Search Results (29)

The performance of SBS (Styrene Butadiene Styrene) modified asphalt mixtures can be enhanced through the addition of fibers including basalt, ceramic, and glass. This study investigates whether a reduced SBS content of 3%, combined with 0.3% fiber reinforcement can match or exceed the performance of a traditional 7% SBS mixture. A comparative analysis was carried out by examining both performance efficiency and life cycle costs across ceramic, basalt, and glass fiber-reinforced mixtures. Maintenance requirements for each scenario were factored into the life cycle analysis. To assess structural integrity, 3D finite element simulations were conducted using the Burger’s logit model while focusing on fatigue and rutting damage. Findings indicate that basalt and ceramic fiber mixtures deliver better asphalt mixtures, thereby outperforming the 7% SBS mix by requiring fewer maintenance interventions. However, due to the higher cost of ceramic fiber mixtures at 831 Eur/m³, basalt fiber emerges as the more cost-effective option, achieving a performance efficiency gain of 20% with reduced costs at 532 Eur/m³. Among the fiber-reinforced variants, glass fiber showed the least improvement in performance, with a difference in 11% and 13% when compared to ceramic fiber and basal fiber, respectively. Full article

The growing need for healthy and sustainable food alternatives has led to a rapid increase in vegan burgers on the market. Specifically, plant-based burgers using legumes as a protein substitute are amongst the most widespread choices for consumers. While these products can offer environmental benefits over traditional meat-based options, further optimization in both ecological and economic aspects can be achieved. This study conducted a life cycle assessment (LCA) and life cycle costing (LCC) analysis to evaluate and optimize the environmental and economic life cycle of a legume-based vegan burger. LCA was performed in accordance with the recommendations of the ISO 14040 and 14044 series, and ReCiPe 2016 Hierarchist served as the impact assessment methodology. For this purpose, a base case scenario, relying on imported raw materials and conventional packaging for a legume-based vegan burger, was established to serve as the comparison benchmark, and various alternative scenarios were examined, focusing on minimizing the distance between cultivation and processing areas for key legume ingredients and improving packaging materials. The results indicate that reducing transportation distances for raw ingredients and using bio-polyethylene packaging significantly enhance sustainability. Specifically, the legume-based vegan burger of the base case scenario had a carbon footprint of 1.30 kg CO₂ eq. and a total life cycle cost of EUR 2.43 per two pieces. In contrast, the optimized scenario, which incorporated shorter transportation distances and bio-polyethylene packaging, achieved a carbon footprint of 0.51 kg CO₂ eq. and a reduced cost of EUR 2.37. The findings of the present work highlight the potential for further environmental and economic improvements in vegan burger production through logistics optimization and selection of climate-friendly packaging solutions, thus contributing to sustainable development. Full article

In this work, we introduce an innovative analytical–numerical approach to solving nonlinear fractional differential equations by integrating the homotopy perturbation method with the new integral transform. The Kawahara equation and its modified form, which is significant in fluid dynamics and wave propagation, serve as test cases for the proposed methodology. Additionally, we apply the fractional new integral transform–homotopy perturbation method (FNIT-HPM) to a nonlinear system of coupled Burgers’ equations, further demonstrating its versatility. All calculations and simulations are performed using Mathematica 12 software, ensuring precision and efficiency in computations. The FNIT-HPM framework effectively transforms complex fractional differential equations into more manageable forms, enabling rapid convergence and high accuracy without linearization or discretization. By evaluating multiple case studies, we demonstrate the efficiency and adaptability of this approach in handling nonlinear systems. The results highlight the superior accuracy of the FNIT-HPM compared to traditional methods, making it a powerful tool for addressing complex mathematical models in engineering and physics. Full article

