Frequency tunable elastic plate wave resonators and filters
The increasing number of frequency bands having to be dealt with in mobile phone systems require a huge number of band pass filters in such systems. In this context, the capability to provide frequency tunable resonators and filters is seen as a key enabling element in future wireless transmission systems.
CEA-LETI has been working for more than 10 years on the development of resonators and filters exploiting the propagation of guided elastic waves in piezoelectric thin films. It has also proposed several concepts for frequency agile resonators and filters.
The purpose of this post-doc will be to further develop these ideas and to apply them to the design of demonstrators matching realistic specifications. In collaboration with the other member of the project team, more focused on fabrication in clean rooms, the candidate will propose innovative structures demonstrating frequency tuning of reconfigurability, and will take in charge their electrical characterization.
Development of a computational framework dedicated to model order reduction by certified reduced basis method.
Many engineering fields require to solve numerically partial differential equations (PDE) modeling physical phenomenon.
When we focus on a mathematical model that describes the physical behavior of a system based on one or more parametrized PDEs (geometrical or physical parameters), it may be desirable to rapidly and reliably evaluate the output of the model (quantity of interest)
for different parameter values.
The real-time context, needed to perform command-control, and contexts asking many evaluations of model outputs (typically for optimization methods or uncertainty and sensitivity analysis) lend themselves perfectly.
The certified reduced basis method is an intrusive reduction method beacause, unkike non-intrusive methods, the reduction is based on the projection of operators associated to physical model PDEs.
This method allow to obtain rapidly, for a given set of parameter values, an approximation of the evaluation of the model output.
One of the strengths of the method is the "certified" aspect to estimate the approximation error of the model output evaluation.
The goal of the post-doctorate is to develop a computational framework for the certified reduced basis method. This framework should be based on the TRUST platform (https://sourceforge.net/projects/trust-platform/) developed at CEA and will be generic enough to be used to deal with different types of problems (linear or not, stationary or not, coercive or not...)
The framework will be used in the case of a two-fluid mixing model.
Cluster dynamic simulations of materials under irradiation
Alloys used in nuclear applications are subjected to neutron irradiation, which introduces large amounts of vacancy and interstitial defects. Over time, these defects migrate, recombine and agglomerate with minor alloying elements to form small clusters. This affects the mechanical properties of ferritic steels and weakens them. In this context, the microstuctural evolution is to be simulated using the rate equation cluster dynamic method. However, this approach becomes ineffecient when several minor alloying elements need being taken into account. The difficulty comes from the huge number of cluster variables to describe. The project aims at optimizing the code efficiency on a distributed parallel architecture by implementing parallelized vector and matrix functions from SUNDIALS library. This library is used to integrate the ordinary differential equation describing the reactions between clusters. Another aspect of the work is more theoretical and involves reformulating the non-linear root-finding problem by taking advantage of the reversibility of most chemical reactions. This property should facilitates the implementation of direct and gradients iterative sparse solvers for symmetric definite positive matrices, such as the multi-frontal Cholesky factorization and the conjugate gradient methods, respectively. One avenue of research will consists of combining direct and iterative solvers, using the former as a preconditioner of the latter.
Ultra Low Power RF Communication Circuit and System Design for Wake-Up Radio
Today, there is a strong demand in developing new autonomous Wake-Up radio systems with tunable performances and independent clocking system. The objectives of the proposed contract it to exploit the capacity of CMOS FD-SOI technologies to develop such devices, improving power consumption and RF performance above the state of the art, thanks to the natural low parasitic and tuning capacity through back biasing of the FD-SOI . A particular attention will be paid to the development of a new power efficient, fast settling, frequency synthesis system.
The chosen candidate will be involved both in RF system and circuit design, with the support of the experienced RF System & Design team.
Implementation of a software package for the simulation of the Infrared Thermography Non Destructive Testing method
The CEA LIST implements simulation tools for several Non Destructively Testing (NDT) techniques, integrated to the CIVA software platform. The different methods used, nowadays in the CIVA platform, concern the ultrasonics, eddy current and radiography techniques. The TREFLE is a reference lab in thermics and had developped some original modelling approachs for the control by Infrared Thermography (IR) method. In the frame of a project funded by the Aquitaine region, these two labs collabore to implement simulation tools for the NDT by the Infrared Thermography technique, dedicated to industrial applications and accesssible to a non-numericians public.
The objective of this post-doc position is the implementation of physical modelling (in a matlab environment) for the resolution of transient thermal problems in multilayers configurations (like composite materials used in aeronautics), eventually anisotropic, for a flash or a periodic excitation with uniform or point irradtiation.
Construction of databases for radionuclide identification based on neural networks (NANTISTA project)
The project NANTISTA (Neuromorphic Architecture for Nuclear Threat Identification for SecuriTy Applications) deals with the prevention of illegal traffic of nuclear materials at international borders. The project aims at the development of a detection platform using plastic scintillators for fast radionuclide identification (such as fissile materials) based on neural networks. The post-doctoral subject consists in the development of the detection system and the construction of databases dedicated to the learning process and the optimization of the neural networks. The databases will be built with experimental measurements given by radioactive sources. Radiation-matter simulations (Monte-Carlo codes Geant4 and Penelope) will also be implemented for the construction of the databases.
