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Engineering Informatics: State of the Art and Future Trends

Li-da Xu

Frontiers of Engineering Management 2014, Volume 1, Issue 3, Pages 270-282 doi: 10.15302/J-FEM-2014038

Abstract: Engineering informatics is an emerging engineering discipline integrating information technology or informaticsnew techniques that can be used for probing industrial information integration including engineering informaticsThese techniques have the potential to contribute to engineering informatics.In this paper, we briefl review the state of the art of engineering informatics as it interfacing with

Keywords： engineering informatics industrial integration industrial information integration engineering (IIIE)

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Informatics: The Frontier of Innovation in Health and Healthcare Views & Comments

Molly Joel Coye, MD, MPH

Engineering 2016, Volume 2, Issue 1, Pages 37-39 doi: 10.1016/J.ENG.2016.01.009

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Data Centric Design: A New Approach to Design of Microstructural Material Systems Article

Wei Chen, Akshay Iyer, Ramin Bostanabad

Engineering 2022, Volume 10, Issue 3, Pages 89-98 doi: 10.1016/j.eng.2021.05.022

Abstract:

Building processing, structure, and property (PSP) relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science. Recent technological advancements in data acquisition and storage, microstructure characterization and reconstruction (MCR), machine learning (ML), materials modeling and simulation, data processing, manufacturing, and experimentation have significantly advanced researchers’ abilities in building PSP relations and inverse material design. In this article, we examine these advancements from the perspective of design research. In particular, we introduce a data-centric approach whose fundamental aspects fall into three categories: design representation, design evaluation, and design synthesis. Developments in each of these aspects are guided by and benefit from domain knowledge. Hence, for each aspect, we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.

Keywords： Materials informatics Machine learning Microstructure Reconstruction Bayesian optimization Mixed-variable

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A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas Article

Marco Massano, Enrico Macii, Andrea Lanzini, Edoardo Patti, Lorenzo Bottaccioli

Engineering 2023, Volume 26, Issue 7, Pages 198-213 doi: 10.1016/j.eng.2022.06.020

Abstract:

The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings' rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO₂ emissions.

Keywords： Energy informatics Geographic information system Load estimation Open data Photovoltaic Residential