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Register a new userUse of detailed kinetic mechanisms for the prediction of autoignitions
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Authors
Frederic Buda
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This paper describes how automatically generated detailed kinetic mechanisms are obtained for the oxidation of alkanes and how these models could lead to a better understanding of autoignition and cool flame risks at elevated conditions. Examples of prediction of the occurrence of different autoignition phenomena, such as cool flames or two-stage ignitions are presented depending on the condition of pressure, temperature and mixture composition. Three compounds are treated, a light alkane, propane, and two heavier ones, n-heptane and n-decane.
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This work reports a theoretical study of the gas phase unimolecular decomposition of cyclobutane, cyclopentane and cyclohexane by means of quantum chemical calculations. A biradical mechanism has been envisaged for each cycloalkane, and the main routes for the decomposition of the biradicals formed have been investigated at the CBS-QB3 level of theory. Thermochemical data (delta H^0_f, S^0, C^0_p) for all the involved species have been obtained by means of isodesmic reactions. The contribution of hindered rotors has also been included. Activation barriers of each reaction have been analyzed to assess the 1 energetically most favorable pathways for the decomposition of biradicals. Rate constants have been derived for all elementary reactions using transition state theory at 1 atm and temperatures ranging from 600 to 2000 K. Global rate constant for the decomposition of the cyclic alkanes in molecular products have been calculated. Comparison between calculated and experimental results allowed to validate the theoretical approach. An important result is that the rotational barriers between the conformers, which are usually neglected, are of importance in decomposition rate of the largest biradicals. Ring strain energies (RSE) in transition states for ring opening have been estimated and show that the main part of RSE contained in the cyclic reactants is removed upon the activation process.
A new steady-state kinetic model of ammonia decomposition is presented and analyzed regarding the electronic properties of metal catalysts. The model is based on the classical Temkin-Ertl mechanism and modified in accordance with Wolkenstein electronic theory by implementing participation of free electrons of the catalyst to change the chemical nature of adsorbed species. Wolkenstein original theory only applied to semiconductors but by including the d-band model, the electronic theory can be extended to metals. For both simplified and full reaction mechanisms, including electronic steps, we present a steady-state rate equation where the dependence on the Fermi level of the metal creates a volcano-shaped dependence. According to the kinetic model, an increasing Fermi level of the catalyst, that approaching the antibonding state with adsorbed nitrogen molecules, will increase the fraction of neutral nitrogen molecules and enhance their the desorption. Concurrently, strong chemisorption of ammonia molecules proceeds easily through participation of additional free catalyst electrons in the adsorbate bond. As a result, the reaction rate is enhanced and reaches its maximum value. A further increasing Fermi level of the catalyst that approaches the antibonding state with ammonia molecules will result in a smaller fraction of negatively charged ammonia molecules and less dehydrogenation. Concurrently, the desorption of neutral nitrogen molecules occurs without impairment. As a result, the reaction rate decreases. The detailed kinetic model is compared to recent experimental measurements of ammonia decomposition on iron, cobalt and CoFe bimetallic catalysts.
Molecular ferroelectrics have captured immense attention due to their superiority over inorganic oxide ferroelectrics, such as environmentally friendly, low-cost, flexible, foldable. However, the mechanisms of ferroelectric switching and phase transition for the molecular ferroelectrics are still missing, leaving the development of novel molecular ferroelectrics less efficient. In this work, we have provided a methodology combining molecular dynamics (MD) simulation on a polarized force field named polarized crystal charge (PCC) and enhanced sampling technique, replica-exchange molecular dynamics (REMD) to simulate such mechanisms. With this procedure, we have investigated a promising molecular ferroelectric material, (R)/(S)-3-quinuclidinol crystal. We have simulated the ferroelectric hysteresis loops of both enantiomers and obtained spontaneous polarization of 7/8 mu C cm-2 and a corresponding coercive electric field of 15 kV cm-1. We also find the Curie temperature as 380/385 K for ferro-/para-electric phase transition of both enantiomers. All of the simulated results are highly compatible with experimental values. Besides of that, we predict a novel Curie temperature of about 600 K. This finding is further validated by principal component analysis (PCA). Our work would significantly promote the future exploration of multifunctional molecular ferroelectrics for the next generation of intelligent devices.
Regression testing is an important phase to deliver software with quality. However, flaky tests hamper the evaluation of test results and can increase costs. This is because a flaky test may pass or fail non-deterministically and to identify properly the flakiness of a test requires rerunning the test suite multiple times. To cope with this challenge, approaches have been proposed based on prediction models and machine learning. Existing approaches based on the use of the test case vocabulary may be context-sensitive and prone to overfitting, presenting low performance when executed in a cross-project scenario. To overcome these limitations, we investigate the use of test smells as predictors of flaky tests. We conducted an empirical study to understand if test smells have good performance as a classifier to predict the flakiness in the cross-project context, and analyzed the information gain of each test smell. We also compared the test smell-based approach with the vocabulary-based one. As a result, we obtained a classifier that had a reasonable performance (Random Forest, 0.83) to predict the flakiness in the testing phase. This classifier presented better performance than vocabulary-based model for cross-project prediction. The Assertion Roulette and Sleepy Test test smell types are the ones associated with the best information gain values.
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