Search Results (6,352)

The aerodynamic performance of nacelle inlets under crosswind conditions is crucial for engine stability and efficiency. Current parametric investigations are predominantly focused on cruise operations, with minimal consideration given to crosswind conditions. This study employs an iCST-based parametric modeling approach to construct geometric models. A systematic examination of key geometric parameters—including the throat axial location, fan face radius, and leading-edge radii of the inner and outer contours is conducted. The reliability of the numerical methodology was established through a two-step validation process using both the iCST-generated non-axisymmetric model and the DLR-F6 benchmark model, followed by a geometric sensitivity analysis based on parametrically generated axisymmetric models. The results demonstrate that the inner contour leading-edge radius (ROC_I/R_hi) has the most substantial influence on flow separation. When ROC_I/R_hi decreases from 7.84% to 3.46%, the peak maximum circumferential total pressure distortion index (IDCmax) is increased by 86.78% with a 53.85% rearward shift in the complete reattachment mass flow rate. Correspondingly, a similar reduction in the outer contour leading-edge radius (ROC_O/R_hi) from 9.38% to 4.69% results in a 55.50% increase in peak IDCmax and a 33.33% rearward shift. Comparatively, the fan face radius shows minimal impact on flow distortion (increases by 9.72%), but more pronounced effects on total pressure recovery, while rearward movement of the throat axial location (35.00% to 69.00%) causes a 30.03% rise in IDCmax and 43.75% complete flow reattachment delay. It is concluded that the leading-edge optimization is crucial for crosswind resilience, with the inner contour geometry being particularly influential, providing parametric foundations for robust inlet design across a wide range of operating regimes. In addition, it is also found that the effects of Reynolds number (Re) lie in two folds: (1) For a fixed model scale, the aerodynamic performance of the inlet suffers a remarkable degradation with rapidly rising IDCmax as the crosswind velocity-based Re is increased to cause significant flow separations. (2) For a fixed crosswind velocity, the peak IDCmax progressively decreases with the increasing scale based Re, while σ exhibits an overall enhancement as Re rises. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

Background/Objectives: In computer graphics, virtual worlds are constructed and visualized through algorithmic processes. These environments are typically populated with objects defined by mathematical models, traditionally based on Euclidean geometry. However, there is increasing interest in exploring non-Euclidean geometries, which require adaptations of the modeling techniques used in Euclidean spaces. Methods: This paper focuses on defining parametric curves and surfaces within elliptic and hyperbolic geometries. We explore free-form splines interpreted as hierarchical motions along geodesics. Translation, rotation, and ruling are managed through supplementary curves to generate surfaces. We also discuss how to compute normal vectors, which are essential for animation and lighting. The rendering approach we adopt aligns with physical principles, assuming that light follows geodesic paths. Results: We extend the Kochanek–Bartels spline to both elliptic and hyperbolic geometries using a sequence of geodesic-based interpolations. Simple recursive formulas are introduced for derivative calculations. With well-defined translation and rotation in these curved spaces, we demonstrate the creation of ruled, extruded, and rotational surfaces. These results are showcased through a virtual reality application designed to navigate and visualize non-Euclidean spaces. Full article

The geometric content of chaos in nonlinear systems with multiple stabilities of high order is a challenge to computation. We introduce a single algorithmic framework to overcome this difficulty in the present study, where a parametrically forced oscillator with cubic–quintic nonlinearities is considered as an example. The framework starts with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which is a self-learned algorithm that extracts an interpretable and correct model by simply analyzing time-series data. The resulting parsimonious model is well-validated, and besides being highly predictive, it also offers a solid base on which one can conduct further investigations. Based on this tested paradigm, we propose a unified diagnostic pathway that includes bifurcation analysis, computation of the Lyapunov exponent, power spectral analysis, and recurrence mapping to formally describe the dynamical features of the system. The main characteristic of the framework is an effective algorithm of computational basin analysis, which is able to display attractor basins and expose the fine scale riddled structures and fractal structures that are the indicators of extreme sensitivity to initial conditions. The primary contribution of this work is a comprehensive dynamical analysis of the DM-CQDO, revealing the intricate structure of its stability landscape and multi-stability. This integrated workflow identifies the period-doubling cascade as the primary route to chaos and quantifies the stabilizing effects of key system parameters. This study demonstrates a systematic methodology for applying a combination of data-driven discovery and classical analysis to investigate the complex dynamics of parametrically forced, high-order nonlinear systems. Full article

