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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Time-domain buffeting analysis for a long-span suspension bridge considering directional discrepancies of non-stationary wind fluctuating components</title>
      <link>https://trid.trb.org/View/2684535</link>
      <description><![CDATA[Conventional non-stationary buffeting analyses for long-span bridges frequently overlook aerodynamic admittance function (AAF) effects or rely on empirical/equivalent models, neglecting directional discrepancies between longitudinal (u-) and vertical (w-) turbulent components by assuming equal u- and w- components of AAF (E-AAF). This simplification potentially inducing catastrophic miscalculations. To distinguish the effects of non-stationary turbulence components (u- and w-) on buffeting responses, this study employs both the E-AAF and the decoupled AAF (D-AAF, includes the longitudinal component u-AAF and the vertical component w-AAF) to reconstruct non-stationary wind fields, generating buffeting forces that reflect the distinct contributions of u- / w- turbulence components. Subsequent time-domain buffeting analyses for a long-span suspension bridge demonstrate three critical findings. First, analysis under consistent conditions demonstrates that neglecting self-excited forces can overestimate displacement responses by up to 51.41%. Second, with all other conditions equal, non-stationary winds produce RMS displacements 45% higher than stationary winds due to dynamic energy amplification; Third, the error in the E-AAF-based response relative to the D-AAF solution shows a pronounced dependence on the angle of attack (AoA); the minimal displacement error (-2.63%) occurs at AoA= 0°, whereas systematic overestimation emerges under non-zero AoAs (2°–8°), with displacement errors peaking (AoA=4°) at 128%, velocity errors reaching 117.86%, and acceleration errors attaining 68.89%. This research contributes to enhancing the accuracy for buffeting response prediction in long-span bridges subjected to non-stationary winds.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684535</guid>
    </item>
    <item>
      <title>Test and Evaluation of the Radar Thunderstorm Turbulence Detection System (Phase I)</title>
      <link>https://trid.trb.org/View/2711594</link>
      <description><![CDATA[A thunderstorm turbulence detection test bed was developed at the Federal Aviation Administration (FAA) Technical Center by the Massachusetts Institute of Technology, Lincoln Laboratory. This consists of a system to measure and process Doppler radar parameters, and an FAA aircraft instrumented to measure turbulence concurrently with the radar observations. The test bed is being used to investigate the relationship between radar- and aircraft-measured turbulence. Radar measurements of the Doppler spectrum width and aircraft measurements of airspeed fluctuations and center-of-gravity normal accelerations were converted to  Ɛ supra ⅓ (cube root of the turbulence dissipation factor) for comparison. Several data collections were made during the summer of 1980. Results of data analysis showed that the major turbulence sequences experienced by the aircraft were essentially reflected by the radar. However, linear correlation coefficients between radar and aircraft Ɛ supra ⅓ were only about 0.5. The low correlations are considered to be due to differences in response to turbulence by the two measuring systems, deficiencies in the radar processing, and radar data interpolation errors between the 80-second radar scans. In a more practical analysis, radar-measured turbulence, classified into ranges of light, moderate, and severe turbulence, showed a potentially useful relationship to aircraft turbulence. The predictive value was enhanced by consideration of radar reflectivity factor as a screening variable.]]></description>
      <pubDate>Tue, 23 Jun 2026 11:09:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711594</guid>
    </item>
    <item>
      <title>Wind Characteristics and Deck Vibration of Xihoumen Bridge during Strong Typhoon Muifa</title>
      <link>https://trid.trb.org/View/2613619</link>
      <description><![CDATA[Field measurements are usually recognized as the most reliable method for investigating wind effects on structures. A limited study has been conducted on the behavior of long-span suspension bridges during the direct attack of strong typhoons based on the in-situ measurements. Recently, a strong typhoon (2212 Muifa) made landfall in the site of Xihoumen long-span suspension bridge, which was the first time that the bridge was directly attacked by a strong typhoon after its completion in 2009. The wind and bridge deck vibrations are captured by the structural health monitoring system (SHMS) installed at the bridge. The wind characteristics in terms of mean wind speed, angle of attack (AOA), gust factor, turbulence intensity, turbulence length scale, and power spectrum were first analyzed utilizing 33 h of data. Modal characteristics are extracted from vibration signals recorded during the typhoon, and two regression analyses are conducted to explore the structural damping ratio. Theoretical buffeting responses are calculated and compared with the measured vibrations. The findings reveal significant variability in the typhoon wind parameters, with an AOA larger than that in normal wind. The gust factor and turbulence intensity initially decrease at low wind speeds (approximately 15  m/s) and remain relatively stable at higher wind speeds. Notably, no amplitude dependence of vertical natural frequencies is observed during the typhoon, while the modal damping ratio increases with mean wind speed. Although the theoretical models generally align with the measured values in trend, they underestimate torsional response and fail to account for the scatter, highlighting the limitations of these approaches.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613619</guid>
