This study presents high-spatial-resolution two-component, two-dimensional particle image velocimetry (PIV) measurements of a self-similar adverse pressure gradient turbulent boundary layer (SS-APG-TBL) at the verge of separation. The experiments were performed in the LTRAC APG-TBL wind tunnel, where the flexible roof was adjusted to impose the pressure distribution required for self-similar development within the test section. The APG-TBL had a Reynolds number based on the boundary-layer thickness of R e δ = 7 . 9 × 1 0 4 , and the Clauser pressure-gradient parameter β = 18 . 8 was maintained throughout the measurement domain. A dual-camera 2C-2D PIV system was employed to capture both large-field and near-wall velocity fields, enabling accurate measurement of the flow adjacent to the wall. Calibration procedures, including lens-distortion correction and camera-overlap alignment, were performed to ensure sub-pixel accuracy in the velocity fields. The probability density and cumulative distribution functions of the instantaneous streamwise velocity adjacent to the wall are used to assess the wall-shear condition and demonstrate that the boundary layer is at the verge of separation. The probability density distribution peaks near zero and exhibits positive skewness, indicating a near-zero mean with intermittent high-velocity events. The results confirm a low-skin-friction state consistent with incipient separation, with nearly constant wall-shear stress across the measurement stations. Furthermore, the mean-velocity defect and Reynolds stresses collapse when expressed in self-similar variables, providing strong experimental evidence of a self-similar APG-TBL at moderate Reynolds number. The present database establishes an experimental benchmark, extending the study of SS-APG-TBLs beyond direct numerical simulation and to higher Reynolds numbers, and offers a new reference for turbulence modelling and theoretical analyses of self-similar adverse pressure gradient turbulent boundary layers. • Self-similar APG-TBL at the verge of separation created with a contoured tunnel roof. • Dual-camera 2C-2D PIV combined high spatial resolution with a large field of view. • Near-zero wall shear stress confirmed by near-wall velocity PDF. • Mean-defect and Reynolds stresses collapse under self-similar scaling. • Experimental database at β ≈ 20 . 3 and Re δ ≈ 7 . 9 × 1 0 4 for SS-APG-TBL studies.

This study investigates the relationship between phase retardation and strain rates in combined extensional–shear flows using the Jeffery–Hamel flow formalism, which yields an analytical velocity solution. Flow birefringence was measured in a 1.0 wt% cellulose nanocrystal (CNC) suspension using a high-speed polarization camera. The velocity field was validated via particle image velocimetry (PIV), which showed good agreement with the analytical solution. In regions dominated by either shear or extensional components, the birefringence behavior was consistent with prior theoretical and experimental findings. In the combined extensional–shear regions of the Jeffery–Hamel flow, the birefringence magnitude followed the root-sum-square (RSS) of the shear- and extension-induced contributions. This observation aligns with the principal stress formulation derived from Mohr’s circle, in which the principal stress is expressed as the RSS of extensional and shear stresses. This finding provides a basis for extending stress–birefringence analysis to flows with coexisting deformation modes. • Flow birefringence in combined extensional–shear fields was investigated using the Jeffery–Hamel flow formalism with an analytical velocity solution. • Phase retardation in cellulose nanocrystal (CNC) suspensions was measured using high-speed polarization imaging, and the velocity field was validated independently by particle image velocimetry (PIV). • In the shear- and extension-dominant regions, the measured birefringence behavior was consistent with prior theoretical and experimental results. • In the combined region, Δ n was successfully described by a root-sum-square of birefringence contributions from shear and extensional deformation. • This study presents the first experimentally validated, interpretable framework for birefringence in combined extensional–shear flows, extending stress-optic applications.

Near-wall parasitic convective flow is a known phenomenon in stratified thermal energy storages (STES) leading to stratification degradation and exergy destruction. While many features of convective flow in STES have been studied previously, the influence of the storage wall thickness on the near-wall flow, heat transfer and exergy destruction has not yet been addressed. Temperature, heat flux density and optical velocity measurements were carried out in a model experiment for STES at different wall thicknesses. It is found that at a thicker wall, the stratification degradation and the exergy destruction is faster than at a lower wall thickness, caused by an enhanced heat transfer between the storage wall and storage fluid. As time proceeds, the temperature distribution in the storage tank and exergy efficiencies converge for both wall thicknesses. In the flow field, this is reflected in higher maximum velocities at the thicker wall. Furthermore, velocity gradients are larger in this case because of thinner boundary layers. Transient flow fluctuations appear to be stronger at the thicker wall as shown by higher amplitudes and broader spectra of the velocity oscillations. Additionally, the occurrence of maxima in the spectra of the fluctuations at the thicker wall which do not appear at the thinner wall may hint at differences in the transition to turbulence caused by the different wall thicknesses. • Higher parasitic heat transfer in stratified thermal storages with thicker walls. • Thermal stratification and exergy degrade faster at thicker storage walls. • Parasitic near-wall flow is faster at thicker storage walls. • Stronger velocity fluctuations in near-wall flow at thicker walls. • Storage wall thickness influences transition of near-wall flow to turbulence.

• NIRS enables fast prediction of RDF moisture content with good accuracy. • Simple regression at selected wavelengths yields precise moisture estimates. • MLP and ridge regression show robust feature selection for NIRS data. • Dry spectra are predicted from wet spectra using autoencoder and random forest. • Random forest achieves the best accuracy in dry spectrum reconstruction. Refuse derived fuel (RDF) sourced from industrial and municipal waste contains primarily plastics, paper/cardboard and textiles. Since RDF has high heating values and low prices, it is used as alternative fuel to coal in cement production, offering CO 2 reductions. However, the cement industry currently lacks real-time quality control of RDF, resulting in unnoticed reductions in heating value caused by elevated moisture content. Moisture content determination by drying takes up to 4 h, which does not allow for quick reactions in operation of the cement kiln. This study addresses the challenge by applying near-infrared spectroscopy (NIRS) to predict the moisture content of paper and cardboard particles, which retains most moisture in RDF. The particles were moisturized by different methods with NIRS measurements at each stage. For the prediction, different approaches ranging from linear regression of selected wavelength absorptions to multiple machine learning regression techniques like ridge, lasso and multilayer perceptron are compared in accuracy and feature representation. The multilayer perceptron showed accurate results and robust feature selection, although also simple linear regression at selected wavelength gave adequate accuracy. In a second step, moisture content and spectra of wet particles were used to predict the spectra of dry particles. These spectra are essential to correctly identify particle types or predict heating values, since training data consists of dry particle data. While linear models are not reaching acceptable results, both an autoencoder and a random forest regressor yielded spectra with correct shapes, which is sufficient for classification with derivative spectra.