Experimental Measurement Error Research Articles

A Multi-Scale Model for Predicting Physically Short Crack and Long Crack Behavior in Metals.

The fatigue behavior of metal specimens is influenced by defects, material properties, and loading. This study aims to establish a multi-scale fatigue crack growth model that describes physically short crack (PSC) and long crack (LC) behavior. The model allows the calculation of crack growth rates for uniaxial loading at different stress ratios based on the material properties and specimen geometry. Furthermore, the model integrates the Gaussian distribution theory to consider material heterogeneity and the experimental measurement errors that cause fatigue scatter. The crack growth rate and fatigue life of metal specimens with different notch geometry were predicted. The curves generated by the multi-scale model were mainly consistent with the test data from the published literature at the PSC and LC stages.

Machine learning based unfolding of x-ray spectra from filter stack spectrometer data.

We demonstrate the application of neural networks to perform x-ray spectra unfolding from data collected by filter stack spectrometers. A filter stack spectrometer consists of a series of filter-detector pairs, where the detectors behind each filter measure the energy deposition through each layer as photo-stimulated luminescence (PSL). The network is trained on synthetic data, assuming x-rays of energies <1 MeV and of two different distribution functions (Maxwellian and Gaussian) and the corresponding measured PSL values obtained from five different filter stack spectrometer designs. Predicted unfolds of single distributions are near identical reproductions of the ground truth spectra, with differences in the values lower than 20% at the higher energy end in some cases. The neural network has also demonstrated robustness to experimental measurement errors of <5% and some capability of performing unfolds for linear combinations of the two distributions without previous training. The network can perform unfolds at rates >1 Hz, ideal for application to some high-repetition-rate systems.

Relative permeability estimation using mercury injection capillary pressure measurements based on deep learning approaches

Relative permeability (RP) curves which provide fundamental insights into porous media flow behavior serve as critical parameters in reservoir engineering and numerical simulation studies. However, obtaining accurate RP curves remains a challenge due to expensive experimental costs, core contamination, measurement errors, and other factors. To address this issue, an innovative approach using deep learning strategy is proposed for the prediction of rock sample RP curves directly from mercury injection capillary pressure (MICP) measurements which include the mercury injection curve, mercury withdrawal curve, and pore size distribution. To capture the distinct characteristics of different rock samples' MICP curves effectively, the Gramian Angular Field (GAF) based graph transformation method is introduced for mapping the curves into richly informative image forms. Subsequently, these 2D images are combined into three-channel red, green, blue (RGB) images and fed into a Convolutional Long Short-Term Memory (ConvLSTM) model within our established self-supervised learning framework. Simultaneously the dependencies and evolutionary sequences among image samples are captured through the limited MICP-RP samples and self-supervised learning framework. After that, a highly generalized RP curve calculation proxy framework based on deep learning called RPCDL is constructed by the autonomously generated nearly infinite training samples. The remarkable performance of the proposed method is verified with the experimental data from rock samples in the X oilfield. When applied to 37 small-sample data spaces for the prediction of 10 test samples, the average relative error is 3.6%, which demonstrates the effectiveness of our approach in mapping MICP experimental results to corresponding RP curves. Moreover, the comparison study against traditional CNN and LSTM illustrated the great performance of the RPCDL method in the prediction of both So and Sw lines in oil–water RP curves. To this end, this method offers an intelligent and robust means for efficiently estimating RP curves in various reservoir engineering scenarios without costly experiments.

Unsupervised learning of quantum many-body scars using intrinsic dimension

Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share high overlap with scar eigenstates, the system undergoes dynamics with atypically slow relaxation and periodic revival. This scarring phenomenon poses a potential avenue for circumventing decoherence in various quantum engineering applications. Given access to an unknown scar system, current approaches for identification of special states leading to non-thermal dynamics rely on costly measures such as entanglement entropy. In this work, we show how two dimensionality reduction techniques, multidimensional scaling and intrinsic dimension estimation, can be used to learn structural properties of dynamics in the PXP model and distinguish between thermal and scar initial states. The latter method is shown to be robust against limited sample sizes and experimental measurement errors.

