NAIRAS Atmospheric and Space Radiation Environment Model
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String fragmentation models such as the Lund Model fit experimental particle production cross sections very well in the high-energy limit. This paper gives an introduction of the massless relativistic string in the Lund Model and shows how it can be modified with a simple assumption to produce formulas for meson production cross sections for space radiation research. The results of the string model are compared with inclusive pion production data from proton-proton collision experiments.
Laser and optical emission-based measurements have been developed and implemented for use on low-power hydrogen arcjet thrusters and xenon-propelled electric thrusters. In the case of low power hydrogen arcjets, these laser induce fluorescence measurements constitute the first complete set of data that characterize the velocity and temperature field of such a device. The research performed under the auspices of this NASA grant includes laser-based measurements of atomic hydrogen velocity and translational temperature, ultraviolet absorption measurements of ground state atomic hydrogen, Raman scattering measurements of the electronic ground state of molecular hydrogen, and optical emission based measurements of electronically excited atomic hydrogen, electron number density, and electron temperature. In addition, we have developed a collisional-radiative model of atomic hydrogen for use in conjunction with magnetohydrodynamic models to predict the plasma radiative spectrum, and near-electrode plasma models to better understand current transfer from the electrodes to the plasma. In the final year of the grant, a new program aimed at developing diagnostics for xenon plasma thrusters was initiated, and results on the use of diode lasers for interrogating Hall accelerator plasmas has been presented at recent conferences.
The radiative forcing of anthropogenic aerosols associated with aerosol–cloud interactions (RF(sub aci)) remains the largest source of uncertainty in climate prediction. The calculation of particle number concentration (PNC), one of the critical parameters affecting RF(sub aci), is generally simplified in climate models. Here we employ outputs from long-term (30-years) simulations of a global size-resolved (sectional) aerosol microphysics model and a machine-learning tool to develop a Random Forest Regression Model (RFRM) for PNC. We have implemented the PNC RFRM in GISS-ModelE2.1 with a mass-based One-Moment Aerosol module, which is one of CMIP6 models. Compared to the default setting, the GISS-ModelE2.1 simulation based on RFRM reduces the changes of cloud droplet number concentration associated with anthropogenic emissions, and decreases the RF(sub aci) from −1.46 W⋅m(exp −2) to −1.11 W⋅m(exp −2). This work highlights a promising approach based on machine learning to reduce uncertainties of climate models in predicting PNC and RF(sub aci) without compromising their computing efficiency.
The primary tasks during January 1990 to June 1990 have been the development and evaluation of various electron and electron-electronic energy equation models, the continued development of improved nonequilibrium radiation models for molecules and atoms, and the continued development and investigation of precursor models and their effects. In addition, work was initiated to develop a vibrational model for the viscous shock layer (VSL) nonequilibrium chemistry blunt body engineering code. Also, an effort was started associated with the effects of including carbon species, say from an ablator, in the flowfield.