Discrete Approach Research Articles

Adoption intensity of soil and water conservation techniques in Burkina Faso is influenced by farmers’ preferences for their attributes

Soil degradation is a major cause of agricultural productivity decrease in sub-Saharan Africa. In Burkina Faso, efforts to reduce this environmental issue has emerged since several decades. However, most of the techniques developed are rarely adopted by farmers. In addition, those adopted are on small areas. This study aims to analyze the effect of farmers’ preferences for soil and water conservation techniques’ attributes on their intensity of adoption. Cross-sectional data was collected from 335 farmers in four rural municipalities in Burkina Faso, using a discrete choice experiment approach. The standard Tobit model was used for data analysis. Results showed that soil and water conservation techniques’ attributes as runoff reduction, soil fertilization and soil restoration explained farmers’ decision to adopt and intensify the adoption of these techniques. In addition, location in the northern sudanian agroecological zone, access to extension services and education influenced positively soil and water conservation techniques’ adoption intensity. However, equipment, labor and cash constraints decrease these techniques’ adoption intensity. These results suggest that incentives for the adoption and intensification policies should be oriented toward farmers’ capacities to produce quantity and quality organic fertilizers reinforcement, build an industrial unit to transform household waste into organic manure for agricultural use.

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care. The objective of this study is to provide an alternative method for distinguishing between radiologists by means of captured eye-tracking data such that the raw gaze (or processed fixation data) can be used to discriminate users based on subconscious behavior in visual inspection. We present a novel discretized feature encoding based on spatiotemporal binning of fixation data for efficient geometric alignment and temporal ordering of eye movement when reading chest x-rays. The encoded features of the eye-fixation data are used by machine learning classifiers to discriminate between faculty and trainee radiologists. A clinical trial case study was conducted using metrics such as the area under the curve, accuracy, F1-score, sensitivity, and specificity to evaluate the discriminability between the 2 groups regarding their level of experience. The classification performance was then compared with state-of-the-art methodologies. In addition, a repeatability experiment using a separate dataset, experimental protocol, and eye tracker was performed with 8 participants to evaluate the robustness of the proposed approach. The numerical results from both experiments demonstrate that classifiers using the proposed feature encoding methods outperform the current state-of-the-art in differentiating between radiologists in terms of experience level. An average performance gain of 6.9% is observed compared with traditional features while classifying experience levels of radiologists. This gain in accuracy is also substantial across different eye tracker-collected datasets, with improvements of 6.41% using the Tobii eye tracker and 7.29% using the EyeLink eye tracker. These results signify the potential impact of the proposed method for identifying radiologists' level of expertise and those who would benefit from additional training. The effectiveness of the proposed spatiotemporal discretization approach, validated across diverse datasets and various classification metrics, underscores its potential for objective evaluation, informing targeted interventions and training strategies in radiology. This research advances reliable assessment tools, addressing challenges in perception-related errors to enhance patient care outcomes.

Didactic transposition of loan repayments in economics and the place of the discrete-continuous interplay

Abstract The study reported in this paper is motivated by an article published on a financial portal that presented loan repayments with a continuous approach, as opposed to the discrete approach of addressing loan repayments applied in mathematics courses for economists. Using the Anthropological Theory of the Didactic, we analyse the current didactic transposition of this content, based on a textbook for mathematics in economics and five interviews with teachers of mathematics and of economics, inquiring about their practices in teaching related to loan repayments in economics. The interviewed teachers indicated that this content is presented in a discrete way in mathematics courses for economics, although for reasons that do not seem directly connected to the content itself. Additionally, although the participants see the interest of considering the use of continuous variables and graphs, several constraints prevent them from their use in the context of loan repayments.

Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

A class of Landweber-type iterative methods based on the Radon transform for incomplete view tomography

Background We study the reconstruction problem for incomplete view tomography, including sparse view tomography and limited angle tomography, by the Landweber iteration and its accelerated version. Traditional implementations of these Landweber-type iterative methods necessitate multiple large-scale matrix-vector multiplications, which in turn require substantial time and storage resources. Objective This paper aims to develop and test a novel and efficient discretization approach for a class of Landweber-type methods that minimizes storage requirements by incorporating the specific structure of the incomplete view Radon transform. Methods We prove that the normal operator of incomplete view Radon transform in these methods is a compact convolution operator, and derive the explicit representation of its convolution kernel. Discretized by the pixel basis, these Landweber-type iterative methods can be implemented quickly and accurately by introducing a discretized convolution operation between two small-scale matrices with minimal storage requirements. Results For the simulated complete and limited angle data, the reconstruction results using various Landweber-type methods with our proposed discretization scheme achieve a 1-5dB improvement in PSNR and require one-third of computation time compared to the traditional approach. For the simulated sparse view data, our discretization scheme yields a valid image with the highest PSNR. Conclusions The Landweber-type iterative methods, when combined with our proposed discretization approach based on the Radon transform, are effective for addressing the incomplete view tomography problem.

Application of a discrete approach to designing the transport component of municipal solid waste logistics

Introduction. The growth of the urban area has led to the need to transfer landfills for the disposal of solid municipal (household) waste (MSW) to more remote locations on new sites. Representations and approaches to cargo transportation planning are an essential factor, separate planning of individual applications does not allow determining the results of future work with sufficient accuracy, since such an approach does not allow taking into account the interaction of rolling stock from different routes with each other and loading machines. The purpose of the study is to design the transportation of MSW in regional communications based on a discrete approach and theoretical provisions of operational planning of road freight transportation.Materials and methods. Within the framework of the conducted research, a normative approach was applied to the planning of freight transportation costs and descriptive models for describing the functioning of road transport systems of regional road freight transportation.Results and discussion. The solution of tasks related to the design of cargo transportation, including in regional communications, is due to the need in advance, prior to experience, knowledge of the results of future activities, since it is impossible to accumulate the work of road transport and then fix it. The results of cargo transportation depend on many factors, including the availability of a design methodology that allows you to adequately reflect the processes of their implementation. The use of mathematical modeling made it possible to reliably build plans for the transportation of MSW and determine the cost of their implementation.Conclusion. It has been revealed that possible downtime of both road trains and excavators is the reason for not fulfilling the operational transportation plan defined by separate planning. The use of the same rolling stock for customers with different transportation distances in the region will be accompanied by different costs.

Localized Evaluation for Constructing Discrete Vector Fields.

Topological abstractions offer a method to summarize the behavior of vector fields, but computing them robustly can be challenging due to numerical precision issues. One alternative is to represent the vector field using a discrete approach, which constructs a collection of pairs of simplices in the input mesh that satisfies criteria introduced by Forman's discrete Morse theory. While numerous approaches exist to compute pairs in the restricted case of the gradient of a scalar field, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. This paper introduces a fast, novel approach for pairing simplices of two-dimensional, triangulated vector fields that do not vary in time. The key insight of our approach is that we can employ a local evaluation, inspired by the approach used to construct a discrete gradient field, where every simplex in a mesh is considered by no more than one of its vertices. Specifically, we observe that for any edge in the input mesh, we can uniquely assign an outward direction of flow. We can further expand this consistent notion of outward flow at each vertex, which corresponds to the concept of a downhill flow in the case of scalar fields. Working with outward flow enables a linear-time algorithm that processes the (outward) neighborhoods of each vertex one-by-one, similar to the approach used for scalar fields. We couple our approach to constructing discrete vector fields with a method to extract, simplify, and visualize topological features. Empirical results on analytic and simulation data demonstrate drastic improvements in running time, produce features similar to the current state-of-the-art, and show the application of simplification to large, complex flows.

