Maria Tănase

When used to analyze the wear of rough surfaces, Archard’s law implicitly assumes that the surface subjected to wear is homogeneous and that its hardness is equal to that of the bulk material, an assumption that does not apply to asphalt mixtures, where, in the case of road pavements, the wearing surface is a mosaic of aggregate particles and binder (bitumen). This study examines this assumption by comparing two models of the wheel–asphalt pavement contact for four BA16 asphalt mixtures: a reference specimen (M0), a modified specimen (M1, characterized by high porosity), a specimen with aggregate replacement in its composition (M2, containing 3% plastic material in flake form), and a specimen with aggregate replacement in its composition (M3, containing 3% cellulosic material in pellet form). The macro-equivalent model treats the specimens as homogeneous media, whereas the meso-structural model explicitly incorporates 93 polyhedral aggregate particles within a representative volume element of 30 mm × 30 mm × 30 mm, under three loading scenarios: severe dry, wet, and thermal conditions, with extreme temperatures of 5 and 40 °C, respectively. The results were assessed using two independent indicators: a composite tribological degradation indicator and an indicator derived from Archard’s law. The explicit modeling shows that the wearing surface contains 16.2% mineral aggregate, compared with 25.2% in the bulk asphalt mixture, while the effective elastic modulus at the wearing surface is 10.5% lower than that of the bulk asphalt mixture. This ratio remains nearly constant (0.894–0.897) for all mixtures and loading scenarios. Both analyses yield the same ranking of wear susceptibility—M0 < M3 < M2 < M1—in all scenarios, with deviations below 4.3%. Tangential loading at the wearing surface triples the stress in the binder and has a predominant effect on wear compared with the normal tire pressure, while the maximum stress occurs precisely at the aggregate–binder interface.

The increasing environmental concerns associated with petroleum-based packaging materials have accelerated the development of biodegradable alternatives, with starch-based films emerging as promising candidates. This study investigates the influence of grape seed oil (5–15 wt.%) and storage time (up to 5 months) on the mechanical, thermal, structural, and surface properties of potato-starch-based films for biodegradable packaging applications. Oil incorporation improved the initial film performance, increasing tensile strength (1.27–3.11 MPa), enhancing thermal stability (melting temperature up to ~118 °C), and promoting higher surface hydrophobicity (contact angle up to ~87°). The differential scanning calorimetry (DSC) analysis indicated time-dependent changes in the thermal transitions associated with the reorganization of ordered starch domains with oil addition, from 39.96% for samples with 5% oil to 36.40% for samples with 15% oil. After three months, all oil-containing samples exhibited a substantial reduction in ultimate tensile strength, with normalized stress values decreasing to 0.74–1.18 depending on oil content. However, after 5 months, the evaluated properties had values close to or above the reference. Statistical evaluation confirmed that storage time is the dominant factor governing property evolution, exceeding the influence of oil concentration.

The evaluation of vibro-acoustic comfort in vehicle cabins is frequently limited by fragmented treatment of noise and vibration indicators and by the absence of structured, reproducible assessment frameworks. This study proposes an advanced, correlation-driven and process-oriented methodology for vibro-acoustic comfort evaluation, designed to support systematic analysis and decision-making across varying vehicle operating conditions. The proposed framework is formulated as a sequential process comprising experimental data acquisition, signal preprocessing, statistical correlation analysis, and decision-oriented interpretation. The framework was experimentally validated on five special-purpose armored platforms under both stationary and dynamic operating regimes, with repeated measurement trials to ensure robustness. Interior and exterior sound pressure levels, together with vibration-related parameters, are experimentally measured under stationary and dynamic operating regimes. Pearson correlation coefficients are employed to quantify interdependencies among vibro-acoustic variables and identify dominant contributors affecting comfort-related conditions. The results indicate statistically significant correlations between interior noise levels and selected vibration indicators, revealing distinct correlation patterns associated with different operating states. Based on these findings, correlation strength was classified as weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.6), and strong (|r| ≥ 0.6), enabling structured contributor ranking. The primary contribution of this work consists in elevating correlation analysis from a descriptive statistical technique to a formalized assessment process suitable for integration into predictive modeling and optimization workflows. The framework provides a transferable methodological structure, validated within the investigated vehicle category.

