The local burning velocity and flame displacement speed are the major properties of premixed flames. The local burning velocity, which is the instantaneous quantity based on the local consumption rate of the unburnt mixture, is considered to be the most appropriate burning velocity in terms of the definition. The local burning velocity can be evaluated theoretically and numerically; however, it is almost impossible to obtain it experimentally using the current technology of measurement. The flame displacement speed can be evaluated more easily than the local burning velocity and compared with the flame displacement speed obtained from experiments. However, the local burning velocity and flame displacement speed have been discussed separately in turbulent premixed flames. In this study, to clarify the relation between the local burning velocity and the flame displacement speed, numerical analyses were performed using the DNS database of statistically steady and fully developed turbulent premixed flames with different density ratios of the unburnt mixture to the burnt product and with different Lewis numbers. It was found that for different density ratios, the local burning velocity was little sensitive to the flame displacement speed in any case under the unity Lewis number. This means the correlation between the local burning velocity and the flame displacement speed is little affected by the dilation of a flame. For different Lewis numbers, the correlation between the local burning velocity and the flame displacement speed was negative in Le = 0.8, and positive in Le = 1.2. This can be explained by the effect of the Lewis number on the local burning velocity, and the flame displacement speed was little affected by the Lewis number in the correlation between the local burning velocity and the flame displacement speed.
Mechanical engineering and machinery, Mechanics of engineering. Applied mechanics
This paper presents a perspective in which Direct Simulation Monte Carlo (DSMC) is viewed not in its traditional role as an algorithm for solving the Boltzmann equation but as a numerical method for statistical mechanics. First, analytical techniques such as the collision virial and Green-Kubo relations, commonly used in molecular dynamics, are used to study the numerical properties of the DSMC algorithm. The stochastic aspect of DSMC, which is often viewed as unwanted numerical noise, is shown to be a useful feature for problems in statistical physics, such as Brownian motion and thermodynamic fluctuations. Finally, it is argued that fundamental results from statistical mechanics can provide guardrails when applying machine learning to DSMC.
The nonmagnetic kagome metal ScV6Sn6 displays an unconventional charge order (CO) accompanied by signatures of an anomalous Hall effect, hidden magnetism, and multiple lattice instabilities. In this study, we report the observation of unconventional anomalous thermoelectric properties. Notably, unexpected anomalous transverse Nernst signals reach a peak value of ~4 μV/K near the TCDW ~92 K in ScV6Sn6, and these signals persist in the charge-ordered state as the temperature decreases to 10 K. Furthermore, both thermopower and thermal conductivity exhibit significant changes under magnetic fields, even in the nonmagnetic ground state. These observations strongly suggest the emergence of time-reversal symmetry breaking in ScV6Sn6, as supported by muon spin relaxation (μSR) measurements. While hidden magnetism represents the most plausible origin, alternative mechanisms involving orbital currents and chiral charge order remain possible.
This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.
Ivan Panfilov, Alexey N. Beskopylny, Besarion Meskhi
Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver’s blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport.
Betty ariani, Frengki Mohammad Felayati, Moh. Arif Batutah
The energy crisis and the threat of global climate change have spurred various research efforts and alternative initiatives to find substitutes for fossil fuels, improve energy efficiency, and reduce emissions, especially greenhouse gases. The shipping industry is one of the contributors to global emissions that has received particular attention due to the increasing demand for maritime transportation services. The use of natural gas is considered a potential solution due to its relatively clean nature, abundant availability, and competitive pricing. The CNG-Diesel Dual Fuel Engine design is developed with the principle of using natural gas as an alternative fuel without replacing the existing diesel engine. Minimal modifications are made to the intake manifold to accommodate CNG entry. Despite its advantages, the development of dual fuel engines faces challenges, such as increased methane emissions due to the potential for incomplete combustion. This research conducts experimental studies on the use of a Venturi-like mixer in the intake manifold to enhance the homogeneity of the CNG-air mixture before entering the combustion chamber. Testing the mixer's influence is carried out under various CNG injection durations at low and high engine loads at constant speeds. The results indicate that the addition of the mixer does not immediately improve combustion quality or reduce emissions. Attention to conditioning the homogenous mixture at the required air-fuel ratio before entering the combustion chamber is crucial. The selection of the appropriate mixer design, diameter size, and placement of holes needs careful consideration
Mechanical engineering and machinery, Mechanics of engineering. Applied mechanics
Julia Karaeva, Svetlana Timofeeva, Marat Gilfanov
et al.
