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The problem of shimmy of a castering wheel, such as the nose wheel of a tricycle gear airplane, is treated analytically. The flexibility of the tire is considered to be the primary cause of shimmy. The rather simple theory developed agrees rather well with previous experimental results. The author suggests that shimmy may be eliminated through a suitable choice of landing gear dimensions in lieu of a damper.
Herein we report an approach to synthesis Ti 3 AlC 2 MAX phase and Ti 3 C 2 T z MXene from low-cost precursors, viz. recycled carbon recovered from waste tire, recycled aluminum scrap, and titanium oxide. By adjusting the ratios of the initial materials, we determined that 3TiO 2 +6Al+1.9C resulted in the purest sample of Ti 3 AlC 2 when heated at 1,350 °C for 1 h. The MXene phase was synthesized by modified minimally intensive layer delamination and acid etching under N2 purging. The final Ti 3 C 2 T z films demonstrated conductivity of 5857 ± 680 S/cm and capacitance of 285 F/g (1,012 F/cm 3 ) at 20 mV/s scan rates, which are comparable with that produced from MAX phase of high-purity elemental precursors (viz. Ti, Al and C). Choosing readily available and inexpensive precursor materials such as these allows for a drastic reduction in production cost of the MXene, as well as reducing the environmental impact of the industry.
Today's modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS). There is a high degree of coupling, cohesiveness, and interactions among vehicle's CPS components (e.g., sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers. Cyber-attacks in the sensing or communication layers can compromise the security of the control layer. This paper provides a detailed review of potential cyber threats related to the sensing layer. Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (e.g., Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (e.g., Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units). Furthermore, the paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions.
Skidding accident reduction investigated for runway and highway safety noting worn tires, smooth surface texture, locked wheels and pilot technique
The rise in universal population and accompanying demands have directed toward an exponential surge in the generation of polymeric waste. The estimate predicts that world-wide plastic production will rise to ≈590 million metric tons by 2050, whereas 5000 million more tires will be routinely abandoned by 2030. Handling this waste and its detrimental consequences on the Earth's ecosystem and human health presents a significant challenge. Converting the wastes into carbon-based functional materials viz. activated carbon, graphene, and nanotubes is considered the most scientific and adaptable method. Herein, this world provides an overview of the various sources of polymeric wastes, modes of build-up, impact on the environment, and management approaches. Update on advances and novel modifications made in methodologies for converting diverse types of polymeric wastes into carbon nanomaterials over the last 5 years are given. A remarkable focus is made to comprehend the applications of polymeric waste-derived carbon nanomaterials (PWDCNMs) in the CO 2 capture, removal of heavy metal ions, supercapacitor-based energy storage and water splitting with an emphasis on the correlation between PWDCNMs' properties and their performances. In conclusion, this review offers insights into emerging developments in the upcycling of polymeric wastes and their applications in environment and energy.
Here, in this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.
Here, this study investigates the impact of ambient temperature on the range of electric vehicles (EVs) by analyzing its effects on usable battery energy (UBE), heating, ventilation, and air conditioning (HVAC) energy consumption, and powertrain energy losses. Chassis dynamometer tests within a thermal chamber were conducted under various temperature conditions to investigate these impacts. The results indicate that lower temperatures lead to a decrease in UBE for lithium-ion batteries in EVs. At −18 °C, the UBE exhibited reductions of 4---8 % compared to the UBE at 22 °C. Battery thermal management strategies significantly affected the UBE loss, with different strategies resulting in distinct UBE reductions. HVAC energy consumption, especially for interior heating, proved to be the most dominant variable affecting EV driving range. Larger discrepancies between the HVAC target temperature (22 °C) and the ambient temperature increased HVAC energy usage. The type of HVAC system also influenced energy consumption, where EVs equipped with heat pumps demonstrated lower energy consumption for heating compared to those relying solely on resistance heaters. Ambient temperature also influenced motor energy consumption due to increased frictions, powertrain losses and tire rolling resistance at lower temperatures; consequently, regenerative braking energy decreased in cold conditions. Combining these effects influenced the overall energy consumption and driving range of EVs. At −18 °C, the driving range saw a substantial decrease of up to 60 % compared to 22 °C, while a slight decrease was observed at 35 °C.
