Multilayer Model Predictive Control for a Voltage Mode Digital Power Amplifier

The application of the finite control set model predictive control to cascaded inverters is severely limited by its computational complexity. In this paper, a load observer based multilayer model predictive control is proposed for the voltage mode digital power amplifier employing cascaded full-bridge neutral point clamped inverter, which can avoid the use of load current sensor and greatly reduce the controller computation without affecting its dynamic performance. The discrete mathematical model of the voltage mode digital power amplifier employing cascaded full-bridge neutral point clamped inverter is established with filter inductor current and filter capacitor voltage as state variables. A load current observer is designed based on this to avoid the use of load current observer. Based on the discrete model and the observed load current, the upper layer of the multilayer model predictive control determines the optimal level that minimizes the cost function. The middle layer allocates the optimal level to each submodule in order to achieve capacitor voltage balancing. The lower layer determines the switching state of each submodule in order to reduce switching actions. Finally, the experimental results based on the designed nine-level prototype show that the develop multilayer model predictive control lead to acceptable steady state, dynamic and robust performance, with only 1.37% of the run time of the traditional model predictive control.

A Multilayer Model for Early Detection of COVID-19

ABSTRACTCurrent efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information: 1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264,516 COVID-19 PCR-tests, out of which 16,512 were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore, considering only information collected before the testing episode – that is, before the individual may had the chance to report on any symptom – our model could reach a considerably high AUC of 79.5%. Namely, most of the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be utilized for a more efficient testing policy.

Multilayer-HySEA model validation for landslide-generated tsunamis – Part 2: Granular slides

The final aim of the present work is to propose a NTHMP-benchmarked numerical tool for landslide-generated tsunami hazard assessment. To achieve this, the novel Multilayer-HySEA model is validated using laboratory experiment data for landslide-generated tsunamis. In particular, this second part of the work deals with granular slides, while the first part, in a companion paper, considers rigid slides. The experimental data used have been proposed by the US National Tsunami Hazard and Mitigation Program (NTHMP) and were established for the NTHMP Landslide Benchmark Workshop, held in January 2017 at Galveston (Texas). Three of the seven benchmark problems proposed in that workshop dealt with tsunamis generated by rigid slides and are collected in the companion paper (Macías et al., 2021). Another three benchmarks considered tsunamis generated by granular slides. They are the subject of the present study. The seventh benchmark problem proposed the field case of Port Valdez, Alaska, 1964 and can be found in Macías et al. (2017). In order to reproduce the laboratory experiments dealing with granular slides, two models need to be coupled: one for the granular slide and a second one for the water dynamics. The coupled model used consists of a new and efficient hybrid finite-volume–finite-difference implementation on GPU architectures of a non-hydrostatic multilayer model coupled with a Savage–Hutter model. To introduce the multilayer model more fluidly, we first present the equations of the one-layer model, Landslide-HySEA, with both strong and weak couplings between the fluid layer and the granular slide. Then, a brief description of the multilayer model equations and the numerical scheme used is included. The dispersive properties of the multilayer model can be found in the companion paper. Then, results for the three NTHMP benchmark problems dealing with tsunamis generated by granular slides are presented with a description of each benchmark problem.

Mass accommodation and gas–particle partitioning in secondary organic aerosols: dependence on diffusivity, volatility, particle-phase reactions, and penetration depth

Mass accommodation is an essential process for gas–particle partitioning of organic compounds in secondary organic aerosols (SOA). The mass accommodation coefficient is commonly described as the probability of a gas molecule colliding with the surface to enter the particle phase. It is often applied, however, without specifying if and how deep a molecule has to penetrate beneath the surface to be regarded as being incorporated into the condensed phase (adsorption vs. absorption). While this aspect is usually not critical for liquid particles with rapid surface–bulk exchange, it can be important for viscous semi-solid or glassy solid particles to distinguish and resolve the kinetics of accommodation at the surface, transfer across the gas–particle interface, and further transport into the particle bulk. For this purpose, we introduce a novel parameter: an effective mass accommodation coefficient αeff that depends on penetration depth and is a function of surface accommodation coefficient, volatility, bulk diffusivity, and particle-phase reaction rate coefficient. Application of αeff in the traditional Fuchs–Sutugin approximation of mass-transport kinetics at the gas–particle interface yields SOA partitioning results that are consistent with a detailed kinetic multilayer model (kinetic multilayer model of gas–particle interactions in aerosols and clouds, KM-GAP; Shiraiwa et al., 2012) and two-film model solutions (Model for Simulating Aerosol Interactions and Chemistry, MOSAIC; Zaveri et al., 2014) but deviate substantially from earlier modeling approaches not considering the influence of penetration depth and related parameters. For highly viscous or semi-solid particles, we show that the effective mass accommodation coefficient remains similar to the surface accommodation coefficient in the case of low-volatility compounds, whereas it can decrease by several orders of magnitude in the case of semi-volatile compounds. Such effects can explain apparent inconsistencies between earlier studies deriving mass accommodation coefficients from experimental data or from molecular dynamics simulations. Our findings challenge the approach of traditional SOA models using the Fuchs–Sutugin approximation of mass transfer kinetics with a fixed mass accommodation coefficient, regardless of particle phase state and penetration depth. The effective mass accommodation coefficient introduced in this study provides an efficient new way of accounting for the influence of volatility, diffusivity, and particle-phase reactions on SOA partitioning in process models as well as in regional and global air quality models. While kinetic limitations may not be critical for partitioning into liquid SOA particles in the planetary boundary layer (PBL), the effects are likely important for amorphous semi-solid or glassy SOA in the free and upper troposphere (FT–UT) as well as in the PBL at low relative humidity and low temperature.