Добірка наукової літератури з теми "Non-Intrusive PGD"

Abstract Understanding the failure of brittle heterogeneous materials is essential in many applications. Heterogeneities in material properties are frequently modeled through random fields, which typically induces the need to solve finite element problems for a large number of realizations. In this context, we make use of reduced order modeling to solve these problems at an affordable computational cost. This paper proposes a reduced order modeling framework to predict crack propagation in brittle materials with random heterogeneities. The framework is based on a combination of the Proper Generalized Decomposition (PGD) method with Griffith’s global energy criterion. The PGD framework provides an explicit parametric solution for the physical response of the system. We illustrate that a non-intrusive sampling-based technique can be applied as a post-processing operation on the explicit solution provided by PGD. We first validate the framework using a global energy approach on a deterministic two-dimensional linear elastic fracture mechanics benchmark. Subsequently, we apply the reduced order modeling approach to a stochastic fracture propagation problem.

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.

AbstractReduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PED) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. By starting from a database of high-fidelity solutions related to a certain values of the parameters, we apply the proper orthogonal decomposition with interpolation (PODI) and then reconstruct the variables of interest for new values of the parameters, i.e. different values from the ones included in the database. Furthermore, we present a preliminary web application through which one can run the ROM with a very user-friendly approach, without the need of having expertise in the numerical analysis and scientific computing field. The case study we have chosen to test the efficiency of our algorithm is represented by the aortic blood flow pattern in presence of a left ventricular (LVAD) assist device when varying the pump flow rate.

Despite significant development in facial recognition (FR), current FR systems are exposed to spoofing attacks like printed photo attacks, 3D mask attacks, video replay attacks, and many more. Several anti-spoofing approaches have been proposed to assess whether the person in front of the camera is real or fake. Developing effective protection mechanisms against these threats is a challenging task. This paper gives a brief overview of various presentation attack detection (PAD) techniques, which are categorized into intrusive and non-intrusive approaches. Each technique is examined in terms of its execution, benefits, and drawbacks and also provides information on modern anti-spoofing techniques.

Woven reinforced composites are often hindered by challenges in accurately predicting their mechanical behavior. This obstacle primarily stems from the heterogeneous nature of these materials. Consequently, employing multi-scale approaches becomes imperative to ascertain their overall responses under complex loading conditions, incorporating detailed descriptions of microstructure and the constitutive laws governing their components. However, effectively incorporating these methodologies into real-scale applications, particularly within FE² analyses, remains challenging due to the significant computational requirements. This challenge intensifies when numerous direct calculations are necessary for testing various configurations, a critical aspect in optimization, inverse analysis, or real-time simulations. The need for such calculations adds to the computational demands, posing a significant obstacle to integrated into practical applications. To address these issues, while considering the scale effects, this thesis aims to develop efficient numerical tools to achieve accurate and fast predictions of woven composite response. First, we develop virtual twins (multiparametric solution) for real-time prediction of composite response, using non-intrusive Proper Generalized Decomposition (PGD) based methods. This aims at providing an accurate approximation of these high-dimensional problems, that involved several microstructural parameters, with limited dataset. These multiparametric solutions are constructed for both linear and nonlinear behavior including history- and rate-dependent behaviors. Second, we develop an approach based on Artificial Neural Networks (ANNs) to perform a macroscopic surrogate model of composites. This model, referred to as Multiscale Thermodynamics Informed Neural Networks (MuTINN), is founded on thermodynamic principles and introduces specific quantities of interest that serve as internal state variables at the macroscopic level. This captures efficiently the state and evolution laws governing the history-dependent behavior of these composites while retaining the thermodynamic admissibility and the physical interpretability of their overall responses. This approach has successfully associated with FE code, streamlining the application of multiscale FE-MuTINN approach for composite structure computations. The prediction capabilities of the proposed approach are demonstrated across the material scales, exemplified through diverse instances of woven composite structures. These applications account for anisotropic yarn damage and an elastoplastic polymer matrix behavior. This promises a potential solution to alleviate the computational challenges associated with multiscale simulations of large composite structures and paving the way for the development of a hybrid twin solution
Although woven reinforced composites are experiencing rapid growth across various engineering and industrial domains, their widespread adoption is often hindered by challenges in accurately predicting their mechanical behavior. This obstacle primarily stems from the heterogeneous nature of these materials. Consequently, employing multi-scale approaches becomes imperative to predict their overall response under complex loading conditions, incorporating detailed descriptions of microstructure and the constitutive laws governing their components. However, effectively incorporating these methodologies into real-scale applications, particularly within FE² analyses, remains challenging due to the significant computational requirements they entail. This challenge intensifies when numerous direct calculations are necessary for testing various configurations, a critical aspect in optimization, inverse analysis, or real-time simulations. The need for such calculations adds to the computational demands, posing a significant obstacle to integrated into practical applications. To address these issues, while considering the scale effects, this thesis aims to develop efficient numerical tools to achieve accurate and fast predictions of woven composite response. First, we develop virtual twins (multiparametric solution) for real-time prediction of composite response, using non-intrusive Proper Generalized Decomposition (PGD) based methods. This aims at providing an accurate approximation of high-dimensional problems, that involved several microstructural parameters, with limited dataset. These multiparametric solutions are constructed for both linear and nonlinear behavior including history- and rate-dependent behaviors. Second, we develop an approach based on ANN to perform a macroscopic surrogate model of composites. This model, referred to as Multiscale Thermodynamics Informed Neural Networks (MuTINN), is founded on thermodynamic principles and introduces specific quantities of interest that serve as internal state variables at the macroscopic level. This captures efficiently the state and evolution laws governing the history-dependent behavior of these composites while retaining the thermodynamic admissibility and the physical interpretability of their overall responses. This approach has successfully associated with FE code, streamlining the application of multiscale FE-MuTINN approach for composite structure computations. The prediction capabilities of the proposed approach are demonstrated across the material scales, exemplified through diverse instances of woven composite structures. These applications account for anisotropic yarn damage and an elastoplastic polymer matrix behavior. This promises a potential solution to alleviate the computational challenges associated with multiscale simulations of large composite structures and paving the way for the development of a hybrid twin solution

