Engineering topics

Fisher, K. A.

Publications and source records attributed to Fisher, K. A..

9 records

DOE OSTI2023

Shooter Alarm System Final Report FY23

Lawrence Livermore National Laboratory (LLNL) is developing a multiphysics sensor package for automatic live arms fire detection for use in schools, houses of worship, hospi tals, etc. The goal is to have a package the size of a common smoke or CO2 detector with a price point of $50 or less which may be deployed throughout the aforementioned facilities. The commercial device will detect live gun fire with low probability of false alarm and auto matically alert first responders. A prototype board with four accelerometers, two infrared (IR) and two microphones has been developed and deployed at two experiments at LLNL’s Small Firearms Training Facility (SFTF). The device successfully and consistently measured signals on the accelerometer and acoustic channels. IR responses were measured depending upon the environment and sound source.

42 ENGINEERING↗

DOE OSTI2022

Resonant Ultrasonic Spectroscopy: A Modal-Analysis Approach for Additive Manufacturing

Resonant ultrasonic spectroscopy is a methodology capable of measuring a change in modal resonant frequencies of material structures. It encompasses an inversion technique based on the eigen-frequencies of a simple regular sample geometry to estimate the elastic tensor of a solid. Obtaining an accurate and complete set of resonant frequencies is a critical first step in the RUS process. In this paper a variety of techniques to extract and validate material resonances from complicated RUS measurement spectra are developed. Various processing methodologies employing modal analysis techniques are applied to estimate the underlying resonances that lead to the extraction of the elastic coefficients characterizing the specimen under test. A case study of a simple isotropic material (304 SS) is investigated to analyze the algorithms and evaluate their performance

Coal, lignite, and peat↗

DOE OSTI2022

Processing of Ultrasonic Measurements for an Additive Manufacturing Application

Resonant ultrasonic spectroscopy is an effective methodology capable of measuring a change in modal resonant frequencies of material structures. It encompasses techniques based on ultrasonic frequencies employed to estimate elastic coefficients in solids. It is these frequencies that are uniquely characterized by the material shape, coefficients, symmetry and density that are used to improve and detect changes especially during the additive manufacturing (AM) process. In this report a variety of techniques to extract and validate material resonances are developed starting first with the background theory required to comprehend the approaches. Once the theory is established, various signal processing methodologies are applied to estimate the underlying resonances leading to the extraction of the critical elastic coefficients characterizing the specimen under test. A detailed case study based on wire arc additive manufacturing is discussed demonstrating the applicability of the various approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Vibrational Energy Harvesting Using a Cantilever Model

Vibrational energy harvesting (VEH) is a method of capturing incidental mechanical vibrational energy and converting it to electrical energy. This is enabled by two technologies: Electromagnetic induction via a cantilever or piezoelectric devices. When designing a VEH system, a fast forward model is desired for response determination and optimal parameter estimation. An ordinary different equation (ODE) system model is developed for the cantilever system based upon the derivations of [1] and [2], and compared with a full electromechanical COMSOL model.

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DOE OSTI2021

Vibration-Based Sensor Design: A Grey-Box Approach

Knowledge of the internal structure of an object or device under investigation proceeds from the basic idea of constructing its dynamic behavioral relations governed by a set of differential/algebraic equations that characterize its response. These equations can be partial differential equations leading to finite element or finite difference relations requiring a complex numerical solution on a super computer or ordinary differential equations requiring sophisticated numerical integration techniques to obtain the desired solution. Discrete dynamic systems evolving from digitized data acquisition are typically captured by sampled-data (continuous-to-discrete) representations characterized by a set of difference equations specifying the underlying system dynamics. In any case, with a mathematical description in hand, Grey-Box modeling techniques have evolved, concerned with the estimation of model parameters embedded in a prescribed set of equations (the system) governing its behavior, while capturing the underlying physical phenomenology of the problem at hand.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Multichannel deconvolution of vibrational signals: A state-space inverse filtering approach

