Coupling of nanofluid flow, heat transfer and nanoparticles sedimentation using OpenFOAM

Nanofluid is a suspension containing a certain quantity of nanoscaled solid particles in a conventional cooling liquid. Compared to pure liquid in micro channels, nanofluid shows notably better heat transfer performance but without erosion and clogging problems as normal two-phase suspensions. Due to such advantages, nanofluid is increasingly applied as an ideal coolant in engineering. For a better understanding of nanofluid flow and heat transfer performance, many investigations have been carried out recently in both experimental and numerical ways. In numerical investigations, computational fluid dynamics (CFD) is playing a dominant role due to its maturity in the area of fluid flow and heat transfer research. However, in previous CFD studies, the problem of nanoparticles sedimentation is always ignored based on the assumption that nanofluid is stable with homogeneous properties throughout the simulation. To some extreme cases in which nanoparticles sedimentation would happen soon after nanofluid preparation, such assumption could induce inaccurate numerical results. To investigate the relationships between nanofluid flow, heat transfer and nanoparticles sedimentation, an open source CFD package, OpenFOAM is employed as the basis to develop several numerical solvers in multi-phase way for the first time. More specifically, nanofluid CFD simulations are carried out by several newly developed OpenFOAM solvers under both Eulerian-Langrangian and Eulerian-Mixture (a simplified Eulerian-Eulerian approach) frames. By comparing present numerical results to previous published experimental and numerical investigations, it can be concluded that the newly developed solvers under both Eulerian-Langrangian and Eulerian-Mixture frames are capable to investigate nanofluid flow and heat transfer performance coupling with nanoparticles sedimentation. However, with the considerations of computational resource requirement, Eulerian-Mixture approach is believed to be better to achieve the balance between accuracy and computational effort. With an assumption that no appropriate stabilizing treatments have been applied after nanofluid preparation, CFD simulations are carried out for 0.64% Al²O³/water nanofluid in three most typical geometries by the newly developed solver 'nanofluidMixtureFoam'. According to the present research, it can be confirmed that nanofluid heat transfer and nanoparticles sedimentation have considerable impacts to each other other in nanofluid natural convections (in both two- and three-dimensional cases). More specifically, temperature driven flow leads to ticker nanoparticles sedimentation layer than that in normal sedimentation case. On the other hand, nanoparticles sedimentation layer induces worse nanofluid natural convection heat transfer performance. Furthermore, for forced convection problems in a horizontal channel with an open cavity, nanoparticles sedimentation is likely to occur at cavity bottom and leads to higher temperature at heating surface. For better heat transfer performance of the cooling blocks with similar geometries, lower fins (cavity depths) in blocks are recommended to reduce possible nanoparticles sedimentation. In summary, the newly developed OpenFOAM solvers and numerical observations in this thesis are expected to guide future nanofluid CFD study and correlative practical applications

MCFNet: Multi-scale Covariance Feature Fusion Network for Real-time Semantic Segmentation

The low-level spatial detail information and high-level semantic abstract information are both essential to the semantic segmentation task. The features extracted by the deep network can obtain rich semantic information, while a lot of spatial information is lost. However, how to recover spatial detail information effectively and fuse it with high-level semantics has not been well addressed so far. In this paper, we propose a new architecture based on Bilateral Segmentation Network (BiseNet) called Multi-scale Covariance Feature Fusion Network (MCFNet). Specifically, this network introduces a new feature refinement module and a new feature fusion module. Furthermore, a gating unit named L-Gate is proposed to filter out invalid information and fuse multi-scale features. We evaluate our proposed model on Cityscapes, CamVid datasets and compare it with the state-of-the-art methods. Extensive experiments show that our method achieves competitive success. On Cityscapes, we achieve 75.5% mIOU with a speed of 151.3 FPS

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer

Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism

Numerical study of natural convection in a horizontal cylinder filled with water-based alumina nanofluid

