Different Levels Of Detail Research Articles

Sustainability of underground infrastructure – Part 1: Digitalisation-based carbon assessment and baseline for TBM tunnelling

Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for “build clever and efficiently”. This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions.

Methodology for the Integration of Structural Health Assessment of Masonry Bridges into HBIM

ABSTRACT A methodology is proposed to efficiently develop BIM models of masonry arch bridges, with the objective of generating databases (Level of Information, LoI) associated with 3D geometrical models, incorporating different levels of detail (LoD). The use of parametric libraries is implemented to develop the models. The first step is to perform an experimental campaign to obtain initial data. With these data and the parametric library previously created, a parametric model of the structure is generated (low LoD). A map of the vulnerable areas of the structure is created on this model and the available information is linked to it. A structural model (high LoD) is also created and calibrated. With all this information, an HBIM file is generated. A second experimental campaign is performed on the structure using non-destructive techniques. Point clouds, OMA data, photographs, and visual inspections from the different experimental campaigns are compared to the original HBIM file. In addition, to validate the usefulness of the parametric library, its implementation is carried out on three other historic masonry structures. This comprehensive and technologically advanced approach proves to be a promising strategy for the conservation and sustainable management of architectural heritage.

МОДЕЛИ ТУРБУЛЕНТНОСТИ В РАСЧЕТАХ АЭРОДИНАМИЧЕСКИХ ХАРАКТЕРИСТИК ГАЗОВЫХ ПОТОКОВ

Abstract. The review of the most common turbulence models in engineering calculations, which allow approximate description of turbulent flows using the Reynolds equations of motion, is carried out, and their areas of application are shown. To calculate turbulent flows by solving the Reynolds equations, a closure hypothesis is adopted for apparent turbulent stresses and other variables included in the equations.
 It is noted that Businex is considered one of the main ones, which is confirmed by experimental studies of turbulence, which show that the turbulent flow is characterized by the presence of an energy cascade from large to smaller scales.
 In addition, the authors considered: the semi-empirical Prandtl theory based on the assumption that turbulent motion consists of an infinite number of vortices of various scales that interact with each other and transfer energy from large scales to smaller ones, models specially adapted to a specific type of problem, for example, LES for modeling the interaction of turbulent flows, a generalized theory of developed turbulence (GDT, Generalized Theory of Turbulence), which is a theory, developed for the description and simulation of turbulent flows in various technical devices.
 It is proved that one of the most common approaches is to use models with different levels of detail, starting with simpler models and moving on to more complex ones, if necessary to achieve the required accuracy. This approach is called the "ladder of models" or RANS/LES method. 
 Subject of research: turbulence models for calculating aerodynamic characteristics of gas flows.
 Materials and methods: the basis of the performed research is a number of scientific works of domestic and foreign researchers in this field and the results obtained by the authors in the course of the work.
 A systematic approach is used, meaning consideration and study of the object of research as an integrated system consisting of many interrelated elements.
 Results: The authors note that one of the most common approaches is to use models with different levels of detail, starting with simpler models and moving on to more complex ones if necessary to achieve the required accuracy. This approach is called the "ladder of models" or RANS/LES method. 
 Conclusions: The choice of a turbulence model for specific cases depends on a number of factors, including flow characteristics, available data, accuracy requirements and computational capabilities. The selection criteria include: physical properties and flow mode; the degree of accuracy of calculations and calculation capabilities.

Complex Systems in Interdisciplinary Interaction

Complex systems consist of many components that interact with each other, and nonlinearity is fundamental in the dynamics that arise as a result of the interaction of these components and cannot be studied by observing only a small part of the system. A large number of them make fluctuations an integral part of their dynamics. Through interdisciplinary research, we integrate different disciplines and methodological approaches. We study complexity from different angles through topics that include neuroscience, artificial intelligence, and quantum physics. We have sought to explore these topics that have gained progressive interest in scientific circles as well as outside academic circles, influencing many areas outside academic research. Two goals connect our research. First, theoretical insights from neuroscience, artificial intelligence and quantum physics of the financial stock market. “Financial stock markets are sophisticated machines intertwined with scientific networks” (Ana Negovanović, 2024). They are the core of a system of different blocks that interact on the financial market in an interdisciplinary interaction of the most complex structures, neural networks with interconnected nodes. Second, new technologies- convergence of technologies, new industries, space industry through chaos theory. Investigating the attributes of complex systems is structure. Systems are structured in such a way that some parts of the system are specialized, and systems can naturally be divided into different levels of organization, which should be modeled at different levels of detail. An important component of systems understanding is the ability to break down a system model into its component parts and levels to see how the parts behave and how to connect the parts and levels to understand the overall dynamics and structure of the system. Perhaps one of the most transparent examples of the importance of structure in thinking about complex systems is the space program, where one must equally consider the behavior of individual components and the dynamics of how those components interact with each other.

Hierarchies of description enable understanding of cognitive phenomena in terms of neuron activity.