Driven by the dual-carbon goals, photovoltaic (PV) battery systems at renewable energy stations are increasingly clustered on the distribution side. The rapid expansion of these clusters, together with the pronounced uncertainty and spatio-temporal heterogeneity of PV generation, degrades battery utilization and forces conservative dispatch. To address this, we propose a “spatio-temporal clustering–deep estimation” framework for short-term interval forecasting of PV clusters. First, a graph is built from meteorological–geographical similarity and partitioned into sub-clusters by a self-supervised DAEGC. Second, an attention-based spatio-temporal graph convolutional network (ASTGCN) is trained independently for each sub-cluster to capture local dynamics; the individual forecasts are then aggregated to yield the cluster-wide point prediction. Finally, kernel density estimation (KDE) non-parametrically models the residuals, producing probabilistic power intervals for the entire cluster. At the 90% confidence level, the proposed framework improves PICP by 4.01% and reduces PINAW by 7.20% compared with the ASTGCN-KDE baseline without spatio-temporal clustering, demonstrating enhanced interval forecasting performance. Full article

The design of fiber-reinforced composite (FRC)-based components for marine energy applications necessitates a fundamental understanding of material properties and the resulting geometry to predict long-term performance. In this work, we present a modeling workflow to predict linear elastic and diffusive bulk properties at the mesoscale for an idealized geometry based on knowledge of fiber and resin properties. A parametric study was performed to identify the key model input parameters that influence bulk properties. Furthermore, we demonstrate how bulk properties can be leveraged in high-fidelity image-based simulations, where imperfections in tow geometry and voids captured during X-ray computed tomography imaging are explicitly represented within the simulation. Bulk properties of interest include moduli, Poisson’s ratios, hygroscopic swelling, diffusivity, and moisture uptake, which are key parameters for characterizing FRC performance within marine environments. Modeling predictions agreed well with experimental data, except for estimating swelling coefficients, likely due to crack accumulation as a function of moisture uptake. The mesoscale modeling workflow ultimately highlights a versatile framework for understanding the influence of material and geometric properties, which can be leveraged to rapidly assess new FRC-based components. Full article

Background: Conflicts are an inevitable part of interpersonal relationships, and personality traits influence how they are resolved. In the nursing work environment, conflicts often arise from poor communication and stress, negatively impacting nurses’ well-being and quality of care. The “Big Five” personality model highlights how traits such as extraversion, agreeableness, and emotional stability shape conflict approaches. Understanding these traits aids in developing effective conflict management strategies. This study investigates intragroup conflicts among nurses by identifying their types and examining how sociodemographic factors and personality traits predict their occurrence. The aim is to provide insights that support targeted interventions and improve team dynamics in nursing practice. Methods: The study was conducted as a cross-sectional analysis within the University Hospital Centre Osijek from March to August 2024, involving nurses and technicians. Data was collected using structured questionnaires with clearly defined inclusion and exclusion criteria. The questionnaire included the Process Conflict Scale, the Big Five Inventory, and a Demographic questionnaire. Appropriate statistical analyses were conducted, including descriptive statistics, normality testing with the Kolmogorov–Smirnov test, non-parametric Spearman and Point-Biserial correlations, and linear regression to examine predictors of intragroup conflicts. All assumptions for regression were met, with significance set at p < 0.05, and analyses were performed using JASP software version 0.17.2.1. Results: The research reveals significant differences among various types of team conflicts, where personality traits such as neuroticism increase, while conscientiousness decreases conflicts. The professional competence of respondents also positively correlates with logistical conflicts, and personality explains the variance in conflicts among nurses. Conclusions: Intragroup conflicts among nurses, particularly task-related, stem from communication issues and high care standards. Neuroticism negatively affects team dynamics, while conscientiousness can reduce conflicts but may also lead to disagreements if expectations are unmet. Education on conflict management and clearly defined roles can improve teamwork and quality of care. Full article

Concrete slabs in composite bridges are inevitably subjected to heavy vehicular loads during their service life. To evaluate the fatigue performance of the prestressed concrete slabs in composite bridges, two full-scaled models of prestressed concrete slabs were first designed and tested, with the load amplitude was selected as the variable. To simulate the damage caused by the initial passage of heavy vehicles, this was simplified into the form of a static cyclic load. The mechanical deformation state and crack distribution of the slab were analyzed. Further, a finite-element model was established, and a parametric analysis based on the variation in loading form, such as monotonic displacement loading, static cyclic loading followed by monotonic displacement loading, and cyclic displacement loading, was conducted to discuss the performance-enhancement mechanism of prestressed concrete slabs. Finally, in consideration of the influence of static cyclic damage on the fatigue performance of prestressed concrete slabs, evaluation parameters were proposed to account for static cyclic damage by considering the effects of stresses in concrete, tensile rebar, prestressed tendons, and external loading. A comprehensive fatigue performance evaluation method for prestressed concrete slabs, which neglects the tensile hardening behavior of cracked concrete in the tension zone, was established and verified by test results. The results indicate that the damage caused by static cyclic loading has a significant influence on the fatigue performance of the slab. Applying prestress can significantly mitigate the influence of initial damage on the mechanical and deformation behavior of the slab, which benefits from the prestress compensating for the cracking stress at the bottom of the slab. The proposed fatigue performance-evaluation method for prestressed concrete slabs, which considers static cyclic damage, can predict fatigue deformation behavior with an error of less than 10%, while reasonably determining the fatigue life and failure modes of prestressed concrete slabs. The parametric analysis reveals that when the prestress value exceeds 9 MPa, the failure mode of the prestressed concrete slab transfers from rebar fracture to concrete failure. Full article

