退任記念論文集～症例から学ぶ～Festschrift for Professor Hiroyuki Mineta In Hornor of His Retirement as Chairman of Hamamatsu University School of MedicineNeck cystic masses of various causes are frequently encountered in otorhinolaryngological practice. Papillary thyroid carcinoma is also known to be associated with cystic cervical lymph node metastasis. Measurement of the thyroglobulin level in the cyst fluid is considered to be useful for the diagnosis. Herein, we report a case of cervical schwannoma that was initially suspected as a cystic lymph node metastasis secondary to a papillary thyroid carcinoma. A 49-year-old woman was diagnosed as having thyroid papillary carcinoma and cervical lymph node metastases. She was treated by total thyroidectomy and cervical lymph node resection. Three years after the operation, she developed a cystic mass in the upper left neck, which began to gradually increase in size. We resected this cystic mass in the upper neck under the suspicion that it was a cystic lymph node metastasis secondary to the papillary thyroid carcinoma, but histopathology of the resected specimen revealed the diagnosis of schwannoma.journal articl

Large-eddy simulation of particle-laden isotropic turbulent flows and sub-grid scale models

&nbsp; &nbsp; 携带颗粒的湍流二相流在环境流动和工业应用中广泛存在。与直接数值模拟 (DNS) 相比，大涡模拟 (LES) 作为湍流二相流工程预测的下一代主要工具，大大降低了计算支出，能够应用于更高雷诺数的湍流。然而，降低计算量的同时，LES 由于小尺度湍流的缺失，无法准确地模拟颗粒与湍流的相互作用。而将 LES 应用于湍流混合与输运过程的研究，需要其准确地预测湍流中的颗粒相对弥散，或者至少准确预测流场的拉格朗日统计量。因此，本文针对 LES 的小尺度运动缺失问题，分别研究滤波和亚格子模型误差对流体颗粒弥散的影响；构造运动学-反卷积混合颗粒亚格子模型，同时恢复 LES 的解析尺度和亚格子尺度对颗粒运动的贡献；根据大量湍流 DNS 数据，采用人工神经网络 (ANN) 建立数据驱动的亚格子模型。此外，对于球形颗粒在静止流体中靠近壁面运动的受力和力矩，本文基于传统模型，发展适用于有限颗粒雷诺数情况的亚格子尺度模型。 &nbsp; &nbsp; 本文的主要创新性工作包括以下四个部分： (一). 研究滤波和亚格子模型误差对流体颗粒弥散的影响 &nbsp; &nbsp; 该部分通过开展各向同性湍流的 DNS、滤波的 DNS (FDNS) 和 LES，比较单颗粒、颗粒对和四颗粒弥散，分别研究滤波和谱涡粘模型误差对流体颗粒弥散的影响。对于单颗粒弥散，LES 略微高估了一点两时间速度关联函数，但准确预测了单颗粒位移。对于颗粒对弥散，当初始分离距离较小时，与 DNS 相比，LES 低估了分离距离的平均值和方差以及相对扩散、高估了速度关联函数，FDNS 的结果处于二者之间。当初始分离距离较大时，速度关联函数曲线在初始阶段短暂抬升，我们理论推导出关联函数随时间变化的表达式，证明了这一抬升现象。对于四颗粒弥散，与 DNS 相比，LES 低估了四面体平均表面积和体积，且表征形状变化的无量纲系数曲线出现了明显滞后，FDNS 结果仍处于二者之间。此外，LES 和 FDNS 的颗粒对速度关联函数相对误差随着雷诺数的增大而减小。 (二). 构造运动学-反卷积混合颗粒亚格子模型 &nbsp; &nbsp; 该部分通过构造运动学-反卷积 (KSAD) 混合颗粒亚格子模型，提升 LES 对颗粒弥散的预测精度。对各向同性湍流的 LES，采用近似反卷积模型 (ADM) 恢复解析尺度流场；基于 LES+ADM 速度场，采用运动学模型 (KS) 构造亚格子尺度流场。通过参数研究，发现可采用较少数目的波数模态和矢量构造 KS，减少计算支出。随后详细评估 KSAD 模型对流体颗粒弥散的作用。对于颗粒对统计量，包括分离距离的平均值与方差、相对扩散以及两点一时间拉格朗日速度关联函数，模型几乎完全补偿了 LES 和 DNS 的偏差；对于四颗粒统计量，模型显著提升了 LES 对四面体的平均表面积、体积以及无量纲系数的预测。最后，将 KSAD 模型应用于 LES 预测惯性颗粒聚团，定量准确地补偿了所有&nbsp;St&nbsp;下 LES 与 DNS 的径向相对速度误差和&nbsp;St&ge;2.0 的径向分布函数误差。 (三). 人工神经网络建立数据驱动的湍流亚格子模型 &nbsp; &nbsp; 该部分基于大量的湍流 DNS 数据，采用 ANN 建立数据驱动的亚格子模型。对各向同性湍流 DNS 进行高斯滤波，获得不同雷诺数和滤波宽度的解析尺度流场与亚格子应力张量数据。随后选取速度梯度张量和滤波宽度作为输入特征、亚格子应力张量作为输出标签，采用单隐层前馈 ANN 进行训练，并讨论了隐层神经元数目的影响。训练成功后，对 ANN 模型进行先验评估和后验验证。在先验评估中，ANN 模型预测的关联系数基本都大于 0.9，与梯度模型结果相近，远高于 Smagorinsky 模型；ANN 模型预测的能量传输率相比梯度模型结果有显著的提升。在后验验证中，采用修正的 ANN 模型进行各向同性湍流 LES，其预测的能谱满足惯性区的&nbsp;k-5/3&nbsp;标度律，预测的流体颗粒对统计量接近于 FDNS 结果。 (四). 发展球形颗粒在流体中靠近壁面运动的受力和力矩模型 &nbsp; &nbsp; 对球形颗粒在静止流体中靠近壁面的运动，该部分基于 Stokes 流动假设下的传统模型，发展有限颗粒雷诺数下的颗粒受力和力矩模型。将颗粒靠近壁面的一般运动划分为四种基本运动，采用格子 Boltzmann 方法进行解析颗粒的直接数值模拟。在确定合适的计算区域尺寸并进行网格无关性检验后，设置不同的颗粒与壁面的间距以及颗粒雷诺数，计算颗粒受力和力矩的无量纲系数。随后对数据进行拟合处理，提出颗粒受力和力矩的有限颗粒雷诺数模型。接下来，我们对模型的正确性与适用性进行验证。验证包括两部分，其一为颗粒不同基本运动组合成的复合运动模拟，数值结果与提出的模型基本吻合；其二为引入不同文献中的相似算例，发现文献结果与提出的模型也有很好的一致性。</p

