Mechanical Analysis Research Articles

Design and analysis of a solid surface deployable antenna mechanism based on flasher rigid origami

This study proposes a mechanism for solid surface deployable antennas based on flasher rigid origami. The mechanism scheme design and degree-of-freedom analysis were conducted utilizing a Kirigami release constraint strategy. Subsequently, a spatial geometric model of the key points was established incorporating the folding and unfolding characteristics and geometric relationships within the mechanism. Furthermore, a comprehensive 3D model of the antenna was developed by determining the hinge locations, geometric parameters, and driving mechanism, followed by a kinematic simulation analysis. Finally, a prototype was constructed, and a microgravity deployment experiment was conducted to verify the feasibility of the mechanism.

Design and kinematic analysis of a deployable mechanism for soft sheets grasping

In the automation process of the garment industry, a notable challenge arises from the presence of flexible materials characterized by irregular shapes, posing difficulties for robotic grasping. This article addresses this challenge by proposing a gripper manipulator centered around the 4 P-RRP quadrilateral cone folding mechanism as the core driving mode, with the angled four-bar linkage mechanism serving as the extension arm. Utilizing screw theory, the developability of the 4 P-RRP four-pyramid mechanism with one degree of freedom is verified, forming the basis for its adoption as the central driving mechanism. Furthermore, an in-depth analysis of the geometric properties of the robot’s extension arm leads to the derivation of an optimal folding ratio structure, which is seamlessly integrated into the robot design. Kinematic equations for the mechanism are formulated and solved using the D-H parameter method to obtain the kinematic solution. The investigation demonstrates that the manipulator utilizing the quadrilateral cone folding mechanism significantly enhances adaptability to irregular shapes. By incorporating this proposed mechanism, robots can efficiently manipulate flexible materials across various garment manufacturing applications.

CFD Analysis of Laminar Mixing Mechanism and Performance in an Oscillatory Baffled Reactor

AbstractThe mixing mechanism in an oscillatory baffled reactor (OBR) at the low oscillatory Reynolds number was analyzed using numerical simulation. OBR is a tubular reactor in which baffles are placed at equal intervals, and the interaction between the baffles and oscillatory flow mixes the fluid. At the low oscillatory Reynolds number, mixing induced by the folding and stretching of the fluid was observed at each baffle section. This was caused by the radial flow in the vicinity of the baffles when the direction of the oscillatory flow was reversed. The mixing performance was quantified by applying virtual particle tracing, setting up a virtual boundary surface, and following the changes in its area over time. The area increased exponentially, confirming the chaotic mixing characteristics.

Seasonality regulates the distinct assembly patterns of microeukaryotic plankton communities in the Three Gorges Reservoir, China.

The hydrographic and environmental factors along the Three Gorges Reservoir (TGR) have been significantly altered since the Three Gorges Dam (TGD) began working in 2006. Here, we collected 54 water samples, and then measured the environmental factors, followed by sequencing of the 18S rRNA gene and subsequent analysis of community assembly mechanisms. The findings indicated that the majority of environmental variables (such as AN, TP, Chl-a, CODMn, and Cu) exhibited both temporal and spatial variations due to the influences of the TGD. The distribution of different environmental factors and microeukaryotic plankton communities is influenced by the changing seasons. The community structure in TGR showed variations across three seasons, possibly due to variations in their environmental preferences, inherent dissimilarities, and seasonal succession. Furthermore, different communities exhibited a comparable distance-decay trend, suggesting that distinct taxa are likely to exhibit a similar spatial distribution. In addition, the community formation in TGR was influenced by both deterministic and stochastic factors, with the balance between them being mainly controlled by the season. Specifically, deterministic processes could explain 33.9-51.1% of community variations, while stochastic processes could contribute 23.5-32.2%. The findings of this research demonstrated that the varying ecological processes' significance relied on environmental gradients, geographical scale, and ecological conditions. This could offer a fresh outlook on comprehending the composition, assembly mechanisms, and distribution patterns of microeukaryotic plankton in reservoir ecosystems.

