This paper introduces a recent innovation in dealing with non-periodic behavior often referred to as transients. These transients can be the result from unforced response due to the initial condition and other drifts which are a source of error when performing and interpreting Fourier analysis on measurement data. Fourier analysis is particularly relevant in system identification used to build feedback controllers and the analysis of various pulsed experiments such as heat pulse propagation studies. The basic idea behind the methodology is that transients are continuous complex-valued smooth functions in the Fourier domain which can be estimated from the Fourier data. Then, these smooth functions can be approximately subtracted from the data such that only periodic components are retained. The merit of the approach is shown in two experimental examples, i.e., heat pulse propagation (core transport analysis) and radiation front movement due to gas puffing. The examples show that the quality of the data is significantly improved such that it allows new interpretation of the results even for non-ideal measurements.

中文翻译：

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修正微扰实验中的非周期性行为：应用于热脉冲传播和调制喷气实验

本文介绍了处理通常称为瞬态的非周期性行为的最新创新。这些瞬变可能是由于初始条件和其他漂移引起的非受迫响应的结果，这些漂移是对测量数据执行和解释傅立叶分析时的误差来源。傅立叶分析与用于构建反馈控制器的系统识别和各种脉冲实验（例如热脉冲传播研究）的分析特别相关。该方法背后的基本思想是瞬态是傅立叶域中的连续复值平滑函数，可以从傅立叶数据中估计出来。然后，可以从数据中近似减去这些平滑函数，从​​而只保留周期分量。该方法的优点在两个实验示例中显示，即热脉冲传播（核心传输分析）和由于气体喷出引起的辐射前沿运动。这些示例表明，数据质量得到了显着提高，因此即使对于非理想的测量，它也允许对结果进行新的解释。