湍流中颗粒相对弥散的大涡模拟亚格子模型

湍流中颗粒的相对弥散在大气污染的预测和控制、化工反应过程的优化等领域有着重要应用。相对弥散主要受小尺度湍流结构的控制,这为该过程的大涡模拟精准预测提出了一个根本性的挑战。针对大涡模拟预测颗粒相对弥散的主要误差来源,即小尺度湍流的缺失和亚格子模型的过度耗散,我们提出了一种运动学模型与反卷积方法耦合的大涡模拟亚格子模型,这种耦合通过解析尺度和未解尺度在截断波数处的湍流能量通量得以实现。反卷积方法用于提高截断波数附近的湍流能量,而运动学模型用于构造缺失的亚格子湍流。为了验证提出的模型,我们通过与直接数值模拟对比,研究了流体质点的相对弥散和惯性颗粒的相对速度和聚团统计量,发现该模型显著提升了大涡模拟对流体质点的相对弥散和惯性颗粒的相对速度的预测,而对小Stokes数颗粒的聚团预测仍需要进一步改进

有限雷诺数下颗粒靠近壁面运动的受力和力矩模型

本文旨在建立有限颗粒雷诺数下球形颗粒靠近壁面运动的受力和力矩模型。传统的润滑力模型是基于斯托克斯流动理论发展得到的,只适用于低颗粒雷诺数情况。为了研究有限颗粒雷诺数对颗粒靠近壁面运动的受力和力矩模型的影响,本文采用格子Boltzmann方法对颗粒靠近壁面的四种基本运动情况进行数值模拟,在分别确定合适的计算区域尺寸并进行网格无关性检验后,设置不同的颗粒与壁面间距和颗粒雷诺数,解析颗粒与流场的相互作用,从而得到颗粒的受力和力矩的无量纲系数。将获得的数值结果与传统的低雷诺数润滑力模型进行定量比较,提出了有限颗粒雷诺数下颗粒靠近壁面运动的受力和力矩模型。随后模拟了颗粒的一般运动情况,获得的结果与提出的受力和力矩模型符合得很好。此外,将提出的颗粒受力和力矩模型与其它文献的相应结果进行对比,验证了模型的正确性与适用性。在解析颗粒的直接数值模拟中,当颗粒与壁面的间隙中的流体运动无法求解时,本文提出的颗粒受力和力矩模型可作为亚格子尺度模型被采用;在使用点颗粒模型时,本文提出的模型则可以作为颗粒靠近壁面的运动方程中的受力模型

Homogeneity constraints on the mixed moments of velocity gradient and pressure Hessian in incompressible turbulence

