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

Identifying crystal accumulation in granitoids through amphibole composition and in situ zircon O isotopes in North Qilian Orogen

Granitoids are the main constituents of the continental crust, and an understanding of their petrogenesis is key to the origin and evolution of continents. Whether crystal fractionation is the dominant way to generate evolved magmas has long been debated, mostly because such processes would produce large volumes of complementary cumulates, which remains elusive. Mafic magmatic enclaves (MMEs) are ubiquitous in granitoids and their presence was initially recognized as cumulates. However, because many MMEs lack obvious evidence of accumulation, such as the classic cumulate textures and modal layering, the cumulate origin of MMEs has been abandoned and the model of magma mixing between mafic and felsic magmas has become popular. In this study, we conduct a combined study of amphibole composition and in situ O isotopes in zircons on three suites of orogenic granitoids with MMEs from the North Qilian Orogenic Belt (NQOB). We find that the MMEs and their host granodiorites show overlapping zircon δ18O values, affirming that they share the same parental magmas. The amphibole compositions indicate that amphiboles from the MMEs are not in equilibrium with a melt whose composition was that of the bulk-rock. These new data, together with the published bulk-rock data, suggest that the MMEs in our study have clear cumulate signatures and are thus of cumulate origin. Our study provides evidence for crystal accumulation in granitoids in the NQOB. This new understanding calls for re-examination on the petrogenesis of some intermediate magmatic rocks (granitoid/andesite) in discussing models of continental crustal growth

DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions

3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion

The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational cost. To tackle these issues, a 3D deep learning scheme, called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR C-scans. The proposed scheme leverages a prior 3D convolutional neural network with a feature attention mechanism to suppress the noise in the C-scans due to subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder network with multi-scale feature aggregation modules is designed to establish the optimal inverse mapping from the denoised C-scans to 3D permittivity maps. Furthermore, a three-step separate learning strategy is employed to pre-train and fine-tune the networks. The proposed scheme is applied to numerical simulation as well as real measurement data. The quantitative and qualitative results show the network capability, generalizability, and robustness in denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface objects

A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver

Control system of spontaneous combustion in coal gangue dumps – a case study at Yuquan coal mine in China

Spontano zapaljenje na odlagalištima jalovine uglja dovodi do zagađenja okoliša i oštećenja ekosustava i ljudskog zdravlja. Voditelji ugljenokopa obično ne poduzimaju učinkovite mjere sprečavanja spontanog zapaljenja zbog nepravovremenog otkrivanja mjesta izbijanja spontanog zapaljenja. Zbog toga je u ovom radu dizajniran i konstruiran sustav kontrole spontanog zapaljenja na odlagalištima jalovine. Taj se sustav sastoji od sustava za otkrivanje područja spontanog zapaljenja, pokretnog sustava za prevenciju i gašenje spontanog zapaljenja primjenom tekućeg ugljičnog dioksida (LCD - Liquid Carbon Dioxide) i sustava za provjeru učinka spontanog zapaljenja u odlagalištu jalovine. Prvo je sustavom otkrivanja otkriveno područje spontanog zapaljenja na odlagalištu, Zatim je u zapaljeno područje uštrcan LCD pomoću opreme za uštrcavanje kako bi se temperatura u odlagalištu smanjila ispod točke zapaljenja ugljene jalovine i kako bi se kisik izbacio iz odlagališta. Konačno, sustavom za provjeru učinkovitosti ispitali su se učinci dvaju sustava. Spontano zapaljenje na odlagalištima jalovine provjereno je na analizi slučaja u Yuquan rudniku. Rezultati pokazuju da su koncentracije plinova CO i H2S na odlagalištu jalovine ugljenokopa Yuquan ispod 24 i 6,6 ppm, a unutarnja temperature je ispod 70 °C. Cijena po kvadratnom metru sustava kontrole spontanog zapaljenja iznosi približno $8. Prema tome, predloženim se kontrolnim sustavom može ekonomično i učinkovito nadgledati spontano zapaljenje na odlagalištima jalovine uglja, stoga i spriječiti zagađenje okoliša.Spontaneous combustion in coal gangue dumps leads to pollution in the environment and damages ecosystem and human health. Mine operators usually fail to take effective measures against spontaneous combustion because of the inaccurate detection of the location of spontaneous combustion bursts. To address this issue, a control system of spontaneous combustion in coal gangue dumps was designed and constructed in this study. The control system is composed of a detection system of spontaneous combustion areas, a mobile prevention and extinguishing system of spontaneous combustion using liquid carbon dioxide (LCD), and an effect-checking system of spontaneous combustion in coal gangue dump. First, the detection system was used to detect the spontaneous combustion areas in the coal gangue dumps. LCD was then injected into the combustion areas with its pressure injection equipment to lower the temperature in the coal gangue dumps below the ignition point of coal gangue and to force oxygen out of the dumps. Finally, the effect-testing system was used to test the effects of the two systems. The spontaneous combustion in coal gangue dumps was controlled through a case study at Yuquan coal mine. Results show that the concentrations of index gases CO and H2S in the coal gangue dumps of Yuquan coal mine are below 24 and 6,6 ppm, and the internal temperature is below 70 °C, The cost per square meter of the control system of spontaneous combustion is approximately$8. Therefore, the proposed control system can economically and effectively control spontaneous combustion in coal gangue dumps, thereby preventing pollution in the environment

