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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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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Thermal Enhancement of Additively Manufactured Windings via Embedded Cooling Channels for High-Performance YASA Motors</title>
      <link>https://trid.trb.org/View/2686047</link>
      <description><![CDATA[To achieve net-zero carbon emissions, electrified and hybrid propulsion systems in air-transport increasingly demand high torque density motors. High torque density is invariably accompanied by high loss density, where winding copper losses typically constitute the primary source of total motor losses. This directly increases the risk that the winding temperature will exceed the threshold, leading to dielectric failure of the insulation. Thus, enhancing winding heat dissipation becomes a core approach to breaking through the power density improvement bottleneck. Additive manufacturing (AM) enables innovative winding designs. This article compares three AM windings with integrated cooling channels, which enhances heat dissipation by increasing the winding cooling area and optimizing coolant flow paths. A computational fluid dynamics (CFD) model is established to compare the temperature distribution of three winding structures, as well as the flow characteristics and pressure drop of the coolant under different structures. The best-performing O-type AM winding exhibits a steady-state average temperature rise of only  34.38° C at a current density of 33.5 A/mm2. Finally, the prototype windings are manufactured and tested to verify the feasibility of the concept, and satisfactory results are achieved.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:15:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686047</guid>
    </item>
    <item>
      <title>Economic growth, mobility, and the additive manufacturing in Europe</title>
      <link>https://trid.trb.org/View/2768461</link>
      <description><![CDATA[The present study analyses the interrelationship between the European Union's macroeconomic indicators and the Additive Manufacturing (AM) market. While the EU's GDP and transport sector have demonstrated moderate, linear growth since 1990, the European AM technology market has embarked on an exponential growth trajectory. The research indicates that within the European Union, the automotive and aerospace industries have become the primary drivers of AM adoption, particularly due to the transition to electric mobility and the imperative for weight reduction. Projections indicate that by the year 2030, the AM segment within the EU's transport sector will attain a valuation of 21 billion USD, thereby effecting a substantial reconfiguration of the Union's supply networks through the substitution of physical warehousing with digital inventory management systems.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2768461</guid>
    </item>
    <item>
      <title>Integrated job shop and transportation scheduling under time-of-use electricity pricing and human-robot interactions</title>
      <link>https://trid.trb.org/View/2714590</link>
      <description><![CDATA[The transition toward Industry 5.0 challenges manufacturers to balance profitability and sustainability. While prior studies address energy-aware scheduling, transportation, or human-centric manufacturing separately, their combined effects remain underexplored. This paper proposes a bi-objective job shop scheduling framework with integrated transportation under stochastic human–robot interactions (HRIs) and time-of-use (ToU) electricity pricing. A simulation–optimization framework is developed, combining system-level modeling with an ϵ-constraint approach to minimize total energy cost (TEC) under makespan constraints. Tasks are scheduled following several high-pricing-period avoidance rules, while the scheduling horizon is progressively reduced to generate a representative set of non-dominated solutions. Computational results indicate that allowing up to 33% of production during high-pricing periods yields the best trade-offs, whatever the scheduling approach (static or dynamic) used. HRIs exhibit a limited overall impact but become significant under prolonged interactions, whereas flow-shop-like job sets tend to produce infeasible schedules under stricter avoidance rules.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714590</guid>
    </item>
    <item>
      <title>Experiments on Effects of Self-Locking on Mechanical Response of Origami Metamaterials</title>
      <link>https://trid.trb.org/View/2778035</link>
      <description><![CDATA[Motivated by the negative Poisson’s ratio tetrahedral-trihedral polyhedron (TMP), this study systematically examines the role of self-locking mechanisms in determining the mechanical response and energy absorption capacity of rigid origami metamaterials. Quasi-static compression tests were conducted on specimens exhibiting three distinct geometries (B19, B22, B23) and four wall thicknesses (0.8–2.0 mm). The results of these tests revealed two unique self-locking behaviors. Type I self-locking originates from inter-wall interlocking, characterized by progressively decreasing inter-wall spacing during compression; Type II self-locking originates from interlocking between creases, characterized by creases contacting each other during compression. The fabrication of the specimens was accomplished through the utilization of FDM-based additive manufacturing, employing PEEK material. The results obtained from this study