Distributed optimal planning of energy resources. Application to district heating
Heating district networks in France fed more than one million homes and deliver a quantity of heat equal to about 5% of the heat consumed by the residential and tertiary sector. Therefore, they represent a significant potential for the massive introduction of renewable and recovery energy. However, heating networks are complex systems that must manage large numbers of consumers and producers of energy, and that are distributed in extended and highly branched geographical zones. The aim of the STRATEGE project, realized in collaboration among the CEA-LIST and the CEA-LITEN, is to implement an optimal and dynamic management of heating networks. We propose a multidisciplinary approach, by integrating the advanced network management using Multi-Agent Systems (MAS) and by considering simplified physical models of transport and recovery of heat developed on Modelica.
The post-doc’s goal is to design mechanisms of planning and optimization for allocation of heat resources that consider the geographical information from a GIS and the predictions of consumption, production and losses calculated with the physical models. In this way, several characteristics of the network will be considered: the continuous and dynamic aspect of the resource; sources with different behaviors, capabilities and production costs; the dependence of consumption/production to external aspects (weather, energy price); the internal characteristics of the network (losses, storage capacity). The developed algorithms will be implemented in a existing MAS management plateform and will constitute the main brick of a decision-support engine for the management of heating systems. It will initially operate in a simulated environment and in a second time online on a real system.
Compressed Sensing for ultrasonic imaging: disruptive method development and prototyping
In non-destructive ultrasonic testing, multi-element sensors are used for the inspection of structures to ensure the safety of people and infrastructures. Currently, one of the driving factor of an ultrasonic method is the number of elements of the sensor, influencing the speed and efficiency of the inspection but also the cost and the volume of the equipment. This project aims at developing a prototype of a multi-element sensor with a limited number of elements compared to current state of the art equipment, without losing imaging resolution. To achieve this goal, Compressed Sensing (CS), a recent technique of signal processing allowing to go beyond the traditional sampling theorems and to reconstruct data from severely undersampled measurements, will be used. The ultrasonic inspection procedure will need to be entirely rethought to meet the CS requirements, specifically the sparsity of the measured data and the incoherence of the measurement process. The expected results is a significant reduction (of the order of 5) of the number of elements to conduct imaging, which would be a true revolution in NDT with direct applications in various industrials sectors.
The following laboratories, all located in Saclay (France) of the CEA (the French atomic commission), will participate to the project: the NDT department for its expertise in multi-element ultrasonic testing and Neurospin and Cosmostat for their expertises in the field of CS, mainly applied to medical RMI imaging and astrophysics, respectively. The collaboration between these three labs, each among the worldwide leading institutes in their respective fields, will ensure the creation of a new and disruptive family of sensors.
Distributed multiagent resources allocation. Application to district heating
Heating district networks in France fed more than one million homes and deliver a quantity of heat equal to about 5% of the heat consumed by the residential and tertiary sector. Therefore, they represent a significant potential for the massive introduction of renewable and recovery energy. However, heating networks are complex systems that must manage large numbers of consumers and producers of energy, and that are distributed in extended and highly branched geographical zones. The aim of the SIGMA project, realized in collaboration among the CEA-LIST and the CEA-LITEN, is to implement an optimal and dynamic management of heating networks. We propose a multidisciplinary approach, by integrating the advanced network management using Multi-Agent Systems (MAS), by taking into account spatial constraints using Geographic Information Systems (GIS) and by considering simplified physical models of transport and recovery of heat.
The post-doc’s goal is to design mechanisms for dynamically allocating resources that consider the geographical information from the GIS and the predictions of consumption, production and losses calculated with the physical models. In this way, several characteristics of the network will be considered: the continuous and dynamic aspect of the resource; sources with different behaviors, capabilities and production costs; the dependence of consumption / production to external aspects (weather, energy price); the internal characteristics of the network (losses, storage capacity). The coupling with a GIS should allow implementing self-configuration mechanisms for the management of different networks and different levels of granularity obtained by reduction of the original GIS. The MAS should dynamically establish the link between the suitable simplified models and the desired level of granularity and then it will create the agents needed to represent the system.
Real time low cost algorithms for brain computer interface with multiple degrees of freedom
The topic of the postdoctoral project is the optimization of BCI methods and algorithms for medical application in humans (quadriplegic subjects).
Namely the particular goal of the postdoctoral fellow will be optimization and the acceleration of calculation to allow multiple degrees of freedom (up to 26) in real time. Selecting the appropriate features subset will improve the computational efficiency and the quality of control. To this purpose the algorithms of sparse modeling will be applied.
To map ECoG recordings to the spatial-temporal-frequency space, continuous wavelet transform (CWT) is applied. Optimization will include the implementation of low cost CWT and C++ coding.
The project will include the test and the adaptation of BCI algorithms to wireless signal transmission with the implant WIMAGINE.
Finally the adaptation of algorithms to medical environment of quadriplegic subjects (the use of imaginary tasks, presence of stimuli in the signal, the restricted duration of experiments) will be under responsibility of postdoctoral scientist.