Study on the Design of Supersonic Axisymmetric Multicompression and Quasi-isentropic Inlets

AbstractBased on the characteristics method, the design method of axisymmetric multi-compression and quasi-isentropic compression inlets are developed. For the multi-compression inlets, the solution strategy of shock wave angle for the leasttotal pressure loss is designed. For quasi-isentropic compression inlets, the design criteria of wall of quasi-isentropic compression inlets is come up with that the left Mach lines starting from the wall should intersect at the lip. The wall of axisymmetric multicompression and quasi-isentropic compression inlets under the condition of mach 4 is designed, and the numerical simulation results calculated by FLUENT shows that the distribution of the shock waves meet the design requirements, which validates the design method of this paper. The comparative study of the two kinds of inlets shows that: the quasi-isentropic compression has the advantage of total pressure recovery, while the drag on the wall of the quasi-isentropic compression is similar with that of three shock compression

Salicylic Acid Modulates the Osmotic System and Photosynthesis Rate to Enhance the Drought Tolerance of <i>Toona ciliata</i>

Toona ciliata M. Roem. is a valuable and fast-growing timber species which is found in subtropical regions; however, drought severely affects its growth and physiology. Although the exogenous application of salicylic acid (SA) has been proven to enhance plant drought tolerance by regulating the osmotic system and photosynthesis rate, the physiological processes involved in the regulation of drought tolerance by SA in various plants differ. Therefore, drought mitigation techniques tailored for T. ciliata should be explored or developed for the sustainable development of the timber industry. We selected 2-year-old T. ciliata seedlings for a potting experiment, set the soil moisture at 45%, and subjected some of the T. ciliata seedlings to a moderate drought (MD) treatment; to others, 0.5 mmol/L exogenous SA (MD + SA) was applied as a mitigation test, and we also conducted a control using a normal water supply at 70% soil moisture (CK). Our aim was to investigate the mitigating effects of exogenous SA on the growth condition, osmotic system, and photosynthesis rate of T. ciliata under drought stress conditions. OPLS–VIP was used to analyze the main physiological factors that enable exogenous SA to alleviate drought-induced injury in T. ciliata. The results indicated that exogenous SA application increased the growth of the ground diameter, plant height, and leaf blades and enhanced the drought tolerance of the T. ciliata seedlings by maintaining the balance of their osmotic systems, improving their gas exchange parameters, and restoring the activity of their PSII reaction centers. The seven major physiological factors that enabled exogenous SA to mitigate drought-induced injury in the T. ciliata seedlings were the soluble proteins (Sp), net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), stomatal opening window (Sow), activity of the photosystem II reaction center (ΦPSII), and electron transfer rate (ETR). Of these, Sp was the most dominant factor. There was a synergistic effect between the osmotic system and the photosynthetic regulation of drought injury in the T. ciliata seedlings. Overall, our study confirms that exogenous SA enhances the drought tolerance of T. ciliata by modulating the osmotic system and photosynthesis rate

Development of reduced and optimized reaction mechanism for potassium emissions during pulverized-biomass combustion based on genetic algorithms

International audienceA reduced mechanism for potassium chemistry under combustion conditions is derived from a detailed chemical mechanism for alkali metal emissions (Glarborg and Marshall, 2005), which could be useful for three-dimensional (3D) numerical simulations of potassium emissions by biomass combustion furnaces. An automated chemistry reduction and optimization approach relying on canonical micro-mixing problem is applied to develop the reduced mechanism, whose performance is then evaluated in two-dimensional (2D) carrier-phase direct numerical simulation (DNS) of pulverized-biomass combustion. Good agreements are achieved between predictions of the reduced and the detailed mechanisms on the four major potassium species, i.e., K, KOH, KCl and K 2 SO 4. The prediction capabilities of the reduced mechanism for various K/Cl/S ratios in the volatiles are further investigated by a parametric study with 14 two-dimensional DNS cases. The potassium chemistry under those various conditions are predicted well by the reduced potassium mechanism with a CPU cost reduction reaching up to 71.3% compared to the detailed reference mechanism

Reduced chemical reaction mechanisms for simulating sodium emissions by solid-fuel combustion

International audienceStarting from a reference and comprehensive chemical mechanism for alkali metal emissions (Glarborg and Marshall, 2005), combined with an hydrocarbon oxidation described with a skeleton mechanism (Kazakov and Frenklach, 1994), reduced and optimized chemical kinetics are derived. The objective is to provide a set of chemical schemes useful for three-dimensional (3D) numerical simulations of alkali metal emissions by pulverized solidfuel combustion systems. An automated procedure relying on one-dimensional (1D) premixed flames is applied to obtain a combined reduced mechanism, whose performance is then evaluated in one-dimensional strained diffusion flames, micro-mixing based canonical problems and three-dimensional carrier-phase direct numerical simulation (DNS) of coal combustion. Predictions of the reduced mechanism on major sodium species, i.e., Na, NaOH, NaCl and Na 2 SO 4 agree well with that of the detail reference scheme under all the considered conditions. A parametric study with 14 two-dimensional (2D) DNS cases is then performed to better understand the reactive flow properties and estimate the prediction capabilities of the reduced mechanism for various Na/Cl/S ratio in the volatiles. After pursuing the chemistry reduction, a global sodium mechanism with only 9 species and 8 reaction-steps is also discussed. The systematic comparison between the 3D DNS results obtained with the reference chemical scheme against those with the reduced ones confirm the validity of the reduction strategy. A reduction of up to 84% in computational cost is reached with the optimized global scheme, thus allowing for addressing real pulverized-coal combustion systems

Prediction of ignition delay times of Jet A-1/hydrogen fuel mixture using machine learning

International audienceTo control the global warming trends, carbon footprint of all the human activities needs to be restricted, including the aviation industry. Mixing hydrogen with commercial kerosene jet fuels appears as a promising alternative fuel to reduce the carbon dioxide emissions of aviation engines. The addition of hydrogen can significantly impact the auto-ignition process of aviation fuels, which is a key ingredient of engine reliability. However, accurate calculations or measurements of ignition delay times (IDTs) over a wide range of pressures, temperatures and fuel blending ratios are complicated and time-consuming. To achieve real-time prediction of ignition delay time for hydrogen-blended jet fuels under various operating conditions, machine learning methods are introduced to build a data-driven proxy model in this work. First, the ignition delay times of Jet A-1/hydrogen fuel mixture are simulated using the well-known HyChem combustion reaction mechanism under different pressures, temperatures, equivalence ratios and blending molar ratios of hydrogen. After some validation against experimental results, an artificial neural network (ANN) model is trained using the database of ignition delay times. Furthermore, a sub-ANN is nested to the original ANN model as an improvement on certain local conditions. The results show that the single original ANN model leads to a large local relative error for IDT , generally referring to high temperatures and pressures conditions. With the help of the nested sub-ANN approach, the improved model achieves a significantly better accuracy for very fast ignition. Compared with other machine learning models such as random forest, the nested sub-ANN model is more efficient to predict the ignition delay times of Jet A-1/hydrogen fuel mixture, still mitigating computational cost. The sensitivity analysis shows how the nested sub-ANN model is less sensitive to the uncertainties of the input parameters than the original ANN model