One objective of neuroscience is to understand a wide range of specific cognitive processes in terms of neuron activity. The huge amount of observational data about the brain makes achieving this objective challenging. Different models on different levels of detail provide some insight, but the relationship between models on different levels is not clear. Complex computing systems with trillions of components like transistors are fully understood in the sense that system features can be precisely related to transistor activity. Such understanding could not involve a designer simultaneously thinking about the ongoing activity of all the components active in the course of carrying out some system feature. Brain modeling approaches like dynamical systems are inadequate to support understanding of computing systems, because their use relies on approximations like treating all components as more or less identical. Understanding computing systems needs a much more sophisticated use of approximation, involving creation of hierarchies of description in which the higher levels are more approximate, with effective translation between different levels in the hierarchy made possible by using the same general types of information processes on every level. These types are instruction and data read/write. There are no direct resemblances between computers and brains, but natural selection pressures have resulted in brain resources being organized into modular hierarchies and in the existence of two general types of information processes called condition definition/detection and behavioral recommendation. As a result, it is possible to create hierarchies of description linking cognitive phenomena to neuron activity, analogous with but qualitatively different from the hierarchies of description used to understand computing systems. An intuitively satisfying understanding of cognitive processes in terms of more detailed brain activity is then possible.

Analyzing land cover change dynamism through a GIS-based method: application to Gran Canaria (Canary Islands, Spain)

Landscapes are dynamic areas which can be studied from the perspective of different, but related, disciplines. The application of GIS methods to study landscape change is an interesting resource to help understand and explain landscape dynamics. Thus, the objective of this paper is to characterize land cover dynamism and identify land cover change ‘hotspots’. The method used here generates raster maps in which each pixel value represents the number of times each pixel has changed land cover between 1990-2018. Furthermore, it provides three of these maps, since it works with the 3 taxonomic levels of Corine Land Cover datasets, giving us different levels of detail in the analysis. On the other hand, the statistical treatment of the data has been done at the municipal level. The most important results reveal that Agüimes is the municipality with the most dynamic land cover change in the three levels, while Tejeda, in level 1, and Valsequillo, in levels 2 and 3, have the least changing land cover. These outputs are complemented with other statistical analyses which allow the integration of different data types such as those related to population, tourism and agriculture. Subsequently, some of the methodological issues and findings are discussed and put in context with the scientific literature.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

Balancing resources for dynamic vehicle routing with stochastic customer requests

AbstractWe consider a service provider performing pre-planned service for initially known customers with a fleet of vehicles, e.g., parcel delivery. During execution, new dynamic service requests occur, e.g., for parcel pickup. The goal of the service provider is to serve as many dynamic requests as possible while ensuring service of all initial customers. The allocation of initial services impacts the potential of serving dynamic requests. An allocation aiming on a time-efficient initial routing leads to minimal overall workload regarding the initial solution but may congest some vehicles that are unable to serve additional requests along their routes. An even workload division is less efficient but grants all vehicles flexibility for additional services. In this paper, we investigate the balance between efficiency and flexibility. For the initial customers, we modify a routing algorithm to allow a shift between efficient initial routing and evenly balanced workloads. For effective dynamic decision making with respect to the dynamic requests, we present value function approximations with different feature sets capturing vehicle workload in different levels of detail. We show that sacrificing some initial routing efficiency in favor of a balanced vehicle workload is a key factor for a flexible integration of later customer requests that leads to an average improvement of 10.75%. Further, we show when explicitly depicting heterogeneity in the vehicle workload by features of the value function approximation provides benefits and that the best choice of features leads to an average improvement of 5.71% compared to the worst feature choice.

Multi-scale simulations of pulmonary airflow based on a coupled 3D-1D-0D model

Pulmonary airflow simulation is a valuable tool for studying respiratory function and disease. However, the respiratory system is a complex multiscale system that involves various physical and biological processes across different spatial and temporal scales. In this study, we propose a 3D-1D-0D multiscale method for simulating pulmonary airflow, which integrates different levels of detail and complexity of the respiratory system. The method consists of three components: a 3D computational fluid dynamics model for the airflow in the trachea and bronchus, a 1D pipe model for the airflow in the terminal bronchioles, and a 0D biphasic mixture model for the airflow in the respiratory bronchioles and alveoli coupled with the lung deformation. The coupling between the different components is achieved by satisfying the mass and momentum conservation law and the pressure continuity condition at the interfaces. We demonstrate the validity and applicability of our method by comparing the results with data of previous models. We also investigate the reduction in inhaled air volume due to the pulmonary fibrosis using the developed multiscale model. Our method provides a comprehensive and realistic framework for simulating pulmonary airflow and can potentially facilitate the diagnosis and treatment of respiratory diseases.

