Damien Guého

Damien Guého

Aerospace Engineer

Biography

I am an aerospace engineer and a PhD candidate in the CASS Lab in the Department of Aerospace Engineering at The Pennsylvania State University. I graduated from Centrale Lyon in 2019 with an engineering degree and am currently working with Prof. Puneet Singla to obtain my PhD in 2022.

My research focuses on a wide range of topics in data-driven analysis of dynamical systems, with particular interests for high-dimensional and complex dynamical systems, data-driven system identification, reduced-order modeling, stochastic analysis, uncertainty quantification and data-driven control. I also acquired a rich expertise in astrodynamics, conjunction assessment and optimal control throughout my time at Penn State and during my last internship.

I would like to devote my professional life to the pursuit of novel research in the areas of data-driven analysis of complex dynamical systems, especially for space, air or robotics applications. My focus is to develop a rigorous and analytical mind, and to study the most challenging projects using a combination of newly developed and existing methods in engineering.

Download my résumé.

Interests
  • Complex dynamical systems
  • Data-driven system identification and reduced order modeling
  • Stochastic Analysis and Uncertainty Quantification
  • Astrodynamics
  • Optimization
Education
  • PhD in Aerospace Engineering, 2022

    The Pennsylvania State University, University Park, USA

  • MS in Aerospace Engineering, 2019

    The Pennsylvania State University, University Park, USA

  • BS and MS Engineering Sciences, 2019

    École Centrale de Lyon, Lyon, France

Projects

System Identification Technologies

System Identification Technologies

An educational and tutorial website on dynamical systems and system identification.

systemID

systemID

A python package for linear, bilinear and nonlinear system identification.

systemID Documentation

systemID Documentation

A documentation with tutorials and examples for the python package systemID.

Research Experience

 
 
 
 
 
CASS Lab
Research Assistant
Aug 2018 – Present The Pennsylvania State University, University Park, USA

My research focuses on a wide range of topics in data-driven analysis of dynamical systems, with particular interests for high-dimensional and complex dynamical systems, data-driven system identification, reduced-order modeling, stochastic analysis and data-driven control. Here are some current Ph.D. and Masters level research projects:

  • Development of a unified and robust data-driven framework for reduced-order modeling and system identification
    The objective is to develop a computationally fast, robust and accurate data-driven framework (as a Python package) that combines the latest techniques in time-varying subspace realization methods, sparse representation and embeddings. Eventually, I would like this framework to be operated real-time, with real-time data collection, process, visualization, and all achieved on-board (applications for autonomous aerospace vehicles, space missions). I want to extend the system identification module with an estimation and uncertainty quantification module, a real-time learning module, and a data-driven control and parameters update module. The research work and the implementation is still in progress

  • Development of an educational and tutorial website for system identification of dynamical systems
    This project is to expose as many people (undergraduate/graduate students and professional, engineers) in the field of aerospace to data-driven modeling and system identification of dynamical systems. This website offers theoretical knowledge for dynamical systems and time-domain system identification as well as a full section that allows the user to apply the concepts of linear system identification to online real-time simulations. Pick a premade system or build your own system, define all the parameters yourself, pick and input signal and launch the simulation. You’ll be able to see the results of the identification process and access all the relevant quantities involved. The version 2 of this website is scheduled to launch in February 2022 (with many new features!) and to be hosted on Penn State servers.

  • Reduced-order modeling and analysis for high-fidelity aero-thermo-servo-elasticity (ATSE) simulation for hypersonic vehicles
    This research work is on the development of new algorithms to study the nonlinear coupled dynamics between structural dynamics, heat transfer, and hypersonic aerothermodynamics. Several subspace realization techniques as well as embeddings and sparse representation methods are used to provide a linear-time varying model or a sparse model to reproduce the aerothermoelastic response of a hypersonic vehicle and to study the effect of a bifurcation parameter. To validate the developed approach, numerical simulations involving the nonlinear dynamics of a heated panel model as well as high-fidelity simulations are considered. This eventually will enable accurate hypersonic aerothermoelastic analysis and control with tractable computational cost.