Expanded beads of polyethylene moldable at low steam pressure

In this study, we explored an innovative approach to extrude expanded polyethylene (ePE) bead foam by utilizing a blend of two types of polyethylene, specifically low-density and linear low-density polyethylene. The intention behind this mixture was to enhance the molding process’s flexibility during steam chest molding. The outcomes of our investigation reveal the adaptability of the devised method and formulation, enabling the production of a broad spectrum of foam densities ranging from 20 to 100 kg/m³. Additionally, we achieved successful molding at remarkably low steam pressures, below 0.6 bar. Our research focuses on the strategic adjustment of processing parameters, including temperature and pressure, along with optimized die geometry for the extrusion foam process. To comprehensively evaluate the foamed molded components, we conducted a comprehensive mechanical and optical analysis. The findings underscore the flexibility and cost-effectiveness of ePE produced through foam extrusion. This method emerges as a promising alternative to expandable polystyrene, especially in applications requiring flexible materials and convenient recyclability without contributing to microplastic pollution in the environment.

Evaluating CO 2 retention risk of geological sequestration sites: physical, time-scale, and site style considerations

With some exceptions, such as fluid phase, pressure evolution, and reservoir geometry, evaluation of CO 2 retention for geological sequestration sites primarily involves well-established seal and trap concepts and methods developed by the petroleum industry. Inputs to a CO 2 retention evaluation (e.g. bed seal capacity analysis) include CO 2 phase, CO 2 and brine density, reservoir pressure and temperature, rock properties, and stress state. The inputs are used to perform capillary and mechanical seal analyses similar to the petroleum industry, but unlike hydrocarbons, CO 2 retention also occurs within the reservoir pore volume by capillary and solubility trapping, adding additional requirement for analyses using reservoir engineering concepts and methodology. Reservoir geometry types for CO 2 storage include conventional traps (depleted hydrocarbon fields, brine-filled traps) and brine-filled reservoirs that lack a conventional trap geometry (e.g. a monocline). Each geometry style and project phase (injection, near-term post-injection, long-term static) requires different retention considerations, for example the CO 2 plume extent and height. Based on a retention evaluation, retention risk and uncertainty may be articulated using a qualitative risk matrix that facilitates early screening, identification of data needs, possible mitigating strategies, and comparisons across a portfolio of potential sites. Thematic collection: This article is part of the Geoscience workflows for CO 2 storage collection available at: https://www.lyellcollection.org/topic/collections/geoscience-workflows-for-CO2-storage

The tardigrade-derived mitochondrial abundant heat soluble protein improves adipose-derived stem cell survival against representative stressors

Human adipose-derived stem cell (ASC) grafts have emerged as a powerful tool in regenerative medicine. However, ASC therapeutic potential is hindered by stressors throughout their use. Here we demonstrate the transgenic expression of the tardigrade-derived mitochondrial abundant heat soluble (MAHS) protein for improved ASC resistance to metabolic, mitochondrial, and injection shear stress. In vitro, MAHS-expressing ASCs demonstrate up to 61% increased cell survival following 72 h of incubation in phosphate buffered saline containing 20% media. Following up to 3.5% DMSO exposure for up to 72 h, a 14–49% increase in MAHS-expressing ASC survival was observed. Further, MAHS expression in ASCs is associated with up to 39% improved cell viability following injection through clinically relevant 27-, 32-, and 34-gauge needles. Our results reveal that MAHS expression in ASCs supports survival in response to a variety of common stressors associated with regenerative therapies, thereby motivating further investigation into MAHS as an agent for stem cell stress resistance. However, differentiation capacity in MAHS-expressing ASCs appears to be skewed in favor of osteogenesis over adipogenesis. Specifically, activity of the early bone formation marker alkaline phosphatase is increased by 74% in MAHS-expressing ASCs following 14 days in osteogenic media. Conversely, positive area of the neutral lipid droplet marker BODIPY is decreased by up to 10% in MAHS-transgenic ASCs following 14 days in adipogenic media. Interestingly, media supplementation with up to 40 mM glucose is sufficient to restore adipogenic differentiation within 14 days, prompting further analysis of mechanisms underlying interference between MAHS and differentiation processes.

