Geospatial Model of COVID-19 Spreading and Vaccination With Event Gillespie Algorithm

We developed a model and a software package for stochastic simulations of transmission of COVID-19 and other similar infectious diseases, that takes into account contact network structures and geographical distribution of population density, detailed up to a level of location of individuals. Our analysis framework includes a surrogate model optimization process for quick fitting of the model’s parameters to the observed epidemic curves for cases, hospitalizations and deaths. This set of instruments (the model, the simulation code, and the optimizer) is a useful tool for policymakers and epidemic response teams who can use it to forecast epidemic development scenarios in local environments (on the scale from towns to large countries) and design optimal response strategies. The simulation code also includes a geospatial visualization subsystem, presenting detailed views of epidemic scenarios directly on population density maps. We used the developed framework to draw predictions for COVID-19 spreading in the canton of Geneva, Switzerland.

SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS

This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.

SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS

This paper investigates the applicability of surrogate model optimization (SMO) usingdeep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.

Mass-Optimized Design of Guyed Wind Turbine Tower Using Struts

Continuing upscaling trends in turbine height, rotor diameter, and rated power have resulted in massive, expensive tower structures. A modified guyed tower concept with struts (GTS) is proposed for saving material, and the basic design is made for a 2.5MW turbine. The tower and cable dimensions are optimized for lowest system cost given yield constraints. DACE (design and analysis of computer experiments) methods of sampling and surrogate model optimization are used for efficient parameter study and optimization of the ABAQUS finite element model using DAKOTA software. The resulting design is highly effective in transferring turbine loads from the tower to the cables. A mass savings of 41% is calculated vs. conventional structures, and further investigation of the GTS is recommended for both onshore and offshore applications. Additionally, the concept of retowering older turbines is introduced and proposed as an economic alternative to the common practice of repowering old wind farms with larger, new machines. The GTS is specifically suited to this application. Lastly, the design methodology developed for this study is shown to be effective and efficient; it can be applied for the massoptimization of similar cablesupported truss structures.