Design and Implementation of Sports Network Teaching Platform Based on Discrete Similarity Method

Sport is the most important topic in education, which individually shows motor skills in health activities. Athletic training should focus graduates on sports, low-involvement task activities, and physical fitness or exercise. Teaching concepts and methodologies are innovative, coaching methods and procedures, evaluation of coaching sessions in sports-all this is accompanied by development in the sports hall and successful improvement of sports performance. Here, he provides extraordinary assistance to students by predicting early school leaving and improving the potential application of wireless platforms in sports programs and changing the nature of sports, including visualization and repetition, and incorporating it into sports training. A sports network learning platform based on the discrete similarity approach is proposed for design and implementation in this paper. First, data is collected from a real-time data set. After that, the data submitted for preprocessing removes noise and imperfect records, which can be removed using a global constant or a most likely learning method using an Adaptive Savitsky-Golay Filtering (ASGF) method. The preprocessing method is fed into the feature extraction utilizing Multi‐Hypothesis Fuzzy‐Matching Radon Transform (MHFMRT). MHFMRT extract statistical features such us kurtosis, variance, energy, mean and standard deviation. Then, the Similarity-Based Convolutional Neural Network (SBCNN) method is optimized by the Chaotic Satin Bower Bird Optimization (CSBBO) algorithm, which effectively classifies the visualization and a satisfactory training platform of the sports network. The proposed system is executed in the MATrix LABoratory (MATLAB) platform and the performance of the proposed methods attains 28.5%, 27.9% and 29.5% higher accuracy, 28%, 21% and 26.5% higher precision, 21.2%, 28.75% and 21.3% higher recall compared with the existing methods like Recurrent Neural model to analyze the effect of Physical Training and Treatment in Relation to Sports Injuries (PTT-RSI-RNN), recognizing Sports Activities from Video Frames using Deformable Convolution and Adaptive Multiscale Features (SAVF-DCN) and Sports Match Prediction model for training and Exercise using attention-based LSTM network (SMPE-LSTM) respectively

A multiphysics hybrid continuum - agent-based model of in vitro vascularized organoids.

Organoids are 3D in vitro models that fulfill a hierarchical function, representing a small version of living tissues and, therefore, a good approximation of cellular mechanisms. However, one of the main disadvantages of these models is the appearance of a necrotic core due to poor vascularization. The aim of this work is the development of a numerical framework that incorporates the mechanical stimulation as a key factor in organoid vascularization. Parameters, such as fluid velocity and nutrient consumption, are analyzed along the organoid evolution. The mathematical model created for this purpose combines continuum and discrete approaches. In the continuum part, the fluid flow and the diffusion of oxygen and nutrients are modeled using a finite element method approach. Meanwhile, the growth of the organoid, blood vessel evolution, as well as their interaction with the surrounding environment, are modeled using agent-based methods. Continuum model outcomes include the distribution of shear stress, pressure and fluid velocity around the organoid surface, in addition to the concentration of oxygen and nutrients in its interior. The agent models account for cell proliferation, differentiation, organoid growth and blood vessel morphology, for the different case studies considered. Two main conclusions are achieved in this work: (i) the results of the study quantitatively predict in vitro data, with an enhanced blood vessel invasion under high fluid flow and (ii) the diffusion and consumption model parameters of the organoid cells determine the thickness of the proliferative, quiescent, hypoxic and necrotic layers.

Three limited interaction approaches to understanding the epidemiology of HIV among YMSM in the US

BackgroundUsing a theoretically-grounded approach to the epidemiological study of HIV incidence among a national, diverse sample of sexual and gender minority (SGM) men (age 17 -29 years), as well as examining HIV incidence through an innovative geospatial lens, is of considerable public health significance. Our overarching objectives are to assemble a U.S.-based national cohort of diverse SGM men: (1) to estimate HIV incidence in SGM men followed every 6 months for up to 24 months, (2) to assess the association of individual and geospatial factors associated with HIV incidence and (3) to determine the relative efficiency and acceptability of three different, discrete study enrollment approaches (including completion of remote HIV testing). The purpose of this manuscript is to describe the study protocol.MethodsThe cohort is composed of English- and/or Spanish-speaking SGM men at risk for HIV, age 17–29 years and living in the United States and its territories. We used multiple methods to recruit our sample including social networking apps like GrindrTM. If a participant was eligible for the study, they completed an address intake form so an HIV test could be mailed to their home or chosen address. We assembled three cohorts using different enrollment approaches. Cohort 1 used Zoom video calls with study staff observing participants use of OraQuick test with oral swabs at the baseline visit. Cohort 2 used No Zoom and OraSure oral fluid tests that participants mailed to an external lab. Cohort 3 used No Zoom/self-administration of OraQuick tests and participants uploading test results to an online portal (REDCap).DiscussionThis study will provide important data on multilevel determinants of HIV incidence among SGM men at the national level, allowing us to examine important differences by local jurisdiction, region and state and to better understand the impact of individual, social and geospatial factors on HIV incidence to help inform future prevention strategies.

