To address uneven air supply among multiple needle tubes during the drying of high-density forage bales, this study investigated the airflow characteristics and structural optimization of the upper and lower air distribution chambers of a needle-type forage dryer. A three-dimensional CFD model was established, and airflow performance was evaluated using the velocity non-uniformity coefficient M and the inlet-to-outlet total pressure drop Δp. Response surface methodology was used to optimize the key structural parameters. For the upper chamber, installation of a T-shaped baffle and optimization of the cavity height Hc, diffuser angle α, and top-plate opening area ratio Ra yielded an optimal combination of Hc = 133.29 mm, α = 12.51°, and Ra = 1.12, reducing M from 11.2264% to 3.3886%. For the lower chamber, a strip-perforated airflow equalizing plate with Hb = 74.82 mm, D = 23.79 mm, and W = 25.03 mm reduced M from 9.8772% to 1.5484%, with Δp of approximately 130 Pa. Mesh-refinement and turbulence-model sensitivity analyses supported the robustness of the numerical predictions. Repeated outlet-velocity measurements yielded mean absolute relative errors of 3.09%–4.58%. Smoke visualization and grayscale analysis further indicated that the optimized structures enhanced airflow diffusion and redistribution. The results provide guidance for air distribution chamber design in needle-type forage dryers.
Traditional identification of accident-prone road sections relies on fixed-unit statistics, which has disadvantages that include missing spatial continuity of risk, boundary distortion, and information “averaging,” and struggles to support differentiated safety management. To address this issue, this study proposes a road accident risk field reconstruction model integrating dynamic spatial attenuation and probability-severity dual-dimensional coupling. First, based on the information diffusion principle and Gaussian kernel density estimation (KDE), a framework for converting discrete accident points into a continuous risk probability field is constructed, and a dynamic standard deviation function is proposed. This function enables the Gaussian kernel standard deviation to dynamically adapt to road design speed and cross-sectional type (with/without median divider), clarifying its physical correlation with drivers’ sight distance requirements. Second, by integrating casualties and direct economic losses, a continuous accident severity field is established through an accident equivalent loss function; the two fields are discretized using the quantile method, and combined with a risk matrix to generate comprehensive risk ratings (Levels I–V) via coupling. Verification using accident data from a Class II mountain highway in Guangxi shows the following: (1) the boundaries of the risk field generated by the model fit the road alignment well, with a probability field gradient smoothness of 0.0068, which can mitigate the boundary effect and step effect of the fixed-unit method; (2) compared with KDE with fixed bandwidth (0.35 km) and fixed-length segmentation method (0.5 km), the area under the curve (AUC) of the model’s probability field reaches 0.9641 (increased by 12.79% and 15.31%, respectively), the AUC of the severity field is 0.8669 (increased by 15.24% and 5.88%, respectively), and the Pearson correlation coefficient between the probability field and accident data is 0.4772 (increased by 11.01% and 3.77%, respectively), indicating that the identification accuracy and data fit have been improved; and (3) dual-dimensional coupling can distinguish risk patterns of high-frequency low-loss, low-frequency high-loss, and high-frequency high-loss, providing a quantitative basis for differentiated management. This model promotes the evolution of road risk assessment from discrete statistics to continuous field analysis, and can offer technical support for the optimal allocation of safety resources.
Jianfeng Liu, Fuyuan Luo, Jianqiu Chen et al.· Journal of Transportation En...· 0 citations
Accurate prediction of centrifugal-pump performance under viscous operating conditions remains challenging, particularly for low specific-speed pumps operating at low Reynolds numbers. This study develops a Reynolds-number-based correction-factor framework derived from a physically based energy-loss analysis. The method explicitly accounts for major internal loss mechanisms, including hydraulic losses, disk friction, leakage flow, recirculation, mixing and diffusion losses, slip-factor deviation, and blade blockage. The model was calibrated using water-test data from an FM-50 centrifugal pump at 1200 rpm and validated using an independent dataset at 900 rpm. The validation results showed accurate head prediction, with and . Efficiency prediction showed larger deviation, with percentage points, reflecting the sensitivity of efficiency to measurement uncertainty and combined loss mechanisms. After validation, the model was extended to viscous-flow conditions and used to derive compact analytical correction factors for head and efficiency as functions of Reynolds number. The proposed expressions showed strong cross-validation performance within the investigated range, with mean for the head correction factor and for the efficiency correction factor. Comparison with ANSI/HI, KSB, and Gülich methods shows that the proposed formulation follows the expected Reynolds-number-dependent trend while providing a more physically interpretable basis for viscous-performance correction. The proposed method offers a practical alternative to conventional correction charts for low specific-speed centrifugal pumps operating under viscous or low-Reynolds-number conditions.
A. Kara Omar, A. Khaldi, A. Ladouani et al.· Journal of Applied Fluid Mec...· 0 citations
SyntheGrAnon is introduced, a framework for evaluating synthetic graph anonymity that primarily targets the singling out, linkability, and inference risks outlined in the EU GDPR at the node and community levels, while also including edge-level attacks as an extension of the node-level setting.
Abele Malan, Ahmad Al Kurdi, Stefanie Roos et al.· Proceedings on Privacy Enhan...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.