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International audience; Previous studies on sampling probe effects in the premixed flat flame showed that the temperature in the preheat zone can be lowered down to low-temperature oxidation regime (e.g., 400–800 K). In order to investigate the contribution of the low-temperature chemistry in flame-sampling experiments, a stoichiometric laminar premixed flat flame of ethylene/oxygen/argon was investigated at 30 Torr using synchrotron vacuum ultraviolet photoionization mass spectrometry (SVUV-PIMS) in this work. Ethyl hydroperoxide (C2H5OOH) was identified and quantified in the present experiment, providing an experimental evidence for the existence of low-temperature chemistry in flame-sampling experiments. In addition to C2H5OOH, the formation of several other intermediates like ethanol and formaldehyde was also influenced by the low-temperature chemistry in the present flame-sampling experiment. A literature kinetic model (Hashemi et al., 2017) was used for predictions and analyses. The difference between the predicted maximum mole fractions of C2H5OOH with the perturbed and unperturbed temperature profiles can reach up to five orders of magnitude. The great improvement of the predictions with the perturbed temperature profiles indicates that the observed low-temperature chemistry in the present flame sampling experiment originates from the probe-induced perturbations, which lowers down the temperature window of the preheat zone and leads to a temperature drop of more than 400 K compared with the unperturbed temperature profile. Through modeling analysis, the low-temperature oxidation chemistry of ethylene involved in the present flame-sampling experiment was discussed. The influence of low-temperature chemistry in the present experiment has also been demonstrated by comparing the model predictions with/without key reactions at low temperatures. It is concluded that predicted maximum mole fractions of several low-temperature chemistry related intermediates, i.e. C2H5OOH, ethanol and formaldehyde, are strongly reduced without these reactions, while low-temperature chemistry only has negligible influence on the predictions of the fuel and the majority of flame intermediates. Furthermore, preliminary experiments were also conducted in ethane, propene and n-butane flames under similar conditions, where hydroperoxides can also be observed.

This work presents a comprehensive experimental and kinetic modeling study on the combustion of n -propylbenzene. Flow reactor pyrolysis of n -propylbenzene at 0.04, 0.2 and 1 atm and laminar premixed flames of n -propylbenzene at 0.04 atm with equivalence ratios of 0.75 and 1.00 were investigated with synchrotron vacuum ultraviolet photoionization mass spectrometry. Jet stirred reactor (JSR) oxidation of n -propylbenzene at 10 atm with equivalence ratios of 0.5, 1.0, 1.5 and 2.0 was investigated with gas chromatography. A detailed kinetic model for n -propylbenzene combustion with 340 species and 2069 reactions was developed and validated against the data measured in this work. Model analyses such as rate of production analysis and sensitivity analysis were also performed to reveal the key pathways in the consumption of fuel and formation of polycyclic aromatic hydrocarbons (PAHs). The analysis results demonstrate that the benzylic C C bond dissociation reaction is crucial for the decomposition of n -propylbenzene in the pyrolysis and rich flame. Low temperature oxidation reactions play important roles in the high pressure JSR oxidation of n -propylbenzene. In addition, the formation pathways of PAHs are strongly related to the fuel structure, especially for the formation of bicyclic PAHs such as indene and naphthalene. Furthermore, the present model was also validated against previous experimental data of n -propylbenzene combustion under a wide range of conditions, including ignition delay times, laminar flame speeds, extinction strain rates, speciation profiles in atmospheric pressure JSR oxidation, flow reactor oxidation and high pressure shock tube pyrolysis and oxidation.

International audience; Home health care is one of the recent service systems where human resource planning has a great importance. The assignment of patients to care givers is a relevant issue that the home health care service provider must address before generating the daily routes. The assignment decision is typically made without knowing the visiting sequence, which creates some uncertainties and disparities regarding the effective workload of care givers. However, taking into account travel times in the care giver workload while solving the assignment problem is not straightforward, because travel times can also be affected by clinical conditions of patients and their homes. Providing good travel time estimates that would be used in the assignment decision is the specific topic this paper focuses on. In particular, we propose a data-driven method to estimate the travel times of care givers in the & Semih Yalçındag ˘ assignment problem when their routes are not available yet. The method, based on the Kernel regression technique, uses the travel times observed from previous periods to estimate the time necessary for visiting a set of patients located in specific geographical locations. The main advantage offered by this technique is the empirical modelling of the travel routes generated by care givers. Numerical results based on realistic problem instances indicate that the proposed estimation method performs better than the average value and k-nearest neighbor search methods and can be successfully used in a two-stage approach that first assigns patients to care givers and then defines their routes.

Due to the increased complexity of engineering products and design process, concurrent engineering practices still face enormous challenges. Design teams often provide the knowledge involving multiple disciplines in natural language at conceptual design stage. As a result, conflicts often arise during communications. To increase the efficiency of design process, it is necessary to avoid interpretation conflicts. This article presents a system framework to resolve terminology conflicts. First, a semantic hierarchical structure of knowledge is proposed. Knowledge is decomposed into terms and predicates. Second, a fuzzy knowledge fusion framework is proposed to identify and resolve terminology conflicts. The conflict is resolved by unifying the usage of terminologies. By using the fuzzy linguistic computational model based on extension principle to measure the similarity of terms, the terminology conflict problem is converted into a linguistic decision-making problem. Finally, the methodology is applied to a piece of automotive electronics knowledge. The best term to describe electronic control unit is identified.

International audience