Numerical investigation of flow unsteadiness and heat transfer on suction surface of rotating airfoils within a gas turbine cascade

The effects of the periodical turbulence and pressure fluctuation on suction surface heat transfer over airfoils of a row of rotor blades with a certain type have been investigated numerically in this paper. The calculation is performed using model with the numerical results of pressure fluctuation and heat transfer performance over 4 sample points being analyzed and compared with existing experimental data. It shows that the static pressure change has significant impact on heat transfer performance of the fore suction surface, especially in the active region of the shock waves formed from the trailing edge of upstream nuzzles. While, for the rear suction surface, the flow turbulence contributes more to the heat transfer change over the surface, due to the reduced pressure oscillation through this region. Phase shifted phenomenon across the surface can be observed for both pressure and heat transfer parameters, which should be a result of turbulence migration and wake passing across the airfoil

Application of CFD, Taguchi Method, and ANOVA Technique to Optimize Combustion and Emissions in a Light Duty Diesel Engine

Some previous research results have shown that EGR (exhaust gas recirculation) rate, pilot fuel quantity, and main injection timing closely associated with engine emissions and fuel consumption. In order to understand the combined effect of EGR rate, pilot fuel quantity, and main injection timing on the NOx (oxides of nitrogen), soot, and ISFC (indicated specific fuel consumption), in this study, CFD (computational fluid dynamics) simulation together with the Taguchi method and the ANOVA (analysis of variance) technique was applied as an effective research tool. At first, simulation model on combustion and emissions of a light duty diesel engine at original baseline condition was developed and the model was validated by test. At last, a confirmation experiment with the best combination of factors and levels was implemented. The study results indicated that EGR is the most influencing factor on NOx. In case of soot emission and ISFC, the greatest influence parameter is main injection timing. For all objectives, pilot fuel quantity is an insignificant factor. Furthermore, the engine with optimized combination reduces by at least 70% for NOx, 20% in soot formation, and 1% for ISFC, in contrast to original baseline engine

Vehicle stability criterion based on three-fold line method

Stable boundary is analysed and corresponding stability criterion is proposed based on sideslip angle speed- sideslip angle phase plane. First, we analyse the impact of adhesion coefficient, longitudinal speed and front wheel angle on phase plane stable boundary, then we simplify the hyperbolic boundary with polyline. Stability criterion is then built based on the distance between locus and stable boundary. The proposed stability criterion is integrated to vehicle stability control system, and simulations are run under Matlab/Simulink-Carsim co-simulation platform. The results show that stability criterion based on sideslip angle speed- sideslip angle phase plane can evaluate vehicle stability state; under SWD/SIS steering condition and DLC condition, stability control syste

Effects of Intake Components and Stratification on the Particle and Gaseous Emissions of a Diesel Engine

It is of great significance to improve the performance of diesel engines by adjusting the intake components and their distribution. In this work, various proportions of exhaust gas recirculation (EGR) gas and oxygen (O2) have been introduced to the intake charge of a diesel engine and the effects of different intake components and stratification conditions on pollutant emissions, especially for particles, have been explored. The results show that the introduction of O2 into the intake charge is beneficial to alleviate the deterioration of particles and hydrocarbon (HC) emissions caused by high EGR rates. Compared with the pure air intake condition, the introduction of moderate O2 at high EGR rate conditions can simultaneously reduce nitrogen oxides (NOx) and particles, when the intake oxygen content (IOC) is 0.2 and the EGR rate is 20%, the NOx and particles are reduced by 45.66% and 66.49%, respectively. It is worth noting that different intake components have a significant impact on the particle size distribution (PSD) of diesel engines. In addition, the in-cylinder O2 concentration distribution formed by the stratified intake is advantageous for further improving the combined effect of NOx, particles and HC emissions relative to the homogeneous intake. At a condition of 0.2 IOC and 20% EGR rate, the NOx, particles, and HC emissions are about 8.8%, 14.3%, and 26% lower than that of intake components nonstratification, respectively

Experimental and kinetic study on the laminar burning speed, Markstein length and cellular instability of oxygenated fuels

The laminar burning speed, Markstein length and cellular instability of three oxygenated fuels, polyoxymethylene dimethyl ether 3 (PODE3), dimethyl carbonate (DMC) and n-butanol (NB), were experimentally studied using spherical flame propagation method. Both of the three fuels are potential alternatives for petrochemical gasoline and diesel. Laminar burning speeds and Markstein lengths were measured at ambient pressure and elevated temperature (363 K-423 K) with three extrapolation models including linear and non-linear employed to extract the unstretched flame speed. Onset of flame cellular instability of the three fuels was determined at high pressure (0.5–0.75 MPa) which was favored to the occurrence of cellular instability. Three well-validated mechanisms for PODE3, DMC and NB respectively were used to numerically analyze the flame structure and then understand the underlying effect of the molecular structure of oxygenated fuels on laminar flame propagation. Results show that PODE3 has the highest laminar burning speed among the three, supporting by both thermal effect and kinetic effect. While the laminar burning speed of NB is higher than that of DMC, which is due to the combined effect of thermal factor and kinetic factor. The molecular structure of oxygenated fuels exerts an influence on the laminar flame propagation through the fuel-specific cracking pathway and resulting formed intermediates with different reactivity. The absence of C–C bond within PODE3 and DMC leads to the formation of substantial oxy-intermediates (CH2O) with high reactivity during fuel decomposition. PODE3 has the most stable flame among the three because of the strong stretching of PODE3 flame. The flame stability of DMC and NB is approximately similar especially at high initial pressure