Blood donation drive management: a discrete event simulation-based approach for enhanced operational efficiency

PurposeThis study aims to optimize blood donation drive efficiency by addressing operational bottlenecks and improving resource deployment, focusing on enhancing donor experience and reducing camp duration.Design/methodology/approachThe research uses a mixed-method approach, combining qualitative insights from blood banking officer interviews with quantitative data from 58 camp observations. “Simio” simulation software models various operational configurations, while mathematical techniques like queuing theory analyze key performance metrics. The process involves creating a baseline model, proposing optimizations and validating recommendations through real-world implementation.FindingsThe study achieved significant improvements: Reduced average donor time from 1.79 to 0.79 hours (56.4% improvement); Shortened camp operation time from 7.98 to 4.85 hours (39.2% improvement); Decreased waiting times at the medical check station from 45.23 to 0.60 minutes; Improved service rates across all stations, notably at registration (233.79% increase); and Streamlined processes through digitization and health check consolidation.Practical implicationsThe study provides actionable recommendations for blood bank managers, including digital pre-registration and optimized staff allocation, leading to substantial time and resource savings while enhancing donor experience.Social implicationsBy improving donation camp efficiency and experience, this research could increase donor retention rates and lead to a more stable blood supply, crucial for medical care.Originality/valueThis research presents a novel approach by combining discrete event simulation with real-world implementation and validation, offering an innovative solution to common bottlenecks in blood donation drives.

Angiogenesis Dynamics: A Computational Model of Intravascular Flow Within a Structural Adaptive Vascular Network.

Understanding vascular development and the key factors involved in regulating angiogenesis-the growth of new blood vessels from pre-existing vasculature-is crucial for developing therapeutic approaches to promote wound healing. Computational techniques offer valuable insights into improving angiogenic strategies, leading to enhanced tissue regeneration and improved outcomes for chronic wound healing. While chorioallantoic membrane (CAM) models are widely used for examining fundamental mechanisms in vascular development, they lack quantification of essential parameters such as blood flow rate, intravascular pressure, and changes in vessel diameter. To address this limitation, the current study develops a novel two-dimensional mathematical model of angiogenesis, integrating discrete and continuous modelling approaches to capture intricate cellular interactions and provide detailed information about the capillary network's structure. The proposed hybrid meshless-based model simulates sprouting angiogenesis using the in vivo CAM system. The model successfully predicts the branching process with a total capillary volume fraction deviation of less than 15% compared to experimental data. Additionally, it implements blood flow through the capillary network and calculates the distribution of intravascular pressure and vessel wall shear stress. An adaptive network is introduced to consider capillary responses to hemodynamic and metabolic stimuli, reporting structural diameter changes across the generated vasculature network. The model demonstrates its robustness by verifying numerical outcomes, revealing statistically significant differences with deviations in key parameters, including diameter, wall shear stress (p < 0.05), circumferential wall stress, and metabolic stimuli (p < 0.01). With its strong predictive capability in simulating intravascular flow and its ability to provide both quantitative and qualitative assessments, this research enhances our understanding of angiogenesis by introducing a biologically relevant network that addresses the functional demands of the tissue.

NMPC-Based Path Tracking Control Through Cascaded Discretization Method Considering Handling Stability for 4WS Autonomous Vehicles Under Extreme Conditions

In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking.

A discretized representation for Monte Carlo simulation of deformed semiflexible chains.

In this study, we present a novel orientation discretization approach based on the rhombic triacontahedron for Monte Carlo simulations of semiflexible polymer chains, aiming at enhancing structural analysis through rheo-small-angle scattering (rheo-SAS). Our approach provides a more accurate representation of the geometric features of semiflexible chains under deformation, surpassing the capabilities of traditional lattice structures. Validation against the Kratky-Porod chain system demonstrated superior consistency, underscoring its potential to significantly improve the precision of uncovering geometric details from rheo-SAS data. This approach opens new avenues for investigating the conformations of semiflexible polymers and mechanically induced phase transitions in more complex polymer structures, offering deeper insights into their behavior under various conditions.

