Simulation of ultrasonic waves interaction phenomena with metal microstructure for imaging and characterisation purposes

The interaction of waves with matter is highly dependent on the frequency of these waves and the scale of their wavelengths in relation to the properties of the medium under consideration. In the context of ultrasound imaging applications, particularly concerning metals, the scales considered are generally on the order of a millimeter (ranging from a tenth to several tens of millimeters). However, depending on the manufacturing processes used, metallic materials, often anisotropic, can also have a microstructure with heterogeneities of similar dimensions. Consequently, ultrasonic waves propagating through metals may, under certain circumstances, be significantly influenced by their microstructures. This can either pose limitations to certain ultrasonic techniques (e.g., attenuation, structural noise) or provide an opportunity to estimate local properties of the inspected metal.
The general objective of the proposed thesis is to gain a deeper understanding of the link between microstructure and ultrasonic wave behaviour for large classes of material by benefiting from the combined knowledge of LEM3 for the generation of virtual microstructure and of the CEA for the simulation of ultrasonic wave propagation.
This work will combine the acquisition and analysis of experimental data (material and ultrasound), the use of simulation tools, and the statistical processing of data. This will enable an analysis of behaviors based on material classes, and possibly the implementation of inversion procedures to characterize a microstructure from a set of ultrasonic data. The combination of these methods will enable a holistic approach, contributing to significant advances in this field.

Smart & Scalable Orchestration to ensure system performance and reliability throughout Cloud Native ecosystems

The Cloud Computing paradigm has brought a new way of building application systems by strongly decoupling applications and the platforms on which they operate. This is particularly thanks to virtualization techniques (virtual machines, containers, and soon WASM) and the associated orchestration technologies (OpenStack, Kubernetes, and CloudWASM), which make it possible to port, deploy, and manage applications on heterogeneous resources and at large scale.
The objective of this thesis is to provide strategies and mechanisms to ensure the reliability of virtualized systems. In particular, orchestrating these systems involves managing multiple tasks such as deploying containers, scaling resources, managing updates, handling errors, and balancing the load across different nodes. Therefore, the orchestrator(s) play a critical role in the successful implementation of virtualized processing.
In this context, we aim to build a modeling and analysis/remediation engine that ensures SLOs in container orchestration both at design time and runtime. The ultimate goal is to enrich the orchestration process with a system that guarantees and optimizes the management of virtualized resources.

Study and exploitation of Barkhausen noise spectral information for the characterization of steels

The use of magnetic Barkhausen noise (MBN) measurements for assessing the structural health of magnetic materials has become an important industrial technique the last years. The interest in the application of this technique stems from the strong dependence of the MBN signals on the material microstructure as well as its stress level and its chemical composition.
The development of robust and reliable analysis tools based on MBN signals is however greatly impeded by the complexity of the underline physics and its sensitivity upon the details of the microstructure. Although a number of models has been proposed in the last decades and significant progress has been reported in terms of the understanding of the phenomenon, a complete theory is still lacking.
Due to this lack of understanding and the complexity of the MBN signals, the current state of the art from the non-destructive testing (NDT) perspective is almost entirely based on the measurement and the analysis of the signal envelope. The spectral information although rich in content is ignored at this level. Yet, it has been demonstrated that the MBN spectrum can give rise to classification of the magnetic materials at different universality classes based on microstructural features, notably the degree of disorder.
The proposed Ph.D. aims to contribute in the use of spectrum measurements for the characterisation of magnetic materials, notably steels. Accurate MBN measurements obtained from different microstructures using a dedicated setup (developed in the context of the Ph.D. work) will be analysed and compared with theoretical simulations based on tools previously developed by the host institute in order to
• Validate and fine-tune the theoretical models
• Study the impact of the microstructure (grain size, dislocations) to the spectrum features
• Explore the classification of the considered microstructures in different classes
Starting from well-known model materials (FeSi and FeCo), for which a great amount of published results exist and hence can be used as reference, the study will be then focused on some important industrial steel grades like the interstitial-free (IF) and low-carbon (LC) steels.
The proposed Ph.D. thesis will be jointly directed and supervised by the French atomic and alternative energies commission (commissariat à l'énergie atomique et aux énergies alternatives, CEA) and the CEIT Institute. The main part of the work will be hosted at the CEA research centre at Saclay, France with possible stays at the CEIT institute in San Sebastian, Spain.
The sought candidate profile is compatible with physicists and engineers with a good background in solid-state physics and a solid understanding of electromagnetism. Basic metallurgical notions and a familiarisation with standard laboratory equipment is also expected. Basic programming knowledge will be helpful. The candidate is also assumed to have good communication skills in English.
The candidate will benefit from access to the experimental facilities of both centres, the central CEA library and the CEA transport network as well as the restaurant facilities.