This study investigates the wear behavior and multi-technique characterization of 3D printed thermoplastic polyurethane (TPU) intended for friction layers in transmission belts used in pharmaceutical manipulators. Two flexible TPU grades—TPU 51A and TPU 60A—were printed using fused deposition modeling (FDM) with varying printing temperatures (255–265 °C for 51A; 225–235 °C for 60A) and layer counts (three or four layers). Specimens were evaluated for Shore A hardness, wear resistance (mass loss using a Baroid lubricity tester under dry sliding against carton), tensile properties, crystallinity (XRD), chemical structure (FTIR), thermal stability (TGA), and scanning electron microscopy (SEM). The results show that printing parameters significantly influence the mechanical and tribological behavior of the materials. For TPU 51A, increasing the printing temperature to 265 °C and using four layers led to a substantial reduction in cumulative mass loss, although hardness decreased. In contrast, for TPU 60A, higher printing temperature and layer count increased hardness but also resulted in higher wear. Tensile tests indicated that specimens printed with fewer layers exhibited higher yield strength and strain, indicating improved interlayer bonding. XRD analysis confirmed the predominantly amorphous nature of the printed samples, with a reduction in crystallinity compared to the raw filaments. FTIR spectra showed no significant chemical degradation during printing, while thermogravimetric analysis revealed good thermal stability up to approximately 250–260 °C. The results demonstrate that wear behavior is governed by a combination of hardness, interlayer cohesion, and microstructural organization rather than crystallinity alone. Among the investigated conditions, TPU 51A printed at 265 °C with four layers exhibited the most favorable balance between wear resistance and mechanical properties, highlighting its suitability for friction layer applications.

This study utilizes machine-learning algorithms to reinterpret existing datasets originally plotted using Decline-Curve Analysis (DCA), aiming to enhance predictive accuracy without requiring new field-data acquisition. Historical production records were compiled: monthly oil/gas rates, bottom-hole pressures, and cumulative productions, which were fitted to Arps equations via least-squares optimization, and key decline parameters, such as initial rate, nominal decline rate, and hyperbolic exponent, served as input data. Four machine-learning models were trained and validated: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Regression (LR), using 80/20 train–test splits and 5-fold cross-validation. Models were evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). The ANN emerged as the best-performing method, achieving near-unity predictive accuracy (R² ≈ 1) on the independent test set, with low error values (MSE = 0.0012 Ncm²/month², RMSE = 0.035 Ncm/month, MAE = 0.028 Ncm/month) for oil production rates. Similar levels of accuracy were obtained for gas rates and pressures. These results reflect the strong and highly regular relationships present in the dataset analyzed rather than an exact zero-error fit. The multi-layer architecture of the ANN effectively captured the nonlinear interactions between Arps parameters and transient flow regimes, outperforming the empirical and physics-constrained approaches. Linear regression yielded strong results (R² = 0.98, RMSE = 0.15 Ncm/month) but faltered in high-decline scenarios, failing to model exponential tails accurately. SVM exhibited the highest deviations (RMSE = 0.42 Ncm/month, R² = 0.89), attributable to kernel sensitivity in sparse, noisy decline data. RF provided intermediate performance (R² = 0.97). This ANN-driven approach redefines decline analysis by automating parameter tuning and uncertainty quantification, reducing forecasting errors by 85% versus classical Arps methods.

Vibrations occurring in the impellers of centrifugal compressors are among the primary factors that influence the reliability and service life of main gas pumping station units. A promising approach to improving the dynamic performance of centrifugal compressors is the use of modern metal-matrix composite materials for impellers. This article presents a prediction of the dynamic behavior of an impeller under actual operating conditions with the aim of eliminating resonance vibrations. Detailed geometric and finite-element modeling enabled an investigation of the prestressed state caused by centrifugal forces on the natural frequency spectrum of the impeller. To investigate the influence of the material’s physical properties, calculations were performed for boron-aluminum composites with varying boron carbide particle content. For boron-aluminum impellers, the effect of centrifugal forces on the natural frequencies did not exceed 2%–3%. However, increasing the boron carbide content from 1.5% to 7.5% results in an increase in the first 10 natural frequencies of up to 6.8%. Campbell diagrams were constructed for the fundamental and harmonic frequencies of the disturbing forces, taking into account the interaction of the blades of the compressor’s stator and rotor components.

