RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence. Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5% from direct numerical simulation (DNS) databases into RANS equations leads to velocities with large errors (up to 35%). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e., flow over periodic hills and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e., deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.Comment: 35 pages, 18 figure

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

The ability of Lisa, Taiji, and their networks to detect the stochastic gravitational wave background generated by Cosmic Strings

The cosmic string contributes to our understanding and revelation of the fundamental structure and evolutionary patterns of the universe, unifying our knowledge of the cosmos and unveiling new physical laws and phenomena. Therefore, we anticipate the detection of Stochastic Gravitational Wave Background (SGWB) signals generated by cosmic strings in space-based detectors. We have analyzed the detection capabilities of individual space-based detectors, Lisa and Taiji, as well as the joint space-based detector network, Lisa-Taiji, for SGWB signals produced by cosmic strings, taking into account other astronomical noise sources. The results indicate that the Lisa-Taiji network exhibits superior capabilities in detecting SGWB signals generated by cosmic strings and can provide strong evidence. The Lisa-Taiji network can achieve an uncertainty estimation of $\Delta G\mu/G\mu<0.5$ for cosmic string tension $G\mu\sim4\times10^{-17}$, and can provide evidence for the presence of SGWB signals generated by cosmic strings at $G\mu\sim10^{-17}$, and strong evidence at $G\mu\sim10^{-16}$. Even in the presence of only SGWB signals, it can achieve a relative uncertainty of $\Delta G\mu/G\mu<0.5$ for cosmic string tension $G\mu<10^{-18}$, and provide strong evidence at $G\mu\sim10^{-17}$

Probing the gravitational wave background from cosmic strings with Alternative LISA-TAIJI network

As one of the detection targets of all gravitational wave detectors at present, stochastic gravitational wave background (SGWB) provides us an important way to understand the evolution of our universe. In this paper, we explore the feasibility of detecting the SGWB generated by the loops, which arose throughout the cosmological evolution of the cosmic string network, by individual space detectors (e.g.~LISA, TAIJI) and joint space detectors (LISA-TAIJI). For joint detectors, we choose three different configurations of TAIJI (e.g.~TAIJIm, TAIJIp, TAIJIc) to form the LISA-TAIJI networks. And we investigate the performance of them to detect the SGWB. Though comparing the power-law sensitivity (PLS) curves of individual space detectors and joint detectors with energy density spectrum of SGWB. We find that LISA-TAIJIc has the best sensitivity for detecting the SGWB from cosmic string loops and is promising to further constrains the tension of cosmic sting $G\mu=O(10^{-17})$

Upper Gastrointestinal Endoscopy Detection of Synchronous Multiple Primary Cancers in Esophagus and Stomach: Single Center Experience from China

The present study was undertaken to clarify the prevalence and clinicopathological features of synchronous multiple primary cancers (SMPCs) under upper gastrointestinal endoscopic examination. We enrolled 45,032 consecutive patients who underwent upper gastrointestinal endoscopic examination for digestive disease from January 2006 to December 2007 in our hospital and analyzed the clinicopathological features of SMPCs in esophagus and stomach. SMPCs are defined as two or over two different cancerous lesions developing in the same or other organs within 6 months. SMPCs were identified in 46 patients (0.1%). The gender ratio was 5.6 : 1 (male/female) and the mean age was 59.4 years. Synchronous esophageal and gastric cancers were the most frequent, being seen in 32 patients (0.07%). The most common histological types of SMPCs were squamous cell carcinoma in esophagus and adenocarcinoma in stomach, respectively. There were 27 (59%) SMPCs patients who had the history of simultaneous exposure to tobacco smoking and alcohol drinking. Additionally, 32 (78%) esophageal squamous cell cancers were associated with tobacco use. And 23 adenocarcinomas of the stomach were associated with Helicobacter pylori infection