The interaction between parallel jets plays a critical role in determining the characteristics of the momentum and heat transfer in the flow. Specifically for next generation VHTR, the output temperature will be about 900 °C, and any thermal oscillations will create safety issues. The mixing variations of the coolants in the reactor core may influence these power oscillations. Numerous numerical tools such as computational fluid dynamics (CFD) simulations have been used to support the reactor design. The validation of CFD method is important to ensure the fidelity of the calculations. This requires high-fidelity, qualified benchmark data. Particle image velocimetry (PIV), a non-intrusive measuring technique, was used to provide benchmark data for resolving a simultaneous flow field in the converging region of two submerged parallel jets issued from rectangular channels. The jets studied in this work had an equal discharge velocity at room temperature. The turbulent characteristics including the distributions of mean velocities, turbulence intensities, Reynolds stresses and Z-component vorticity were studied. The streamwise mean velocity measured by PIV and LDV were compared, and they agreed very well.

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Turbulent mixing of parallel jet flows has broad engineering applications. For example, in Gen IV conceptual nuclear reactors, high-temperature flows mix in the lower plenum before entering the secondary cooling system. The mixing condition needs to be accurately estimated and fully understood. In addition, massive computational works involved in the design process necessitate high-fidelity experimental data sets for benchmarking simulation results. The purpose of this study is to use laser Doppler anemometry, a non-intrusive measuring technique, to evaluate the mixing characteristics of two submerged parallel jets issuing from two rectangular channels. The jets with a small spacing ratio of 3.1 were at room temperature. Flow characteristics including distributions of mean velocities, turbulence intensities, and Reynolds stresses were studied for the cases with equal and non-equal discharge velocities. The merging point (MP) was found to be between y/a = 1.72 and y/a = 3.45. The combining point (CP) was at y/a = 15.52. The Reynolds shear stress, a measure of the intensity of momentum transfer, reached its maximum after the merging point. An uncertainty analysis indicated that the average standard deviations of the streamwise mean velocity U and turbulence intensity Urms at all locations in the five days' measurements were 1.5% and 1.6%, respectively. Spectral analyses including fast Fourier transform, power spectral density estimation and continuous wavelet transform revealed the scale and the evolution in time of varied-size eddies in the mixing region of the flow. Repeating flow structures were observed in different time segments. The experimental data obtained from the LDA measurements of the averaged quantities and transient are not only valid for benchmarking steady-state numerical simulations using turbulence models to solve RANS equations but they also enlarge the database of the experimental data for twin jets.

Background and Objectives Proteome analysis of periodontal ligament stem cells (PDLSCs) could be used to study the function of PDL tissue. We used a label‐free quantitative proteomic technique to investigate differentially expressed proteins (DEPs) in human PDLSCs (hPDLSCs) compared to human bone marrow mesenchymal stem cells (hBMSCs) and identify proteins specific to hPDLSCs. Material and Methods hPDLSCs (n = 3) and hBMSCs (n = 3) were cultured and harvested for protein extraction and trypsin digestion. The proteomes of both cell types were analyzed by nano‐liquid chromatography/tandem mass spectrometry. DEPs in hPDLSCs compared to hBMSCs were detected by label‐free quantification and evaluated through signal transduction pathway and gene ontology (GO) analysis. Results In total, 690 and 771 proteins were identified from hPDLSCs and hBMSCs, of which 561 proteins were in common and 124 DEPs were found between hPDLSCs and hBMSCs. Fifty‐eight proteins were expressed at significantly higher levels in hPDLSCs, whereas 66 proteins were expressed at lower levels compared to hBMSCs. The more highly expressed proteins were associated with translation and initiating protein synthesis, and lower expressed proteins were related to cell aging and metabolic processes. Proteins unique to hPDLSCs and hBMSCs were associated with translation and metabolic processes, respectively. Conclusion Our results demonstrate evidence of distinct differences in protein expression between hPDLSCs and hBMSCs by using label‐free quantitative proteomic analysis which was the first attempt in this field. DEPs included previously reported hPDLSC marker proteins and novel marker candidates, such as microtubule‐associated protein, CTP synthase 1 and stathmin, which could be the markers for developing periodontal disease diagnostics and therapies.

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