revealed two distinct locking behaviors: It has been demonstrated that type I locking enables sustained deformation without load reduction. In contrast, type II locking has been shown to result in premature collapse and diminished energy absorption capacity. The B22 configuration has been demonstrated to trigger both locking mechanisms concurrently, thereby significantly enhancing performance metrics. This has been evidenced by improvements in both crush force efficiency (CFE) and specific energy absorption (SEA), whilst also delaying densification. In contrast, structures dominated by a single locking mechanism exhibit premature failure (B19) or inefficient energy absorption (B23). These findings emphasize the pivotal role of synchronized self-locking activation and geometric configuration in enhancing impact resistance and energy dissipation, thereby establishing a foundational theoretical framework for the design of advanced metamaterials in protective engineering.]]></description>
      <pubDate>Wed, 16 Sep 2026 09:22:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2778035</guid>
    </item>
    <item>
      <title>Adaptive-Weighted Coupling-Aware Gaussian Processes for AFP</title>
      <link>https://trid.trb.org/View/2777950</link>
      <description><![CDATA[Composite materials have gained widespread application in the aerospace field due                     to their advantages, such as high specific strength, high specific modulus, and                     corrosion resistance. Automated placement technology, as an emerging automated                     manufacturing method, is gradually replacing traditional manual placement                     processes and demonstrating significant advantages in composite manufacturing.                     Currently, the automated placement process for composite materials faces                     challenges such as insufficient experimental samples and strong coupling                     relationships between process parameters, leading to low fitting accuracy in                     process parameter optimization models. To address this, this paper proposes a                     placement process parameter optimization method based on model weight adaptive                     allocation. This method integrates three key technologies: a coupling-aware                     Gaussian process based on combined kernel functions, a weight allocation                     ensemble model based on leave-one-out cross-validation, and a multi-criteria                     adaptive sampling mechanism. Experimental validation demonstrates that the                     integrated model achieves a coefficient of determination R^2 = 0.82,                     which represents a superior fit compared to the R^2 = 0.65 achieved by                     a single-kernel Gaussian model and the 0.76 obtained from a single sampling.                     Furthermore, both the Root Mean Square Error (RMSE=0.92) and Mean Absolute Error                     (MAE=0.70) are lower than those of traditional baseline models. This framework                     provides an effective solution for optimizing parameters in the automated                     placement process for composite materials.]]></description>
      <pubDate>Wed, 16 Sep 2026 09:22:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2777950</guid>
    </item>
    <item>
      <title>Battery Resizing Enabled by Vehicle Lightweighting: Implications for Battery-Pack Manufacturing Emissions, Cost, and Critical Mineral Demand in Battery Electric Vehicles</title>
      <link>https://trid.trb.org/View/2705454</link>
      <description><![CDATA[Battery electric vehicles (BEVs) are widely promoted as a pathway to decarbonize transportation, yet their large battery packs create economic, environmental, and resource challenges. This study develops an integrated modeling framework to evaluate how vehicle mass reduction through lightweighting influences battery resizing, energy demand, range, cost, and greenhouse gas (GHG) emissions. The framework incorporates standard drive cycles, vehicle dynamics, and iterative feedback between vehicle mass and pack size to capture secondary benefits of lightweighting. Building on previous studies that have already quantified the direct use‑phase energy savings from vehicle lightweighting, this work isolates the additional, secondary benefits that arise through changes in battery manufacturing and pack sizing. Scenario analysis demonstrates that downsizing the battery (Shrink scenario) reduces pack mass, cost, and emissions by roughly 8% without sacrificing range, while reallocating mass savings to larger packs (Increase scenario) extends range but with higher costs and embodied emissions. Comparisons of lithium-ion, sodium ion, and solid-state batteries further highlight trade-offs: sodium-ion offers near-term reductions in cost and emissions despite lower energy density, while solid-state batteries provide unmatched performance at prohibitive cost. Linking these findings to International Energy Agency (IEA) projections, battery downsizing emerges as a crucial strategy to narrow the widening gap between critical mineral demand and supply. The results underscore the dual role of lightweighting in improving BEV sustainability and enhancing supply chain resilience through both efficiency gains and chemistry diversification.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705454</guid>
    </item>
    <item>
      <title>Determinants of investment risk: A comparative analysis of China’s port and manufacturing investments along the maritime silk road</title>