    </item>
    <item>
      <title>Inflow-Dependent Sensitivity of Layout Optimization Performance: A
          Case Study of the Lillgrund Wind Farm</title>
      <link>https://trid.trb.org/View/2706264</link>
      <description><![CDATA[Layout optimization is one of the most effective approaches to reduce the power                     loss induced by turbine wakes. However, the performance of a wind farm is                     strongly affected by the inflow direction. This paper conducted a sensitivity                     analysis on a realistic wind farm, Lillgrund Wind Farm, to investigate the                     sensitivity of inflow direction on the power production of the initial layout                     and optimal limits. A wake model considering ambient turbulence intensity is                     adopted together with the wake superposition method to efficiently resolve the                     flow field in the wind farm. The results indicate that the power production of                     the initial layout had a significant discrepancy under different inflow                     directions, and relies on the consistency of inflow direction and layout array                     directions. The feature of the two main directional sectors is observed from a                     realistic wind rose. Therefore, two-sector wind roses are adopted in                     optimization, and the angles of sectors vary among 51 cases. After optimization,                     the fin-shape layout trends are observed. More importantly, the optimal limits                     of the layout are similar under different sector angles. The normalized power                     performance of the initial layout has an average of 72% and a variation of 24%.                     Meanwhile, the optimal layout can reach an average of 80% with a variation of                     7%. The results indicate that the design boundaries of Lillgrund Wind Farm do                     not have a significant constraining effect on the optimal limit of layout                     limit.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:09:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706264</guid>
    </item>
    <item>
      <title>Computational Analysis of Platform Motion and Wave Effects on the
          Aerodynamics and Wake of the IEA 22 MW Floating Wind Turbine</title>
      <link>https://trid.trb.org/View/2706260</link>
      <description><![CDATA[This study investigates the unsteady aerodynamic response, wake evolution, and                     vortex dynamics of an ultra-large floating offshore wind turbine (FOWT) under                     coupled motion–wave conditions. A high-fidelity aero–hydrodynamic CFD model is                     employed for the IEA 22 MW reference turbine. Platform pitch and surge motions                     are prescribed via sinusoidal functions, and wave conditions are independently                     introduced by considering two representative sea states (H = 4 m and 7 m) and a                     no-wave case. Results show that pitch and combined pitch–surge motions                     significantly amplify unsteady aerodynamic effects, increasing peak power from                     81.1 MW (P5S0) to 92.6 MW (P5S5), with periodic negative power output and severe                     dynamic stall. Under strong motion, waves further raise peak power to 93.4 MW                     (H7P5S5), indicating a coupled amplification effect. Dynamic stall is mainly                     triggered by pitch motion, expanding in scope and duration with motion                     amplitude; wave effects on stall remain limited. Platform motion also enhances                     wake recovery by increasing inflow shear and turbulence, leading to higher                     turbulent kinetic energy (TKE) and a reduced velocity deficit (ΔŪ). Waves                     compress the low-speed wake core and reduce ΔŪ from 0.248 (no-wave case) to                     0.204 under H7 conditions at x/D = 3.0, with the effect being particularly                     evident under combined motion. Vortex visualization reveals that platform                     movement leads to vortex merging, ring thickening, and deflection, with combined                     motion creating the strongest mixing. Wave-generated vortices interact with tip                     vortices near the surface, becoming more intense under larger wave heights. In                     general, platform motion is the main factor in FOWT unsteady aerodynamics, while                     waves have secondary but cooperative effects by changing inflow structures and                     aiding wake recovery. This study offers theoretical support and engineering                     guidance for aerodynamic design optimization and wind farm layout of                     next-generation ultra-large floating offshore wind turbines.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:09:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706260</guid>