The influence of carbon emission trading on the optimization of regional energy structure

The optimization of the energy structure is a crucial element in the development of green and low-carbon economies and societies. Market-based environmental regulations, such as carbon emissions trading, play a significant role in facilitating this transition. This study analyzes panel data from 30 provinces in China from 2007 to 2020. It empirically examines the impact and mechanism of carbon emissions trading pilot schemes on the transformation of energy consumption structure at the macro level, using the difference-in-differences method. The research findings indicate that provinces with carbon emissions trading pilots have experienced significant reductions in carbon emissions and carbon intensity, demonstrating clear emission reduction effects. Additionally, the transformation process of energy consumption structure has been notably accelerated, highlighting the correlation between initial carbon emissions trading and the regional energy utilization framework. Furthermore, in our extended mechanism analysis, we introduce two new variables - green finance and environmental regulation. Our analysis reveals that the level of green finance in carbon emissions trading pilot provinces significantly influences the degree of carbon emissions in these areas. Additionally, environmental regulation has a significant positive impact on the optimization of energy consumption structure.

The impact of long-term care insurance on family care for older adults: The mediating role of intergenerational financial support.

Rapid population aging has been placing heavy tolls on Chinese family caregivers. Previous empirical evidence from multiple countries have shown that establishing national long-term care insurance was effective in reducing family care burdens. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) wave 2011 to 2018, this study examined the effects of implementing the pilot long-term care insurance program on family care received by the Chinese older adults, by using a time-varying Difference-in-Differences (DID) method. The results showed that: (1) the implementation of the pilot long-term care insurance program has led to a 17.2% decline in general for family care received by the Chinese older adults. (2) The effect of participating in the pilot program on family care received differed by respondent's household registration, health status, marital status, and possesion of retirement pension, and were specifically pronounced among those who were urban residents, having spouse, living with disabilities, and living with no retirement pension. (3) Further results from the mechanism analyses showed that the pilot long-term care insurance program decreased the level of family care by reducing the dual intergenerational financial support between older adults and their adult children. (4) Although participating in the pilot program decreased older adult's dependence on their adult children, their physical and mental health status were not negatively affected. This study contributes to the existing literature by evaluating the effects of implementing the pilot long-term care insurance program on family care received by the Chinese older adults, and lends supports to the previous studies that participating in long-term care insurance significantly reduces old adults' demand for family care, but not in sacrifice of their physical and mental well-being.

Human capital upgrading and enterprise innovation efficiency

This research utilizes data from listed enterprises on the Shanghai and Shenzhen stock exchanges in China from 2011 to 2022 to explore how upgrading human capital affects the innovation efficiency of enterprises and investigates related heterogeneity. Through regression test, it investigates the effect of upgrading human capital on innovation efficiency and its mechanisms. The results show a positive relationship between human capital upgrading and enterprise innovation efficiency. Mechanism analysis suggests that human capital upgrading promotes enterprise digital transformation and risk-taking levels to enhance innovation efficiency. Furthermore, the impact of human capital upgrading on innovation efficiency shows significant heterogeneity across different industries and scales, providing valuable insights into effective human capital management within enterprises and enhancing their innovation efficiency.

Development of a Digital Model for Predicting the Variation in Bearing Preload and Dynamic Characteristics of a Milling Spindle under Thermal Effects