      <link>https://trid.trb.org/View/2737034</link>
      <description><![CDATA[Under rising geopolitical tensions, trade conflicts, policy uncertainty, and host-country risks, global logistics systems increasingly require diversified and resilient corridors to maintain trade continuity and transportation efficiency. In this context, China’s port and manufacturing investments along the Maritime Silk Road (MSR) may support corridor resilience by improving logistics node capacity, maritime connectivity, industrial support, and cargo-generation potential. However, investment failure risk may constrain this expected resilience-enhancing role by affecting logistics capacity, industrial support, and corridor diversification. To address these challenges, this study develops a risk analysis framework for port and manufacturing investments along the MSR, incorporating the dimensions of socioeconomic condition, bilateral relations, institutional distance, and sector-specific risk. A binary logit model is used to assess their associations with investment returns, while historical data are used to estimate the probability of investment failure. The results show that: (1) the risk attributes of the port and manufacturing investments differ substantially, i.e., for ports the risk is more closely associated with capital amount, whereas for manufacturing it shows a stronger association with macroeconomic indicators such as Gross Domestic Product (GDP), economic growth rate, openness, and railway network scale; (2) regarding bilateral relations, high-level reciprocal visits demonstrate a long-term cumulative effect, with a four-year lag being more pronounced than a two-year lag; (3) interactions between institutional distance and indicators such as capital amount and logistics performance are significantly associated with variations in risk levels. These results provide evidence-based insights for identifying key risk drivers and for supporting more resilient decision-making on port and manufacturing investments under uncertain geopolitical and trade environments.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737034</guid>
    </item>
    <item>
      <title>Trade-offs of recycling strategies under carbon tax and recycling target responsibility system: Trade-in vs. deposit-refund</title>
      <link>https://trid.trb.org/View/2743183</link>
      <description><![CDATA[Under the dual pressures of global climate change and sustainable resource management, carbon taxes and recycling target responsibility systems are synergistically fostering the green transformation of the manufacturing industry through integrated policy combinations. However, optimizing the selection of recycling strategies within reverse logistics under this complex regulatory framework remains a formidable managerial challenge. To explore the operational boundaries of two mainstream recycling strategies, namely Trade-in (TI) and Deposit-refund (DR), we develop a manufacturer-led Stackelberg game model to compare their performance under dual environmental regulations. The findings reveal distinct cost thresholds for strategy selection. TI relies on an internal subsidy mechanism and is particularly effective for products with low production costs and rapid turnover. Its core advantage lies in leveraging discount incentives underpinned by high profit margins to significantly bolster demand for new products. Conversely, DR, which utilizes an external deposit mechanism, yields higher profits in high-cost industries. By externalizing incentive costs, DR maintains a more competitively priced system and effectively steers price-sensitive consumers toward remanufactured products. Interestingly, aligning policy design with the appropriate recycling strategy is key to achieving a triple-win outcome. When production costs are low and carbon taxes are high, TI more effectively balances economic growth, environmental protection, and social welfare. In contrast, DR emerges as the superior choice when production costs are high and carbon taxes are moderate. Our findings provide strategic guidance for firms to optimize recycling choices in reverse logistics and for regulators to orchestrate complementary environmental policies.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743183</guid>
    </item>
    <item>
      <title>Artificial Intelligence-Enabled Generative Design for Additive Manufacturing Inventory Pooling: An Economic and Environmental Sustainability Analysis in Aviation Spare Parts Supply Chain</title>
      <link>https://trid.trb.org/View/2709788</link>
      <description><![CDATA[The potential for artificial intelligence (AI)-enabled generative design (GD) algorithms for additive manufacturing (AM) is vast. By employing GD, parts are optimized for material efficiency, structural performance, longevity, and reduced waste, aligning with circular economy principles. This study provides a quantitative comparison of the cost and environmental impacts of conventional manufacturing (CM) and generatively designed additive manufacturing (GDAM) within the context of centralized spare-parts supply chain (SPSC) inventory pooling in the aviation industry. We analyze two scenarios and estimate the total annual and lifecycle costs of each using a cost model that integrates established inventory management models. Our sensitivity analysis shows that, assuming equal part longevity, the GDAM scenario becomes cost-competitive when the weight reduction exceeds approximately 84% of the CM part’s weight. We also discovered that part durability is a more influential driver