Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Electric versus conventional vehicles: social costs and benefits in france

Abstract: This article compares the social costs of electric vehicles with those of conventional, thermal vehicles for typical passenger use in the Ile-de-France region (Greater Paris), a case of particular interest because nearly 80% of the electricity is generated by nuclear power plants. A four-seat electric car is compared to a new conventional car of the same make and model; for the latter both the gasoline and the diesel version are considered because almost half of new car sales in France are diesel. These results are also compared to typical existing diesel and gasoline vehicles in the current French fleet. The methodology developed by the ExternE (External Costs of Energy) Project of the European Commission is used to estimate the costs associated with atmospheric pollution due to power plants, refineries and tail pipe emissions. Our discussion of externalities is limited to air pollution thus excluding others such as costs associated with noise or accidents. Our results imply that the external costs are large and significant, even when one considers the uncertainties. If internalized by government regulations, these externalities can render the total cost of an electric vehicle more competitive with that of currently available thermal vehicles in large urban centers if the electricity is produced by sources with low pollution. However, the current generation electric vehicles are so expensive that internalization of pollution damage would not give it a very clear advantage.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

Abstract: Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Converting a successful research project into a sustainable service: The case of the SoDa Web service

Abstract: Information on solar radiation is a critical issue in several environmental domains. The means for accessing information suffer from severe drawbacks. The SoDa project was launched (2000–2003, IST program of the European Commission) to bring solutions by an efficient use of information and communication technologies. Integration of information sources of different natures was realized by the SoDa Intelligent System. Surveys of users demonstrated that large gains in terms of efficiency, costs, etc. were expected by engineers, companies, agencies and research institutes. The SoDa Service was created in 2003. During the past three years, it has undergone several improvements, including works on the user interface and on the presentation of the services, all aiming at consolidating it with respect to access by users. Promotion efforts were made in the media, including TV. The use of the SoDa Service is increasing from year to year: in 2003, 10,000 requests were satisfied, in 2004, 17,000 requests and 27,000 in 2005. This article presents the lessons learned from the past years and the perspectives of the SoDa Service. We discuss the sustainability of the SoDa Service, the technologies used and the approach to customers, in the perspective of building a B2B merchant site.

Thermal-hydraulic CFD study in louvered fin-and-flat-tube heat exchangers

Abstract: Heat transfer performance prediction by CFD codes is of major interest. Usually air-side heat transfer characteristics of fin-and-tube heat exchangers are determined from limited experimental data. The ability of CFD code to predict flow patterns and thermal fields allows determining the heat transfer characteristics by performing ‘numerical experiments’. CFD calculations of a 1-row automotive condenser are compared to experimental results and correlations of the literature matching the fin design and the flow conditions. Calculations are performed for different air frontal velocities. 2D models, with uniformly constant fin temperature overestimate significantly the heat transfer coefficient. 3D models, taking into account tube effects, conjugate heat transfer and conduction through the fin are in better agreement with the experimental results. However, even if an offset in noticed between CFD calculations and the experimental results, the trends are comparable and CFD study permits to reach local information, leading to better understanding of the physical phenomena involved in compact heat exchangers. An attempt for 2D unsteady flow has also been performed. Results are discussed in terms of flow pattern and heat transfer coefficient behaviour.