The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload through sensory instruments. Therefore, this study aimed to establish a digital thermal–mechanical model to investigate the thermal-induced effects on the spindle tool system. The technologies involved include the following: Run-in experiments of the milling spindle at different speeds, the establishment of the thermal–mechanical model, identification of the thermal parameters, and prediction of the thermal-induced preload of bearings in the spindle. The speed-dependent thermal parameters were identified from thermal analysis through comparisons with transient temperature history, which were further used to model the thermal effects on the bearing preload and dynamic compliance of the milling spindle under different operating speeds. Current results of thermal–mechanical analysis also indicate that the internal temperature of the bearing can reach 40 °C, and the thermal elongation of the spindle tool is about 27 µm. At the steady state temperature of 15,000 rpm, the bearing preload is reduced by 40%, which yields a decrease in the bearing rigidity by approximately 16%. This, in turn, increases the dynamic compliance of the spindle tool by 22%. Comparisons of the experimental measurements and modeling data show that the variation in bearing preload substantially affects the modal frequency and stiffness of the spindle. These findings demonstrated that the proposed digital spindle model accurately mirrors real spindle characteristics, offering a foundation for monitoring performance changes and refining design, especially in bearing configuration and cooling systems.

Artificial intelligence based smoke flow mechanism analysis and prediction patterns of fire for large space building

The deposition and dynamics of smoke layers in large-space building fires are governed by a complex interplay of factors, making the prediction of such dynamics using traditional mathematical models challenging. In response, this study introduces a Back Propagation (BP) neural network model, devised from field simulation data, to efficiently forecast the temporal progression of smoke layers. It was observed that the model exhibits minimal training errors and high processing speeds, thereby fulfilling the stringent accuracy demands of fire engineering. Specifically, the model achieved a minimum relative error of 0.0005 and a maximum of 0.0845 across various prediction points, underscoring its reliability and precision. The ability of this BP neural network model to predict smoke layer changes significantly enhances the design optimization of smoke control systems swiftly and accurately in large buildings and supports rapid, informed decision-making during fire emergencies. Moreover, the model facilitates the development of engineering calculation models tailored for the quick prediction of fire smoke dynamics, which are essential for both theoretical research and practical applications. This approach not only conserves experimental resources but also advances the implementation of scientific, effective rescue operations in the event of large space building fires.

Influence of reactive diluent composition on properties and bio-based content of itaconic acid-based additive manufacturing materials

Among the additive manufacturing techniques, UV-curing processes are of special interest, as they allow for the fabrication of thermosetting materials with high resolutions and optical clarity. Traditionally, these processes require the use of formulations, consisting of polyester or polyurethane oligomers, combined with photoinitiators and reactive diluents. The latter are usually vinyl monomers such as acrylates or acrylamides, used to reduce the viscosity of the formulation and render it processable by additive manufacturing machines. In this work, we investigate whether a combination of diluents can be used to tune the thermomechanical properties of the printed materials, and if it can be exploited to increase their overall bio-based content without compromising their performance. To do so, a series of itaconic acid-based polyesters with different chemical structures (aliphatic–aromatic) were synthesized and formulated with reactive diluents acryloyl morpholine (ACMO) and isobornyl acrylate (IBOA). The physicochemical properties of the prepared formulations, together with their reactivity towards UV-light were assessed via photo differential scanning calorimetry (photo-DSC) and photo-rheology measurements. The same formulations were then used to fabricate test specimen via digital light processing (DLP) 3D printing, which were characterized on their thermomechanical properties by means of dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) measurements. The glass transition temperature of the printed samples reached a maximum of 98 °C, while the diluent ratio could be used to manipulate the Tg in a linear trend. All materials of this study exhibited Td,5% above 250 °C, suggesting good thermal stability. These results show that materials with a very high bio-based content (up to 85%) and very promising thermomechanical properties could be obtained by employing a diluent mixture during the formulation phase.