Catalytic conversion of waste rubbers and plastics into aromatic hydrocarbons is a promising approach to waste management and energy recovery. In the present study, acidic HY zeolites were supported by cobalt, iron, and zirconium, and the catalysts were characterized by powder X-ray diffraction, nitrogen adsorption-desorption, ammonia temperature programmed desorption, X-ray photoelectron spectroscopy, and pyridine-Fourier transform infrared spectroscopy. Here, the catalytic degradation of waste polybutadiene rubbers (BR) was conducted to investigate the degradation mechanism and evaluate the catalytic activity of supported zeolites. Experimental results indicated that HY loaded by zirconium and iron led to a higher content of Lewis acid sites as opposed to cobalt supported one. Compared with the non-catalytic pyrolysis of BR, the zirconium supported HY (Zr/HY) led to a 10-fold increase in aromatic hydrocarbons production with a distinctively high selectivity of 97.9%. A series of waste polymers including waste tires (WT), polyethylene (PE), polycarbonate (PC), and BR, were subjected to catalytic pyrolysis to explore the effects of polymer type on aromatic hydrocarbons generation, and BR was the most effective substrate, with yield enhancement reaching 2.4 over Zr/HY. Catalytic co-pyrolysis of waste rubbers and plastics was conducted to probe the effect of polymer structure on aromatic hydrocarbons formation, where a significant synergistic effect was observed in the PE co-fed with PC run.
Value-added utilization of waste tires in cementitious materials has been studied for decades, whereas the synergy between rubber and alkali-activated cements deserves more investigations. To this end, we compared physico-mechanical and durability properties of alkali-activated slag (AAS) and OPC-based composites containing NaOH-pretreated/as-received crumb rubber (CR). It was found that the incorporation of CR had both positive and negative effects on specimens due to its special attributes. CR reduced electrical conductivity which was associated with the ion transport ability, since it worked as an electrical insulator and increased hydrophobicity of pore walls. The sulfate expansion decreased with CR content because the flow of sulfate ions was demobilized, and the flexibility of CR relieved the internal stress. CR also improved the freeze-thaw durability of composites if the total CR content was less than 30% in specimens. However, the low Young's modulus of CR impaired the compressive strength and failed to effectively control the total shrinkage upon drying. The composites with CR must avoid high temperature exposure which resulted in the pyrolysis of rubber. In addition, the NaOH pretreatment enhanced adhesion between CR and hydrated cement and improved overall performances of composites. The FTIR spectra confirmed the NaOH-induced accelerated oxidation of CR surface and the generation of polar functional groups including carbonyl and carboxyl groups, increasing the total surface energy of rubber. More importantly, the alkali-activation process for AAS could treat the CR aggregate in the meantime, which means the use of CR in alkali-activated cements does not require the pretreatment of CR before blending.
Compounding of commercial nanocomposites usually involves the addition of viscosity enhancers such as binder resins in ink jet inks, and paints. Contrary to this, plasticizers such as process oils are added to reduce the melt viscosity and ease processability of reinforced elastomers. Nanofillers such as silica and carbon black are typically added to reinforce rubber and enhance performance of automotive tire treads. Filler dispersion has traditionally been qualitatively (indirectly) assessed by measuring the properties of reinforced elastomers. While dispersion can be quantified by examining filler agglomeration through surface roughness measurements and microscopy, the size-scale dependence for these hierarchical fillers has usually been ignored. Here, we have recently devised a method to quantify nano-scale dispersion of fillers using Ultra small-angle X-ray scattering (USAXS) techniques. This method is advantageous since it directly links the controllable processing/compounding parameters such as the mixing speed, mixer geometry, residence time (or mixing duration), melt density, flow gap distance, and melt viscosity to nano-scale dispersion. While our previous studies have explored the impact of different processing parameters, this study specifically investigates the impact of melt viscosity on nano-scale filler dispersion in elastomer compounds. Commercially available polybutadienes with different Mooney viscosities were used in conjunction with different grades and amounts of process oils to modify the melt viscosity.
Thousands of chemical contaminants threaten watersheds but are time and cost prohibitive to monitor. Identifying their sources, transport, and ecological risk is limited in heterogeneous urban watersheds. We present an integrative watershed approach using source-specific indicator compounds, common water quality measures, and ecotoxicity assays to examine the distribution of contaminant mixtures in an urbanized watershed. Indicator compound concentrations were temporally and spatially distributed for treated/untreated sewage (sucralose, artificial sweetener), road runoff (diphenyl-guanidine [DPG] and 6PPD-quinone [6PPD-Q], automobile tire additives), and lawncare runoff (aminomethanephosphonic acid (AMPA), major degradant of the herbicide glyphosate). Sucralose was predominately sourced from treated wastewater; measurable concentrations in tributaries indicated raw sewage inputs. DPG and 6PPD-Q concentrations correlated to road density during base flow and were elevated during stormflow. AMPA was measurable spring through fall, especially where lawns were dense. When specific sources dominated flow, water quality measures correlated with wastewater (sulfate, potassium, chloride, and sodium) and road runoff (chromium and lead) indicators. The limited behavioral toxicity observed in exposed zebrafish (Danio rerio) (18%) was not well explained by source-indicators. PFAS concentrations were highly variable spatially but not well explained by our source-specific indicator compounds. Here, more costly compound-specific monitoring may be necessary when multiple sources exist or when unexpected toxicity trends occur.
