Scipedia
Not a member yet
33380 research outputs found
Sort by
Two iterative methods for sizing pipe diameters in gas distribution networks with loops
Get PDFClosed-loop pipe systems allow the possibility of the flow of gas from both directions across each route, ensuring supply continuity in the event of a failure at one point, but their main shortcoming is in the necessity to model them using iterative methods. Two iterative methods of determining the optimal pipe diameter in a gas distribution network with closed loops are described in this paper, offering the advantage of maintaining the gas velocity within specified technical limits, even during peak demand. They are based on the following: (1) a modified Hardy Cross method with the correction of the diameter in each iteration and (2) the node-loop method, which provides a new diameter directly in each iteration. The calculation of the optimal pipe diameter in such gas distribution networks relies on ensuring mass continuity at nodes, following the first Kirchhoff law, and concluding when the pressure drops in all the closed paths are algebraically balanced, adhering to the second Kirchhoff law for energy equilibrium. The presented optimisation is based on principles developed by Hardy Cross in the 1930s for the moment distribution analysis of statically indeterminate structures. The results are for steady-state conditions and for the highest possible estimated demand of gas, while the distributed gas is treated as a noncompressible fluid due to the relatively small drop in pressure in a typical network of pipes. There is no unique solution; instead, an infinite number of potential outcomes exist, alongside infinite combinations of pipe diameters for a given fixed flow pattern that can satisfy the first and second Kirchhoff laws in the given topology of the particular network at hand
EmoGame: Unraveling Emotional Correlations in Teen Video Gamers for Health Monitoring Insights - Biomedical Analysis of the Emotional Impact of Fortnite and Minecraft
Get PDF
Wind Turbine Gearbox Condition Monitoring Using Vibration Data and Mel-Frequency Cepstral Coefficients
Get PDFDetection of Lossy Bolts in a Jacket-Type Wind Turbine Support Using a Vibration-Only Response Mechanism Based on Accelerometer Data
Get PDFFBG based strain monitoring system to assess load distribution and differential settlements in a 33-story building: The constructive life cycle behavior
Get PDF
Digitization of subsurface geological stratigraphy using machine learning and neighborhood aggregation
Get PDFIn engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological deposition process. Explicit knowledge of subsurface stratigraphy is a critical input for the analysis, design, and construction of geotechnical engineering systems. However, the accurate and reliable modelling of subsurface geological stratigraphy is challenging due to the limited number of available boreholes in practice and the complex nature of soil stratigraphy. This paper presents an innovative machine learning framework built upon the neighborhood aggregation technique for the prediction of digitized subsurface geological stratigraphy. To predict the stratigraphy at a given point of interest, neighborhood aggregation is first performed to intelligently consolidate the stratigraphy information from its neighboring boreholes, resulting in additional features associated with the target location. By combining the extra stratigraphy information with conventional location-specific features, the framework enhances the predictive capabilities of classical machine learning models at a finer scale. The proposed framework is implemented using common machine learning models and is validated using a simulated benchmark 3D example. The results of leave-one-out cross-validation demonstrate that the proposed framework can improve the performance of classical machine learning models, leading to more reasonable stratigraphy transition and associated uncertainty quantification
Application of Physics-Informed Machine Learning to Geotechnical and Geophysical Site Investigation Data To Define Centimetre-Scale Design Parameters for Offshore Wind
No full textOffshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (ð) from seismic CPT. Subsequently, porosity and P-wave velocity (ð) are derived using a ð to bulk density correlation and a dynamic poroelastic model. The DNN is trained and tested on a dataset comprising 5284 instances of public-domain geotechnical CPT test data, including depth, tip resistance, sleeve friction, and ð from seismic CPT. During testing, the DNN model demonstrates a mean absolute error of 55 m s-1 between predicted and measured ð values. The uncertainty in ð predictions is attributed to factors such as (i) limited training data for some soil types such as gravelly sands, (ii) intricate relationship between geotechnical CPT features and seismic properties influencing ð, (iii) the presence of CPT features and ð combinations that lie well outside the region from most combinations (i.e. outliers), and (iv) CPT features and ð measurements that are averaged over different depth ranges. The derived porosity and
INDUSTRY 4.0 and SOCIOECONOMIC PROGRESS
No full textCatalonia is a country with a long industrial tradition, made up of an important fabric of SMEs and large companies, indigenous and multinational. This industrial tradition, which over the decades has materialized with an important contribution to the GDP and leading the job generation rankings, saw it lose strength at the end of the last century and the beginning of this one, as a result of the process of offshoring arose as a result of globalization and the growth of manufactured products in countries with low labor costs and few environmental protection rules.
Over the last few years it has become evident that the paradigm of Asia as the world's factory was beginning to falter, the energy costs in terms of transport, the factors associated with product customization, proximity services, the dangers of covid, the change in culture from using and throwing to the culture of conserving and reusing explain the need to develop policies aimed at promoting reindustrialization with sustainability criteria in a global competition framework and generating quality employment. Requirements that will only become possible with a strong, innovative and productive industrial sector that assumes the criteria of Industry 4.0.
Industry 4.0, intelligent factories and products leads to an increase in the competitiveness of the economy based on five components: Innovation, technology, talent, sustainability and inclusive growth.
The challenge of Industry 4.0 can be assumed by Catalonia given that Catalan industry and manufacturing are highly competitive at an international level, as shown by the amounts of exports. To make it possible, involves considering manufacturing as a key sector for economic competitiveness and job creation considering three objectives: the first, making it possible to turn scientific progress into social progress by enhancing the transfer of knowledge and the symbioses between the world of science and the productive world. The second is to facilitate the emergence of new initiatives in areas of emerging knowledge and to facilitate their interrelationship with established companies with proven productive capacity. The third, articulating an administration close to the company, familiar with its problems and challenges with the ability to listen to proposals and turn them into action programs considering the reality of the Catalan productive fabric and the entirety of the Catalan territory
Achieving this challenge in the context described entails the development of an innovative industrial policy that contemplates digital transformation, strengthening the capitalization of companies and bringing research and the University closer to companies and in promotion policies to mergers and alliances between companies to reach the required volume