Fe-Phenanthroline derived FeNx coordinated chitosan effectively activates peroxymonosulfate to degrade tetracycline through both radical and non-radical pathways

Fe-Nx sites can activate peroxymonosulfate (PMS) in advanced oxidation processes (AOPs) to produce various reactive oxygen species, and have great potential for removing tetracycline (TC), but their formation and catalytic mechanism are rarely studied. Herein, the Fe/N-C catalyst was prepared by crosslinking chitosan (CS) using a coordination complex of Fe2+ and phenanthroline as the precursor for FeNx. The results of crystal structure, lattice constant, chemical state of N element and structural analysis have demonstrated the successful construction of FeNx sites. The degradation experiments confirmed that the removal efficiency of tetracycline reached up to 90.7 % within 15 min, with a degradation rate of up to 0.184 min−1. Moreover, the material has exhibited excellent performance in complex water environments and within a broad pH range (pH = 2.30 ∼ 9.88). Mechanism analysis proves that the active substances in the system are 1O2, •O2−, SO4•−, •OH and FeIV = O, of which 1O2 played the principal role. The FeNx structure is the primary active site, where the redox cycle between Fe2+ and Fe3+, electron transport between pyridinic N, electron-deficient C and PMS produces active components. In addition, based on the various intermediate products detected, two possible TC degradation pathways are reasonably speculated. In this study, the highly active FeNx sites were successfully constructed, and elucidated their activation mechanism towards PMS, which proved the broad prospect of Fe/N-C materials in pollutant degradation.

Encapsulation strategies for mechanical impact and damp heat reliability improvement of lightweight photovoltaic modules towards vehicle-integrated applications

Lightweight modules are essential for next-generation vehicle-integrated photovoltaic (VIPV) applications, such as solar-powered cars, allowing integration of solar cells beyond the roof, and on the hood, boot and body panels, and thereby extending the driving range. However, the lightweight module's reliability and corresponding degradation mechanisms under various environmental stresses are less researched. In this work, we investigate interconnection and encapsulation strategies to improve reliability against damp heat and mechanical impact. We fabricated lightweight mini modules, weighing around 3.45 kg/m2, and conducted hail impact and damp heat tests. These tests result in different failures, such as cracks in the solar cell, module delamination, and microcracks in the backsheet. By carrying out failure mechanism analysis and altering the fiber reinforcement in backsheet and encapsulation materials, we can increase resilience to these failure modes, thus providing guidance for the design of lightweight PV modules for next-generation VIPV.

Does environmental public interest litigation improve the urban land green use efficiency?-Evidence from a quasi-natural experiment in China.

Environmental public interest litigation (EPIL) is a significant part of the judicial system; it is aimed at strengthening judicial protections and safeguarding public interests. Based on the quasi-natural experimental setting of China's EPIL pilot project, this study examines the impact of EPIL on the country's urban land green use efficiency (ULGUE). The findings show that effectively implementing EPIL enhances ULGUE. Specifically, this policy has led to a 6.6% increase in ULGUE in pilot cities, and its impact has grown stronger over time. Mechanism analysis results show that EPIL mainly enhances ULGUE by strengthening environmental supervision and law enforcement, by increasing public participation in environmental governance, and by promoting green innovation and industrial structure upgrades. Furthermore, heterogeneity analysis revealed that the positive effects of this policy implementation are more pronounced in resource-based cities, cities with open environmental information, and cities with high marketization. This paper provides empirical evidence for the effectiveness of environmental governance via EPIL.

Investigation of Thermomechanical and Dielectric Properties of PLA-CA 3D-Printed Biobased Materials

Renewable dielectric materials have attracted the attention of industries and stakeholders, but such materials possess limited properties. This research focused on studying polylactic acid (PLA)/cellulose acetate (CA) blends produced by 3D printing to facilitate their integration into the electrical insulation field. The dielectric findings showed that a blend containing 40% of CA by weight had a dielectric constant of 2.9 and an electrical conductivity of 1.26 × 10−11 S·cm−1 at 100 Hz and 20 °C while exhibiting better mechanical rigidity in the rubbery state than neat PLA. In addition, it was possible to increase the electrical insulating effect by reducing the infill ratio at the cost of reduced mechanical properties. The differential scanning calorimetry, broadband dielectric spectroscopy, and dynamic mechanical analysis results showed that the PLA plasticizer reduced the energy required for PLA relaxations. These preliminary results demonstrated the benefits of using a combination of PLA, CA, and 3D printing for electrical insulation applications.