Filled rubber materials are key in many technologies having broad impact on the economy and sustainability, the most obvious being tire technology. Adding filler dramatically improves the strength of rubber by reinforcement and tailoring the type of filler and the chemistry of the interface between filler and rubber matrix is important for optimizing performance metrics such as fuel efficiency. In a highly loaded, silica-filled, crosslinked model rubber closely mimicking commercial materials, both the filler network structure and the dynamics of the silica filler particles change when the silica surface is modified with silane coupling agents. Reduction in size scales characteristic of the structure are quantified using ultra-small-angle X-ray scattering (USAXS) measurements and the particle dynamics probed with X-ray photon correlation spectroscopy (XPCS). While the structure averaged over the scattering volume changes little with aging after step strain, the dynamics slow appreciably in a manner that varies with the treatment of the silica filler. The evolution of filler particle dynamics depends on the chemical functionality at the silica surface and observing these differences suggests a way of thinking about the origins of hysteresis in nanoparticle-reinforced rubbers. These microscopic filler dynamics are correlated with the macroscopic stress relaxation of the filled materials. The combination of static and dynamic X-ray scattering techniques with rheological measurements is a powerful approach for elucidating the microscopic mechanisms of rubber reinforcement.
The underlying microscopic response of filler networks in reinforced rubber to dynamic strain is not well understood due to the experimental difficulty of directly measuring filler network behavior in samples undergoing dynamic strain. This difficulty can be overcome with in situ X-ray photon correlation spectroscopy (XPCS) measurements. The contrast between the silica filler and the rubber matrix for X-ray scattering allows us to isolate the filler network behavior from the overall response of the rubber. This in situ XPCS technique probes the microscopic breakdown and reforming of the filler network structure, which are responsible for the nonlinear dependence of modulus on strain, known in the rubber science community as the Payne effect. These microscopic changes in the filler network structure have consequences for the macroscopic material performance, especially for the fuel efficiency of tire tread compounds. In this work, we elucidate the behavior with in situ dynamic strain XPCS experiments on industrially relevant, vulcanized rubbers filled (13 vol %) with novel air-milled silica of ultrahigh-surface area (UHSA) (250 m 2 /g). The addition of a silane coupling agent to rubber containing this silica causes an unexpected and counterintuitive increase in the Payne effect and decrease in energy dissipation. For this rubber, we observe a nearly two-fold enhancement of the storage modulus and virtually equivalent loss tangent compared to a rubber containing a coupling agent and conventional silica. Interpretation of our in situ XPCS results simultaneously with interpretation of traditional dynamic mechanical analysis (DMA) strain sweep experiments reveals that the debonding or yielding of bridged bound rubber layers is key to understanding the behavior of rubber formulations containing the silane coupling agent and high-surface area silica. These results demonstrate that the combination of XPCS and DMA is a powerful method for unraveling the microscale filler response to strain which dictates the dynamic mechanical properties of reinforced soft matter composites. With this combination of techniques, we have elucidated the great promise of UHSA silica when used in concert with a silane coupling agent in filled rubber. Such composites simultaneously exhibit large moduli and low hysteresis under dynamic strain.
Accurately estimating rolling and air resistance is essential for predicting the energy consumption of vehicles. This study presents a field-based approach using a rolldown test to simultaneously determine rolling and air resistance coefficients. Unlike prior methods that frequently used simulations or models, we employ a goal programming methodology to improve precision and evaluate the actual vehicle and environmental conditions. Our methodology was tested using a Class 8 Freightliner eCascadia on a surveyed road section, ensuring controlled conditions for data collection. By analyzing the time–velocity relationship across multiple test runs, we derived resistance coefficients for both loaded and unloaded conditions. The study confirms that rolling resistance is largely independent of velocity at low speeds but exhibits a nonlinear dependency at higher speeds. Additionally, road surface conditions, tire condition, axle configuration, aerodynamic properties, and weather conditions significantly impact resistance values, emphasizing the need for real-world testing rather than relying solely on standardized projections. Our results align with existing literature while demonstrating the efficacy of the goal programming approach in refining resistance estimates. This work contributes to improved vehicle energy modeling, offering practical insights for fleet operators and policymakers seeking accurate energy consumption predictions for electric trucks operating under varying environmental conditions.
