Methods, apparatuses, and embodiments related to a technique for monitoring construction of a structure. In an example, a robot with a sensor, such as a LIDAR device, enters a building and obtains sensor readings of the building. The sensor data is analyzed and components related to the building are identified. The components are mapped to corresponding components of an architect's three dimensional design of the building, and the installation of the components is checked for accuracy. When a discrepancy above a certain threshold is detected, an error is flagged and project managers are notified. Construction progress updates do not give credit for completed construction that includes an error, resulting in improved accuracy progress updates and corresponding improved accuracy for project schedule and cost estimates.

The invention relates to a computer implemented method and tool for determining pressure-drop in multiphase pipeline flow where the effective surface roughness, k.sub.eff, of liquid film coated sections of the inner pipeline wall is assumed to be equal to the maximum hydraulic roughness, k.sub.s.sup.max. The maximum hydraulic roughness is further assumed to be proportional to a maximum stable droplet size, d.sub.droplet.sup.max, i.e.: k.sub.eff=k.sub.s.sup.max=K.Math.d.sub.droplet.sup.max, where K is a correlation coefficient. The invention further relates to applying the computer implemented method for designing a pipeline-based fluid transport system for transport of multiphase fluids.

The invention relates to a computer implemented method and tool for determining pressure-drop in multiphase pipeline flow where the effective surface roughness, k.sub.eff, of liquid film coated sections of the inner pipeline wall is assumed to be equal to the maximum hydraulic roughness, k.sub.s.sup.max. The maximum hydraulic roughness is further assumed to be proportional to a maximum stable droplet size, d.sub.droplet.sup.max, i.e.: k.sub.eff=k.sub.s.sup.max=K.Math.d.sub.droplet.sup.max, where K is a correlation coefficient. The invention further relates to applying the computer implemented method for designing a pipeline-based fluid transport system for transport of multiphase fluids.

A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

Gross energy load can be determined by combining periodic net load statistics, such as provided by a power utility or energy agency, with on-site power generation, such as photovoltaic power generation, as produced over the same time period. The gross energy load provides an indication upon which other types of energy investment choices can be evaluated. These choices can include traditional energy efficiencies, such as implementing electrical efficiency measures, which includes cutting down on and avoiding wasteful energy use and switching to energy efficient fixtures, and improving the thermal efficiency and performance of a building. The choices can also include non-traditional energy efficiencies, such as replacing a gasoline-powered vehicle with an electric vehicle, fuel switching from a water heater fueled by natural gas to a heat pump water heater, and fuel switching from space heating fueled by natural gas to a heat pump space heater.

A method designs nuclear reactors using design variables and metric variables. A user specifies ranges for the design variables and threshold values for the metric variables and selects design parameter samples. For each sample, the method runs three processes, which compute metric variables for thermal-hydraulics, neutronics, and stress. The method applies a cost function to compute an aggregate residual of the metric variables compared to the threshold values. The method deploys optimization methods, either training a machine learning model using the samples and computed aggregate residuals, or using genetic algorithms, simulated annealing, or differential evolution. When using Bayesian optimization, the method shrinks the range for each design variable according to correlation between the respective design variable and estimated residuals using the machine learning model. These steps are repeated until a sample having a smallest residual is unchanged for multiple iterations. The final model assesses relative importance of each design variable.

The invention is a method of constructing a wind farm from a predefined number of wind turbines in a predetermined space. This method comprises two discrete distributions (RD1, RD2), one for the wind speed and one for the wind direction, and a first discrete grid (RD3) of the predetermined space. The method also comprises the probability of occurrence (Prob) of each discrete wind speed value in each discrete wind direction. This method uses a first wind turbine arrangement in the predetermined space, then the position of the wind turbines is modified, one by one (i), by determining discrete positions (PDP_i) around the position to be modified. The position selected (Pos_i) is the one allowing the annual energy produced by the wind farm to be maximized.

The invention is a method of constructing a wind farm from a predefined number of wind turbines in a predetermined space. This method comprises two discrete distributions (RD1, RD2), one for the wind speed and one for the wind direction, and a first discrete grid (RD3) of the predetermined space. The method also comprises the probability of occurrence (Prob) of each discrete wind speed value in each discrete wind direction. This method uses a first wind turbine arrangement in the predetermined space, then the position of the wind turbines is modified, one by one (i), by determining discrete positions (PDP_i) around the position to be modified. The position selected (Pos_i) is the one allowing the annual energy produced by the wind farm to be maximized.

Swimming pools can be visualized according to some aspects described herein. In one example, a system can receive a user selection of a particular liner from among a plurality of liner options for a virtual swimming pool. The system can generate a virtual swimming pool having the particular liner. The virtual swimming pool can be a three-dimensional (3D) rendering of a swimming pool with the particular liner. The system can then output the virtual swimming pool for display on a display device.