Discrete Event Modeling and Simulation Approaches for IIoT

The industry has experienced significant advancements in recent years, primarily focusing on smart manufacturing, culminating in the Industry 4.0 (I4.0) revolution I4.0 emphasizes interconnectivity, real time data capture and transmission among machines, autonomy, and machine learning, providing manufacturing companies numerous growth opportunities. The Industrial Internet of Things (IIoT) is a core component of this revolution, becoming integral to each system and increasing complexity due to the vast number of interconnected devices and diverse physical components. The variety of virtual services distributed across the architectural layers of industrial systems (cloud, fog, edge) and the various connection types between IIoT devices introduce security and privacy challenges, which are critical issues for any system incorporating IIoT. To fully leverage IIoT’s potential, addressing these security and privacy concerns is essential. Research and design in this domain are challenging, particularly when creating a simulation environment to study a system’s behavior over time. Despite the extensive research in IoT and the significant benefits of simulation based approaches, there remains a challenge in creating detailed representations from the underlying IoT nodes to the application layer in the cloud, along with the underlying networking infrastructure. To assist researchers and practitioners in overcoming these challenges, we propose the Discrete Event System Specification (DEVS) formalism. DEVS provides a mathematical framework for modeling systems, whether discrete or continuous events, allowing for the simulation of these systems within the DEVS environment. Every system, whether real or conceptual, has a time base, inputs, outputs, and functions to determine the next state, as well as outputs that reflect the current state and inputs. Simulating the system within the DEVS environment allows one to study its behavior to predict and optimize performance patterns.

Simulation optimization of truck queue loading process discrete simulation approach with Promodel Software at PT. XYZ

Simulation optimization of truck queue loading process discrete simulation approach with Promodel Software at PT. XYZ

Equilibrium dynamical models in the inner region of the Large Magellanic Cloud based on Gaia DR3 kinematics

Context. The Large Magellanic Cloud (LMC) contains complex dynamics driven by both internal and external processes. The external forces are due to tidal interactions with the Small Magellanic Cloud and the Milky Way, while internally its dynamics mainly depend on the stellar, gas, and dark matter mass distributions. Despite this complexity, simple physical models often provide valuable insights into the primary driving factors. Aims. We used Gaia Data Release 3 (DR3) to explore how well equilibrium dynamical models based on the Jeans equations and the Schwarzschild orbit superposition method are able to describe the LMC’s five-dimensional phase-space distribution and line-of-sight (LOS) velocity distribution, respectively. In the Schwarzschild model, we incorporated a triaxial bar component for the first time and derived the LMC’s bar pattern speed. Methods. We fit comprehensive Jeans dynamical models to all Gaia DR3 stars with proper motion and LOS velocity measurements found in the footprint of the VISTA near-infrared survey of the Magellanic System using a discrete maximum likelihood approach. These models are very efficient at discriminating genuine LMC member stars from Milky Way foreground stars and background galaxies. They constrain the shape, orientation, and enclosed mass of the galaxy under the assumption of axisymmetry. We used the Jeans model results as a stepping stone to more complex two-component Schwarzschild models, which include an axisymmetric disc and a co-centric triaxial bar, which we fit to the LMC Gaia DR3 LOS velocity field using a χ2 minimisation approach. Results. The Jeans models describe the rotation and velocity dispersion of the LMC disc well, and we find an inclination angle of θ = 25.5° ±0.2°, line of nodes orientation of ψ = 124° ±0.4°, and an intrinsic thickness of the disc of q0d = b/a = 0.23 ± 0.01 (minor to major axis ratio). However, bound to axisymmetry, these models fail to properly describe the kinematics in the central region of the galaxy dominated by the LMC bar. We used the derived disc orientation and the Gaia DR3 density image of the LMC to obtain the intrinsic shape of the bar. Using these two components as input to our Schwarzschild models, we performed orbit integration and weighting in a rotating reference frame fixed to the bar, deriving an independent measurement of the LMC bar pattern speed of Ω = 11 ± 4 km s−1 kpc−1. Both the Jeans and Schwarzschild models predict the same enclosed mass distribution within a radius of 6.2 kpc of ∼ 1.4 × 1010 M⊙.