Natural heat convection of water-based alumina (Al2O3/water) nanofluids (with volume fraction 1% and 4%) in a horizontal cylinder is numerically investigated. The whole three-dimensional computational fluid dynamics (CFD) procedure is performed in a completely open-source way. Blender, enGrid, OpenFOAM and ParaView are employed for geometry creation, mesh generation, case simulation and post process, respectively. Original solver 'buoyantBoussinesqSimpleFoam' is selected for the present study, and a temperature-dependent solver 'buoyantBoussinesqSimpleTDFoam' is developed to ensure the simulation is more realistic. The two solvers are used for same cases and compared to corresponding experimental results. The flow regime in these cases is laminar (Reynolds number is 150) and the Rayleigh number range is 0.7 x 10(7) similar to 5 x 10(7). By comparison, the average natural Nusselt numbers of water and Al2O3/water nanofluids are found to increase with the Rayleigh number. At the same Rayleigh number, the Nusselt number is found to decrease with nanofluid volume fraction. The temperature-dependent solver is found better for water and 1% Al2O3/water nanofluid cases, while the original solver is better for 4% Al2O3/water nanofluid cases. Furthermore, due to strong three-dimensional flow features in the horizontal cylinder, three-dimensional CFD simulation is recommended instead of two-dimensional simplifications

Short-term fatigue analysis for tower base of a spar-type wind turbine under stochastic wind-wave loads

Due to integrated stochastic wind and wave loads, the supporting platform of a Floating Offshore Wind Turbine (FOWT) has to bear six Degrees of Freedom (DOF) motion, which makes the random cyclic loads acting on the structural components, for instance the tower base, more complicated than those on bottom-fixed or land-based wind turbines. These cyclic loads may cause unexpected fatigue damages on a FOWT. This paper presents a study on short-term fatigue damage at the tower base of a 5 MW FOWT with a spar-type platform. Fully coupled time-domain simulations code FAST is used and realistic environment conditions are considered to obtain the loads and structural stresses at the tower base. Then the cumulative fatigue damage is calculated based on rainflow counting method and Miner's rule. Moreover, the effects of the simulation length, the wind-wave misalignment, the wind-only condition and the wave-only condition on the fatigue damage are investigated. It is found that the wind and wave induced loads affect the tower base's axial stress separately and in a decoupled way, and the wave-induced fatigue damage is greater than that induced by the wind loads. Under the environment conditions with rated wind speed, the tower base experiences the highest fatigue damage when the joint probability of the wind and wave is included in the calculation. Moreover, it is also found that 1 h simulation length is sufficient to give an appropriate fatigue damage estimated life for FOWT

An ice material model for assessment of strain rate, temperature and confining pressure effects using finite element method

This paper addresses an investigation of ice constitutive laws modeling with strain rate, temperature and\ua0confining pressure effects of interest in modeling ice compressive behaviour. For the proposed\ua0phenomenological model consisting of elastic, delayed elastic and viscous components, strain rate is\ua0taken into account by introducing a viscous term based on Glen’s law. The effects of temperature and\ua0confining pressure are also included in the ice model. With the consideration that the viscous term and\ua0delayed elastic term are affected by temperature, the pressure hardening and pressure softening\ua0phenomena are embedded in the constitutive model. The proposed three-dimensional constitutive\ua0model is implemented in explicit LS-DYNA as a user-defined material model, and the numerical\ua0simulations of constant strain rate and creep experiments are conducted to verify the proposed ice\ua0material model. Ice strength and strain-time curves at different strain rates, temperatures and confining\ua0pressures are obtained and compared with experimental results

Assessing the activity of nonsense-mediated mRNA decay in lung cancer

Abstract Background Inhibition of nonsense-mediated mRNA decay (NMD) in tumor cells can suppress tumor growth through expressing new antigens whose mRNAs otherwise are degraded by NMD. Thus NMD inhibition is a promising approach for developing cancer therapies. Apparently, the success of this approach relies on the basal NMD activity in cancer cells. If NMD is already strongly inhibited in tumors, the approach would not work. Therefore, it is crucial to assess NMD activity in cancers to forecast the efficacy of NMD-inhibition based therapy. Methods Here we develop three metrics using RNA-seq data to measure NMD activity, and apply them to a dataset consisting of 72 lung cancer (adenocarcinoma) patients. Results We show that these metrics have good correlations, and that the NMD activities in adenocarcinoma samples vary among patients: some cancerous samples show significantly stronger NMD activities than the normal tissues while some others show the opposite pattern. The variation of NMD activities among these samples may be partly explained by the varying expression of NMD effectors. Conclusions In sum, NMD activity varies among lung cancerous samples, which forecasts varying efficacies of NMD-inhibition based therapy. The developed metrics can be further used in other cancer types to assess NMD activity

Additional file 4: of Assessing the activity of nonsense-mediated mRNA decay in lung cancer

This document contains information of the PTC inducing SNVs among patients. (TXT 25 kb