Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Phase Field Models and Undestanding Interface Evolution at Crystal Growth Initial States

Département d'Optronique (LETI)

Laboratoire des Matériaux pour la photonique

01-10-2020

SL-DRT-20-0924

marc.parent@cea.fr

Photonics, Imaging and displays (.pdf)

The proposed research concerns modeling the physics of crystal growth involved in Vertical Gradient Freeze (VGF), a method that allows the manufacturing of large ingots for the semi-conductor industry. In this process, the material is molten then solidifies as the temperature is slowly decreased with spatially controlled temperature profiles, in order to favor the orientation and the low defect density of the resulting crystal. One of the main objectives of this project is to design numerical models that could provide an accurate description of the evolution of the liquid-solid interface in a II-VI alloy at the early stages of a crystal growth, so as to gain understanding of how one could control the system thermal state evolution and improve the quality of the crystallization. In particular, an approach based on phase field approximations of the solidification front will be developed. From the numerical point of view, such representation of the front should allow a more robust modeling of topology and phase changes, while also providing insight on the relevant relationships between the mesoscopic and macroscopic scales. Underlying experimental and industrial goals are also to help scientists analyze the process observable signals and determine the optimal conditions that need to be setup in order to achieve desired material properties.

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Bio sensir using near field propagation of millimeter waves

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-10-2020

SL-DRT-20-0933

frederic.hameau@cea.fr

Health and environment technologies, medical devices (.pdf)

In the context of new bio-medical applications, we propose to use solutions from the radio-frequency domain, namely using millimeter wave systems, which had to radiate with nearfield antenna. Depending on the antenna neighborhood, the behavior of the radiated wave changes with its frequency and amplitude. This PhD aims to detecte physiological parameters using this signature of the environnement at different wavelength, signal amplitude and even signal shape (chirp). This physiological parameter could be the sweat, the hartbeat, melanoma, but not only. Target frequency could be from 20GHz to 120GHz which are easy for CMOS integration. From an existing study, the PhD student will have to developpe an accurate solution, which could be based on the antenna impedance variation due to the environement (Power Amplifier output impedance modification tracking) or the analysis of the reflected signal thought a polar receiver (radar mode).

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Digital twin in structural health monitoring for aerospace using machine learning

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND

01-06-2020

SL-DRT-20-0938

olivier.mesnil@cea.fr

Artificial intelligence & Data intelligence (.pdf)

This PhD is funded by the Marie Curie program of European Union through the innovative training network (ITN) GW4SHM on Guided Waves for Structural Health Monitoring. Structural Health Monitoring (SHM) relies on the permanent integration of sensors to create smart structures by enabling the monitoring of structural health over time. In the aerospace industry, an SHM system installed on an aircraft or a reusable launcher would provide a prognostic of the residual life of the structure, leading to safer and cheaper structures through a customized maintenance plan. In SHM, the use of Guided elastic Wave (GW) is particularly promising in the aerospace industry due to the limited added mass of the sensors and high defect sensitivity. Typically in GW-SHM, a sensor network is instrumented on the structure in order to generate and measure propagated elastic waves, carrying information on invisible but potentially harmful defect(s). Advanced post-processing techniques can then be employed to reliably detect, locate and quantify the defect(s). Despites its promises and relative maturity, the field of GW-SHM does not currently have any large scale application in the aerospace industry. The underlying reason is the very high sensitivity of the GW to a large set of parameters, including of course the presence of the defect, but also the local micro structure of the propagating medium, the environmental and operating conditions (such as temperature or loads) or any geometric singularity. The interpretation of GW signals outside a laboratory environment on a known sample is therefore a challenge. Moreover, in order to demonstrate the performance of SHM towards the certification of such systems, extensive simulation campaigns must be conducted to study the results in a large set of conditions and avoid prohibitively expensive experimental trials, which requires a digital twin (i.e. a faithful digital replica of a structure instrumented by an SHM system). However, the digital twin concept only holds in SHM if all the influencing parameters are fully known, which is barely possible for the simplest configurations in well-controlled environments. The goal of this thesis is to expand the concept of digital twin for GW-SHM in order to enable the use of simulation for the reliable interpretation of GW-SHM data in uncontrolled experimental conditions for the detection and characterization of flaws. To achieve this goal, simulation tools developed at CEA-List and integrated in the CIVA software will be extensively used to generate a set of simulations describing a not-fully known experiment. Machine learning tools will then trained to exploit this a priori knowledge in order to extract the defect(s) signature(s) from the signals, enabling the application of diagnostic processes such as guided wave imaging. Reliability of the developed methodology will be of interest to demonstrate its efficiency in representative conditions. The process will then be tested and validated for the SHM of advanced aeronautics composites structures in realistic environments. Working Context The PhD thesis will be hosted by CEA-List at Saclay, France (20km from Paris) with a secondment at Safran Tech at Magny-Les-Hameaux (7km from Saclay) to study the reliability of the developed approach on realistic use-cases. At CEA-List, the department of imaging and simulation for nondestructive testing (DISC) is a department of about 120 people dedicated to the R&D related to simulation, instrumentation and methods for nondestructive testing and SHM. In SHM, activities revolve around novel instrumentations, integrated SHM systems, passive monitoring techniques, simulation, performance demonstration and artificial intelligence, with applications in multiple industries including aerospace, nuclear, petrochemical or land transportation. Safran inaugurated on January 2015, Safran's new Research & Technology Center, Safran Tech, at France's leading science and technology cluster, in Saclay, near Paris. The Safran Tech center houses a research workforce of around 500 scientists and engineers organized in six research units and four platforms. The research units are covering energy & propulsion, materials & processes, sensors, electronics, information & communication technologies, and digital simulation for engineering. Eligibility criteria Marie Sklodowska-Curie Actions Eligibility rules apply. Particularly: Mobility Rule: The researcher (any nationality) must not have resided or carried out his/her main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to his/her recruitment (excluding vacations and short stays). Early Stage Researchers (ESR): ESR shall, at the date of recruitment, be within the first four years of full-time equivalent research experience(*) and have not been awarded a doctoral degree. The 4-years period is measured from the date when the researcher obtained the degree which formally entitle him/her to embark on a doctorate either in the country in which the degree was obtained or the country in which the researcher is recruited. (*) Research and development activities (R&D) in any institution (companies, research organizations, government agencies, universities, etc.) are considered as research experience. Teaching in universities is also considered as research experience since it's part of the research & academic career. Mandatory skills: - Scientific curiosity and motivation for challenging tasks - M2 or equivalent in one of the following area (or related fields) o Mechanical engineering ? elastic or acoustic waves o Machine learning ? data analysis ? signal processing Optional skills: - Hands-on experience in machine learning and data analysis is a plus - Experience in a laboratory and data acquisition

