The calculation of time-varying meshing stiffness caused by the alternate contacting of the gear tooth is an essential prerequisite to obtain real and effective nonlinear dynamic characteristics of the transmission system, so that the significance of which cannot be overemphasized. Accordingly, this work proposes an improved method to get meshing stiffness with taking fillet-foundation and gear rim deflection into consideration. Compared to the traditional potential energy method, the proposed method has more superior accuracy and performance, and its effectiveness has been further verified by the finite element analytical model. After that, an ideal eight degree of freedoms (DOFs) dynamic model of one stage mass-spring-damper involute spur gear, including lateral and torsional motions, is established to study the dynamic characteristics. Due to the complexity of the gear system operating conditions, we also investigate the influence of various parameters including hub bore radius, transmitting load, and rotation speed on dynamic features, especially in heavy-load and high-speed conditions. From the results, it can be concluded that these parameters will play a prominent role in the spur gear pair dynamic behaviors, providing a certain guidance for gear design.
Abstract Background Thermogravimetric analysis (TGA) is an important method to characterize the pyrolysis reaction characteristics of tobacco, reflecting the chemical composition of tobacco from the perspective of chemical reaction. However, quantitative analysis of tobacco chemical indexes based on TG curve has not yet been reported due to the complexity to the best of our knowledge. Results By the combination of TG curve and partial least squares (PLS) algorithm, we established quantitative analysis models of six routine chemical constituents in tobacco, including total sugar, reducing sugar, total nitrogen, total alkaloids, chlorine and potassium. We also performed an in-depth analysis of the chemical mechanism revealed by the result of the quantitative model, namely the regression coefficient which reflected the correlation degree between the six chemicals and different stages of tobacco thermal decomposition process. Conclusions The quantitative analysis model of chemical constituents in tobacco based on TG curve can be used for the rapid and accurate analysis of compound content. From the perspective of pyrolysis reaction mechanisms, the quantification of total sugar and reducing sugar mainly depended on the Maillard reaction in which carbohydrate compounds participate. The correlation of total nitrogen and total alkaloids with TG curves in the whole temperature range was affected by total sugar. Although chlorine was detrimental to Maillard reaction in general, it has different effects on the different elementary steps of the Maillard reaction. Potassium can promote the thermal decomposition of hemicellulose, pectin, and water-soluble carbohydrates such as glucose and fructose.
Low-temperature-initiated cracking technology has significant potential to develop new hydrocarbon pyrolysis with better atomic economy and higher yields of light olefins. The impacts of three representative initiators, triethylamine (TEA), nitromethane (NM), and di-tert-butyl peroxide (DTBP), were compared. Sufficiently detailed co-cracking reaction networks of three initiators with n-hexane were obtained through an automatic reaction network generator RMG, and the corresponding steam cracking tubular reactor simulation was performed. The results showed that the initiator with a lower cracking temperature brought about a more noticeable decrease in the cracking temperature of n-hexane. Both TEA and NM increased the carbon atom conversion from n-hexane to ethylene and propylene, especially propylene. NM was more significant, while DTBP was almost ineffective. Through kinetic parameter analysis, it was found that the temperature dependence of the key reaction caused different initiator performance. NM completely cracked at an appropriate temperature where the rate constants of the hydrogen abstraction and ethylene generation had reached a high level, and the released free radicals were effective. Visual reaction network analysis revealed that NM had a strong ability to release free radicals, and these free radicals significantly adjusted the competitive reaction path to produce ethylene and propylene, resulting in the most obvious effect on improving the conversion of n-hexane and the yield of propylene. The action laws of NM described above under hydrocarbon pyrolysis conditions indicate that NM may be the most suitable initiator for low-temperature-initiated cracking of hydrocarbons among these three initiator types.
Methyl 4-nitrophenyl ketone was brominated by copper(II) bromide in methanol; the ketone reacts with the solvent in situ to yield the title compound, C10H12BrNO4, which exists as a monomeric molecule.
Abstract A time‐integrated species flux analysis (TSFA) method for the systematic reduction of large detailed kinetics in the pyrolysis process is proposed. The obtained reaction paths are based on the species flux throughout temporal evolutions rather than at transitory moments. This allows considering species flux at different reaction moments. The proposed strategy is verified by experiments and numerical simulation of the cracking process of n ‐hexane and n ‐butanol. The kinetics of n ‐hexane cracking was reduced from the original 328 species to 94 species, while the resulting product yield difference is less than 1.5%. In addition, a global reaction pathway of n ‐butanol pyrolysis was discovered. Compared with literature reports, we found that hydroxyl radicals also played an important role in n ‐butanol pyrolysis. The proposed framework provides a new strategy for simplifying large‐scale reaction kinetics and conducting global reaction pathway analysis in the pyrolysis process.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='translate(288.889 -214.211)'/%3e%3cuse xlink:href='%23a' transform='translate(108 342.749)'/%3e%3cuse xlink:href='%23a' transform='translate(469.189 342.749)'/%3e%3c/svg%3e)