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1. Low-Mach-Number Simulation of Diffusion Flames with the Chemical-Diffusive Model

   https://doi.org/10.2514/6.2019-2169  

   Chung, Joseph D.; Zhang, Xiao; Kaplan, Carolyn R.; Oran, Elaine S. (January 2019, AIAA Scitech 2019 Forum)

   This work describes and tests the calibration process of the chemical-diffusive model (CDM) for the simulation of non-premixed diffusion flames. The CDM is an alternative, simplified approach for incorporating the effects of combustion in a fluid simulation, based on the ideas of regulating the rate of energy release such that the properties of combustion waves (e.g. flames and detonations) are reproduced. Past implementations of the CDM have considered single-stoichiometry fuel-air mixtures or mixtures with variable stoichiom- etry but with premixed modes of combustion. In this work, the CDM is tested and shown to work for non-premixed, low-Mach-number flames (i.e., diffusion flames) by incorporat- ing it into a numerical model which solves the reactive and compressible Navier-Stokes equations with the barely implicit correction (BIC) algorithm, which removes the acoustic limit on the integration time-step size. Simulations of one-dimensional premixed laminar flames reproduce the required premixed laminar flame speed, thickness, and temperature. A two-dimensional, steady-state, laminar coflow diffusion flame is computed, and the result demonstrates the ability of the algorithm to compute a non-premixed flame. Lastly, a two- dimensional simulation of two opposing jets of fuel and air show that the CDM approach can compute the structure of a counter-flow diffusion flame. 

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2. Ionization disequilibrium in K- and L-shell ions

   https://doi.org/10.1063/5.0151931  

   Bishel, D T; Marley, E V; Schneider, M B; Liedahl, D A; Heeter, R F; Foord, M E; Kemp, G E; Frank, Y; Emig, J A; Pérez-Callejo, G; et al (July 2023, Physics of Plasmas)

   Time-gated Sc K-shell and Ge L-shell spectra are presented from a range of characterized thermodynamic states spanning ion densities of 1019–1020cm−3 and plasma temperatures around 2000 eV. For the higher densities studied and temperatures from 1000 to 3000 eV, the Sc and Ge x-ray emission spectra are consistent with steady-state calculations from the modern atomic kinetics model SCRAM. At the lower ion densities achieved through plasma expansion, however, the model calculations require a higher plasma temperature to reproduce the observed Ge spectrum. We attribute this to ionization disequilibrium of the Sc because the ionization time scales exceed the hydrodynamic timescale when the inferred temperatures diverge. 

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3. Measurement of the longitudinal diffusion of ionization electrons in the MicroBooNE detector

   https://doi.org/10.1088/1748-0221/16/09/P09025  

   Abratenko, P.; An, R.; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; et al (September 2021, Journal of Instrumentation)

   Abstract Accurate knowledge of electron transport properties is vital to understanding the information provided by liquid argon time projection chambers (LArTPCs). Ionization electron drift-lifetime, local electric field distortions caused by positive ion accumulation, and electron diffusion can all significantly impact the measured signal waveforms. This paper presents a measurement of the effective longitudinal electron diffusion coefficient, D L , in MicroBooNE at the nominal electric field strength of 273.9 V/cm. Historically, this measurement has been made in LArTPC prototype detectors. This represents the first measurement in a large-scale (85 tonne active volume) LArTPC operating in a neutrino beam. This is the largest dataset ever used for this measurement. Using a sample of ∼70,000 through-going cosmic ray muon tracks tagged with MicroBooNE's cosmic ray tagger system, we measure D L = 3.74 +0.28 -0.29 cm 2 /s. 

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4. Controlling the breakdown delay time in pulsed gas discharge

   https://doi.org/10.1088/1361-6595/ac417a  

   Schweigert, I. V.; Hopkins, M. M.; Barnat, E.; Keidar, M. (March 2022, Plasma Sources Science and Technology)

   Abstract In experiment and 2D3V PIC MCC simulations, the breakdown development in a pulsed discharge in helium is studied forU= 3.2 kV and 10 kV andP= 100 Torr. The breakdown process is found to have a stochastic nature, and the electron avalanche develops in different experimental and simulation runs with time delays ranging from 0.3 to 8μs. Nevertheless our experiments demonstrate that the breakdown delay time distribution can be controlled with a change of the pulse discharge frequency. The simulation results show that the breakdown process can be distinguished in three stages with (a) the ionization by seed electrons, (b) the ions drift to the cathode and (c) the enhanced ionization within the cathode sheath by the electrons emitted from the cathode. The effects of variation of seed electron concentrations, voltage rise times, voltage amplitudes and ion–electron emission coefficients on the breakdown development in the pulsed gas discharge are reported. 

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5. Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals

   https://doi.org/10.1109/IJCNN55064.2022.9892272  

   Sun, Q; Vo, K; Lui, KK; Nunez, M; Vandekerckhove, J; Srinivasan, R (January 2022, Nyssa)

   Human decision making behavior is observed with choice-response time data during psychological experiments. Drift-diffusion models of this data consist of a Wiener first-passage time (WFPT) distribution and are described by cognitive parameters: drift rate, boundary separation, and starting point. These estimated parameters are of interest to neuroscientists as they can be mapped to features of cognitive processes of decision making (such as speed, caution, and bias) and related to brain activity. The observed patterns of RT also reflect the variability of cognitive processes from trial to trial mediated by neural dynamics. We adapted a SincNet-based shallow neural network architecture to fit the Drift-Diffusion model using EEG signals on every experimental trial. The model consists of a SincNet layer, a depthwise spatial convolution layer, and two separate fully connected layers that predict drift rate and boundary for each trial in-parallel. The SincNet layer parametrized the kernels in order to directly learn the low and high cutoff frequencies of bandpass filters that are applied to the EEG data to predict drift and boundary parameters. During training, model parameters were updated by minimizing the negative log likelihood function of WFPT distribution given trial RT. We developed separate decision SincNet models for each participant performing a two-alternative forced-choice task by discriminating whether a Gabor patch presented with noise is high or low spatial frequency. Our results showed that single-trial estimates of drift and boundary performed better at predicting RTs than the median estimates in both training and test data sets, suggesting that our model can successfully use EEG features to estimate meaningful single-trial Diffusion model parameters. Furthermore the shallow SincNet architecture identified time windows of information processing related to evidence accumulation and caution and the EEG frequency bands that reflect these processes within each participant. 

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