L'un des principaux avantages de la méthode «Proper Generalized Decomposition», par rapport à d'autres méthodes de réduction de modèles, réside dans son adéquation pour calculer des représentations séparées dans l’espace pour des domaines dégénérés de type cartésien, tels que des plaques ou des coques. L'objectif principal de cette thèse est de généraliser les représentations séparées dans l’espace aux domaines non cartésiens, en introduisant la notion de représentations séparées. Les représentations séparées de type global-local peuvent être comprises comme une décomposition multiplicative dans laquelle les modes locaux capturent la solution à une échelle fine, tandis que les modes globaux résolvent une échelle grossière. Pour ce faire, deux stratégies sont proposées. La première proposition est basée sur la partition de l'unité et combine les niveaux de discrétisation globale et locale, basés sur une partition du domaine. Cette approche construit une représentation séparée qui fournit l'enrichissement local, sans qu'il soit nécessaire de connaître a priori la solution, ni de mettre en oeuvre des problèmes locaux auxiliaires pour déterminer l'enrichissement. La deuxième stratégie est consacrée à la construction de représentations séparées de type global-local de manière moins intrusive, compatible avec le standard des éléments finis. Par conséquent, nous nous basons sur l’assemblage FEM standard des opérateurs et utilisons la PGD comme résolveur algébrique itératif. La continuité sur les limites de la partition du domaine n'a pas besoin d'être imposée explicitement, car elle constitue une propriété intégrée dans les opérateurs FEM
One of the main advantages of the Proper Generalized Decomposition method, when compared to other model reduction methods, lies in its adequacy to compute space separated representations in Cartesian-like degenerated domains, such as plates or shells. The main objective of this thesis is to generalize space separated representations to non-Cartesian domains, by introducing the notion of Global-Local separated representations. Global-Local separated representations can be understood as a multiplicative decomposition in which the local modes capture the solution at the finer scale, while the global modes solve the coarser scale. To this aim, two strategies are proposed. The first proposal is based on the partition of unity, and combines the global and local discretization levels, based on a partition of the domain. It builds a separated representation that provides the local enrichment, without the need for a priori knowledge of the solution, nor the implementation of auxiliary local problems to determine the enrichment. The second strategy is devoted to the construction of Global-Local separated representations in a less intrusive manner, compatible with the finite element standard. Therefore, we rely on standard FEM assembly of the operators and use the PGD as an algebraic iterative solver. Continuity on the boundaries of the domain’s partition does not need to be imposed explicitly, as it comes as a built-in property of the FEM operators

A robust shape optimization procedure based on a multi-objective genetic algorithm coupled to a non-intrusive uncertainty quantification analysis was applied to a transonic inviscid flow of a dense gas over a plane turbine cascade. The goal was to simultaneously improve the mean turbine performance and the system stability under fluctuating thermodynamic inlet conditions. Despite an elevated computational cost, the optimization procedure was capable of generating a Pareto front of turbine geometries which improved the mean isentropic turbine efficiency μ(ηs) over the baseline profile, while limiting the solution variability in terms of the coefficient of variation of the power output CV(P2D). In addition to demonstrating an excellent parallel scalability over 1600 processors, the robust optimization revealed that variability of CV(P2D) depends more on the variation of inlet conditions than turbine geometry. A posteriori stochastic analyses on selected optimized turbine geometries allowed an investigation of flow behavior variability, as well as propositions for the improved selection of robust optimization cost criteria in future simulations.