    </item>
    <item>
      <title>Flight Mastery in Turbulent Skies: Shared Control and Curriculum Reinforcement Learning for Crosswind Landing</title>
      <link>https://trid.trb.org/View/2617735</link>
      <description><![CDATA[Landing in crosswind conditions poses significant challenges for aircraft, as traditional control methods often fail to ensure stability in rapidly changing wind environments. While reinforcement learning offers a promising alternative, it typically suffers from low sample efficiency and limited generalization under stochastic wind fields. To address these challenges, we propose a Shared Control and Curriculum Reinforcement Learning framework. We model the crosswind landing task as a Markov Decision Process (MDP), explicitly defining the state space, action space, and wind field representation. To initialize learning, we decompose the multi-objective landing task into four sub-tasks—altitude, attitude, heading, and speed control—and train expert policies for each. These are then distilled into a shared control model via behavior cloning, providing a pre-trained policy with basic flight control capabilities. We further fine-tune this model using curriculum reinforcement learning, progressively increasing the complexity of wind conditions to enhance robustness and generalization. Experimental results across multiple aircraft and wind scenarios show that our method improves landing success rates and trajectory smoothness, while generalizing more effectively to unseen wind conditions, outperforming PID controllers, imitation learning, and mainstream RL baselines.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617735</guid>
    </item>
    <item>
      <title>Enhancing metamaterial performance for acoustic sensing in flow</title>
      <link>https://trid.trb.org/View/2700556</link>
      <description><![CDATA[Turbulent pressure fluctuations can compromise the accuracy of far-field acoustic measurements when microphone arrays are flush-mounted on surfaces exposed to fluid flow. To address this, recent advances in acoustic metamaterials have introduced novel approaches to enhance the signal-to-noise ratio of these measurements. In this paper, new techniques for coupling the sound and flow field to a meander metasurface are discovered through computational acoustics modeling and examined through wind tunnel testing in Virginia Tech’s Subsonic Modular Anechoic Research Tunnel (SMART). The results provide insight into the optimal shape for allowing sound waves into a metamaterial while attenuating turbulent boundary layer noise.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700556</guid>
    </item>
    <item>
      <title>From turbulence modelling to machine learning integration in computational ship hydrodynamics using RANS</title>
      <link>https://trid.trb.org/View/2690425</link>
      <description><![CDATA[This comprehensive review examines the application of Reynolds-Averaged Navier-Stokes (RANS) modelling in computational ship hydrodynamics, starting from fundamental theory, numerical implementation, validation procedures, and diverse practical applications. The paper systematically addresses governing equations and turbulence modelling approaches, while analyzing their strengths and limitations in ship flow simulations. Detailed discussions of discretization schemes, grid generation strategies, free surface modelling techniques, and convergence algorithms provide practical guidance for CFD practitioners. The review emphasizes verification and validation methodologies through benchmark cases and uncertainty quantification, highlighting best practices from ITTC guidelines and international workshops. Extensive applications are presented across calm water resistance prediction, hull form optimization, bulbous bow design, energy-saving devices, seakeeping analysis and fluid - structure interaction. Current challenges are critically assessed, including turbulence modelling limitations, scale effects, and free surface accuracy. The integration of machine learning with RANS simulations is explored as an emerging frontier for accelerated design optimization, with particular focus on resistance prediction, hull optimization, and wake field analysis. The paper concludes with future perspectives on hybrid RANS-LES approaches, increased availability of CFD services and ultimately, AI-augmented ship design workflows, providing a comprehensive reference for researchers and engineers in computational ship hydrodynamics.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:19:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690425</guid>
    </item>
    <item>
      <title>Real Turbulent Flow in Vehicle Aerodynamics: Overview of On-Road
          Measurements and Turbulence Generation in Wind Tunnels</title>
      <link>https://trid.trb.org/View/2696801</link>
      <description><![CDATA[Flow conditions on the road are quite different from the conditions used to                     develop vehicle aerodynamics. However, a significant amount of statistical data                     now exists that describes realistic road conditions. Some of these on-road flow                     characteristics can be replicated in wind tunnels. This paper reviews technical                     facilities designed to simulate on-road flow characteristics, such as turbulence                     intensity, turbulent length scales, and flow angle distribution. Reconstruction                     of a flow field that matches real road conditions is made possible by using                     active or passive turbulence generators within the wind tunnel. This review                     provides a comprehensive overview of these facilities, offering readers key                     insights into the challenges involved in replicating real-world flow conditions                     in wind tunnels.]]></description>
