Dogan A. Timucin

MULTILAYER VOLUME HOLOGRAPHIC OPTICAL MEMORY

We demonstrate a scheme for volume holographic storage based on the features of shift selectivity of a speckle reference-wave hologram. The proposed recording method permits more-efficient use of the recording medium and yields greater storage density than spherical or plane-wave reference beams. Experimental results of multiple hologram storage and replay in a photorefractive crystal of iron-doped lithium niobate are presented. The mechanisms of lateral and longitudinal shift selectivity are described theoretically and shown to agree with experimental measurements.

A Model-Based Probabilistic Inversion Framework for Characterizing Wire Fault Detection Using TDR

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable handheld devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S -parameter-based forward-modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

An algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally and locally coupled noisy oscillators.

Bayesian Framework for In-Flight SRM Data Management and Decision Support

We report progress in the development of a novel Bayesian framework for an in-flight failure decision and prognostic (FD&P) system for solid rocket boosters (SRBs) based on a combination of low-dimensional performance models and a Bayesian framework for diagnostics and prognostics of the parameters of nonlinear flow of combustion products in the combustion chamber. To simulate faults we introduce high-fidelity models of these faults based on stochastic partial differential equations (SPDE). To infer parameters of the model, the SPDE is reduced to a low dimensional performance model (LDPM). It is shown by example of the nozzle blocking fault that using a novel Bayesian framework, it becomes possible both to infer the variations of SRB parameters stimulated by the fault and to predict values of the pressure and time of the overpressure fault even in the case of highly nonlinear fault dynamics. The extension of the method to the diagnostic and prognostic of the case burning fault is discussed.

Simulation of guided-wave ultrasound propagation in composite laminates: Benchmark comparisons of numerical codes and experiment

HighlightsGuided wave propagation in composite plates is simulated using four different tools.Results are benchmarked against experiment and theory in wavenumber and time domain.Mesh refinement study results are also reported for the four simulation tools.Results are reported for a pristine and a delaminated (Teflon insert) specimen.Performance of simulation tools is reported for accuracyand computational demand. ABSTRACT Ultrasonic wave methods constitute the leading physical mechanism for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials, such as carbon fiber reinforced polymer (CFRP) laminates. Computational models of ultrasonic wave excitation, propagation, and scattering in CFRP composites can be extremely valuable in designing practicable NDE and SHM hardware, software, and methodologies that accomplish the desired accuracy, reliability, efficiency, and coverage. The development and application of ultrasonic simulation approaches for composite materials is an active area of research in the field of NDE. This paper presents comparisons of guided wave simulations for CFRP composites implemented using four different simulation codes: the commercial finite element modeling (FEM) packages ABAQUS, ANSYS, and COMSOL, and a custom code executing the Elastodynamic Finite Integration Technique (EFIT). Benchmark comparisons are made between the simulation tools and both experimental laser Doppler vibrometry data and theoretical dispersion curves. A pristine and a delamination type case (Teflon insert in the experimental specimen) is studied. A summary is given of the accuracy of simulation results and the respective computational performance of the four different simulation tools.

A model-based probabilistic inversion framework for wire fault detection using TDR

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Fault diagnostics and prognostics for large segmented SRMs

We report progress in development of the fault diagnostic and prognostic (FD&P) system for large segmented solid rocket motors (SRMs). The model includes the following main components: (i) 1D dynamical model of internal ballistics of SRMs; (ii) surface regression model for the propellant taking into account erosive burning; (iii) model of the propellant geometry; (iv) model of the nozzle ablation; (v) model of a hole burning through in the SRM steel case. The model is verified by comparison of the spatially resolved time traces of the flow parameters obtained in simulations with the results of the simulations obtained using high-fidelity 2D FLUENT model (developed by the third party). To develop FD&P system of a case breach fault for a large segmented rocket we notice [1] that the stationary zero-dimensional approximation for the nozzle stagnation pressure is surprisingly accurate even when stagnation pressure varies significantly in time during burning tail-off. This was also found to be true for the case breach fault [2]. These results allow us to use the FD&P developed in our earlier research [3]-[6] by substituting head stagnation pressure with nozzle stagnation pressure. The axial corrections to the value of the side thrust due to the mass addition are taken into account by solving a system of ODEs in spatial dimension.

Model Based IVHM System for the Solid Rocket Booster

We report progress in the development of a model-based hybrid probabilistic approach to an on-board IVHM for solid rocket boosters (SRBs) that can accommodate the abrupt changes of the model parameters in various nonlinear dynamical off-nominal regimes. The work is related to the ORION mission program. Specifically, a case breach fault for SRBs is considered that takes into account burning a hole through the rocket case, as well as ablation of the nozzle throat under the action of hot gas flow. A high-fidelity model (HFM) of the fault is developed in FLUENT in cylindrical symmetry. The results of the FLUENT simulations are shown to be in good agreement with quasi-stationary approximation and analytical solution of a system of one-dimensional partial differential equations (PDEs) for the gas flow in the combustion chamber and in the hole through the rocket case.

Development of an on-board failure diagnostics and prognostics system for Solid Rocket Booster

We develop a case breach model for the on-board fault diagnostics and prognostics system for sub-scale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

Time-Varying Cardiovascular Oscillations

Signals derived from the human cardiovascular system (CVS) are exceptionally complex, being time-varying, noisy, and of necessarily limited duration. Yet an appropriate analysis of them may be expected to yield detailed information about the dynamics of the underlying physiological processes. A new approach to the analysis and modelhng of CVS signals is proposed. It combines decomposition of the signals into principal modes and a novel method of parameter identification in nonlinear stochastic systems based on Bayesian inference. The scheme is tested on a noisy Van der Pol oscillator, for which it yields rapid convergence and correct inference of the known parameters. Preliminary applications to CVS data are discussed.