Microstructural characterization by bulk laser-ultrasounds tomography

The proposed thesis falls into the framework of designing innovative methods in the materials characterization. The thesis aims to develop a new tomographic technique for characterizing microstructures using bulk laser ultrasound. In the state of the art, acoustic methods such as the scanning acoustic microscope and surface wave optoacoustic spectroscopy lead to grain imaging but only at the surface of the sample. However, industrial manufacturing processes (in metallurgy, welding, additive manufacturing...) can reveal spatial inhomogeneity of the microstructure, such as grain size gradients with depth within the component. Electron backscatter diffraction (EBSD) also provides surface imaging of grains but has disadvantages, including restriction on sample size and the need to make transversal cuts through the sample to image its volume.
The proposed idea is to develop a bulk laser ultrasound tomography technique able to determine the grain size in areas of a component or even to obtain imaging of large-grain microstructures and a local determination of crystallographic orientation. Therefore, the thesis's main objective will be to design such an experimental characterization tool and optimize its design using a digital twin to develop.

High speed/High capacity distributed Fiber Bragg Grating sensing technique for Structural Health Monitoring (SHM) applications

Schedule-driven Non-Destructive Evaluations (NDE) are carried out during structure/equipment’ life to detect major degradations endangering safety and impairing service availability. In addition to NDE, Structural Health Monitoring (SHM) involves the use of in-situ Fiber Bragg Grating (FBG) sensing systems and algorithms to evaluate structure worthiness. FBGs are mostly used as strain/temperature sensors but are also used for acoustic sensing, as substitutes to piezoelectric actuators. The SHM of large structures or acoustic measurements for passive/active tomographic techniques simultaneously require a high capacity and readout rate. However, commercially available FBG readout units rely upon Wavelength-Division Multiplexing (WDM) or Optically Frequency-Domain Reflectometry (OFDR) techniques. WDM-based units are limited in capacity (several tens of sensors) but may reach high scan rate (MHz or beyond MHz). Conversely, OFDR-based units are limited in scan rate (typically several tens of Hz) but may accommodate large number of sensors (typically up to 2000). Tomography with acoustic techniques requires both high capacity and high scan rate with the aim to improve quality of image reconstruction. Optical Time-Stretch (OTS) is a time-domain technique that has potential to improve both capacity and scan rate and to open the way to efficient tomography reconstruction processes. The basics of OTS is to use a pulsed laser, a highly dispersive medium and a high bandpass photodetector in order to convert a Bragg wavelength shift into a time delay. The doctoral candidate will investigate several ways to implement OTS to SHM. Draw-Tower Gratings (DTG) and chirped gratings will be used for the measurement of strain profiles and acoustic field emission on metallic and carbon fiber-reinforced plastics (CFRP) composite structures. The candidate will first assess the performance of the OTS technique in laboratory (LSPM) with piezoelectric actuators and laser-ultrasonics (if available, with CNRS/PIMM). Then, the OTS device will be tested onto several demonstrators provided by partners within the MSCA USES 2 doctoral network: civil engineering structure (BAM, Berlin), hydrogen storage canister (Faber, Cividale del Friuli) and CEA DAM (Le Ripault) and finally onto a metallic pipeline for fluid transport (ENI, Milano). The doctoral candidate will move onto those test sites during three 2-month periods. He will implement the OTS technique and gather experimental feedback.

High mobility mobile manipulator control in a dynamic context

The development of mobile manipulators capable of adapting to new conditions is a major step forward in the development of new means of production, whether for industrial or agricultural applications. Such technologies enable repetitive tasks to be carried out with precision and without the constraints of limited workspace. Nevertheless, the efficiency of such robots depends on their adaptation to the variability of the evolutionary context and the task to be performed. This thesis therefore proposes to design mechanisms for adapting the sensory-motor behaviors of this type of robot, in order to ensure that their actions are appropriate to the situation. It envisages extending the reconfiguration capabilities of perception and control approaches through the contribution of Artificial Intelligence, here understood in the sense of deep learning. The aim is to develop new decision-making architectures capable of optimizing robotic behaviors for mobile handling in changing contexts (notably indoor-outdoor), and for carrying out a range of precision tasks.

Study of inversion methods based on simulation and machine learning for defect characterisation in ultrasonic array imaging

The thesis work is part of the activities of CEA-List department dedicated to Non-Destructive Testing (NDT), and aims to study simulation-based inversion methods to characterise defects from ultrasonic images, such as TFM (Total Focusing Method) or PWI (Plane Wave Imaging) images. The inversion methodology will rely on machine learning algorithms and numerical training databases generated with the CIVA software platform. A first part will study the ability of such an inversion method to characterise a defect (location, size, orientation...) without any a priori information, by exploiting the noise and reconstruction artefacts due to the use of unsuitable propagation modes. In a second part, the simulation-based inversion will be evaluated in more realistic situations where images are of poor quality due to uncertainties on the properties of the component and/or on the experimental setup. In order to reduce the generation time of the training database, and to gain in robustness and accuracy, the feasibility of inverting fast imaging (e.g.: combining PWI and fast reconstruction algorithms in the Fourier domain) will be studied, as well as the feasibility of directly inverting signals or spectra without the need to compute images. The inversion method will be experimentally evaluated with different mock-ups representative of industrial components and, at the end of the thesis, a real-time proof of concept will be demonstrated by implementing the imaging and inversion algorithms in a laboratory prototype system.