In homogeneous turbulent flow, a relation for the correlation between velocity gra dient mij = aui ax j and pressure Hessian hpij = a2 p axiaxjwas found recently: (tr(mhpm)) = 2((tr(m2))2). We discuss the implications of this relation to the velocity gradient dy 1 namics: together with the Poisson equation for pressure, the homogeneity relation yields an identity between ((tr(m2))2) and the integration of a two point fourth order correlation function of velocity gradient for isotropic flows. Our results indicate that the main contri butions to (tr(mhpm)) come from scales less than roughly 20 times the Kolmogorov scale. Also, the homogeneity relation provides restrictions to the parameters in the closure models of pressure Hessian in velocity gradient dynamics. We further discuss the generalization of this homogeneity relation to turbulent shear flows, and we show numerically that this relation between (tr(mhpm)) and ((tr(m2))2) is approximately satisfied even in the presence of a shear and of a wall, as it occurs in turbulent channel flows

前馈神经网络构造周期山状流的大涡模拟壁模型

自然界以及工程流动通常处于高雷诺数、非定常的湍流状态,大涡模拟是预测非定常湍流的重要工具。在具有强压力梯度、分离和再附等流动特征的复杂壁湍流中,基于均衡假设的传统大涡模拟壁模型常常难以准确计算壁面切应力,从而无法为外区流动提供准确的边界条件。针对传统壁模型无法准确预测复杂壁湍流的问题,我们以周期山状流为研究对象,以壁面解析的大涡模拟数据为基础,采用机器学习方法构造数据驱动的壁模型。首先进行数据准备,我们输出较长时间的展向截面瞬时流场,从下壁面的不同流向位置,沿法向取点,插值得到近壁流动数据。随后以壁面法向距离、近壁速度和压力梯度为输入特征,以流向和展向壁面切应力为输出标签,对其分别进行线性归一化,采用多隐层前馈神经网络训练壁模型。模型训练成功后,对其进行先验研究。在先验研究中,对于训练集内的瞬时应力和时均应力,模型都可以准确地预测,表明模型具有较高的预测精度;对于训练集以外的流动截面,模型预测与真实的时均应力在不同流向位置也有较好吻合,关联系数接近于1、相对误差接近于0,预测与真实的瞬时应力的变化趋势基本一致,脉动幅值略有差异,表明训练的壁模型具有较强的泛化能力

Reynolds number effect on statistics of turbulent flows over periodic hills

The wall-resolved large-eddy simulations of turbulent flows over periodic hills are carried out to study the Reynolds number effect on flow statistics. Five different Reynolds numbers ranging from 2800 to 37 000 are considered. The present simulations are validated by comparing the time-averaged flow statistics with those from the literature. The Reynolds number effect is first examined on the skin friction and pressure coefficients, the isosurfaces of p & PRIME; and Q criteria, and the vertical profiles of flow statistics. The results show that (1) at most locations the magnitude of friction coefficient decreases with the increase in Reynolds number, while the pressure coefficient varies in the opposite direction; (2) smaller turbulence structures arise at higher Reynolds numbers; and (3) the mean velocities and Reynolds stresses in general exhibit asymptotic behaviors with the increase in Reynolds number. The statistical properties of turbulence structures are further examined via the probability density function and time correlation of velocity fluctuations. At last, the dynamics in the separation bubble is investigated by examining the flow statistics and the budget equation of mean kinetic energy (MKE) on the coordinate with its origin fixed at the recirculation center, and the power spectral density of the velocity fluctuations. Similarities are in general observed for the mean velocities, Reynolds stresses, and the MKE budget in the rear part of the separation bubble. The mean convection term and turbulence convection term are observed playing a key role on the decrease in bubble size with the increase in Reynolds number

Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation

A super-resolution reconstruction model for the subgrid scale (SGS) turbulent flow field in large-eddy simulation (LES) is proposed, and it is called the meta-learning deep convolutional neural network (MLDCNN). Direct numerical simulation (DNS) data of isotropic turbulence are used as the dataset of the model. The MLDCNN is an unsupervised learning model, which only includes high-resolution DNS data without manually inputting preprocessed low-resolution data. In this model, the training process adopts the meta-learning method. First, in the a priori test, the SGS turbulent flow motions in the filtered DNS (FDNS) flow field are reconstructed, and the energy spectrum and probability density function of the velocity gradient of the DNS flow field are reconstructed with high accuracy. Then, in the a posteriori test, the super-resolution reconstruction of the LES flow field is carried out. The difficulty of LES flow field reconstruction is that it contains filtering loss and subgrid model errors relative to the DNS flow field. The super-resolution reconstruction of the LES flow field achieves good results through this unsupervised learning model. The proposed model makes a good prediction of small-scale motions in the LES flow field. This work improves the prediction accuracy of LES, which is crucial for the phenomena dominated by small-scale motions, such as relative motions of particles suspended in turbulent flows. (c) 2022 Author(s)