for the GDAM scenario than lightweighting. In the GDAM scenario, we observed that when the part is 53% lighter than the CM part, if the GDAM part can also deliver a 100% increase in longevity compared to the CM part, then the result is a 24.6% cost advantage for the GDAM scenario relative to the CM scenario. Moreover, the GDAM scenario is shown to offer substantial sustainability advantages, including reductions in fuel consumption, CO₂ emissions and raw material use. These environmental benefits become increasingly evident under high weight-reduction scenarios. This paper contributes to the literature by integrating GDAM into SPSC cost analysis. We propose a cost model for GDAM utilization in a centralized SPSC pool in the airline industry and compare its cost components, using a jet-engine bracket case study, with those of a CM-based centralized SPSC pool. We also provide insights into when GDAM production-capacity pooling can be viable, particularly for non-critical-to-flight parts. These findings inform strategic decisions for supply chain managers and policymakers by quantifying the economic and environmental trade-offs associated with adopting GDAM-based inventory pooling in aviation.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709788</guid>
    </item>
    <item>
      <title>A Data-Driven Method for Predicting The Operational Status of The Aircraft Assembly System</title>
      <link>https://trid.trb.org/View/2742495</link>
      <description><![CDATA[Aircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The proposed methodology facilitates timely anomaly detection and early warning, allowing proactive risk mitigation and ensuring sustained production system stability. This research contributes to advancing intelligent monitoring and control strategies for aircraft assembly operations within data-driven manufacturing environments.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:46:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742495</guid>
    </item>
    <item>
      <title>Stewart Platform Position and Attitude Error Compensation Based on Improved Particle Swarm Optimization</title>
      <link>https://trid.trb.org/View/2742487</link>
      <description><![CDATA[The six-degree-of-freedom Stewart platform, as a high-precision parallel robot, is widely used in fields such as aerospace and precision manufacturing. However, its complex structure and diverse sources of error (such as manufacturing errors, assembly errors, rod deformation, etc.) make it difficult to effectively control position and attitude errors. This article proposes a Stewart platform position and attitude error compensation method, relying on the improved particle swarm optimization (IPSO) algorithm. By establishing a position and attitude error model for the platform and optimizing the driving joint error using the IPSO optimization, the position and attitude error of the platform have been significantly reduced, providing a new solution for error compensation of high-precision parallel robots.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:46:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742487</guid>
    </item>
    <item>
      <title>Effect of particle shape on the mechanical properties of 3D-printed transparent soils</title>
      <link>https://trid.trb.org/View/2701836</link>
      <description><![CDATA[Transparent soil experimental technique employs transparent particles as a substitute for natural sand, where particle shape, as a fundamental characteristic, significantly influences the mechanical properties of the transparent soil. In this study, a mathematical particle model generation method based on spherical harmonics (SH) analysis was adopted. Combined with 3D printing technology, transparent soil specimens with controllable multi-scale particle morphology were fabricated. Direct shear tests were then conducted on specimens with varying elongation, roundness, and roughness to investigate the influence of particle shape on the mechanical properties. The results indicate that decreasing the elongation and roundness, or increasing the roughness of transparent particles typically results in more complex geometric shapes, thereby enhancing the peak state and critical state friction angles. Under a vertical stress of 200 kPa, linear fittings (R² > 0.9) of the peak state friction angle against elongation and the spherical harmonic factors that control roundness and roughness yield slopes of 2.89, 3.72, and 2.3, respectively, with roundness contributing most significantly to the improvement of shear strength. Based on the experimental results, predictive equations for the peak state and critical state friction angles that comprehensively consider multi-scale shape parameters were established. This study provides a reference to fabricate transparent granular soils with specific mechanical properties, enhancing the capability of transparent soil experimental technique in solving practical engineering problems such as pile foundations, excavations, and slopes]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701836</guid>
    </item>
    <item>
      <title>Research on CODP Localization Decision Model of Automotive Supply Chain Based On Delayed Manufacturing Strategy</title>
      <link>https://trid.trb.org/View/2729782</link>