Models of Inter-Moult Period for Antarctic Krill - Lack of Progress and Promulgation of Unreliable Models Calibrated Using Indirect Observations

Aims: Two methods of calibrating regression models of inter-moult period (IMP) as a function of temperature exposure (T) for crustaceans, in particular, Antarctic krill (Euphausia superba) are reviewed in terms of both theoretical and empirical properties in order to make recommendations on the application of the methods and/or the use of the resultant fitted models. Methodology: The method and fitted model that used a meta-analysis of published results from laboratory-reared krill using means of directly observed IMP for a range of controlled, constant temperature regimes has valid theoretical and empirical support. The alternative used moult frequencies obtained as a “byproduct” of 5-d Instantaneous Growth Rate (IGR) experiments carried out at sea for which individual IMPs were not directly observed. Instead mean IMP given T and animal total length (L) was predicted using the moult frequencies disaggregated to binary data of moulted versus not-moulted as dependent variable in the calibration of a logistic regression on T and L. The shape of the daily development rate, R, the inverse of IMP, versus T response curve fitted using direct observations is a classical monotonically increasing curve whereas for combinations of sex/maturity classes the curves fitted using indirect observations are parabola-like with sexually dimorphic concavities of either up or down. Four sources of bias in predictions of mean IMP using the indirect observations and estimation method are described. One source due to an unrepresentative sampling frame can lead to large positive bias in estimated mean IMP based on theory which has been absent until now and that applies a discrete uniform distribution for next moult date corresponding to ideal asynchrony. This bias and that due to IGR experimental measurement error in T cannot be remedied. Conclusion: The indirect method and the corresponding fitted models are unreliable and should not be used.

Semi-Empirical and Theoretical Calculation of L1, L2, and L3 Subshell Fluorescence Yields

In this study, semi-empirical Li subshells fluorescence yields (ωLi) were calculated from the available experimental data covering the period from 1955 to 2022 for elements with 28 ≤ Z ≤ 96 for L1 subshell, and 23 ≤ Z ≤ 96 for L2 and L3 subshells. First, we critically examine these data and obtain weighted average values (ωL1W,ωL2W,andωL3W) using a formula based on experimental (ωEXP) values and measurement errors. New recommended values ωWR are obtained by calculating the ratio S = ωEXP/ωW and removing out-of-range values (less than 0.8 or greater than 1.2). Semi-empirical values were derived for the three subshells using two interpolations: the analytical function [ωWR/(1 − ωWR)]1/4 and the fitting ratio S = ωEXP/ωWR, both as function of the atomic number Z. Furthermore, new theoretical calculations based on the Multiconfiguration Dirac-Fock Method have been performed for some elements and are presented in this work. Finally, our semi-empirically and theorical calculated Li subshell fluorescence yields were compared with other theoretical, experimental, and empirical values from the literature.

Using XAS to monitor radiation damage in real time and post-analysis, and investigation of systematic errors of fluorescence XAS for Cu-bound amyloid-β.

X-ray absorption spectroscopy (XAS) is a promising technique for determining structural information from sensitive biological samples, but high-accuracy X-ray absorption fine structure (XAFS) requires corrections of systematic errors in experimental data. Low-temperature XAS and room-temperature X-ray absorption spectro-electrochemical (XAS-EC) measurements of N-truncated amyloid-β samples were collected and corrected for systematic effects such as dead time, detector efficiencies, monochromator glitches, self-absorption, radiation damage and noise at higher wavenumber (k). A new protocol was developed using extended X-ray absorption fine structure (EXAFS) data analysis for monitoring radiation damage in real time and post-analysis. The reliability of the structural determinations and consistency were validated using the XAS measurement experimental uncertainty. The correction of detector pixel efficiencies improved the fitting χ2 by 12%. An improvement of about 2.5% of the structural fitting was obtained after dead-time corrections. Normalization allowed the elimination of 90% of the monochromator glitches. The remaining glitches were manually removed. The dispersion of spectra due to self-absorption was corrected. Standard errors of experimental measurements were propagated from pointwise variance of the spectra after systematic corrections. Calculated uncertainties were used in structural refinements for obtaining precise and reliable values of structural parameters including atomic bond lengths and thermal parameters. This has permitted hypothesis testing.