As a novel pavement wear layer material, the micromechanical mechanisms of High-toughness Ultra-thin Friction Course (HUFC) have not been fully elucidated. This paper presents a new method for the three-dimensional micromechanical simulation of high-toughness asphalt mixtures based on a viscoelastic parameter calibration model. X-ray Computerized Tomography (CT) was employed to scan samples of high-toughness asphalt mixtures to obtain detailed information on the internal structure (aggregate, fine aggregate matrix FAM and voids), and a three-dimensional micromechanical model was constructed based on the real-scale distribution of these components. Aggregates in the high-toughness asphalt mixture were modeled as elastic bodies, while FAM was treated as a viscoelastic material characterized by the Burgers model. Using the Boltzmann linear superposition principle and Laplace transform theory, the viscoelastic properties of FAM were converted into Prony parameters recognizable by finite element software, and the viscoelastic parameters were calibrated. Micromechanical simulations were conducted for three different gradings of high-toughness asphalt mixtures, and the results show that the predicted deformation closely matched the measured deformation. This method accurately reflects the deformation characteristics of different gradings of high-toughness asphalt mixtures, overcoming the limitations of traditional numerical simulations based on homogeneous material models. It represents an advancement and refinement of micromechanical simulation methods for high-toughness asphalt mixtures. Full article

Calcareous sand has been widely used as a construction material for offshore projects; however, the problem of foundation settlement caused by particle crushing cannot be ignored. Although many methods for reinforcing calcareous sands have been proposed, they are difficult to apply on-site. In this study, a permeable polyurethane polymer adhesive (PPA) was used to reinforce calcareous sands, and its mechanical properties after reinforcement were investigated through compression creep, direct shear, and triaxial shear tests. The reinforcement mechanism was analyzed using optical microscopy, CT tomography, and mercury intrusion porosimetry. The experimental results indicate that there is a critical time during the compression creep process. Once the critical time is surpassed, creep accelerates again, causing failure of the traditional Burgers and Murayama models. The direct shear strength of the fiber- and geogrid-reinforced calcareous sand reinforced by PPA was approximately nine times greater than that without PPA. The influence of normal stress was not significant when the moisture content was less than 10%, but when the moisture content was more than 10%, the shear strength increased with an increase in vertical normal stress. Strain-softening features can be observed in triaxial shear tests under conditions of low confining pressure, and the relationship between the deviatoric stress and strain can be described using the Duncan–Chang model before softening occurs. The moisture content also has a significant influence on the peak strength and cohesive force but has little influence on the internal friction angle and Poisson’s ratio. This influence is caused by the different PPA structures among the particles. The higher the moisture content, the greater the number of pores left after grouting PPA. Full article

At present, some ingredients called “novel foods”, such as seaweed, are being incorporated into meat products. Therefore, this study aimed to evaluate the use of Durvillaea antarctica meal as an extender of traditional beef burgers and its effect on quality, fatty-acid profile, and general acceptability. Prototypes including 0.5, 1.0, 1.5, and 3.0% Durvillaea antarctica meal were developed and measured for color, pH, water-holding capacity, fatty acids, and cholesterol profile. A trained sensory panel evaluated the organoleptic properties. The results show that as the amount of Durvillaea antarctica meal increases, the pH decreases less sharply compared to the control, while the water-holding capacity was similar to, but not better than, the control when including 3.0% of seaweed. On the other hand, the redness significantly decreased, affecting the sensory attributes of the product. The lipid profile was partially altered by the inclusion of the meal; it was observed that the percentage of saturated fats was reduced, and the levels of some omega3 fatty acids increased. Beef burgers made with 0.5% Durvillaea antarctica meal showed better acceptability and flavor. The use of seaweed, such as Durvillaea antarctica, could be a new alternative for the transformation of traditional meat products into new-generation foods. The evaluation of the functional and microbiological properties of the meat matrix, as well as nutraceutical properties and cost effectiveness, will be addressed in a future study. Full article