<i>Amaranthus retroflexus</i> or redroot pigweed is a second generation lignocellulosic fuel. Each biomass sample (leaves, inflorescences and stems) was pyrolyzed in a lab-scale furnace, in a nitrogen atmosphere under non-isothermal conditions at heating rates of 10 °C/min until the furnace temperature reached 550 °C. The pyrolysis characteristics of the three major components were also studied through thermogravimetric analysis. The thermal decomposition of the biomass samples is similar to the process of pyrolysis of lignocellulosic materials and proceeds in three main stages: dehydration, devolatilization, and carbonation. The highest bio-oil yield was obtained for inflorescences (55%) and leaves (45%). Gas chromatography—mass spectrometry analysis was carried out for oil fractions of the pyrolysis liquid from <i>Amaranthus retroflexus</i>. The composition of the pyrolysis oil fraction from the leaves had an overbearing aliphatic hydrocarbon nature whereas the oil fraction from inflorescences and stems was composed mainly of oxygen-containing components. The use of <i>Amaranthus retroflexus</i> biochars can lead to slag formation in power equipment, so it is advisable to use them to produce composite fuel, for example, mixed with coal. The results would help to better understand the thermal behavior of <i>Amaranthus retroflexus</i> biomass and its utilization for fuels or chemicals.
Progress in the industry is accompanied by the development of new materials and more efficient technological production processes. At present, additive production is becoming very attractive in all industries (research, development, production), which brings a number of advantages compared to subtractive methods (customization, production speed, control of material properties by users, etc.). The main advantage of 3D printing is the controlled deposition of material in defined places. Instead of demanding manual labour, fully automated production via computers leads to the manufacturing of complex components from materials whose production in conventional ways would be problematic or even impossible. Because these are new technologies, the main direction of research at present is to identify the basic physical properties of these materials under different types of loading. The main goal of this article is to observe the dependence of the behaviour of the extruded material (thermoplastic reinforced with chopped carbon fibre) on the printing parameters (thickness of the lamina, the orientation of the fibres of the printed material, etc.). Based on published scientific works, it appears that these settings have a significant impact on the achieved physical properties. This is the reason why the authors decided to analyze the influence of these parameters on the basis of processed data from experimental measurements of mechanical properties in the MATLAB program. As this is FFF printing, an essential condition is to identify and specify the directional dependence of the behavior of the printed material. This physical phenomenon is a necessary condition for gradual knowledge for the purposes of a subsequent mathematical description of the material properties. According to the authors, for the purposes of modeling these materials in FEM-based programs, it is essential to define the directional dependence in the plane of the lamina.
Mining engineering. Metallurgy, Materials of engineering and construction. Mechanics of materials
Grid-stiffened composite shells are one of the most important structures in many industries. These structures based on their fabrication method, provide both high strength and light structural weight. In this study, buckling analysis under external hydrostatic pressure is performed to obtain critical buckling pressure and the optimum values of parameters for stiffeners. First-order shear deformation theory (FSDT) based on the Ritz method is used to calculate the critical buckling load of these structures. The effects of shell thickness, angle of helical stiffeners, rib section area, and the stiffeners number into the buckling load are determined. Comparing the calculated buckling load for stiffened and non-stiffened structures shows that stiffeners significantly optimize structural performance. Furthermore, optimization of stiffener parameters is done by Genetic Algorithm. The results show that the introduced structure has the minimum mass. So, the stiffener parameters would be better. According to the results, the optimum dimensions for stiffener buckling load for the optimal stiffener have been increased by about 80% compared to non-stiffened.
P. Hariprasath, P. Sivaraj, V. Balasubramanian
et al.
DMR 249A is a micro-alloyed high strength low alloy (HSLA) steel particularly designed for shipbuilding structures due to its excellent strength-to-weight ratio. In this investigation, the rotating-beam fatigue test was performed on the flux-cored arc welded (FCAW) butt joints of DMR 249A steel. And the fatigue test results compared to mechanical properties and microstructural characteristics. The transverse tensile, microhardness, impact, and fatigue properties were evaluated from across the butt weld. The S-N curve was constructed from the experimental data for the stress ratio R = -1. The evaluated fatigue life of the FCAW joint and parent metal was 366 MPa and 394 MPa, respectively. The maximum achieved transverse tensile strength and impact energy absorption of the FCAW joint was 578 MPa &174 J. The weld region contains an acicular ferrite microstructure, which exhibited improved properties of strength, toughness, and fatigue crack resistance. From the results, the fatigue strength of FCAW is 92.8% that of the parent metal and 62.8% equal to the tensile strength of the FCAW joint.