      <pubDate>Mon, 27 Apr 2026 16:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696801</guid>
    </item>
    <item>
      <title>Enhancing the reliability of marine pipeline transportation systems: A flow safety monitoring method for sand-carrying churn flows via multi-migration collision behavioral responses</title>
      <link>https://trid.trb.org/View/2607027</link>
      <description><![CDATA[Marine pipeline transportation systems frequently encounter sand-carrying churn flows, wherein persistent sand particle-wall collisions lead to structural degradation of pipelines. This paper proposes a flow safety monitoring method for sand-carrying churn flows based on multi-migration collision behavior responses. Based on the Robust Empirical Mode Decomposition (REMD) algorithm, this study first establishes a multi-frequency scale vibration response characterization method of sand particles for sand-carrying churn flow. Then, a lightweight deep learning architecture based on Depthwise Separable Convolution (DSC) is constructed, achieving an average recognition accuracy of 87.17 % for sand features with contents ranging from 0g to 20g (in 5g increments) across three distinct datasets. Furthermore, the Bidirectional Long Short-Term Memory (BiLSTM) module and Self-Adaptive Temporal Transformer (SATT) module into the DSC framework, thereby enhancing bidirectional full-sequence time-delay feature extraction capability and adaptive weight-matching capacity for holistic particle characteristic information. The DSC-BiLSTM-SATT recognition model improves the average recognition accuracy by 8 %, achieving a final accuracy of 95.17 %. The model shows excellent generalization capability even on low signal-to-noise ratio (SNR) datasets, and the average recognition accuracy for three low SNR datasets reaches 89.73 %. The framework with high accuracy significantly contributes to improve the flow safety and reliability of marine pipeline transportation systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607027</guid>
    </item>
    <item>
      <title>Numerical study of turbulence intensity effects on energy-extraction performance of a semi-activated hydrofoil</title>
      <link>https://trid.trb.org/View/2606844</link>
      <description><![CDATA[Turbulence is a typical and key environmental dynamic factor influencing the performance of tidal current energy devices. This study numerically investigates the effect of turbulence intensity on the energy-extraction performance of a semi-activated hydrofoil. Three turbulence intensities of 0.9 %, 6.8 %, and 13.6 % were generated and calibrated. Results show that both the heaving response and energy-extraction performance first increase and then decrease significantly with rising turbulence intensity. Compared with the case of 0.9 % turbulence intensity, the maximum efficiency and power coefficient at turbulence intensity of 6.8 % were increased by 5.9 % and 9.7 %, respectively. In contrast, these metrics at turbulence intensity of 13.6 % decrease by 15.9 % and 18.6 %. At moderate turbulence intensity, strong vortical structures enhance fluid–hydrofoil interaction and improve hydrodynamic performance, whereas high turbulence has the opposite effect. To clarify the mechanism, power spectral density of lift, output power, and pressure, along with turbulent kinetic energy and proper orthogonal decomposition of velocity were analyzed. Results indicate that moderate turbulence promotes vortex formation and shedding, while high turbulence disrupts these processes and accelerates vortex dissipation through intensified interactions between small-scale vortices and the boundary layer.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606844</guid>
    </item>
    <item>
      <title>Entrainment mechanism in a turbulent offset jet</title>
      <link>https://trid.trb.org/View/2604955</link>
      <description><![CDATA[In this study, a turbulent offset jet with an offset ratio of 5 and a Reynolds number of 7500 is numerically investigated using Large Eddy Simulation (LES), aiming to explore the entrainment mechanisms based on the characteristics of the turbulent/non-turbulent interface (TNTI). First, the distance from the mean position of the TNTI to the jet centerline increases significantly faster in the pre-attachment zone than in the wall jet zone, suggesting a stronger entrainment process in the former region. Second, the conditionally averaged profiles show larger jumps in the velocity and vorticity across the TNTI in the pre-attachment zone, thereby promoting the entrainment of fluid from the non-turbulent region into the turbulent region. Finally, the entrainment is further analyzed through the mechanisms of the nibbling and engulfment. The results show that the contribution of engulfment to the entrainment is less than 2 %, indicating that engulfment is not a significant mechanism in this flow. This indirectly suggests that entrainment is primarily driven by the nibbling process. By quantifying the nibbling flux, it is found that the nibbling flux in the pre-attachment zone is approximately double that observed in the wall jet zone, further explaining higher entrainment in the former region.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604955</guid>