The car-following model (CFM) utilizes intelligent transportation systems to gather comprehensive vehicle travel information, enabling an accurate description of vehicle driving behavior. This offers valuable insights for designing autonomous vehicles and making control decisions. A novel extended CFM (ECFM) is proposed to accurately characterize the micro car-following behavior in traffic flow, expanding the stable region and improving anti-interference capabilities. Linear stability analysis of the ECFM using perturbation methods is conducted to determine its stable conditions. The reductive perturbation method is used to comprehensively describe the nonlinear characteristics of traffic flow by solving the triangular shock wave solution, described by the Burgers equation, in the stable region, the solitary wave solution, described by the Korteweg–de Vries (KdV) equation, in the metastable region, and the kink–antikink wave solution, described by the modified Korteweg–de Vries (mKdV) equation, in the unstable region. These solutions depict different traffic density waves. Theoretical analysis of linear stability and numerical simulation indicate that considering both the lateral gap and the optimal velocity of the preceding vehicle, rather than only the lateral gap as in the traditional CFM, expands the stable region of traffic flow, enhances the anti-interference capability, and accelerates the dissipation speed of disturbances. By improving traffic flow stability and reducing interference, the ECFM can decrease traffic congestion and idle time, leading to lower fuel consumption and greenhouse gas emissions. Furthermore, the use of intelligent transportation systems to optimize traffic control decisions supports a more efficient urban traffic management, contributing to sustainable urban development. Full article

In deep high-geostress and high-temperature environments, understanding the creep deformation of deep coal is of great significance for effectively controlling coal deformation and improving gas control efficiency. In this paper, the Abel dashpot is defined based on the conformable derivative, and a damage variable is introduced into the conformable derivative order, thereby constructing a damaged Abel dashpot. Combining the Weibull distribution and the Drucker–Prager yield criterion, the thermo-mechanical coupling damage variable is defined, and the coupling damage variable is introduced into the damaged Abel dashpot to establish a thermo-mechanical coupling damaged Abel dashpot. Based on the traditional framework of the Burgers creep model, a three-dimensional fractional creep model of deep coal considering the influence of thermo-mechanical coupling damage is proposed. Experimental data on coal creep under different temperatures and stress conditions are utilized to validate the effectiveness and applicability of the proposed three-dimensional fractional creep model and to determine the model parameters. A comparison between experimental data and model results reveals that the creep model effectively characterizes the time-dependent deformation of coal samples under varying temperature and stress influences. Additionally, an in-depth analysis is carried out on the influence mechanism of key parameters in the creep model, particularly focusing on the effects of stress levels and temperature on creep deformation. Full article

Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers’ equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm’s efficacy. Full article

As the world attempts to decarbonise the food industry and limit greenhouse gas (GHG) emissions, plant-based meat analogues (PBMAs) have emerged as a sustainable alternative to traditional meat. The objective of this study is to assess the environmental impacts of PBMAs compared to traditional beef burgers, aiming to address the research gap in the life cycle assessments (LCAs) of publicly available PBMA recipes. Utilising a cradle-to-fork system boundary, this research conducted a rigorous LCA on a 100 g plant-based burger patty and its beef burger (BB) counterpart, each produced in the UK but sourced from different global locations. The results demonstrated that the plant-based burger had significantly lower environmental impacts across several categories, including a 65% reduction in global warming potential and a 45% reduction in water consumption. A simple extrapolation illustrated that if the UK population switched from beef to meat analogue patties, 3 million tonnes of CO₂e could be saved annually, corresponding to 0.74% of the country’s yearly territorial GHG emissions. Scenario analyses displayed how the environmental impact of the MA patty remained stable regardless of changes in exportation, ingredient origin or soy protein sourcing. Moreover, a sensitivity analysis conducted with an alternative characterisation method corroborated the initial findings, whilst uncertainty analysis ensured that nearly all of the conclusions generated from the original comparison were robust. Future studies should conduct LCAs on PBMA patties with commercial recipes using varied plant-based sources, as well as fully understanding any potential health implications of long-term PBMA consumption. Full article

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional, and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogue market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations. Full article

To investigate the vortical wake pattern generated by water flow past an oscillating symmetric airfoil, using experimental velocity fields from particle image velocimetry (PIV), a novel combinatorial vortex detection (CVD) algorithm is developed. The primary goal is to identify and characterize vortices within the wake. Experimental flows introduce complexities not present in numerical simulations, posing challenges for vortex detection. The proposed CVD approach offers a more robust alternative, excelling in both vortex detection and quantification of essential parameters, unlike widely-used methods such as Q-criterion, λ₂-criterion, and Δ-criterion, which rely on subjective and arbitrary thresholds resulting in uncertainty. The CVD algorithm effectively characterizes the airfoil wake, identifying and analyzing vortices aligning with the Burgers model. This research enhances understanding of wake phenomena and showcases the algorithm’s potential as a valuable tool for vortex detection and characterization, particularly for experimental fluid dynamics. It provides a comprehensive, robust, and non-arbitrary approach, overcoming limitations of traditional methods and opening new avenues for studying complex flows. Full article