Glass-fiber-reinforced polymer (GFRP) pipes are increasingly used in aggressive environments due to their high corrosion resistance and favorable mechanical properties. However, long-term exposure to saline environments and elevated temperatures can lead to degradation of their structural performance. This study investigates the influence of accelerated saltwater aging on the tensile behavior and structural characteristics of GFRP pipes and proposes machine-learning-based predictive models for the ultimate tensile strength (UTS). Experimental specimens were immersed in a 3.5% NaCl solution under controlled temperature and exposure time conditions. Tensile testing revealed that the unexposed samples exhibited a maximum UTS of 79.63 MPa, while aged specimens showed a gradual reduction in strength, although more than 80% of the initial tensile strength was retained after 60 days of exposure. Statistical analysis indicated that temperature was the dominant factor, contributing 60.24% to the variation in UTS, followed by exposure time with 33.72%, with the regression model explaining 93.96% of the total variance (R2 = 0.9396). X-ray diffraction analysis revealed a decrease in the degree of crystallinity from 20.49% in the reference sample to 15.05% in the most degraded specimen, corresponding to an approximate 26.5% reduction, which correlated with the observed decline in mechanical strength. Several machine learning approaches were evaluated, including Artificial Neural Networks (ANN), Exponential Gaussian Process Regression, and Boosted Trees. Among them, ANN provided the highest predictive accuracy, demonstrating strong agreement between predicted and experimental UTS values. The results confirm that hydrothermal aging significantly affects both the microstructural and mechanical properties of GFRP pipes, while machine learning models represent effective tools for predicting their long-term performance under aggressive environmental conditions.

The increasing occurrence of corrosion-related damage in steel pipelines has led to the growing use of composite-based repair techniques as an efficient alternative to traditional replacement methods. Computer modeling and structural analysis were performed for the repair reinforcement of a steel pipeline with a composite bandage. A preliminary analysis of possible contact interaction schemes was implemented based on the theory of cylindrical shells, taking into account transverse shear deformations. The finite element method was used for a detailed study of the stress state of the composite bandage and the reinforced section of the pipeline. The limit state of the reinforced section was assessed based on the von Mises criterion for steel and the Tsai-Wu criterion for composites. The effectiveness of the repair was demonstrated on a pipeline whose wall thickness had decreased by 20% as a result of corrosion damage. At a nominal pressure of P = 6 MPa, the maximum normal stress in the weakened area reached 381 MPa. The installation of a composite bandage reduced this stress to 312 MPa, making the repaired section virtually as strong as the undamaged pipeline. Due to the linearity of the problem, the results obtained can be easily used to find critical internal pressure values.

This study investigates chlorine-induced aging of high-density polyethylene (HDPE) through a 3 × 3 factorial matrix combining three temperatures (20, 40, 60 °C) and three chlorine concentrations (5, 10, 20 ppm) over 45 days. Tensile tests revealed progressive embrittlement, with elongation at break decreasing sharply under severe aging; samples exposed to 60 °C and 20 ppm exhibited premature brittle failure despite peak stresses remaining near ~22 MPa. XRD results showed a reduction in crystallinity from 67.07% (reference) to 61.06–61.31% under the most aggressive conditions, accompanied by a decrease in crystallite size from 5.60 nm to 2.10–2.50 nm. FTIR analysis confirmed oxidation through increased carbonyl absorption at 1716 cm⁻¹ and new bands at 1608–1635 cm⁻¹. TGA revealed substantial thermal deterioration, with T5% falling from 450 °C (reference) to 327 °C at 60 °C/20 ppm, along with an additional degradation peak at 398 °C. DSC showed a melting temperature decrease from 136.32 °C to 131.67 °C and an increase in crystallinity from 41.07% (unexposed sample) to 59.19% (60 °C/20 ppm). Statistical analysis of the results established that degradation is governed by different dominant factors depending on the measured property: Chlorine concentration was found to be the dominant factor for XRD crystallinity and thermal decomposition T5%, confirming that surface structural damage and early molecular weight loss are driven primarily by chlorine-induced oxidation. Conversely, DSC crystallinity was governed primarily by temperature, reflecting thermally driven molecular reorganization within the bulk material. Overall, chlorine exposure, amplified by temperature, accelerates chemical oxidation, structural degradation, and mechanical embrittlement, reducing the long-term reliability of HDPE in chlorinated water systems. The findings provide critical data for predicting the service life and informing material selection for HDPE components used in high-temperature or high-chlorine water distribution systems.