Longitudinal vehicle motion control is essential for enhancing performance and optimizing a vehicle’s energy usage. However, it remains a challenging task due to the nonlinear and uncertain nature of vehicle dynamics, along with varying driving conditions. This paper presents a novel ultra-local optimal control approach based on Pontryagin’s Minimum Principle (PMP) that circumvents the need for detailed system identification by employing an ultra-local model. The control objective is to minimize the total energy consumption under boundary conditions while ensuring smooth traction force generation. The proposed approach is evaluated using a high-fidelity vehicle model in three representative scenarios: (i) nominal driving, (ii) a change in tire road friction coefficient (TRFC) from 0.5 to 0.65 and road slope from 0% to 5% during the maneuver, with target velocity unchanged, and (iii) a change in target velocity from 20 m/s to 0 m/s during the maneuver, while maintaining nominal TRFC and slope conditions. The simulation results demonstrate that the proposed method delivers robust performance, effectively balancing consumption and tracking accuracy in all tested scenarios.
While controlling underwater adhesion is critical for designing biological adhesives and in improving the traction of tires, haptics, or adhesives for health monitoring devices, it is hindered by a lack of fundamental understanding of how the presence of trapped water impedes interfacial bonding. Here, by using well-characterized polycrystal diamond surfaces and soft, nonhysteretic, low–surface energy elastomers, we show a reduction in adhesion during approach and four times higher adhesion during retraction as compared to the thermodynamic work of adhesion. Our findings reveal how the loading phase of contact is governed by the entrapment of water by ultrasmall (10-nanometer-scale) surface features. In contrast, the same nanofeatures that reduce adhesion during approach serve to increase adhesion during separation. The explanation for this counterintuitive result lies in the incompressibility-inextensibility of trapped water and the work needed to deform the polymer around water pockets. Unlike the well-known viscoelastic contribution to adhesion, this science unlocks strategies for tailoring surface topography to enhance underwater adhesion.
Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.
The Cryogenic Carbon Capture™ (CCC) process separates CO2 from light gases in essentially any continuous process. CCC cools the gases to the frost or desublimation point of CO2 (-100 to -135 °C), separates and pressurizes the solids, and warms all streams to produce a CO2-depleted stream at ambient pressure and a pure (99+%) pressurized liquid CO2 stream typically to about 150 bar, both at ambient temperature. The process also recovers all gas moisture and most gas impurities less volatile than CO2 (NOx, SOx, Hg, PM, UHC, CCC, etc.) in separable streams. CCC nearly eliminates refrigeration energy for sensible temperature changes through heat integration. CCC does require energy to change the CO2 phase from a mixed vapor to a pressurized fluid, which represents the minimum energy required of any process for this separation. CCC uses additional energy for turbomachinery inefficiencies, heat losses, moisture removal and overall process pressure drop. Aside from these real-world energy demands, CCC operates near the minimum energy required to perform this gas separation by minimizing stream recycling. CCC compresses CO2 as a liquid, which is one of several reasons it costs about about half as much and consumes about half as much energy as an amine process when using flue gases with about 15% CO2. The process also has several major additional advantages, including (a) it is a bolt-on retrofit technology that does not need steam or any modification of existing equipment, (b) it recovers water and nearly all pollutants in addition to CO2 from the flue gas, (c) it enables highly efficient and cost effective energy storage at grid scale and on time scales of minutes, (d) it enables NG storage if the energy storage option is used, and (d) it has a small footprint and is minimally disruptive to existing plants, requiring only electrical power and a gas source to operate. Sustainable Energy Solutions (SES) has scaled this technology through several levels, the largest of which captures nominally 1 tonne of CO2/day and is called the skid system. Skid system field tests include utility-scale power plants, cement plants, heating plants, and other utility or industrial sites that burn natural gas, biomass, coal, shredded tires, municipal waste, and combinations of these fuels. These field tests produced 95-99% CO2 capture with CO2 purities of 99+% and initial CO2 contents that range from 4 to 28%. SES currently seeks to scale the system to merchant scale (10-80 tonnes of CO2 per day). In the process of doing so, SES has demonstrated the potential for CCC to contribute to energy storage and direct air capture in innovative and cost-effective ways. This presentation discusses the overall process and highlights results from field and in-house tests. These include (a) measured CO2 capture rates and operating conditions from in-house and field tests, and (b) predicted utility-scale costs and energy demands. This discussion also includes the application of the CCC technology to energy storage and direct air capture.