The architectural heritage of the 20th century has proved to be highly vulnerable to the test of time, with slight variations in different geographical contexts. The lack of value recognition, restrictions imposition, and resulting protection has led to the loss of memory of material and immaterial values. Restoring dignity has been the primary goal of those who have given substance and vitality to the theme of Modern Restoration, inheriting from the past the method that requires, in order to catalogue each work, the essential stages of knowledge and documentation, preliminary to conservation and enhancement. It is precisely in this scenario, after analysing the experiences of institutions, bodies and associations in the field of filing and cataloguing, that the needs brought about by the digital transition were taken on board; the aim is to define, within the PRIN 2022 DIMHENSION project, an innovative operative protocol that is economically, socially and technically sustainable, aimed at the computerised management of 20th century architectural heritage. The steps are the identification of the global description of the history of the building, translation of the entire body of data into information assets (H-BIR), and the possibility of consultation using parametric models (H-BIM). A Digital Register has therefore been designed, initially for an international sample of late 20th century façade systems, which goes well beyond their dynamic documentation, creating the conditions for a platform for consulting the complex of information, structured in an H-BIR archive interfaced with an H-BIM object library. Full article

This study proposes a composite acoustic porous duct metamaterial muffler composed of a perforated tortuous channel and an externally wrapped porous layer, integrating both structural resonance and material damping effects. Theoretical models for the perforated plate, tortuous channel, and porous material were established, and analytical formulas for the total acoustic impedance and transmission loss of the composite structure were derived. Finite element simulations verified the accuracy of the models. A systematic parametric study was then performed on the effects of porous material type, thickness, and width on acoustic performance, showing that polyester fiber achieves the best results at a thickness of 30 mm and a width of 5 mm. Further analysis of periodic distribution modes revealed that axial periodic arrangement significantly enhances the peak noise attenuation, radial periodic arrangement broadens the effective bandwidth, and multi-frequency parallel configurations further expand the operating range. Considering practical duct conditions, a single-layer multi-cell array was constructed, and its modal excitation mechanism was clarified. By employing the Particle Swarm Optimization (PSO) algorithm for multi-parameter optimization, the average transmission loss was improved from 26.493 dB to 29.686 dB, corresponding to an increase of approximately 12.05%. Finally, physical samples were fabricated via 3D printing, and four-sensor impedance tube experiments confirmed good agreement among theoretical, numerical, and experimental results. The composite structure exhibited an average experimental transmission loss of 24.599 dB, outperforming the configuration without porous material. Overall, this work highlights substantial scientific and practical advances in sound energy dissipation mechanisms, structural optimization design, and engineering applicability, providing an effective approach for broadband and high-efficiency duct noise reduction. Full article

Background/Objectives: Glioblastoma (GBM) is the most malignant subtype of glioma and shows the poorest prognosis with a median survival time of 15 months. The methylation status of the Methylguanine-DNA Methyltransferase (MGMT) was proven to be a crucial factor in selecting the most appropriate therapy. Currently, it is assessed through brain biopsy, which is a highly invasive and very expensive technique. For these reasons, in recent years, the possibility of inferring this information from multi-parametric Magnetic Resonance Imaging (mpMRI) has been widely explored. However, substantial differences in performance are reported in the literature. Methods: In this study, we developed several models based on either radiomic or deep learning approaches and a mixture of them using mpMRI for the MGMT status assessment using the public dataset UPENN-GBM, available on The Cancer Imaging Archive. Despite the tests performed using all MRI acquisitions and different methodological approaches, we did not obtain sufficiently reliable performance to direct the therapeutic path of patients. We thus investigated the impact of segmentation quality on MGMT status prediction since the UPENN-GBM dataset contains both automatic and manual refined segmentation masks. Results: We found that performance obtained through radiomic features computed on manually segmented tumors was significantly higher compared to that obtained using automatic segmentation, even when the differences between segmentation masks, measured in terms of Dice Similarity Coefficient (DSC), is not significantly different. Conclusion: This could be the reason why very different MGMT classification performance is typically reported and suggests the creation of a benchmark dataset, with high-quality segmentation masks. Full article