In the fields of physics and engineering, it is crucial to understand phase transition dynamics. This field involves fundamental partial differential equations (PDEs) such as the Allen–Cahn, Burgers, and two-dimensional (2D) wave equations. In alloys, the evolution of the phase transition interface is described by the Allen–Cahn equation. Vibrational and wave phenomena during phase transitions are modeled using the Burgers and 2D wave equations. The combination of these equations gives comprehensive information about the dynamic behavior during a phase transition. Numerical modeling methods such as finite difference method (FDM), finite volume method (FVM) and finite element method (FEM) are often applied to solve phase transition problems that involve many partial differential equations (PDEs). However, physical problems can lead to computational complexity, increasing computational costs dramatically. Physics-informed neural networks (PINNs), as new neural network algorithms, can integrate physical law constraints with neural network algorithms to solve partial differential equations (PDEs), providing a new way to solve PDEs in addition to the traditional numerical modeling methods. In this paper, a hard-constraint wide-body PINN (HWPINN) model based on PINN is proposed. This model improves the effectiveness of the approximation by adding a wide-body structure to the approximation neural network part of the PINN architecture. A hard constraint is used in the physically driven part instead of the traditional practice of PINN constituting a residual network with boundary or initial conditions. The high accuracy of HWPINN for solving PDEs is verified through numerical experiments. One-dimensional (1D) Allen–Cahn, one-dimensional Burgers, and two-dimensional wave equation cases are set up for numerical experiments. The properties of the HWPINN model are inferred from the experimental data. The solution predicted by the model is compared with the FDM solution for evaluating the experimental error in the numerical experiments. HWPINN shows great potential for solving the PDE forward problem and provides a new approach for solving PDEs. Full article

Plant-based meat alternatives, exemplified by Impossible Foods’ Impossible Burger, offer a sustainable, ethical substitute for traditional meat, closely mimicking the taste and appearance of meat by utilizing soy leghemoglobin (LegH), a 16 kDa holoprotein found in soy plants structurally similar to heme in animal meat. Cultivation medium plays an important role in bioprocess development; however, medium development or optimization can be labor intensive, and thus the use of previously reported media can be enticing. In this study, we explored the expression of recombinant LegH in Pichia pastoris in various reported cultivation media (BSM, BMGY, FM22, D’Anjou, BSM/2, and RDM) and using different feeding approaches (µ-stat and mixed feed with sorbitol). Our findings indicate that optimization techniques tailored to the specific process did not increase LegH yields, highlighting the need to investigate strain-specific strategies. We also utilized the collected process data to create and train a novel artificial neural network-based soft sensor for estimating cell biomass, relying solely on standard bioreactor measurements (such as stirrer speed, dissolved oxygen, O₂ enrichment, base feed, glycerol feed, methanol feed, and reactor volume). This soft sensor proved to be robust and exhibited a strong correlation (3.72% WCW) with experimental data. Full article

In this article, we discuss the findings of new developments in a class of new triangular functions that blend the quantity functions of the traditional triangular. Considering the significant role played by the triangular functions in applied mathematics, physics, and engineering, it is conceivable to predict that the theory of new triangular functions will provide us with additional interpretations and discoveries in mathematics and physics. The solutions which consider variable separation based on arbitrary functions are constructed to the (3+1)-dimensional Burgers model by presenting the Fibonacci Riccati technique and the linearly independent variable separation approach. This technique’s fundamental concept is to describe the solution of the Burgers model as a polynomial in the Riccati Equation solution that satisfies the symmetrical hyperbolic and triangular Fibonacci functions. Depending on the choice of suitable functions for variable separation, an abundance of new localized solutions were obtained. Moreover, examples such as embedded solitons, rectangle-solitons, plateau-type ring solitons, taper-like solitons, and their interactions with each other, following the symmetrical hyperbolic and triangular Fibonacci functions, as well as the golden mean, could be explored. Full article