  • Optimal Feedback Control under Uncertainty for Hypersonic Re-Entry Vehicles
    The objective of this work is to establish flexible, accurate and navigable flight trajectories for hypersonic vehicles without human interference. On a larger scale, the idea is to accurately plan the path of super-fast vehicles from one point to another while accounting for multi-physics dynamical models and any path or actuation constraints. In this project involving several Universities, I have been developing methods using optimal open-loop solutions to derive appropriate feedback control structure from over an complete dictionary of basis functions with the help of sparse approximation tools. A planar hypersonic maneuver corresponding to maximizing the terminal velocity of the payload has been considered to validate the proposed approach and simulation results clearly demonstrate the efficacy of the method in providing optimal feedback control law for prescibed uncertainty in boundary conditions and model parameters.

  • Computationally Efficient Approach for Stochastic Reachability Set Analysis
    This research work aims to study the significant challenges associated to automating the decision support system of maneuvering Unmanned Autonomous Systems (UAS) in presence of nonlinearities, uncertainties associated with system parameters, states and external disturbances together with an embedded control input. Three different probabilistic approaches to compute the reachability sets for a class of discrete time nonlinear systems are investigated and I have been involved in developing two of these approaches. In the first approach, the central idea is to pose the computation of the state density function at any time as the convolution of two probability density functions to avoid the exponential growth in samples while in the second approach, a quadrature method utilizes the Conjugate Unscented Transform (CUT) to compute the probability density function.

  • Optimal Spacecraft Docking Maneuver Using Direct and Indirect Collocation Method and Heuristic Optimization
    Originally started as a class project, this work used an indirect method combined with a heuristic approach to solve an optimal spacecraft docking maneuver problem. Theoretically, the indirect method presents the difficulty that the problem size is large due to discretization of the costates in addition to requiring good enough initial guesses for the costates variables. With one classmate, we presented a new approach where a heuristic optimization (HO) algorithm is used beforehand to generate a sufficiently accurate initial guess for the costates variables used for the collocation method applied later on.

  • Statistical Orbit Determination Class Project
    The project aims at developing a software to create tracking data of a satellite from multiple tracking sites around the Earth. I used observation data to statistically determine the nominal orbit/tracking parameters and perturbations such as the gravitational parameter, J2, the mean radius of Earth, the drag coefficient of the satellite and the locations of the tracking stations. The final goal was to obtain parameter values and covariances using both a batch filter and a sequential estimation filter for method comparison.

Professional Experience

 
 
 
 
 
CNES (Centre National d'Études Spatiales)
Intern
May 2018 – Jul 2018 LASS, Toulouse, France
  • Investigated different methods to develop an accurate, reliable and efficient method to compute a certified orbital collision probability between two spacecrafts involved in a short-term encounter under Gaussian-distributed uncertainty
  • Derived an analytic expression for the probability integral by use of Laplace transforms and D-finite functions
  • Surveyed different methods to extend this work to long term and multiple encounters
  • Defined a framework to approximately reconstruct the support of the initial collision-prone states in a two-objects long-term encounter
 
 
 
 
 
SAFRAN Aircraft Engines
Intern
Apr 2017 – Jul 2017 Fan Blades Industrial Center of Excellence, Gennevilliers, France
  • Updated the industrial validation files (upgrading to EN 9100) for Chaheng Precision CO. supplier
  • Analyzed control files from GKN Aerospace and built a complete report on the acceptance of their blades
  • Optimized the quality process inspection for Chaheng Precision CO. in Taiwan
 
 
 
 
 
Synerlink
Intern
Jul 2016 – Jul 2016 Electrical Production Int., Ile-de-France, France
  • Operated electric work for Twin Falls (Idaho) packaging machines
  • Provided daily help for pneumatic, automatic and energy work
  • Built a report on the organization of the teams involved in the assembling process
 
 
 
 
 
SAFRAN Aircraft Engines
Intern
Aug 2015 – Aug 2015 Physics Laboratory, Gennevilliers, France
  • Completed physical and chemical sample tests: tensile, compression, twist and bending tests; H2 tests
  • Created a report analysis for crankcase aircraft engines
  • Handled logistic support and restructured the workshop and storage area