Effect of flame thickness on the temperature measurement error for bare-bead thermocouple in open pool fires with different fuels

Accurately obtaining the temperature distribution of the flame is crucial for a comprehensive understanding of the combustion behavior of the pool fire. The correction method is needed to compensate the measurement error when the temperature is measured by bare-bead thermocouple in the open pool experiment, but the effect of flame thickness is rarely considered in the current method. Thus, this work develops a coupled heat transfer model of bare-bead thermocouples in open pool fires and modifies the flame radiation characteristics based on the fuel type and flame thickness. The control mechanism of measurement error in the model is identified, and then the effects of main parameters such as flame thickness and flame temperature on measurement error are analyzed. The results show that the flame emissivity decreases exponentially with the decrease of flame thickness, the minimum emissivity of gasoline flame is 0.26 when flame thickness is 0.1 m. The decrease of flame thickness leads to a sharp increase of temperature measurement error. The maximum increment of error can reach 227.3 K. On this basis, the flame thickness limit value, considering the correction of temperature measurement error in different hydrocarbon pool fire experiments is proposed.

Identifiability of the mechanisms governing the reaction kinetics of MIEC electrodes in solid oxide cells

The oxygen reduction reaction (ORR) is the main phenomenon occurring in mixed ionic and electronic conductors (MIECs) used as air electrodes in solid oxide cells. Their optimisation requires the identification of the ORR regime, which is typically performed via electrochemical impedance spectroscopy (EIS). In this study we present a physics-based model to simulate the impedance spectra of p-type oxygen-deficient perovskite MIEC materials. The EIS response of four extreme kinetic scenarios, characterised by the rate-determining step (electron-transfer or ion-transfer) and the high/low surface coverage of adsorbed oxygen, is mechanistically interpreted for both dense films and porous electrodes. A strategy for kinetic identification is proposed based on distinctive EIS fingerprints at different oxygen partial pressures (pO2) and cathodic bias. However, distinguishing the kinetic scenarios is not free from ambiguity even in dense films since some scenarios are discriminated only via a quantitative analysis, which may be susceptible to experimental errors in real measurements, and reverse behaviours appear when combining cathodic bias with pO2 variation. More difficulties arise in porous electrodes since bulk oxygen vacancy transport interacts with the ORR response. Application of the proposed strategy using literature data for some common MIEC materials shows the typical challenges of kinetic identification when relying solely on EIS.

Experimental analysis of boron dilution transient flow mixing characteristics based on planar laser induced fluorescence measurement

In Reactor Pressure Vessels (RPV), flow mixing between high and low concentration coolants will cause uneven distribution of boric acid. Severe cases will lead to boron dilution transient and re-criticality safe operation accident. Therefore, accurate measurement of coolant flow mixing characteristics and boron concentration distribution in RPV and study of the mechanism affecting the mixing process are of great significance to improve reactor operation safety. Based on the structural characteristics of large advanced pressurized water reactor pressure vessel, a large-scale visual experimental device was built by 1:6 scale modular design. Optical path correction scheme is used to solve the problem of optical path distortion existing in PLIF technology, and improved ratio calibration method is used to reduce the experimental error of concentration field distribution measurement. Flow mixing process and boron concentration field distribution in pressure vessel under single loop conditions were obtained based on plane laser induced fluorescence technique (PLIF). The influence of Reynolds number on boron diffusion behavior was studied. The experimental results show that during single loop operation, the localized eddy current formed in the left area of the lower head will result in a high concentration retention zone and a V-shaped boric acid concentration distribution in the core. With the increase of the ratio of cold tube flow to injection flow, the time when the high concentration coolant reaches the core hardly changes with the boric acid solution flow rate. The mixing diffusion behavior of boron concentration is the best when the ratio of injection to Reynolds number in the cold tube is at a certain value.