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

An experimental investigation of wide distillation fuel based on CTL on the combustion performance and emission characteristics from a CI engine

Coal to liquid (CTL) has promising application prospects as an alternative diesel fuel, but the direct application of coal-based synthetic diesel with high cetane number (CN) in compression ignition (CI) engines also has problems. Therefore, the CTL is blended with gasoline to adjust the physicochemical properties of the fuel, which is expected to meet the requirements of efficient and clean combustion. From the perspective of fuel design and combustion boundary condition control, the effects of CTL/gasoline blends on the combustion performance and emission characteristics in a CI engine are investigated in this study. Meanwhile, the variation in the start of injection (SOI) along with the addition of exhaust gas recirculation (EGR) permit achieving clean combustion with CTL/gasoline blends. Experimental results present that the wide distillation fuel (WDF) formed by adding gasoline to CTL, which is conducive to reducing the required mixing timescale and lengthening the chemical preparation timescale. CTL/gasoline blends bring in a higher premixed combustion ratio (PCR) and keep NOx and soot emissions at the lowest level after introducing EGR. Simultaneously, the inhibition effects of CTL/gasoline blends on particulate emissions are apparent with or without EGR due to prolonged ignition delay (ID) and improved mixing quality of fuel-air mixture, and the mass of the total particulates for CG60 is significantly reduced above 90% compared to pure CTL. In addition, the CTL/gasoline blends show refined engine characteristics for broad SOI, and the addition of gasoline to CTL is valid to alleviate the deterioration of combustion processes and emissions caused by EGR. In brief, Coupling EGR and gasoline addition is an effective way to break the trade-off relationship between NOx and particulate emissions for CTL

Molecular dynamics simulations of wetting behaviors of droplets on surfaces with different rough structures

Aiming at the wall-wetting problem in internal combustion engines, to actively control the behaviors of fuel droplets after hitting the walls, the molecular dynamics method is used to investigate the effects of the surface wettability and rough structure on the static and dynamic wetting behaviors of the droplets. The results show that the droplet diameter has little influence on the intrinsic contact angle. With the decrease of the solid-liquid interaction coefficient, the interaction between the wall and the droplet is weakened, and the wetting state changes from the Wenzel state to the Cassie state, resulting in an increase in the static contact angle. As the ratio of the solid-liquid contact area to the composite contact area decreases, it is easier for the droplet to reach the Cassie state. Compared with the smooth surfaces, the structures of the rough surfaces have an inhibitory effect on the spreading of the droplets. The apparent contact angles of the droplets on the rough surfaces with different structures are larger than their intrinsic contact angles on the smooth surfaces. The secondary boss-shaped structures can significantly enhance the surface oleophobicity. In addition, with the decrease of the solid-liquid interaction coefficient, the contact angle hysteresis reduces. Compared with the Wenzel state, the droplet in the Cassie state has a smaller contact area with the surface, which makes the interaction between the wall and the droplet weaker, leading to a decrease in the contact angle hysteresis

Comparative assessment of n-butanol addition in CTL on performance and exhaust emissions of a CI engine

Coal to liquid (CTL) is a diesel alternative fuel based on Fischer-Tropsch (FT) process, which has shown promising application value. Besides, as an oxygenated biofuel with high oxygen content and volatility, n-butanol can be blended with hydrocarbon fuels to improve engine performance. This study aims to investigate the effects of CTL/n-butanol blends on the performance of the compression-ignition (CI) engine, and to reveal the influence of combustion boundary conditions such as n-butanol blending ratio, the start of injection (SOI), and exhaust gas recirculation (EGR) on the combustion and emissions characteristics. The results show that blending n-butanol with CTL is beneficial to improve the fuel-gas mixture distribution in the cylinder, and the premixed combustion ratio (PCR) increases by 13.66% as the energy ratio of n-butanol increases to 30% (B30) compared with the pure CTL. CTL/n-butanol blends make particulate emission tend to be shifted towards nucleation mode and the particulate mass emission significantly reduced, especially the particulate mass of B30 reduce by 68.6%; meanwhile, the NOx emission shows an upward trend. Compared with n-butanol blended, adjusting the SOI impacts NOx emissions significantly, while its influence on the indicated thermal efficiency (ITE) and particulate emissions is relatively slight. Moreover, through the synergistic control of n-butanol addition and EGR, the trade-off relationship between NOx and particles is mitigated