Vamshi Krishna Chillara

Bio: Vamshi Krishna Chillara is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Guided wave testing & Harmonics. The author has an hindex of 11, co-authored 40 publications receiving 569 citations. Previous affiliations of Vamshi Krishna Chillara include Pennsylvania State University.

A 9-to-12GHz Coupled-RTWO FMCW ADPLL with 97fs RMS Jitter, -120dBc/Hz PN at 1MHz Offset, and With Retrace Time of 12.5ns and 2μs Chirp Settling Time

Abstract: At the center of autonomous driving and range and motion sensing in industrial and healthcare applications are FMCW RADARs, which provide the means for object range and velocity estimation. With future widespread use and availability of more bandwidth for RADAR systems, FMCW generators with short chirp duration and low phase noise will be important to reduce the RADAR doppler-induced range ambiguity and improve its resolution of close targets in multi-target environments. Due to the conflicting tradeoffs between bandwidth and low phase noise, PLLs with two-point modulation (TPM) are commonly used. The TPM architecture suffers from 2 main drawbacks: 1) In analog implementations [1, 2], it requires a low-noise DAC to inject a modulation signal in the sensitive tuning port of the VCO degrading phase noise, 2) Due to the finite matching between the high- and low-pass paths of the PLL, it introduces FM errors, which degrade the linearity of the generated chirps and require calibration. This is true for analog and digital implementations [3]. This work presents a FMCW modulator using an RTWO-based ADPLL to alleviate the phase-noise-versus-settling-time limitations of conventional PLL architectures. It generates sawtooth chirps with slopes up to 65MHz/μs, 2μs settling time, 12.5ns chirp retrace time, and 37kHz rms FM error. This is while achieving phase noise of -120dBc/Hz at a 1MHz offset from a 10GHz RF carrier.

A Physics-Based Signal Processing Approach for Noninvasive Ultrasonic Characterization of Multiphase Oil–Water–Gas Flows in a Pipe

Abstract: A signal processing technique is presented for determining the composition of multiphase oil–water–gas flow in a pipe using noninvasive ultrasonic speed of sound measurements from a transmitter–receiver pair bonded to diametrically opposite sides of a pipe. A linear chirp excitation is used to send broadband ultrasonic energy that propagates in two paths from transmitter to receiver such as: 1) a wave through the pipe wall and then the multiphase mixture and 2) ultrasonic guided waves along the pipe wall in the circumferential direction. As the ultrasonic attenuation of the multiphase mixture increases, the amplitude of the signal through the fluid mixture decreases relative to that of circumferential guided waves, making it difficult to determine the time of arrival of the fluid-path signal and, hence, the speed of sound in the mixture. The proposed signal processing technique overcomes this challenge by using: 1) a guided wave subtraction approach to suppress the strength of guided wave signals relative to the fluid-path signal and 2) a Gaussian reconstruction approach for synthetic enhancement of the fluid-path signal by output signal reconstruction at frequencies corresponding to peak transmission of ultrasonic energy. The efficacy of the technique is demonstrated using experiments carried out in a field-scale flow loop with varying compositions of oil–water–gas mixtures. It is shown that the proposed approach can enhance the signal detectability by approximately 20 dB in comparison with the traditional approach that does not utilize guided wave subtraction and also improves the gas tolerance of composition measurements up to 20% in gas volume fraction.

Scalable time-series feature engineering framework to understand multiphase flow using acoustic signals

Abstract: Time-series signals are central to understand and identify the state of a dynamical system. They are ubiquitous in many areas related to geosciences, energy, and climate. As a result, the theory and techniques for analyzing and modeling time-series have vast applications in many different scientific disciplines. One of the key challenges that the time-series data analysts face is that of data overload. The sheer volume of the data generated at the sensor makes it difficult to transport it to centralized databases. These aspects pose an obstacle in detecting changes in the system response as early as possible. Instead, a workflow for an efficient and automatic reduction of collected data at sensors can enable timely analyses and decrease event detection latency. Such a workflow can be useful for many sensing applications. An attractive way to construct a computationally efficient workflow for automated analysis of sensor data is through machine learning. Herein, we present a novel framework for feature construction, feature selection, and feature filtering. As a first step, we construct comprehensive time-series signal features. In the second step, we perform feature selection using machine learning algorithms. The proposed framework is tested and validated against datasets obtained from multiphase flow loop experiments.Time-series signals are central to understand and identify the state of a dynamical system. They are ubiquitous in many areas related to geosciences, energy, and climate. As a result, the theory and techniques for analyzing and modeling time-series have vast applications in many different scientific disciplines. One of the key challenges that the time-series data analysts face is that of data overload. The sheer volume of the data generated at the sensor makes it difficult to transport it to centralized databases. These aspects pose an obstacle in detecting changes in the system response as early as possible. Instead, a workflow for an efficient and automatic reduction of collected data at sensors can enable timely analyses and decrease event detection latency. Such a workflow can be useful for many sensing applications. An attractive way to construct a computationally efficient workflow for automated analysis of sensor data is through machine learning. Herein, we present a novel framework for feature con...

Noninvasive Acoustic Measurements in Cylindrical Shell Containers

Abstract: Acoustic time-of-flight (ToF) measurements enable noninvasive material characterization, acoustic imaging, and defect detection and are commonly used in industrial process control, biomedical devices, and national security. When characterizing a fluid contained in a cylinder or pipe, ToF measurements are hampered by guided waves, which propagate around the cylindrical shell walls and obscure the waves propagating through the interrogated fluid. We present a technique for overcoming this limitation based on a broadband linear chirp excitation and cross correlation detection. By using broadband excitation, we exploit the dispersion of the guided waves, wherein different frequencies propagate at different velocities, thus distorting the guided wave signal while leaving the bulk wave signal in the fluid unperturbed. We demonstrate the measurement technique experimentally and using numerical simulation. We characterize the technique performance in terms of measurement error, signal-to-noise-ratio, and resolution as a function of the linear chirp center frequency and bandwidth. We discuss the physical phenomena behind the guided bulk wave interactions and how to utilize these phenomena to optimize the measurements in the fluid.

Review of nonlinear ultrasonic guided wave nondestructive evaluation: theory, numerics, and experiments

Abstract: Interest in using the higher harmonic generation of ultrasonic guided wave modes for nondestructive evaluation continues to grow tremendously as the understanding of nonlinear guided wave propagation has enabled further analysis. The combination of the attractive properties of guided waves with the attractive properties of higher harmonic generation provides a very unique potential for characterization of incipient damage, particularly in plate and shell structures. Guided waves can propagate relatively long distances, provide access to hidden structural components, have various displacement polarizations, and provide many opportunities for mode conversions due to their multimode character. Moreover, higher harmonic generation is sensitive to changing aspects of the microstructures such as to the dislocation density, precipitates, inclusions, and voids. We review the recent advances in the theory of nonlinear guided waves, as well as the numerical simulations and experiments that demonstrate their utility.