超声波燃气表矩形流道的雷诺修正系数仿真研究

在超声波流量计测量技术中, 雷诺修正系数相关的研究对于提高计量精度有重要作用. 为研究矩形流道的雷诺修正系数与雷诺数的关系, 对矩形流道在常温常压流量较小情况下进行仿真, 结果发现: 矩形流道层流状态下的雷诺修正系数与雷诺数呈线性相关. 保持压强、体积流量不变, 在不同温度下进行仿真及拟合, 结果表明: 在不同温度下雷诺修正系数与雷诺数的线性关系依然满足. 在上述实验基础上, 对矩形流道湍流状态下的雷诺修正系数与雷诺数关系进行研究, 通过改变温度、压强和体积流量进行仿真及拟合发现, 矩形流道湍流状态下雷诺修正系数与雷诺数呈非线性相关.     

Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

Analysis of a new multispecies tumor growth model coupling 3D phase-fields with a 1D vascular network

In this work, we present and analyze a mathematical model for tumor growth incorporating ECM erosion, interstitial flow, and the effect of vascular flow and nutrient transport. The model is of phase-field or diffused-interface type in which multiple phases of cell species and other constituents are separated by smooth evolving interfaces. The model involves a mesoscale version of Darcy’s law to capture the flow mechanism in the tissue matrix. Modeling flow and transport processes in the vasculature supplying the healthy and cancerous tissue, one-dimensional (1D) equations are considered. Since the models governing the transport and flow processes are defined together with cell species models on a three-dimensional (3D) domain, we obtain a 3D–1D coupled model.     

Distribution dependent stochastic differential equations

Due to their intrinsic link with nonlinear Fokker-Planck equations and many other applications, distribution dependent stochastic differential equations (DDSDEs) have been intensively investigated. In this paper, we summarize some recent progresses in the study of DDSDEs, which include the correspondence of weak solutions and nonlinear Fokker-Planck equations, the well-posedness, regularity estimates, exponential ergodicity, long time large deviations, and comparison theorems.     

A theory of finite tensile deformation of double-network hydrogels

A continuum damage model was developed to describe the finite tensile deformation of tough double-network (DN) hydrogels synthesized by polymerization of a water-soluble monomer inside a highly crosslinked rigid polyelectrolyte network. Damage evolution in DN hydrogels was characterized by performing loading-unloading tensile tests and oscillatory shear rheometry on DN hydrogels synthesized from 3-sulfopropyl acrylate potassium salt (SAPS) and acrylamide (AAm). The model can explain all the mechanical features of finite tensile deformation of DN hydrogels, including idealized Mullins effect and permanent set observed after unloading, qualitatively and quantitatively. The constitutive equation can describe the finite elasto-plastic tensile behavior of DN hydrogels without resorting to a yield function. It was showed that tensile mechanics of DN hydrogels in the model is controlled by two material parameters which are related to the elastic moduli of first and second networks. In effect, the ratio of these two parameters is a dimensionless number that controls the behavior of material. The model can capture the stable branch of material response during neck propagation where engineering stress becomes constant. Consistent with experimental data, by increasing the elastic modulus of the second network the finite tensile behavior of the DN hydrogel changes from necking to strain hardening.     

An efficient edge based data structure for the compressible Reynolds-averaged Navier–Stokes equations on hybrid unstructured meshes

An efficient edge based data structure has been developed in order to implement an unstructured vertex based finite volume algorithm for the Reynolds-averaged Navier–Stokes equations on hybrid meshes. In the present approach, the data structure is tailored to meet the requirements of the vertex based algorithm by considering data access patterns and cache efficiency. The required data are packed and allocated in a way that they are close to each other in the physical memory. Therefore, the proposed data structure increases cache performance and improves computation time. As a result, the explicit flow solver indicates a significant speed up compared to other open-source solvers in terms of CPU time. A fully implicit version has also been implemented based on the PETSc library in order to improve the robustness of the algorithm. The resulting algebraic equations due to the compressible Navier–Stokes and the one equation Spalart–Allmaras turbulence equations are solved in a monolithic manner using the restricted additive Schwarz preconditioner combined with the FGMRES Krylov subspace algorithm. In order to further improve the computational accuracy, the multiscale metric based anisotropic mesh refinement library PyAMG is used for mesh adaptation. The numerical algorithm is validated for the classical benchmark problems such as the transonic turbulent flow around a supercritical RAE2822 airfoil and DLR-F6 wing-body-nacelle-pylon configuration. The efficiency of the data structure is demonstrated by achieving up to an order of magnitude speed up in CPU times.     

Biomimetic Peptide Catalytic Bond-Forming Utilizing a Mild Brønsted Acid

Since the global peptide drug market demand has been predicted to increase, highly efficient and inexpensive mass scale peptides are required. However, the production process raises questions about the cost of energy input, scale-up production, raw materials, and solvents treatment. This paper introduces 2 methods for the 2–4 mer oligopeptides bond formation for batch reaction utilizing 50–100 mol% of a mild Brønsted acid under the mild condition. One of the methods has been capably adapted to flow synthesis at room temperature using organic solvents with boiling points below 100 °C. The method applies the tert-butoxycarbonyl amino methoxy group, forming the desired dipeptide without solvent at mild temperatures. Furthermore, the conversion of the carboxylic acid leaving the group to phenyl ester promotes peptide bond formation, and the reaction were applied to di, tri, and tetrapeptide bond formation in excellent yield without notable racemization at ambient temperature (up to >99 % yield and 99 : 1 dr). Finally, this study proposes this new production method to overcome the limited scale-up production by reaction device scale: liquid phase biomimetic catalytic peptide flow synthesis utilizing a mild Brønsted acid.