The rapid proliferation of unmanned aerial vehicles (UAVs) in urban and peri-urban environments has increased concern regarding drone-generated acoustic emissions, particularly in multirotor platforms whose tonal and broadband noise is strongly influenced by propeller blade geometry. This study presents a CFD-based aeroacoustic assessment framework to examine the influence of key geometric modifications on the acoustic signature of a representative multirotor propeller while preserving aerodynamic performance. A baseline quadrotor propeller was analyzed using Reynolds-Averaged Navier–Stokes (RANS) simulations coupled with the Ffowcs Williams–Hawkings (FW-H) acoustic analogy and Brooks–Pope–Marcolini (BPM) broadband noise estimation. The blade geometry was parameterized in terms of leading-edge sweep, tip chord, blade twist, and trailing-edge serration features, and representative low-noise configurations were evaluated under operating conditions ranging from 3000 to 6000 RPM and advance ratios between 0 and 0.3. The results indicate that combined swept-serrated geometries provide the most favorable noise–performance trade-off, with a predicted reduction of up to 4.8 dB(A) relative to the baseline at the design condition, while maintaining thrust and figure of merit within practical engineering margins. The proposed framework provides a transferable computational basis for the systematic design of low-noise propellers for surveillance UAVs, commercial multirotors, and emerging urban air mobility applications.

The analysis of blood flow behavior has been studied in various aspects. Recently, these studies took a turn when they were developed using Computational Fluid Dynamics (CFD), thereby advancing understanding of fluid behavior. Artery geometries and the properties of flow offer a field of interest for developing studies in which the behavior of liquid and structures can be observed. Biomodel development has evolved to allow for greater ease and precision. With a geometry similar to the real one, mechanical properties can be used to conduct studies with greater precision, yielding greater results on the behavior of a fluid and a structure. This research focuses on developing a biomodel of the right carotid artery. CFD and structural analyses are performed using the generated arterial geometry and a control volume that simulates blood flow. The structural analysis was performed using the Finite Element Method (FEM) to obtain results that provide data on the effects on the arterial walls. The results presented focus on the velocity, pressure, and wall shear stress generated by the flow. In addition, a section on displacement and Von Mises stress results is presented, demonstrating how the structure of the arterial walls is affected by these effects.

Oblique inflow breaks the nominal rotational periodicity of a geometrically symmetric propeller by introducing an azimuth-dependent velocity component across the rotor disk. This study develops a transparent reduced order blade element/momentum framework to examine the resulting once-per-revolution aerodynamic loading of a representative two-bladed small UAV propeller. The formulation combines blade element aerodynamics, momentum-based induction, Fourier decomposition, and advancing–retreating blade diagnostics over moderate oblique inflow conditions. Particular attention is given to distinguishing quantities that arise directly from the assumed harmonic structure from quantities that provide independent aerodynamic information. A first-order analytical expansion shows that, for small in-plane velocity, the normalized first-harmonic loading scales approximately with the in-plane velocity ratio and therefore with J sin(α), consistent with the numerical model. The analysis additionally reports the absolute first-harmonic load amplitude, spanwise development of the asymmetry, cross-plane hub load components, circulation imbalance, and sensitivity to aerodynamic model assumptions. Published axial and non-axial propeller data are used to establish the range over which the reduced order trends can be interpreted. The results indicate that advance ratio and inflow angle govern the modeled cyclic asymmetry, whereas quantitative magnitudes remain sensitive to sectional aerodynamic assumptions and induced flow representation. The framework is therefore intended as a computationally inexpensive screening and interpretation tool for oblique inflow symmetry breaking rather than as a substitute for phase-resolved CFD or experimental measurements.