      <description><![CDATA[Under the market background of increasingly personalized product demand and compressed response cycle, the traditional manufacturing model with standardized mass production as the core has been difficult to meet the dual expectations of customers for differentiation and fast delivery. In order to improve the efficiency of resource allocation and market response, automobile manufacturers need to build a production system that takes into account cost and flexibility. Based on the delayed response manufacturing strategy, this study built an order response node configuration model suitable for automotive manufacturing scenarios, focusing on the positioning of order driven intervention points in the production process. The model comprehensively considers the structural cost changes brought by process adjustment, the dynamic characteristics of the changes of unit manufacturing cost and intermediate inventory cost at different stages with the location of nodes, and introduces delivery time constraints to embed time factors into the inventory decision logic to enhance the practicality of the model and the adaptation of realistic constraints. In terms of solution methods, this paper adopts function fitting and simulation analysis methods, combined with mathematical modeling tools, systematically describes the change trend of total cost, and verifies the rationality and effectiveness of the model structure and solution through actual enterprise cases. The research results provide a theoretical basis and decision support for automobile manufacturing enterprises to realize the synergy of flexible production and cost control in the environment of variable demand, and also provide an empirical reference for the implementation path and system optimization of subsequent relevant strategies.]]></description>
      <pubDate>Fri, 21 Aug 2026 17:00:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2729782</guid>
    </item>
    <item>
      <title>Drive Cycle Based Energy Audit &amp; Range Estimation for 4X2 Electric Truck</title>
      <link>https://trid.trb.org/View/2761921</link>
      <description><![CDATA[Transportation sector in India accounts for 12% of total energy consumption. Demand of energy consumption is being met by the imported crude oil, which makes transportation sector more vulnerable to fluctuating international crude oil prices. India is mindful of its commitment in 2016 Paris climate agreement to reduce GHG emissions intensity of its GDP by 40% by 2030 as compared to 2005 levels. To fast track the decarbonization of transportation sector, commercial vehicle manufacturers have been exploring other viable options such as battery electric vehicles (BEVs) as a part of their fleet. As on today, BEV has its own challenges such as range anxiety & high total cost of ownership. Range anxiety can be certainly addressed by optimum sizing of electric powertrain, reduction in specific energy consumption (SEC) & use of effective regeneration strategies. Higher SEC can be more effectively addressed by doing vehicle energy audit thereby estimating the energy losses occurring at each powertrain component of an electric vehicle.The work illustrated in this paper involves drive cycle-based energy audit & range estimation for 4X2 rigid electric truck using simulation approach. It involves strenuous exercise of simulation specific input data generation by doing rigorous component level tests for battery, motor, tires & auxiliaries. Duty cycle data was acquired for 3000 km & condensed cycle of 32 minutes was formed which represents real world usage pattern. Data recorded in component and vehicle tests was used to build robust simulation model in GT-DRIVE. Simulated SEC was validated within 4% with on road trails. 73.5% of battery discharge energy was used to overcome rolling resistance loss, aerodynamic drag loss, electromechanical conversion loss, auxiliary losses, braking losses & differential losses. Effective power at wheels observed to be 26.5% of total battery discharge energy. Sensitivity analysis for RAR, RRC, coasting & braking regeneration limits was carried out and effect of each parameter on final SEC was studied and optimum set of parameter combination was suggested to the OEM. Outcome of this project has also laid down the sophisticated methodology to carry out energy audit of any electric vehicle, which in turn will help to bring simulation predictions much closer to the real-world scenarios.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761921</guid>
    </item>
    <item>
      <title>Steel-Based Laminates for Lithium-ion Pouch Cell of Electric Vehicles</title>
      <link>https://trid.trb.org/View/2761919</link>
      <description><![CDATA[Aluminum foils have gained traction with EV battery manufacturers for their pouch cell format. Over the years, it has evolved as a material of choice, but it is still plagued by the issues of stress concentration and swelling due to lower strength and lower stiffness of base aluminum layer. Preliminary investigation revealed that laminates using steel foil material (thickness < 0.1mm) could be a potential candidate for EV pouch cell casing. Thus, steel-based laminate was developed meeting key functional requirements (e.g., barrier performance, insulation resistance, peel strength, electrolyte resistance, formable without cracking at edges, and heat sealing compliant). This innovative patented steel-based laminate [1] was further used to manufacture pouch cell prototypes (up to a maximum capacity of 2.8Ah) for key performance evaluation (e.g., cell cycling and nail penetration). The study paves the way for a low cost, sustainable and flexible yet strong steel-based laminate packaging material solution for lithium-ion pouch cells.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761919</guid>
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