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Intensification of carbon dioxide sequestration through microalgae photosynthesis

DPACA (CTReg)

Autre DPACA

01-10-2020

SL-DRT-20-0948

gatien.fleury@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Numerous scientific studies, led in particular by IPCC, have shown that anthropogenic greenhouse gas emissions are responsible for global warming of Earth's atmosphere. Due to the tremendous volumes emitted worldwide annually (more than 30 billion metric tons), CO2 is considered to be one of the main contributors to global warming. Among the methods for sequestering CO2, photosynthesis is particularly attractive, since it makes it possible to create different products through to the capture of solar energy together with CO2. This PhD thesis will focus on the use of microalgae photosynthesis for CO2 sequestration. After a bibliography step which will allow the student to better understand the equilibria at stake and the corresponding equations, first part of the work will focus on the development of an analytical model allowing to simulate different operating conditions by a multidisciplinary approach (in particular fluid mechanics, chemistry and biology). After validation of this model on simple experiments made up with a strain of reference, an innovative culture device (photobioreactor) making it possible to intensify the mass transfers of a gas phase enriched in C02 towards microalgal biomass could be proposed, developed and tested.

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Online characterization and classification of radiological signals using embedded machine learning

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-10-2019

SL-DRT-20-0956

gwenole.corre@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Machine learning classification methods have become ubiquitous in the fields of signal and image processing. Nevertheless, these classification methods remain very little used today in the fields of embedded applications. In fact, several studies have shown that machine learning classification methods provide satisfactory performance in classifying signals received from radiological sensors. However, most of these studies and solutions have been developed and experimented offline, and there are few or no real-time neural network learning based solutions. The aim of the thesis is to propose learning methods that can be embedded in real-time portable measurement devices. The proposed methods have to meet the radiological signal classification constraints and to enhance the performance of these measurement devices.

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Crowdsensing for identifying sources of air pollution using a network of low-cost multimodal gas sensors

Département Systèmes (LETI)

Laboratoire Signaux et Systèmes de Capteurs

SL-DRT-20-0966

sylvain.leirens@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Crowdsensing for the identification of air pollution sources relies on the use of a large number of low-cost multimodal gas sensors. The subject of the thesis covers the implementation of this network of sensors for the identification of high emitters in urban road traffic. It is now recognized that a small number of highly polluting vehicles is responsible for a large fraction of the pollution generated by the road traffic, while urban air quality is becoming a growing concern in Europe. The thesis work will consist in formulating and solving a problem of separation and localization of mobile air pollution sources in urban environment with a network of mobile sensors. Measurement campaigns will be carried out to validate experimentally the approach developed in the thesis.

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