Public-security policy is commonly assessed through operational outputs and crime-related indicators, yet policy performance also depends on the administrative and governance arrangements through which priorities are diagnosed, coordinated, implemented, evaluated, corrected, and subjected to public accountability. This conceptual study develops an integrated governance framework for evidence-informed public-security policy through a theory-driven synthesis of public management, policy capacity, collaborative governance, whole-of-government coordination, evidence-informed policymaking, performance management, organizational learning, public value, police legitimacy, accountability, and security-governance scholarship. The synthesis distinguishes five management functions—strategic diagnosis, policy design, collaborative governance, implementation capability, and evaluation and organizational learning—and connects them to a multilevel capability-to-legitimacy architecture. The framework’s incremental contribution lies not in introducing these constructs individually, but in specifying the interfaces through which mandates, resources, evidence, coordination arrangements, implementation capabilities, performance information, accountability mechanisms, and legitimacy conditions interact across the public-security policy process. Particular attention is given to construct boundaries, level-of-analysis distinctions, feedback mechanisms, and conditions under which otherwise desirable management arrangements may fail or produce trade-offs. Mexico is included as an illustrative documentary vignette rather than as an empirical validation case, using current federal planning and public-security instruments to demonstrate how the proposed analytical categories can be operationalized. The framework is presented as a candidate integrative heuristic whose propositions, measurement structure, and cross-level relationships require future empirical testing.

Small unmanned aerial vehicle (UAV) acoustic signatures have become increas-ingly relevant not only from the perspective of environmental noise mitigation, but also for detectability, surveillance vulnerability, and low-observable aerial system design. While most prior studies focus on rotor-noise reduction through high-fidelity computa-tional fluid dynamics (CFD) or experimental testing, comparatively fewer studies ad-dress reduced-order computational frameworks capable of rapidly predicting both acoustic signatures and detection distances under varying operating conditions. This study presents a physics-informed reduced-order computational aeroacoustic framework integrating blade passing frequency harmonic modeling, aeroacoustic scaling laws, atmospheric propagation, and beamforming-informed detectability metrics for rapid prediction of small UAV acoustic signatures. The methodology combines harmonic spectral synthesis, rotational speed scaling, source propagation modeling, and sig-nal-to-noise-based detection criteria to estimate sound pressure spectra, directional acoustic signatures, and acoustic detection distance envelopes. Computational results indicate strong agreement with trends reported in published UAV aeroacoustic ex-periments and suggest that propeller operating speed dominates both acoustic signature growth and detectability range. For representative multirotor conditions, modeled detection distances vary from approximately 80 m to over 200 m depending on rotational speed and ambient noise floor, while reduced source signature scenarios can reduce detectability by up to 30%. The proposed framework provides a computationally efficient tool for rapid aeroacoustic assessment, acoustic signature management, and preliminary low-observable UAV design.

Small unmanned aerial vehicle (UAV) acoustic signatures are relevant to environmental noise assessment, passive monitoring, and preliminary detectability analysis. This study presents a physics-informed reduced-order framework that combines blade-passing frequency (BPF) harmonic synthesis, rotational-speed acoustic scaling, propagation, and signal-to-noise ratio (SNR) threshold crossing. The RPM–OASPL law was calibrated using ten digitized measurements for a four-rotor DJI Phantom II with Original 9450 propellers and validated, without refitting, against eleven Aftermarket 9443 measurements. The fitted exponent was m = 5.285, and the fitted reference level was Lref = 77.01 dB(A) at 5000 RPM and 1 m. A 100,000-realization Monte Carlo analysis that propagated ±100 RPM and ±0.5 dB digitization bounds, together with residual scatter, produced total 95% intervals of 5.01–5.55 for m and 76.58–77.43 dB(A) for Lref. Calibration yielded RMSE = 0.39 dB and R² = 0.9979; independent-configuration validation yielded MAE = 2.27 dB, RMSE = 2.38 dB, and R² = 0.9168. At 5000 RPM, nominal free-field threshold-crossing distances were 70.9, 22.4, and 7.1 m for quiet rural, semi-urban, and urban scenarios. Combined statistical 95% screening intervals were 41.3–121.7, 13.0–38.5, and 4.1–12.2 m, respectively. Sensitivity analyses show that β controls harmonic-specific range but not normalized OASPL, while ground interference, band-limited masking, and detector processing can materially change operational range. The framework is therefore a rapid, interpretable screening tool rather than a universal detector performance model.