Teaching Experience

 
 
 
 
 
AERSP 597 - System Identification
Co-instructor
Jan 2021 – May 2021 The Pennsylvania State University, University Park, USA
Co-taught the graduate level course AERSP 597 - System Identification with Dr. P. Singla. Shared the duties for lectures, homework assignments and exams. Responsible for grading and half of the office hours. Gave lectures on nonlinear system identification, embeddings and Koopman operator, observer-controller system identification. Taught practical implementation of system identification algorithms in Python.
 
 
 
 
 
AERSP 305 - Aerospace Structures
Teaching Assistant
Jan 2018 – May 2018 The Pennsylvania State University, University Park, USA
Responsible for 10h/week of lab sessions on aerospace structures and vibrations. This involves guiding 25 students every week in performing experiments, grading reports and providing theoretical background as support from the materials taught in class.
 
 
 
 
 
AERSP 313 - Aerospace Analysis
Teaching Assistant
Aug 2017 – Dec 2017 The Pennsylvania State University, University Park, USA
Class of 130+ students. Responsible for grading, office hours and writing homework assignments with 3 other TAs. Responsible for review sessions before exams.

Recent & Upcoming Talks

Access to public presentations only

A Unified and Robust Data-Driven Framework for Analysis and Control
Time-Varying Koopman Operator Theory for Nonlinear Systems Prediction
Advances in System Identification - Theory and Applications
Continuous Bilinear System Identification
Data-Driven Sparse Approximation for the Identification of Nonlinear Dynamical Systems
Koopman Analysis and Finite Linear Representations of Nonlinear Dynamics
Observer Controller Identification Algorithm
Introduction to Koopman Analysis and Finite Linear Representations of Nonlinear Dynamics
Reduced Order Modeling for Hypersonic Aerothermoelastic Analysis
Stochastic Reachability Analysis for the Hypersonic Re-Entry Problem
An Introduction on Neural Networks and Learning Processes
Learning Capabilities of Neural Networks and Keplerian Dynamics
An Introduction on Global Probability of Collision
Investigation of Neural Network Capabilities to Solve the Uncertain Lambert Problem

Volunteer Experience

 
 
 
 
 
Alpha Fire Company
Volunteer Firefighter
Sep 2018 – Present State College, PA, USA

Volunteer Firefighter since September 2018. Joined the Engine Company after completed the in-house Engine PERT program in January 2019. I participated in the Truck PERT program in Fall 2019. Certifications include:

  • Firefighter I
  • FEMA IS-00700.b and IS-00100.c
  • CPR and AED Provider
 
 
 
 
 
Discovery Space Summer Camp
Visiting Speaker
Jul 2021 – Jul 2021 State College, PA, USA
I helped Discovery Space Summer Camp to cover topics in Astrodynamics for 10 students, ages 10‐14. Called ”Notion of Motion”, the camp’s sessions were about Newton’s Laws and conversions of energy. After presentations on rocket propulsion, we helped students build water rockets and launch them in the air.
 
 
 
 
 
Challenge Centrale Lyon
President
Apr 2016 – Apr 2017 Centrale Lyon, Lyon, France

The Centrale Lyon Challenge is the largest student sports tournament for Engineering Schools in France, bringing together more than 3000 students. The Challenge is above all sports but also the opportunity to participate in the largest party of the year animated by professional DJs and singers, meet companies, enjoy a cheerleader contest, marching bands or mascots, and to engage in many activities during the weekend. During one full year, I was the head of the organizing team composed of 16 students and seconded by more than 250 volunteers. In addition to coordinate logistics, security and public relations for the event, we restructured the event to take place during a full 3-day weekend in March. Key figures include:

  • 3 days in March 2017 with 36 schools, 3500 students, 8 nationalities
  • 16 students to organize the event, seconded by 250 volunteers and 40+ contractors
  • A total of 18 sports with 14 sport facilities allocated for the event
  • 200,000€ budget ($230,000)

Video recap