Deconvolution of noisy measurements, especially when they are multichannel, has always been a challenging problem. The processing techniques developed range from simple Fourier methods to more sophisticated model-based parametric methodologies based on the underlying acoustics of the problem at hand. Methods relying on multichannel mean-squared error processors (Wiener filters) have evolved over long periods from the seminal efforts in seismic processing. However, when more is known about the acoustics, then model-based state-space techniques incorporating the underlying process physics can improve the processing significantly. The problems of interest are the vibrational response of tightly coupled acoustic test objects excited by an out-of-the-ordinary transient, potentially impairing their operational performance. Further, employing a multiple input/multiple output structural model of the test objects under investigation enables the development of an inverse filter by applying subspace identification techniques during initial calibration measurements. Feasibility applications based on a mass transport experiment and test object calibration test demonstrate the ability of the processor to extract the excitations successfully.

42 ENGINEERING↗

DOE OSTI2021

Model-Based Ultrasonic Signal Processing for the Nondestructive Evaluation of Additive Manufacturing Components

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detectionof the part under investigation. A primary ingredient in UT besides the required precision robotic hardware for theacquisition of high quality measurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report,we discuss the basic steps in UT signal processing along with current and future capabilities that must be achieved in order to satisfy the critical demands created by the AM process.

42 ENGINEERING↗

DOE OSTI2021

Transient recovery problem in acoustics: A multichannel model-based deconvolution approach

Critical acoustical systems operating in complex environments contaminated with disturbances and noise offer an extreme challenge when excited by out-of-the-ordinary, impulsive, transient events that can be undetected and seriously affect their overall performance. Transient impulse excitations must be detected, extracted, and evaluated to determine any potential system damage that could have been imposed; therefore, the problem of recovering the excitation in an uncertain measurement environment becomes one of multichannel deconvolution. Recovering a transient and its initial energy has not been solved satisfactorily, especially when the measurement has been truncated and only a small segment of response data is available. The development of multichannel deconvolution techniques for both complete and incomplete excitation data is discussed, employing a model-based approach based on the state-space representation of an identified acoustical system coupled to a forward modeling solution and a Kalman-type processor for enhancement and extraction. In conclusion, synthesized data are utilized to assess the feasibility of the various approaches, demonstrating that reasonable performance can be achieved even in noisy environments.

42 ENGINEERING↗

DOE OSTI2020

Multichannel Deconvolution of Vibrational Shock Signals: An Inverse Filtering Approach

When transporting critical systems of national security interest, out-of-the ordinary, impulsive events that can potentially be undetected and affect overall system performance are of great concern. Impulsive events that can occur are essentially pulse-like, transient signals of short duration that evolve from various phenomena. Here the event can be created by either the dropping of a test object, the system, subjecting it to a compact high-energy blow or being struck unintentionally during transit resulting in potential damage. The intensity and location of the strike can cause an inoperability condition that is unacceptable in a national security environment. Therefore, it is essential to detect, classify and localize damage of any test object subjected to an impulsive-event. This effort was targeted to evaluate the vibrational response of test objects that are subjected to “transport” shocks and roadway vibrations during shipping and handling. Any potential damage that could be inflicted during transportation must not only be detected, but also be evaluated to determine the operational readiness of a test object before and after transport. This event is a critical task that must be addressed as part of the Lawrence Livermore National Laboratory (LLNL) national security mission. The estimation of excitation signals from noisy data is termed the deconvolution problem in the signal processing literature. The deconvolution problem is based on recovering the input excitation signal from a system characterized by its impulse response sequence. Using this model of the system, an “inverse” representation or filter is developed to remove the system from the measured data and recover the input. Deconvolution techniques have existed for a long-time; however, transient deconvolution presents a few uncommon problems, since the signal has a finite-length time duration resulting in a limited amount of data containing information about the excitation process. The transient is wideband in the frequency domain relative to any measurement sensor implying that the smaller bandwidth sensor system “filters” the excitation eliminating some of its essential information for recovery. This fact, coupled with the filtering effect of the test object itself makes this excitation recovery (deconvolution) problem a challenge for signal processing.

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