We present an atomic-level study of vibrational excitation and chemical dissociation in molecular nitrogen at high temperature. The computational techniques, quasiclassical trajectory calculations (QCT) and classical trajectory calculation direct simulation Monte Carlo (CTC DSMC), solely rely on the specification of a highly accurate ab initio surface for N2-N2 interactions. We show that the simulations accurately reproduce the vibrational relaxation times obtained from the Millikan-White correlation and further support existing high-temperature corrections. Using both QCT and CTC DSMC methods with the same PES, we show that dissociation proceeds 4 to 5 times faster under equilibrium conditions compared to quasi-steady-state nonequilibrium conditions. At high translational energies (20,000 K and 30,000 K), the overall vibrational energy ladder is depleted, and dissociation proceeds at a rate approximately corresponding to a lower vibrational temperature. Conversely, at lower energies (10,000 K), almost no dissociation occurs from low-lying vibrational energy states. In this case, even a slight depletion of the upper-level vibrational energy states, which preferentially dissociate and are not rapidly re-populated due to slow bound-bound energy transfer mechanisms, causes a remarkable reduction of the dissociation rate. The ab initio CTC DSMC method is able to directly simulate rovibrational excitation and dissociation processes without computing the large number of cross-sections required by a state-resolved approach, and can be used directly to form new CFD models.
|Original language||English (US)|
|Title of host publication||53rd AIAA Aerospace Sciences Meeting|
|Publisher||American Institute of Aeronautics and Astronautics Inc, AIAA|
|State||Published - 2015|
|Event||53rd AIAA Aerospace Sciences Meeting, 2015 - Kissimmee, United States|
Duration: Jan 5 2015 → Jan 9 2015
|Name||53rd AIAA Aerospace Sciences Meeting|
|Other||53rd AIAA Aerospace Sciences Meeting, 2015|
|Period||1/5/15 → 1/9/15|
Bibliographical noteFunding Information:
The research is supported by Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-10-1-0563. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the AFOSR or the U.S. Government. Jason D. Bender was supported in this work by the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) under grant number DE-FG02-97ER25308.