# Estimation of burst-mode LDA power spectra

## TL;DR

This paper introduces a novel spectral algorithm for analyzing LDA data, addressing challenges like bias and intermittency, and demonstrates its effectiveness through experiments on fluid flows.

## Contribution

A new residence-time-weighted spectral algorithm for LDA data analysis is proposed, improving power spectrum estimation in fluid dynamics.

## Key findings

- The new algorithm accurately estimates power spectra from LDA data.
- It outperforms traditional methods like time slot correlation and sample-and-hold.
- Validated on cylinder wake and turbulent jet experiments.

## Abstract

The estimation of power spectra from LDA data provides signal processing challenges for fluid dynamicists for several reasons: acquisition is dictated by randomly arriving particles, the registered particle velocities tend to be biased towards higher values and the signal is highly intermittent. The signal can be interpreted correctly by applying residence time weighting to all statistics and using the residence-time-weighted discrete Fourier transform to compute the Fourier transform. A new spectral algorithm using the latter is applied to two experiments; a cylinder wake and an axisymmetric turbulent jet. These are compared to corresponding hot-wire spectra as well as to alternative algorithms for LDA signals such as the time slot correlation method, sample-and-hold and common weighting schemes.