The effect of the “Oral-Gut” axis on periodontitis in inflammatory bowel disease: A review of microbe and immune mechanism associations

Periodontitis and inflammatory bowel diseases (IBD) are inflammatory diseases of the gastrointestinal tract that share common features of microbial-induced ecological dysregulation and host immune inflammatory response. The close relationship between periodontitis and IBD is characterized by a higher prevalence of IBD in patients with periodontitis and a higher prevalence and severity of periodontitis in patients with IBD, indicating that periodontitis and IBD are different from the traditional independent diseases and form an “Oral-Gut” axis between the two, which affect each other and thus form a vicious circle. However, the specific mechanisms leading to the association between the two are not fully understood. In this article, we describe the interconnection between periodontitis and IBD in terms of microbial pathogenesis and immune dysregulation, including the ectopic colonization of the gut by pathogenic bacteria associated with periodontitis that promotes inflammation in the gut by activating the host immune response, and the alteration of the oral microbiota due to IBD that affects the periodontal inflammatory response. Among the microbial factors, pathogenic bacteria such as Klebsiella, Porphyromonas gingivalis and Fusobacterium nucleatum may act as the microbial bridge between periodontitis and IBD, while among the immune mechanisms, Th17 cell responses and the secreted pro-inflammatory factors IL-1β, IL-6 and TNF-α play a key role in the development of both diseases. This suggests that in future studies, we can look for targets in the “Oral-Gut” axis to control and intervene in periodontal inflammation by regulating periodontal or intestinal flora through immunological methods

Plin4-Dependent Lipid Droplets Hamper Neuronal Mitophagy in the MPTP/p-Induced Mouse Model of Parkinson’s Disease

Epidemiological studies have shown that both lipid metabolism disorder and mitochondrial dysfunction are correlated with the pathogenesis of neurodegenerative diseases (NDDs), including Parkinson’s disease (PD). Emerging evidence suggests that deposition of intracellular lipid droplets (LDs) participates in lipotoxicity and precedes neurodegeneration. Perilipin family members were recognized to facilitate LD movement and cellular signaling interactions. However, the direct interaction between Perilipin-regulated LD deposition and mitochondrial dysfunction in dopaminergic (DA) neurons remains obscure. Here, we demonstrate a novel type of lipid dysregulation involved in PD progression as evidenced by upregulated expression of Plin4 (a coating protein and regulator of LDs), and increased intracellular LD deposition that correlated with the loss of TH-ir (Tyrosine hydroxylase-immunoreactive) neurons in the MPTP/p-induced PD model mouse mesencephalon. Further, in vitro experiments showed that inhibition of LD storage by downregulating Plin4 promoted survival of SH-SY5Y cells. Mechanistically, reduced LD storage restored autophagy, leading to alleviation of mitochondrial damage, which in turn promoted cell survival. Moreover, the parkin-poly-Ub-p62 pathway was involved in this Plin4/LD-induced inhibition of mitophagy. These findings were further confirmed in primary cultures of DA-nergic neurons, in which autophagy inhibitor treatment significantly countermanded the ameliorations conferred by Plin4 silencing. Collectively, these experiments demonstrate that a dysfunctional Plin4/LD/mitophagy axis is involved in PD pathology and suggest Plin4-LDs as a potential biomarker as well as therapeutic strategy for PD