Provided is a pipe network evaluation device that is capable of calculating a pressure loss in a pipe network and a consumption flow rate of compressed air in each terminal equipment without inputting layout information on devices, even in a case where there are some devices, the layout information about which is difficult to ascertain visually or obtain from a building drawing, or the like. Provided is a pipe network evaluation device that estimates the resistance of a pipe, the layout information about which is unknown, on the basis of pressure data on compressed air in an air tank, flow rate data on compressed air supplied from the air tank, layout unknown-part pipe outlet pressure data, for which layout information is unknown, and layout known-part pipe layout information, for which layout information is known.

A method of designing a microwave filter using a computerized filter optimizer, comprises generating a filter circuit design in process (DIP) comprising a plurality of circuit elements having a plurality of resonant elements and one or more non-resonant elements, optimizing the DIP by inputting the DIP into the computerized filter optimizer, determining that one of the plurality of circuit elements in the DIP is insignificant, removing the one insignificant circuit element from the DIP, deriving a final filter circuit design from the DIP, and manufacturing the microwave filter based on the final filter circuit design.

A method of designing a microwave filter using a computerized filter optimizer, comprises generating a filter circuit design in process (DIP) comprising a plurality of circuit elements having a plurality of resonant elements and one or more non-resonant elements, optimizing the DIP by inputting the DIP into the computerized filter optimizer, determining that one of the plurality of circuit elements in the DIP is insignificant, removing the one insignificant circuit element from the DIP, deriving a final filter circuit design from the DIP, and manufacturing the microwave filter based on the final filter circuit design.

Systems and methods for planning a wind farm are provided. One example aspect of the present disclosure is directed to a method for planning a wind farm. The method includes determining, by one or more processors, a load for a plurality of wind conditions for a plurality of circumferential sections for a tower of a wind turbine. The method includes accessing, by the one or more processors, a model that predicts wind conditions over a time period. The method includes determining, by the one or more processors, a load sustainment parameter for the plurality of circumferential sections for the predicted wind conditions over the time period.

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

The invention is a method for exploitation of a sedimentary basin containing hydrocarbons, including optimized scaling of the geological model. Based on categorical property measurements, a first meshed representation of a formation is constructed reflecting the categorical property measurements. At least one second meshed representation having a lower resolution is constructed by assigning a categorical property value to each mesh of the second representation corresponding to a group of meshes of the first representation and storing parameters for changing from the second representation to the first representation with those change parameters enabling reconstitution of the first representation.

Conventional systems for monitoring pipe networks are generally not scalable, impractical in the field with uncontrolled environments or rely of static features of pipes that are vary depending on the pipes under consideration. The ideal sensor-ed monitoring systems are not economically viable. Systems and methods of the present disclosure provide an improved data-driven model to rank pipes in the order of burst probabilities, by including dynamic feature values of pipes such as pressure and flow that depends on network structure and operations. The present disclosure enables estimating approximate values for the dynamic features since they are hard to estimate accurately in the absence of a calibrated hydraulic model. The present disclosure also validates the estimated approximate dynamic feature values for the purpose of estimating bursts likelihood vis-a-vis accurate values of the dynamic metrics.