An active flow control system for reducing distortion in serpentine inlets was developed using non-intrusive microphones as feedback sensors. While the serpentine inlet can provide large benefits to an air vehicle by reducing its overall size and therefore weight, it unfortunately delivers a non-uniform (spatially-distorted) flow to the compressor due to the formation of a secondary flow created by separation of the turbulent boundary layer in the aggressive turn. An active means of controlling distortion has been developed using an array of micro air jet vortex generators. It was hypothesized that microphones in the vicinity of the distorted flow would record higher amplitudes pressure fluctuations compared to those microphones in the vicinity of the undistorted flow. Experiments showed that the difference between the microphone readings in these two flow regimes was correlated to the distortion level. This difference in microphone signals was then used as feedback in a PID control system that regulated spatial distortion levels during steady flight conditions, as well as sudden ramps in aircraft speed.

Abstract Bonding is an attractive assembly solution in many ways. Indeed, it is a non-intrusive and cold solution enables to assemble mono or multi-material structures. This assembly technique is particularly interesting for the installation of equipment or repairs when welding is prohibited, such as in ATEX-type risk areas. For many years, COLD PAD has been developing bonded structural repair solutions and non-intrusive mechanical fasteners for marine environment. Their reinforcement product ColdShield™ [1], has been validated thanks to a previous collaboration with Bureau Veritas. From his side, Bureau Veritas is updating the guidance Note NI613 Adhesive Joints and Patch Repairs [2] to consider new development and innovation achieved in this technical domain. The objective of this paper is initially to present the different methods for evaluating the resistance of bonded assemblies proposed by Bureau Veritas. Two application cases of bonded repair will be detailed with the associated lessons learnt in terms of reliability. The first one concerns a conventional steel-steel bonded repair, one of studied applications within the framework of the JIP Strength Bond Offshore [3]. For this application, the stress approach and the combined stress-energy method will be explained as well as the use of cohesive elements for numerical simulations. The second one is the design assessment of the ColdShield™ reinforcement solution [4], which contributes to hull girder strength for Floating Production Storage and Offloading (FPSO) hull repair, and for which a stress approach is possible due to the specificity of the design. Results of both applications are analysed, compared and commented. In conclusion, the paper will highlight the importance of the calculation methodology and its robustness to avoid a misinterpretation of the strength.

ABSTRACT Due to highly uncertain nature of unconventional reservoirs and difficult monitoring of the engineering effectiveness, a reliable, accurate, and efficient reservoir model calibration workflow is essential to help operators understand/plan their assets. In this paper, a collection of the non-intrusive EDFM (Embedded Discrete Fracture Model), AI (artificial intelligence), and an unconventional-targeting in-house reservoir simulator URSim is built to perform automatic history matching and uncertainty calibration of hydraulic and natural fractures (abbreviated as EDFM-AI). By implementing the proposed workflow, highly uncertain parameters, such as hydraulic fracture half-length, height, conductivity, closure coefficient, etc. are easily characterized. To validate the robustness of the proposed workflow, especially on the level of simultaneous multi-well calibration, a field-scale shale gas reservoir model is prepared with 3 horizontal wells and 200 hydraulic fractures for each well. Known set of hydraulic fracture parameters are inputted, constant gas flow rates are the well constraints, and the simulation outputs are defined as benchmark data. The history matching results showed high accuracy matches. More importantly, the maximum error between calibrated P50 and true value is only 6.8%. This novel EDFM-AI workflow sheds light on post-frac evaluation and completion optimization in unconventional resources. INTRODUCTION Current unconventional reservoir developments are characterized as fast-paced, real-time, and multi-well (pad-wise drilling). Many operators can drill and completion more than 100 unconventional horizontal wells per year. Under this scenario, it is crucial for operators to understand the effectiveness of hydraulic fracturing. Multitude methodologies/studies have been dedicated to study the man-made hydraulic fractures. These include fracturing simulation models (Wan et al., 2020; Weijermars et al., 2020; Leem et al., 2022), experimental methods (Magsipoc et al., 2020; Wei et al., 2021), and diagnostic technologies (Gutierrez et al., 2010; Ugueto et al., 2021; Wang et al., 2022). However, uncertainties associated with fracture geometries still remain as the most challenging problem in the unconventional oil/gas industry.