Validating the Multi-Mode Model’s Ability to Reproduce Diverse Tokamak Scenarios

A large-scale validation exercise was conducted to assess the multi-mode model (MMM) anomalous transport model in the integrated modeling code TRANSP. The validation included 6 EAST discharges, 17 KSTAR discharges, 72 JET ITER-like wall D-D discharges, and 4 DIII-D fusion plasma discharges. Using the MMM, the study computed anomalous thermal, particle, impurity, and momentum transport within TRANSP. Simulations for EAST, KSTAR, and JET focused on electron and ion temperatures and safety factor profiles, while DIII-D simulations also considered electron density, toroidal rotation frequency, and flow shear. The predicted profiles were compared to experimental data at the diagnostic time, quantifying the comparison using root-mean-square (RMS) deviation and relative offsets. The study found an average RMS deviation of 9.3% for predicted electron temperature and 10.5% for ion temperature, falling within the experimental measurement error range 20%. The MMM model demonstrated computational efficiency and the ability to accurately reproduce a wide range of discharges, including various scenarios and plasma parameters, such as plasma density, gyroradius, collisionality, beta, safety factor and heating method variations.

Detonation behaviors in stoichiometric CH4-H2-O2 under different initial pressures conditions

In this study, the detonation behaviors (i.e., detonation limit, detonation overpressure, mean velocity, velocity deficit, and cell sizes) of CH4-H2-O2 mixtures in a small-bore tube at different initial pressures (ranging from 6 kPa to 300 kPa) were investigated. Mixtures with different proportions of H2 in the total fuel (0, 10%, 20%, 25%, 30%, 50%, 75%, 100%) were tested. Steady one-dimensional ZND calculations are used to obtain detonation parameters such as induction zone length, stability parameters, and cell sizes. Results indicate that increasing the hydrogen fraction decreases the detonation limit. The addition of hydrogen can reduce velocity deficit, especially near the detonation limit pressure. Velocity deficits were found to be within 3.5% when the initial pressure higher than 50 kPa. Higher initial pressure may result in the mean detonation velocity surpassing the theoretical CJ velocity. The detonation overpressure increases with the initial pressure and decreases with hydrogen fraction. The influence of initial pressure on cell sizes weakens as hydrogen fraction increases. Besides, the induction zone length and stability parameter decrease as hydrogen fraction increases Notably, the stability parameter of mixtures with different hydrogen fractions shows different trends as the initial pressure increases. A linear relationship between actual measured cell sizes and induction zone length is given as λ = 24.56Δi for hydrogen-methane-oxygen mixtures with the initial pressure range of 100 to 300 kPa. If experimental measurement error is considered, the prediction of the Ng model has reasonable accuracy.

Magnetic microrheometry of tumor-relevant stiffness levels and probabilistic quantification of viscoelasticity differences inside 3D cell culture matrices.

