A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios.
To investigate the evolution of mixing dynamics under different scenarios. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Speed and accuracy of the ensemble and MLP models facilitate uncertainty quantification, which usually requires 1,000s of model run, to estimate the uncertainty bounds on the QoIs. At run time, trained ML emulators are $\approx10^5$ times faster than the high-fidelity numerical simulations. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predict the QoIs. Linear classifiers and regressors fail to reproduce the QoIs however, ensemble methods (classifiers and regressors) and the MLP accurately classify the state of reactive mixing and the QoIs. The ML emulators are specifically trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay, and degree of mixing. The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models. The inputs and outputs of these simulations are concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) emulators to approximate system behavior. Outputs comprise concentration profiles of the reactants and products. performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios.
FOSSIL Q EMULATOR PORTABLE
Using the program Appalm ][ (formerly PalmApple), the wristwatch could could be turned into a portable Apple II.Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. This Dick Tracy watch featured a 160×160 B&W touchscreen and could run most Palm software. It too requires a physical keyboard - no iOS-style virtual keyboards here.īut perhaps most interesting is this Apple II emulator for the Fossil Wrist, a Palm OS-based PDA that was sold 2003–2005. Similarly, Soul Captor for the now-defunct Sega Dreamcast was released in 2002. It supports a USB keyboard as well as GameCube and Wii controllers as a substitute for a standard Apple II joystick. WiiApple was released for the Nintendo Wii back in 2009. Video game consoles are likely targets for emulation, as unintuitive as that may seem.
But the Apple II inspires a hacker’s "can-do" spirit, and for the novelty if no other reason, the Apple II has been emulated in a variety of non-standard environments.
FOSSIL Q EMULATOR FOR MAC OS X
Apple II emulators are most broadly used on modern platforms, such as Sweet16 for Mac OS X or AppleWin for Windows.
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