Modeling and experimental deposition behavior during AlGaAs growth : a comparison for the carrier gases N2 and H2

A modeling and experimental study is carried out to understand why low-pressure metalorganic vapor-phase epitaxy (LP-MOVPE) of AlGaAs in nitrogen atmosphere differs from that in hydrogen in a horizontal tube type of reactor. To this end flow, heat transfer as well as the key chemical species' mass transport are considered. The increased uniformity in N-2 atmosphere is related to the higher molecular weight and, therefore to the higher gas density of the carrier resulting in a flow structure that is more favorable for improved growth rate uniformity of AlGaAs on the substrate. Due to the so called "cold finger" [L. Stock, W. Richter, J. Crystal Growth 77 (1986) 144; D.F. Fotiadis, M. Bockholt, K.F. Jensen, W. Richter, J. Crystal Growth 100 (1990) 577.] effect as well as the enhanced inertia of the carrier gas and lower diffusion coefficients of the growth rate limiting chemical species in N-2, lower total flow rates are found to be optimal for material quality and layer thickness uniformity when using N-2 as carrier gas. The dependence of growth rate uniformity on the carrier gas and total flow rate can only be understood by the detailed numerical modeling of three-dimensional flow, heat and species' mass transfer with resulting layer deposition on the susceptor. The results of experiments are in good agreement with the modeling computations. (C) 2001 Elsevier Science B.V. All rights reserved

A Hybrid Data‐Driven‐Agent‐Based Modelling Framework for Water Distribution Systems Contamination Response during COVID‐19

Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent‐based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID‐19 pandemic and their location in the network. The results indicate that the ANN‐based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]