Astrophysical fluid simulations of thermally ideal gases with non-constant adiabatic index: numerical implementation

An Equation of State (\textit{EoS}) closes the set of fluid equations. Although an ideal EoS with a constant \textit{adiabatic index} $\Gamma$ is the preferred choice due to its simplistic implementation, many astrophysical fluid simulations may benefit from a more sophisticated treatment that can account for diverse chemical processes. Here, we first review the basic thermodynamic principles of a gas mixture in terms of its thermal and caloric EoS by including effects like ionization, dissociation as well as temperature dependent degrees of freedom such as molecular vibrations and rotations. The formulation is revisited in the context of plasmas that are either in equilibrium conditions (local thermodynamic- or collisional excitation- equilibria) or described by non-equilibrium chemistry coupled to optically thin radiative cooling. We then present a numerical implementation of thermally ideal gases obeying a more general caloric EoS with non-constant adiabatic index in Godunov-type numerical schemes.We discuss the necessary modifications to the Riemann solver and to the conversion between total energy and pressure (or vice-versa) routinely invoked in Godunov-type schemes. We then present two different approaches for computing the EoS.The first one employs root-finder methods and it is best suited for EoS in analytical form. The second one leans on lookup table and interpolation and results in a more computationally efficient approach although care must be taken to ensure thermodynamic consistency. A number of selected benchmarks demonstrate that the employment of a non-ideal EoS can lead to important differences in the solution when the temperature range is $500-10^4$ K where dissociation and ionization occur. The implementation of selected EoS introduces additional computational costs although using lookup table methods can significantly reduce the overhead by a factor $3\sim 4$.Comment: 17 pages, 10 figures, Accepted for publication in A&

Track clustering with a quantum annealer for primary vertex reconstruction at hadron colliders

Clustering of charged particle tracks along the beam axis is the first step in reconstructing the positions of hadronic interactions, also known as primary vertices, at hadron collider experiments. We use a 2036 qubit D-Wave quantum annealer to perform track clustering in a limited capacity on artificial events where the positions of primary vertices and tracks resemble those measured by the Compact Muon Solenoid experiment at the Large Hadron Collider. The algorithm, which is not a classical-quantum hybrid but relies entirely on quantum annealing, is tested on a variety of event topologies from 2 primary vertices and 10 tracks up to 5 primary vertices and 15 tracks. It is benchmarked against simulated annealing executed on a commercial CPU constrained to the same processor time per anneal as time in the physical annealer, and performance is found to be comparable for small numbers of vertices with an intriguing advantage noted for 2 vertices and 16 tracks

Unsupervised learning framework for large-scale flight data analysis of cockpit human machine interaction issues

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic clustering techniques are combined with a consensus-based approach to group HMI events and create a data-driven model from the FOQA data. The proposed framework is able to effectively compress a large dataset into a small set of representative clusters within a data-driven model, enabling subject matter experts to effectively investigate identified potential HMI issues

The Role of IRF-1 in Spontaneous Mouse Glioma

Glioblastoma Multiforme has been shown to be one of the deadliest primary brain cancers. One of the reasons why GBM is so deadly, is a unique immunosuppressive tumor microenvironment that promotes GBM growth and progression. Both astrocyte and microglia have been implicated in immunosuppression. In this study, we explored the role of Interferon Regulatory Factor 1 (IRF-1) in astrocytes and glioma cells on the growth of spontaneous glioma tumors. IRF-1 is regulated by the JAK/STAT pathway and induces expression of Programmed death ligand 1 (PD-L1). PD-L1 downregulates immune responses to glioma. We found that IRF-1 had no effect on spontaneous glioma generation nor growth. We also discovered that PD-L1 expression was downregulated in glioma cells. There was still high PD-L1 expression in microglia. Interestingly, we found that tumor vascularization was significantly decreased in animals lacking IRF-1 expression in astrocytes. This has been associated with the loss of CD31 positive staining in endothelial cells as well as the loss of AQP4 expression. Our data suggests that astrocytic IRF-1 may control tumor vascularization via regulating astrocytic endfeet