Transcranial ultrasound modeling using the spectral-element method.

This work explores techniques for accurately modeling the propagation of ultrasound waves in lossy fluid-solid media, such as within transcranial ultrasound, using the spectral-element method. The objectives of this work are twofold, namely, (1) to present a formulation of the coupled viscoacoustic-viscoelastic wave equation for the spectral-element method in order to incorporate attenuation in both fluid and solid regions and (2) to provide an end-to-end workflow for performing spectral-element simulations in transcranial ultrasound. The matrix-free implementation of this high-order finite-element method is very well-suited for performing waveform-based ultrasound simulations for both transcranial imaging and focused ultrasound treatment thanks to its excellent accuracy, flexibility for dealing with complex geometries, and computational efficiency. The ability to explicitly mesh distinct interfaces between regions with high impedance contrasts eliminates staircasing artifacts, which are otherwise non-trivial to mitigate within discretization approaches based on regular grids. This work demonstrates the efficacy of this modeling technique for transcranial ultrasound through a number of numerical examples. While the examples in this work primarily focus on transcranial applications, this type of modeling is equally relevant within other soft tissue-bone systems such as in limb or spine imaging.

Unsteady RANS simulations of under-expanded hydrogen jets for internal combustion engines

Hydrogen can be considered a suitable fuel for heavy-duty reciprocating internal combustion engines (ICEs) in order to limit carbon dioxide emissions. The low volumetric power density of hydrogen and the backfire problem suggest to employing the direct injection technology with relatively high nozzle pressure ratios (NPRs). This paper provides the analysis of under-expanded hydrogen jet dynamics using an open-source high-fidelity simulation tool based on the OpenFOAM framework. The unsteady Reynolds-averaged Navier–Stokes (URANS) equations are solved by an efficient pressure-based solver for compressible flow. URANS equations are attractive for fast engineering analysis of 3D engine cycle and optimization, where large eddy simulation (LES) is too computationally expensive. The accuracy of the simulations is enhanced by employing the weighted essentially non-oscillatory (WENO) approach for the spatial discretization, considering schemes from second-order to fourth-order accuracy. Those schemes are embedded in a pressure-implicit with splitting of operators (PISO) algorithm, obtaining a very robust and accurate numerical method for compressible multi-species flows, which can be shared in an open access framework. Hydrogen injection in air is simulated, with several values of the NPR typical of direct injection ICE in the low-medium range, 8.5≤NPR≤30. The main features of the developing jet are analyzed, such as barrel shock dimensions, cone angle and hydrogen–air mixing. The results are validated with respect to experimental and LES data available in the recent literature, demonstrating the efficiency and the accuracy of the employed URANS approach and evaluating its limits.

Numerical prediction of impact damage in thick fabric composite laminates

A simulation methodology for assessing the damage in thick fabric Carbon Fibre Reinforced Polymer (CFRP) composite laminates under low- and high-velocity impacts is presented. It encompasses steps for calibration, verification, and validation of the elastic and fracture material properties as well as determination of model parameters for the numerical simulations. Damage is modelled using a discrete fracture approach with cohesive interface elements that capture individual cracks occurring in and between plies. For computational efficiency, the method is implemented in a two-dimensional (2D) axi-symmetric model. Results from double-cantilever beam, end-notched flexure, and quasi-static indentation experiments align well with numerical simulations and serve to calibrate and verify the implementation of the discrete fracture approach. The methodology is extended to dynamic impact analysis to predict damage mechanisms, force–displacement histories, and is validated using test results. This methodology combines meaningful insight in the failure mechanisms with a manageable computational effort, achieving a factor 50 improvement compared to a benchmark. A parametric analysis summarised in failure maps relates damage mechanisms to impact energy, mass, and laminate thickness. The proposed methodology strikes a balance between computational efficiency and accuracy, making it a valuable tool for optimum design and certification of thick CFRP composite laminates under impact.