The Influence of the Surface Micro-structure Change on the Stainless Steel Effective Thermal Diffusivity

Somer, Aloisi; Novatski, Andressa; Cruz, Claudia Bonardi Kniphoff da; Serbena, Francisco Carlos; Cruz, Gerson Kniphoff da

doi:10.1007/s10765-022-03072-3

Cited by 3 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is information about how differing martensite concentrations in stainless steel affect the material's effective thermal diffusivity in Ref. [27]. It is well known that defects can influence the thermal properties of materials like occurs with thermal expansion coefficient [17].…”

Section: In Table 1 the Indexmentioning

confidence: 99%

“…A rigorous measurement system calibration results in excellent experimental data fitted by the classical heat propagation model [22,23,24,25,26]. However, in some cases, the values obtained for the thermal diffusivity are far from the expected values [27]. Also, can be obtained different results from Amplitude and Phase individual analysis.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Thermal Fractional Diffusion: Experimental Evidence from the Discrepancies in the Amplitude and Phase in Photothermal Technique

Somer

Novatski

Cruz

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The photoacoustic (PA) signal is the pressure variation in the PA cell and by means of the open photoacoustic cell (OPC) technique, its amplitude and phase components are measured. In this work, the issue of disagreement between experimental measured amplitude and phase for AISI 316 samples with the expected theoretical result from the classical model is studied. The results of the thermal diffusivity obtained from amplitude are inconsistent with phase analysis. This work aims to show a way to get a unique value of thermal parameters from simultaneous analysis of the amplitude and phase of the PA signal. Also, the fractional models were applied to improve the fit of experimental data and provided results closed with the expected thermal diffusivity value.

show abstract

Section: In Table 1 the Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Thermal Fractional Diffusion: Experimental Evidence from the Discrepancies in the Amplitude and Phase in Photothermal Technique

Somer

Novatski

Cruz

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Depending on the thickness of the substrate (usually more than tens of microns), such film thicknesses are usually at the limit of experimental detection [ 16 , 17 , 18 , 19 ]. This means, for example, that the differences in the amplitude of the photoacoustic signals (PAS), generated by a two-layer sample (substrate + thin-film) in the case where only the thickness of the film is changed, are extremely small [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. The analysis of such two-layer samples is also theoretically demanding.…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

et al. 2023

View full text Add to dashboard Cite

In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy.

show abstract

Thermal fractional diffusion: experimental evidence from the discrepancies in the amplitude and phase in photothermal technique

Somer,

Novatski,

da Cruz

et al. 2023

Nonlinear Dyn

View full text Add to dashboard Cite

The Influence of the Surface Micro-structure Change on the Stainless Steel Effective Thermal Diffusivity

Cited by 3 publications

References 47 publications

Thermal Fractional Diffusion: Experimental Evidence from the Discrepancies in the Amplitude and Phase in Photothermal Technique

Thermal Fractional Diffusion: Experimental Evidence from the Discrepancies in the Amplitude and Phase in Photothermal Technique

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

Thermal fractional diffusion: experimental evidence from the discrepancies in the amplitude and phase in photothermal technique

Contact Info

Product

Resources

About