The progression of breast cancer involves cancer-cell invasions of extracellular matrices. To investigate the progression, 3D cell cultures are widely used along with different types of matrices. Currently, the matrices are often characterized using parallel-plate rheometry for matrix viscoelasticity, or liquid-like viscous and stiffness-related elastic characteristics. The characterization reveals averaged information and sample-to-sample variation, yet, it neglects internal heterogeneity within matrices, experienced by cancer cells in 3D culture. Techniques using optical tweezers and magnetic microrheometry have measured heterogeneity in viscoelasticity in 3D culture. However, there is a lack of probabilistic heterogeneity quantification and cell-size-relevant, microscale-viscoelasticity measurements at breast-tumor tissue stiffness up to ≃10 kPa in Young's modulus. Here, we have advanced methods, for the purpose, which use a magnetic microrheometer that applies forces on magnetic spheres within matrices, and detects the spheres displacements. We present probabilistic heterogeneity quantification using microscale-viscoelasticity measurements in 3D culture matrices at breast-tumor-relevant stiffness levels. Bayesian multilevel modeling was employed to distinguish heterogeneity in viscoelasticity from the effects of experimental design and measurement errors. We report about the heterogeneity of breast-tumor-relevant agarose, GrowDex, GrowDex-collagen and fibrin matrices. The degree of heterogeneity differs for stiffness, and phase angle (i.e. ratio between viscous and elastic characteristics). Concerning stiffness, agarose and GrowDex show the lowest and highest heterogeneity, respectively. Concerning phase angle, fibrin and GrowDex-collagen present the lowest and the highest heterogeneity, respectively. While this heterogeneity information involves softer matrices, probed by ≃30 μm magnetic spheres, we employ larger ≃100 μm spheres to increase magnetic forces and acquire a sufficient displacement signal-to-noise ratio in stiffer matrices. Thus, we show pointwise microscale viscoelasticity measurements within agarose matrices up to Young's moduli of 10 kPa. These results establish methods that combine magnetic microrheometry and Bayesian multilevel modeling for enhanced heterogeneity analysis within 3D culture matrices.

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

About the automatic measurement of the dislocation density obtained by R-ECCI

A proof of concept of a new method for automatic characterization of the dislocation density from scanning electron microscopy images is presented. A series of backscattered electron images are acquired while the sample is rotated. For each pixel of the region of interest, the variation of the grey-level intensity as a function of the rotation angle, called the intensity profile, is calculated. This profile can be used to determine the nature of each pixel (dislocation, matrix or noise), such that an automatic dislocation density can be determined within the region of interest. The method is well adapted for dislocation densities ranging from 10 12 to 10 14 m −2 . The simulation of a volume containing dislocations enabled the determination of the maximum and minimum densities attainable as well as the theoretical and experimental measurement errors related to the projection of this volume on a two-dimensional image. The theoretical measurement error due to the projection of dislocation on a surface, is 3% for low dislocation densities (10 12 m −2 ) and 20% for higher dislocation densities (10 14 m −2 ). Experimentally, measurement errors are limited by image analysis conditions, which leads to total measurement errors of 15% for 10 12 m −2 and 34% error for 10 14 m −2 . These uncertainties were obtained considering a given analyzed depth value, that could not be experimentally verified. This uncertainty on the depth value leads to large errors bars in the final measurement, which can reach an order of magnitude. • A new method for automatic characterization of the dislocation density by SEM is presented. • The R-ECCI method and clustering are discussed. • The measurement error of the dislocation density measurement by the R-ECCI method and clustering is estimated.

Mining Order-preserving Submatrices under Data Uncertainty: A Possible-world Approach and Efficient Approximation Methods

Given a data matrix\( D \), a submatrix\( S \)of\( D \)is an order-preserving submatrix (OPSM) if there is a permutation of the columns of\( S \), under which the entry values of each row in\( S \)are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work on OPSM has ever considered uncertain value intervals using the well-established possible world semantics.We establish two different definitions of significant OPSMs based on thepossible world semantics: (1) expected support-based and (2) probabilistic frequentness-based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, with a closed-form formula derived to efficiently handle the special case of uniform value distribution and an accurate cubic spline approximation approach that works well with any uncertain value distributions. To efficiently check the probabilistic frequentness, several effective pruning rules are designed to efficiently prune insignificant OPSMs; two approximation techniques based on the Poisson and Gaussian distributions, respectively, are proposed for further speedup. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori, respectively. We further parallelize our prefix-projection-based mining algorithm using PrefixFPM, a recently proposed framework for parallel frequent pattern mining, and we achieve a good speedup with the number of CPU cores. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches.

METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP

This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate molecules to the selected receptor into active and inactive. Ligands recognized as involved by the first ensemble are fed to the second ensemble, which assumes the degree of their affinity for the receptor in the form of an inhibition factor (Ki). A feature of the method is the rejection of the use of atomic coordinates of individual molecules and their complexes – thus eliminating experimental errors in sample preparation and measurement of nuclear coordinates and the method to determine the affinity of biomolecules with unknown spatial configurations. It is shown that meta-learning increases the response (Recall) of the classification ensemble by 34.9% and the coefficient of determination (R2) of the regression ensemble by 21% compared to the average values. This paper shows that an ensemble with meta-stacking is an asymptotically optimal system for learning. The feature of Super Learning is to use k-fold cross-validation to form first-level predictions that teach second-level models — or meta-models — that combine first-level models optimally. The ability to predict the molecular affinity of six machine learning models is studied, and the efficiency improvement is due to the combination of models in the ensemble by the stacking method. Models that are combined into two consecutive ensembles are shown.

Accurate characterization of surface recombination velocities of silicon wafers with differential nonlinear photocarrier radiometry

The surface recombination velocity (SRV), which reflects the fundamental characteristics of surface defects of semiconductor wafers, is an important parameter in evaluating the quality of surface passivation and electrical performance of surface devices. In conventional photocarrier radiometry (PCR) used for characterizing the electronic transport properties of electronically thick silicon wafers, the rear SRV usually cannot be determined directly due to the relatively low sensitivity of PCR signal to the rear SRV. On the other hand, the determination of front SRV is also very sensitive to the experimental measurement error, especially the measurement error of instrumental frequency response, which is not always easy to be accurately measured in the experiment. In this paper, the front and rear SRVs of silicon wafers are extracted simultaneously with high accuracy by a differential PCR via multi-parameter fitting of the experimental frequency dependences of amplitude ratio and phase difference of PCR signals obtained from the regular measurements and measurements with wafers being flipped respectively to a corresponding differential nonlinear PCR model. The comparison between the front and rear SRVs determined by the conventional and differential PCRs indicates that the differential PCR is highly accurate for the simultaneous determination of the front and rear SRVs of silicon wafers.

Research on Rock Crack Classification Based on Acoustic Emission Waveform Feature Extraction Technology

Abstract Rock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform signal not only reduces the experimental error of the mechanical strength measurement but also simplifies the type and quantity of disaster prediction source data. For the relationship between crack mode and mechanical strength, the acoustic emission (AE) waveform signal is studied. Six coarse-grained sandstone samples were tested by uniaxial compression, AE, and scanning electron microscopy. The results show that the number of microhole cracks in rock is positively correlated with tensile-shear cracks and negatively correlated with mechanical strength. The quadratic function regression curve of the proportion of shear cracks and mechanical strength is more realistic. When crack ratio is less than 0.31, the number of shear cracks is positively correlated with the mechanical strength and vice versa. The waveform mutation coefficient k is defined as the overall change description. It is found that the increase of signal mutation has a positive impact on the mechanical strength of rock. The fitting function of crack and the signal mutation near the peak of rock can be divided into six risk zones in a two-dimensional plane. In addition to these exciting results and discoveries, the determination of the number of tensile-shear cracks and its relationship with mechanical strength provide innovative methods and ideas for crack pattern discrimination and rock burst risk assessment of roadway surrounding rock.

Experimental validation of sonic speed theory for wet steam

In order to obtain the sonic speed of the wet steam under different thermal conditions, and verify the accuracy and the precision of Petr’s wet steam sonic theory, experimental measurements were carried out. Also, the effects of acoustic frequency, pressure, wetness, and droplet size on the sonic speed of wet steam were studied based on Petr’s wet steam sonic theory. Results indicate that deviations between the experimental results and Petr’s wet steam sonic theory is in the range of −4%–4%, which is within the experimental measurement error of ±5%. The variation range of the ratio of the actual sonic speed a to the frozen speed af is 0.89–1. When the sonic speed ratio ( a/ af) is close to 1, it means that the sonic speed of wet steam is close to the frozen speed, and the faster the sound wave propagates in the wet steam. In the acoustic frequency range of 102–105 Hz, the sonic speed of wet steam increases with increase of acoustic frequency, pressure, and water droplet size, and decreases with increasing wetness.