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Mengjie Ding

Publications and source records attributed to Mengjie Ding.

3 recordsLinked to original sources

Higher-order mean velocity profile in the convective atmospheric boundary layer

The higher-order mean velocity profile in the convective atmospheric boundary layer (CBL) is derived using the method of matched asymptotic expansions. The universal expansion coefficients are obtained using field measurement data. The profile accounts for the departures from the (leading-order) log law and local-free-convection scaling as well as the deviations from the Monin-Obukhov Similarity theory (MOST). Invoking MOST and the Multipoint Monin-Obukhov similarity theory, the perturbation equations are obtained from the Reynolds-stress, potential-temperature flux and potential temperature-variance budget equations and the mean momentum and mean potential temperature equations. The small parameters with the most impact in the equations are $(-z_i/L)^{-4/3}$, $(-z_i/L)^{-2/3}$ and $-h_0/L$, where $z_i$, $L$ and $h_0$ are the inversion height, the Obukhov length and the roughness height, respectively. Tong and Ding ({\it J.~Fluid Mech.} 2020) have identified the three-layer structure of the CBL In the present work, asymptotic matching between the outer and inner-outer layers also results in higher-order expansion terms. The expansion coefficients are obtained using measurement data from the recent M$^2$HATS field campaign. Comparisons between the expansions and the measurement show excellent agreement. The higher-order asymptotic expansions show that the convective logarithmic friction law derived by Tong and Ding (2020) is valid to at least the second order. The predicted friction law also agrees well with measurements. The higher-order mean velocity profile can provide improved accuracy over empirical profiles.

physics.flu-dyn

Departure from the statistical equilibrium of large scales in three-dimensional hydrodynamic turbulence

We study the statistically steady states of the forced dissipative three-dimensional homogeneous isotropic turbulence at scales larger than the forcing scale in real separation space. The probability density functions (PDFs) of longitudinal velocity difference at large separations are close to but deviate from Gaussian, measured by their non-zero odd parts. Under the assumption that forcing controls the large-scale dynamics, we propose a conjugate regime to Kolmogorov's inertial range, independent of the forcing scale, to capture the odd parts of PDFs. The analytical expressions of the third-order longitudinal structure functions derived from the Kármán-Howarth-Monin equation prove that the odd-part PDFs of velocity differences at large separations are small but non-zero, and show that the odd-order longitudinal structure functions have a universal power-law decay with exponent $-2$ as the separation tends to infinity regardless of the particular forcing form, implying a significant coupling between large and small scales. Thus, dynamics of large scales depart from the absolute equilibrium, and we can partially recover small-scale information without explicitly resolving small-scale dynamics. The departure from the statistical equilibrium is quantified and found to be viscosity independent. Even though this departure is small, it is significant and should be considered when studying the large scales of the forced three-dimensional homogeneous isotropic turbulence.

physics.flu-dyn

HFContractFuzzer: Fuzzing Hyperledger Fabric Smart Contracts for Vulnerability Detection

With its unique advantages such as decentralization and immutability, blockchain technology has been widely used in various fields in recent years. The smart contract running on the blockchain is also playing an increasingly important role in decentralized application scenarios. Therefore, the automatic detection of security vulnerabilities in smart contracts has become an urgent problem in the application of blockchain technology. Hyperledger Fabric is a smart contract platform based on enterprise-level licensed distributed ledger technology. However, the research on the vulnerability detection technology of Hyperledger Fabric smart contracts is still in its infancy. In this paper, we propose HFContractFuzzer, a method based on Fuzzing technology to detect Hyperledger Fabric smart contracts, which combines a Fuzzing tool for golang named go-fuzz and smart contracts written by golang. We use HFContractFuzzer to detect vulnerabilities in five contracts from typical sources and discover that four of them have security vulnerabilities, proving the effectiveness of the proposed method.

cs.CR