Prediction of liquid holdup and pressure drop in gas-liquid two-phase flow based on integrating mechanism analysis and data mining

Gas-liquid two-phase flow widely exists in gas transportation pipelines, and the prediction of liquid holdup and pressure drop is crucial for the safety and efficiency of pipelines. Due to the complexity of multiphase flow, the mechanism model is difficult to accurately and quickly predict the liquid holdup and pressure drop. Machine learning (ML) models have high accuracy, but the lack of physical meaning limits their application. In order to fully leverage the advantages of mechanism models and machine learning models, a new method using physically guided neural networks (PGNN) to predict pressure drop and liquid holdup is proposed. Based on the flow mechanism analysis of different flow patterns, the structure of PGNN is designed according to the calculation process of the mechanism model. Key physical intermediate variables such as shear stress and friction coefficient are added to the model as neurons. Physical constraints in gas-liquid two-phase flow are added to the loss function to give physical significance to neurons. 1390 publicly available experimental data are collected for model training and testing. Due to the uneven distribution of experimental data, the local outlier factor (LOF) algorithm is used to screen outlier of data. By comparing PGNN with other ML models, empirical models, and semi empirical models, it is proved that integrating mechanism into the ML model improves the accuracy and physical consistency. This research presents a fresh outlook on the investigation of the flow dynamics in gas-liquid two-phase systems.

Medical insurance benefits and health inequality: evidence from rural China.

Alleviating health inequality among different income groups has become a significant policy goal in China to promote common prosperity. Based on the data from the China Health and Retirement Longitudinal Study (CHARLS) covering the period from 2013 to 2018, this study empirically examines the impact of Integrated Medical Insurance System (URRBMI) on the health and health inequality of older adult rural residents. The following conclusions are drawn: First, URRBMI have elevated the level of medical security, reduced the frailty index of rural residents, and improved the health status of rural residents. Second, China exhibits "pro-rich" health inequality, and URRBMI exacerbates health inequality among rural residents with different incomes. This result remains robust when replacing the frailty index with different health modules. Third, the analysis of influencing mechanisms indicates that the URRBMI exacerbate inequality in the utilization of medical services among rural residents, resulting in a phenomenon of "subsidizing the rich by the poor" and intensifying health inequality. Fourth, in terms of heterogeneity, URRBMI have significantly widened health inequality among the older adult and in regions with a higher proportion of multiple-tiered medical insurance schemes. Finally, it is suggested that China consider establishing a medical financing and benefit assurance system that is related to income and age and separately construct a unified public medical insurance system for the older adult population.

Enhancing clayey soil performance with lime and waste rubber tyre powder: Mechanical, microstructural, and statistical analysis

Nowadays, for soil stabilisation and cleaner production of geo-composites, the possibility of utilizing waste rubber is in vogue. The present paper deals with experimentally investigating the mechanical and micro-structural characteristics of weak Indian clayey soil partially substituted with lime (0–3.5%) and waste rubber tyre powder (0–15%). It was observed that, with increasing lime and rubber powder content, the plasticity index of the soil decreases. The shear strength and compaction testing results reveal that adding lime and rubber tyre powder (RTP) enhances the geotechnical performance of clayey soil up to an optimum dosage value. Also, the tri-axial shear testing was performed to obtain stress-strain curves for all considered soil mixes. For modified clayey soil containing 3% lime and 12.5% rubber powder, the cohesion values and bearing capacities improved phenomenally by 36.1% and 88.6% respectively, when compared to clayey soil. Further for this mix, SEM analysis reveals a compacted microstructure which improves dry-density and California's bearing ratio among all modified mixes. The novel co-relations upon regression analysis are found able to predict plasticity index, dry density, bearing capacity and shear strength with higher confidence levels. Overall, the cost-benefit analysis worked out to obtain the optimum cost of construction of footings and flexible pavement shows cost deductions up to 19% and 39% respectively while utilizing modified clay soil mixes containing 3% lime and 12.5% rubber powder in subgrade, ultimately making production stronger, cheaper and environment friendly.