Mechanics of engineering. Applied mechanics, Technology
Abstract Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters, and a mechanical model of a rock tunnel using Markov chain Monte Carlo (MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.
Ice accretion can adversely impact many engineering structures in commercial and residential sectors. Although there are many reports of low-ice-adhesion-strength materials, a scalable and durable deicing solution remains elusive, as ice detachment is dominated by interfacial toughness for large interfaces. In this work, durable metallic coatings based on Al-rich quasicrystalline alloys were prepared and applied on aluminum substrates using high-velocity oxyfuel thermal spray. X-ray diffraction patterns confirmed the quasicrystalline phases of the coating, and its large-scale deicing capability was studied by evaluating the coating's ice detachment mechanics using long lengths of ice. A toughness-controlled regime of interfacial fracture was observed for ice lengths longer than ∼2 cm, and a low shear strength of ∼30 kPa was achieved for a 20 cm ice length. The metallic coatings exhibited excellent ice repellency even after being abraded, scratched, heated, UV-irradiated, and exposed to chemical contaminations, demonstrating promising durability for real-world, large-scale ice removal.
The virtual element method (VEM) for dynamic analyses of nonlinear elasto-plastic problems undergoing large deformations is outlined within this work. VEM has been applied to various problems in engineering, considering elasto-plasticity, multiphysics, damage, elastodynamics, contact- and fracture mechanics. This work focuses on the extension of VEM formulations towards dynamic elasto-plastic applications. Hereby low-order ansatz functions are employed in three dimensions with elements having arbitrary convex or concave polygonal shapes. The formulations presented in this study are based on minimization of potential function for both the static as well as the dynamic behavior. Additionally, to overcome the volumetric locking phenomena due to elastic and plastic incompressibility conditions, a mixed formulation based on a Hu-Washizu functional is adopted. For the implicit time integration scheme, Newmark method is used. To show the model performance, various numerical examples in 3D are presented.
Ester Comellas, Jean-Paul Pelteret, Wolfgang Bangerth
A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is hard to articulate as it relies mostly on the mental catalogues we have built with the experience of past failures. To help newcomers to the field of material modelling, here we attempt to fill this gap and provide a perspective on how to identify and fix mistakes in computational solid mechanics models. To this aim, we describe the components that make up such a model and then identify possible sources of errors. In practice, finding mistakes is often better done by considering the symptoms of what is going wrong. As a consequence, we provide strategies to narrow down where in the model the problem may be, based on observation and a catalogue of frequent causes of observed errors. In a final section, we also discuss how one-time bug-free models can be kept bug-free in view of the fact that computational models are typically under continual development. We hope that this collection of approaches and suggestions serves as a "road map" to find and fix mistakes in computational models, and more importantly, keep the problems solved so that modellers can enjoy the beauty of material modelling and simulation.
Model-Based Systems Engineering aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and workpackage based on the model. Once such a model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema needs to be designed, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modelling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems. The schema and modelling approach are designed to answer questions such as what happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? Or if a condition cannot be met, which alternative routes can be established to reach a certain state of the system. We build on the use case of qualification and operations of a small spacecraft. Structural and behavioral elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by an adapter for MagicDraw to Neo4j. A selection of complex analyses are shown on the example of the MOVE-II space mission.
Abstract A novel analysis-prediction (ANP) approach for geometrically nonlinear problems of solid mechanics is for the first time proposed in this paper. The key concept of this approach is: (1) A part of equilibrium path is traced by numerical analysis; (2) Data of this part is then used for training predictive network; (3) Applying the trained network, the rest of the equilibrium path is simply traced by pure prediction without using any analysis. As an illustration for ANP approach, the analysis package is in this study established based on isogeometric shell analysis using the first-order shear deformation shell theory (FSDT). As the main advantage of the proposed approach, computational cost is significantly lower than that of the conventional approach based on pure numerical analysis. In addition, the predictive networks are built via group method of data handling (GMDH) known as a self-organizing deep learning method for time series forecasting problems without requirement of big data. Some numerical examples are provided to confirm the high accuracy and efficiency of the proposed approach. The approach not only could be applied to a wide range of computational mechanics problems in which nonlinear response occurs but also to other computational engineering fields.