    </item>
    <item>
      <title>An SPH–FEM coupled method incorporating turbulent jet effects and its application to shield tunnel mudcake erosion in offshore viscous strata</title>
      <link>https://trid.trb.org/View/2693665</link>
      <description><![CDATA[Addressing mud cake formation on shield cutter heads in coastal cohesive strata and jet simulation distortions from uniform velocity assumptions in traditional SPH-FEM methods, this paper proposes an SPH-FEM coupling method incorporating turbulent flow velocity distribution. Based on turbulent jet theory, this method assigns radially non-uniform initial velocities to SPH particle sets through secondary development using programming language, constructing a more realistic water jet velocity field. The model's validity was verified through laboratory mud cake scouring tests, achieving an overall similarity of 89% with experimental results. The method was applied to numerical simulation of the full-scale shield cutter head mud cake scouring process, systematically analyzing the influence patterns of scouring time, scouring distance, and mud cake density on scouring effectiveness. The results indicate that: the scouring process can be divided into three stages: efficient destruction, efficiency attenuation, and effect saturation; an optimal range exists for scouring distance (0.8∼1.2 m), within which the mud cake volume destruction ratio reaches 29.7%∼35.3%; mud cake density is positively correlated with its scouring resistance. This study provides validated numerical tools and theoretical foundations for understanding mud cake scouring mechanisms and optimizing scouring system design in Offshore Viscous Strata.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:31:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693665</guid>
    </item>
    <item>
      <title>Project 7: Civil, Supersonic Over-Flight, Sonic Boom (Noise) Standards Development, Study of Variability Effects (Task #1)</title>
      <link>https://trid.trb.org/View/2688766</link>
      <description><![CDATA[The objective of this project was to continue research at the Pennsylvania State University in the ASCENT Center of Excellence to complement the sonic boom standards development ongoing within the Committee for Aviation Environmental Protection's (CAEP) Working Group 1 (Noise Technical), Supersonics Standards Task Group (SSTG). This research aimed to ensure that the behavior of the sonic boom metrics considered in the SSTG discussions were well-understood, as such metrics may be utilized in future sonic boom certification and/or rulemaking. The research focused on investigating the stability of the metrics in the presence of atmospheric turbulence effects and also on an initial investigation to remove turbulence effects (de-turbing) from measured sonic boom signatures. This project showed that some sonic boom metrics are less sensitive to atmospheric turbulence effects than others.]]></description>
      <pubDate>Mon, 20 Apr 2026 18:10:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688766</guid>
    </item>
    <item>
      <title>Aerodynamic Proximity Effects between the DrivAer and AeroSUV Standard Models, Part 1 – Same-Lane Interactions</title>
      <link>https://trid.trb.org/View/2692050</link>
      <description><![CDATA[In this paper, the effects of aerodynamic interactions on the drag of a longitudinally-arranged two-vehicle system are examined by considering the influence of separation distance, cross winds, vehicle size and shape. Testing was undertaken at 30% scale in a large wind tunnel with road-representative freestream turbulence. Separation distances of 0.5, 1.0, and 2.0 vehicle lengths (L) were examined over a range of yaw angles between ±15°. A highlight of the current study is the characterization of platoon drag-reduction benefits for different sizes and shapes of the lead and follower models, by using a DrivAer model and an Aero-SUV model, each with slant-back (Notchback or Fastback) and square-back (Estateback) variants, providing four distinct model pairings. Drag reduction for the lead model appears to be affected mainly by the size of the follower model, while the follower model shows a much greater sensitivity to shape of the lead model. Larger drag reductions were observed at most distances and yaw angles when the lead model had a slant-back configuration (Notchback or Fastback), with smaller drag reductions observed for lead models with square-back configurations (Estateback). This resulted from the different wake structures and their respective influences on the surface-pressure distributions of the follower model. Thrust sheltering is observed as the dominant cause for increased drag at the shortest separation distance. Most of the data show that the drag reductions for the two-vehicle system were larger when the AeroSUV model followed the DrivAer model. This was due to a combination of the greater proportional drag reduction for the leading DrivAer and to the greater relative weighting of the AeroSUV drag reduction due to its larger reference drag area. Peak system-drag reductions of up to 22% were observed at 0.5L separation, decreasing to 18% at 1.0L and 12% at 2.0L.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692050</guid>
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