China
Africa
Explore chapters and articles related to this topic
Convergence of Technologies and IT/OT Integration
Published in Stuart Borlase, Smart Grids, 2018
Stuart Borlase, Michael Covarrubias, Jim Horstman, Greg Robinson, Stuart Borlase, John Chowdhury, Greg Robinson, Tim Taylor
The Time-Series Data Store is optimized for handling time series data, arrays of numbers indexed by time (a date time or a date time range). A time series of energy consumption can be used for understanding a load profile. A database that can correctly, reliably, and efficiently implement query operations is typically specialized for time-series data. Software with complex logic or business rules and high transaction volume for time series data may not be practical with traditional relational database management systems. Flat file databases are not a viable option either, if the data and transaction volume reach a maximum threshold. Queries for historical data, replete with time ranges and roll ups and arbitrary time zone conversions, are more difficult in a relational database. Database that joins across multiple time series data sets is typically only practical when the time tag associated with each data entry spans the same set of discrete times for all data sets across which the join is performed.
A Multi-Peak Evolutionary Model for Stochastic Simulation of Ground Motions Based on Time-Domain Features
Published in Journal of Earthquake Engineering, 2021
Zakariya Waezi, Fayaz R. Rofooei, M. Javad Hashemi
For the problem in hand, four parameters including moment magnitude , closest distance to the rupture plate , hypocentral depth , and shear wave velocity of the upper 30 m of the local soil, , are considered to be the independent scenario-controlled parameters of the event. Even though the database used here is limited to 252 records, it is still possible to generate a relationship between the best-estimated model parameters and their corresponding event data. The data for these records are retrieved from NGA-West2 database flat file [Ancheta et al., 2014]. While some studies do not consider the hypocentral depth variable in their regressive relationship equations, the null hypothesis test with 5% significance for not including this variable in the regression parameters for the current data was rejected. Thus, due to its importance, this parameter was included in the regression equations, as well.
Microstructure modelling for metallic additive manufacturing: a review
Published in Virtual and Physical Prototyping, 2020
Joel Heang Kuan Tan, Swee Leong Sing, Wai Yee Yeong
In this review, the focus is on the microstructural evolutions during the AM processes. The results from these models have the potential to be fed and linked to mechanical properties prediction of the AM parts. Recently, Ge et al. used an integrated modelling of process–structure–property relationship in laser AM for duplex titanium alloy. It is found that the formation of larger grain sizes and more α phase are due to higher scanning speed. This higher composition of α phase is then linked to higher mechanical properties (Ge et al. 2019). At current stage, most of the work done has linked the thermal simulations to the residual stresses in AM parts (Ding et al. 2011; Heigel, Michaleris, and Reutzel 2015; Yang et al. 2016; Zhao et al. 2017). The main challenge to link the different models together to achieve the microstructure to mechanical properties relation is the exchange of information between the individual model. Yan et al. have proposed to use a fixed format, flat-file database as a solution (Yan et al. 2018; Yan et al. 2018). However, more efficient approaches for information exchange have to be developed (Smith et al. 2016).