Abstract Immiscible Water Alternating Gas (iWAG) scheme was adopted in Echo field, offshore Sarawak Malaysia, to increase recovery factor of the matured oil reservoir after more than two (2) decades of peripheral water injection. It was implemented through four (4) horizontal wells located at reservoir’s eastern and western flanks. Since the commencement of iWAG injection, multiple challenges occured interrupting the stable injection that halting the success of this integrated mega scale project. It started with prolonged iWAG performance test run due to surface constraint, measurement and well issues on executing switching test, followed with low injectivity during switching operation. Subsequently, injectivity issues occured in the gas phase after several injection cycles. In addition to that, injector wells facing high downtime due to surface facilities and well integrity issues, causing low injection rates and unavailability to meet cycle volume within the stipulated duration. Reactivation of iWAG benefiter wells also prove to be challenging due to wells have been idle for a long time and multiple interventions required to revive the well. Injection data for both gas and water phase were analysed to improve iWAG operating procedure and understand the wells performance. INJ-J2 was installed with temporary pressure gauge during the water to gas switching, while the other two (2) wells are equipped with Permanent Downhole Gauge (PDG) to monitor the well injectivity. Application of non-intrusive flowmeter was also proven useful in calibrating the Flow Transmitter (FT) for both water and gas injectors, ensuring the accuracy and precision in the water and gas injection measurement. Besides that, fluid temperature trending was referred to validate on the meter measurement. Low injection rate compared to original plan were reviewed with the Reservoir Management Plan (RMP). Several approaches are implemented in order to achieve the iWAG RMP target and idle well reactivation. Analysis of injection data showed that gas injectivity issue occurred after the water to gas switching cycle. Injectivity improves slightly after long duration of continuous gas injection and applying higher Tubing Head Pressure (THP), unfortunately some wells remain with low injectivity because of insufficient discharge pressure to push the water from the near-wellbore deep into the reservoir to improve injection. Low injection rate issue is mitigated by extending injection cycle duration in order to meet the RMP cycle volume. Besides that, wells are normally injected with higher injection rate to cater for the high downtime. Both gas and water injection are balanced to ensure that the wells reached their cycle volume at similar duration. With limited new field discovery by the Operator, tertiary recovery on the mature field is inevitable. However, there is less implementation of iWAG in offshore field. Through this paper, authors wish to provide insights and lesson learnt for others when planning for iWAG tertiary recovery, taking account of various challenges faced.

Abstract Among the many applications of Coiled Tubing (CT) services, milling plugs and wellbore sand cleanout are two of the major applications. The transport of solids to the surface, as well as monitoring the return rates, are two sources of information which can have a significant impact on the execution of these jobs. Traditionally the flowback crew communicates this information to the CT control cab upon request. However, by utilizing an acoustic monitor and ultrasonic flowmeter, it can reduce the dependence on flowback operators and provide real-time solid measurement and return flow rate. The acoustic monitor is a passive, non-intrusive device that is designed to measure the acoustic noise induced into the pipe wall as solids impact the inside wall of the pipe. The ultrasonic flowmeter is a clamp-on device that is designed with two transducers that serve as both a transmitter and receiver. In order to prove the concept, five stages of trials were planned and executed. In stage one, CT was rigged into a 150m vertical test well. The equipment included CT mast unit, CT pump, choke manifold, and acoustic monitoring device. Several debris piles from previous milling operations were introduced to the test well, and a CT cleanout was performed. The acoustic monitor system measured the amount of solid to surface, and the results were evaluated. Solids retrieved were then compared to the initial debris piles and correlated to the data received by the acoustic monitor. On the 2nd stage, the acoustic monitoring device was utilized in actual milling operation. The 3rd stage was a yard trial of ultrasonic flowmeter using a CT pump and data acquisition system to evaluate the working envelope of this device, followed by a field trial, in stage 4, utilizing the flowmeter in actual milling operations. The final stage of this trial was planned and executed in milling operations on a five wells pad, utilizing the combined applications of acoustic monitoring (solid measurement) and ultrasonic flowmeter (return rate) devices. All five stages contributed to proof of concept for the applications of solid measurement and return flow rate devices. These trials were successfully planned, executed, and evaluated. The acquired data throughout the five stages of these trials were utilized during and post job operations as lessons learned to optimize the process for future applications. The real-time measurement of solids and flow rate monitoring, independent of flowback operators, enables the CT operator to make informed decisions throughout milling and cleanout operations. The real-time streaming of solids to surface and return flow rate enables the operator and service company’s Engineering team to evaluate and optimize the execution of milling and sand cleanout operations.