To address path deviation and efficiency reduction issues in traditional interpolation optimization algorithms for complex path machining, this paper proposes a chord error-priority bilevel interpolation optimization method (CPBI). First, arc length parametric modeling of the machining path is performed within the Frenet–Serret framework, yielding curvature and torsion information. After introducing geometric-based multi-machining constraints in the outer layer, the velocity upper limit is established by controlling chord error to dynamically adjust regions with curvature mutation. In the inner layer, combining the velocity limit with bidirectional scanning achieves adaptive optimization of interpolation step size and optimal velocity planning that balances precision and smoothness. Simulation results demonstrate that CPBI effectively reduces the number of interpolation points by 30–50% while ensuring the chord error. Compared with the reference method, the CPBI improved efficiency by 14.31% and 34.72% in machining experiments on S-shaped and wave-shaped paths, respectively. The results validated the CPBI’s high precision and efficiency advantages in complex path machining, providing an effective solution for CNC path optimization in high-end manufacturing. Full article

Balconies are widely recognized for enhancing urban livability, making them attractive elements to incorporate in building renovation projects. However, their impact on energy performance remains insufficiently studied, particularly in temperate climates, like the Mediterranean, where both heating and cooling demands must be considered. This article evaluates the energy impacts of integrating open balconies into housing retrofits on the space conditioning demand of dwellings through spatialized analysis at the urban block scale. Focusing on Barcelona’s Eixample district, a parametric Urban Building Energy Modeling (UBEM) was employed to assess how balcony design interacts with urban morphology (orientation, obstructions), building features (window-to-wall ratio, WWR), and balcony length. Results reveal a seasonal trade-off at the block scale: balconies increase heating demand (0.1–1.6 kWh/m²·yr) by reducing winter solar gain but decrease cooling demand (0.1–3.8 kWh/m²·yr) through summer shading. Net effects vary by unit position, with south-facing and moderately glazed dwellings benefiting the most. Deeper balconies (1.5–2 m) amplify both effects, while optimal depth depends on the window-to-wall ratio. Under future climates, retrofits combining insulation and balconies mitigate rising cooling demands more effectively than insulation alone, reducing block-level demand by up to 16%. Although balconies alone show modest energy savings at the block scale, they enhance localized thermal resilience. The study highlights the need for integrated retrofit strategies that balance thermal insulation with solar protection to address both current and future energy challenges while enhancing occupant well-being. Full article

This study assessed the impact of 39 active and passive energy efficiency measures on the energy demand of a prototype dwelling, modeled through parametric simulations in DesignBuilder across nine climatic zones in Chile, classified according to the Köppen system. Each measure was evaluated individually (single-measure scenarios); three variation levels were evaluated to quantify their relative influence on energy demand. Results indicate that passive strategies are more effective in cold and humid climates, where increasing wall insulation thickness reduced energy demand by up to 45%, and improving airtightness achieved a 43% reduction. In contrast, in tundra climates or areas with high thermal variability, some measures, such as green façades or overhangs, increased energy demand by up to 49% due to the loss of useful solar gains. In desert climates, characterized by high diurnal temperature variation, thermal mass played a more significant role: high-inertia walls without additional insulation outperformed lightweight EPS-based solutions. The findings suggest that measure selection must be climate-adapted, prioritizing high-impact passive strategies and avoiding one-size-fits-all solutions. This work provides quantitative evidence to inform residential thermal design and support climate-sensitive energy efficiency policies. This study delivers a single-measure comparative atlas; future research should integrate multi-measure optimization together with comfort/cost metrics. Full article

In a conventional elastic beam with steady boundary stiffness, vibrational energy tends to concentrate at specific modal frequencies, often resulting in significant resonance phenomena. To address this issue, a novel control strategy is proposed in this study, in which the stiffness of boundary springs is dynamically modulated to alter the resonance characteristics of the beam. The Newmark–Beta method is employed to compute the transient response of the beam with time-varying stiffness in the time domain. A series of numerical simulations is conducted to analyze the vibration behavior of the structure under single-model frequency, multimodal frequency, narrowband, and broadband random excitations. The results indicate that time-varying stiffness effectively redistributes energy from resonance frequencies to other frequency bands, thereby suppressing resonance peaks and reducing displacement amplitudes. Furthermore, parametric analysis reveals that increasing the range of stiffness variation enhances spectral dispersion and improves vibration attenuation performance, and increasing the average stiffness level helps improve energy dispersion; however, it may lead to a slight increase in vibration response at low frequencies. Full article