Spatial analysis of remote sensing and meteorological indices in a drought event in southwestern Spain

The effects of global warming and climate change are being felt through more extreme and prolonged periods of drought. Multiple meteorological indices are used to measure drought, but they require hydrometeorological data; however, other indices measured by remote sensing and used to quantify vegetation vigor can be correlated with the former. This study investigated the correlation between both index types by vegetation type and season. The correlations were also spatially modeled in a drought event in southwestern Spain. In addition, three maps with different levels of detail in terms of vegetation categorization were compared. The results generally showed that grassland was the most well correlated category between the SPEI and the FAPAR, LAI, and NDVI. This correlation was more pronounced in autumn and spring, which is when most changes in vegetation senescence and growth occur. The spatiotemporal analysis indicated a very similar behavior for grasslands grouped in an area indicated by the climate change adaptation maps as having a high evapotranspiration forecast. Finally, in a forest-based forecast analysis, the indices that best explained the performance of the SPEI were again FAPAR, LAI, and NDVI, with a lag of up to 20 days. Therefore, the results showed that remotely sensed indices are good indicators of drought status and can be variably explanatory of traditional drought indicators. Moreover, complementing the study with spatiotemporal analysis made it possible to detect areas particularly vulnerable to climate change.

Producing accessible intersection maps for people with visual impairments: an initial evaluation of a semi-automated approach

ABSTRACT Street intersections are challenging for people with visual impairments. While tactile maps are an important support in both mobility training and independent journeys, the caseload of manual map production has made them less accessible. This paper explores the possibility of (semi-) automatically producing tactile maps for street intersections at large scales, with an initial evaluation focused on the graphic aspect of the produced maps. The automation attempts to identify acceptable default parameters and values and proposes an exploration of possible choices for potentially open decisions. It adapts the classic map production process with parameters to present the information tactilely at the intersection scale, and produces representation meaningful for PVIs and realistic for an automatic procedure, resulting in ready-to-print maps in two scales of three sizes, with different levels of details and styles. The resulting maps are evaluated by professionals involved in tactile graphics through a questionnaire to evaluate the defaults and discuss the possibility of open choices. The professionals validated the maps, and their evaluation emphasized the need to have an acceptable default while keeping some options open to cater to the diversity in the visually impaired audience.

РОЛЬ ФИНАНСОВОГО АНАЛИЗА В ПРОГНОЗИРОВАНИИ ДЕЯТЕЛЬНОСТИ ОРГАНИЗАЦИИ

The article reveals the influence of financial analysis on the forecasting process. The role of financial analysis in the effective management of an enterprise for making strategic decisions is determined. The author emphasizes the variety of financial analysis tools that provide different levels of detail and depth of analysis. The relationship between financial analysis tools and their role in various aspects of organizational management, including the planning process, choosing a development strategy and optimizing the capital structure, has been identified. Forecasting financial results based on current data allows you to assess the future profitability and risks of an investment project. The characteristics of financial indicators, payback estimates and the degree of stability of financial flow are presented, taking into account risks and resource allocation when assessing the effectiveness of investments. An overview of forecasting shows the place of methods in enterprise analysis during planning and the potential effect on the organization’s activities. The key aspects of the use of statistical methods in forecasting financial flows are considered. The selection of sources includes the analysis of various financial sources in order to determine the most effective and suitable for a particular investment project. The main directions for choosing sources of financing for investments are summarized. Analyzing changes in key financial indicators allows you to take proactive measures to manage risks and adapt to changing conditions.

Is time more important than distance to optimize freight delivery routes? An approach using the value of time

Traditional approaches to optimize freight delivery routes are based on minimizing a distance-based cost function. New approaches use also time as the objective function to minimize. However, the trade-off between time and distance is sometimes unclear. This paper presents a new approach to optimize delivery routes in which both time and distance are used together to optimize delivery routes. For this purpose, the operating cost of a vehicle and the value of time have been used to convert time and distance into monetary units. Through the study of three different networks in Spain with different level of detail (the region of Catalonia, the city of Barcelona, and the old part of Pamplona), the results have indicated that minimizing both time and distance provides better results than the traditional approach, especially at a local level where congestion effects are more relevant.

2022 IEEE Scientific Visualization Contest Winner: Multifield Analysis of Vorticity-Driven Lateral Spread in Wildfire Ensembles.

We present an interactive visual analysis tool for analyzing the spread of wildfires and what influences their evolution. Multiple time-varying 3-D scalar and vector fields are investigated and related to each other to identify causes of atypical fire spread. We present a visual analysis approach that allows for a comparative analysis of multiple runs of a simulation ensemble on different levels of detail. Overview visualizations combined with volume renderings and flow visualizations provide an intuitive understanding of the fire spread.

A METHOD FOR EVALUATING EXPLANATIONS IN AN ARTIFICIAL INTELLIGENCE SYSTEM USING POSSIBILITY THEORY

The subject of the research is the process of generating explanations for the decision of an artificial intelligence system. Explanations are used to help the user understand the process of reaching the result and to be able to use an intelligent information system more effectively to make practical decisions for him or her. The purpose of this paper is to develop a method for evaluating explanations taking into account differences in input data and the corresponding decision of an artificial intelligence system. The solution of this problem makes it possible to evaluate the relevance of the explanation for the internal decision-making mechanism in an intelligent information system, regardless of the user's level of knowledge about the peculiarities of making and using such a decision. To achieve this goal, the following tasks are solved: structuring the evaluation of explanations depending on their level of detail, taking into account their compliance with the decision-making process in an intelligent system and the level of perception of the user of such a system; developing a method for evaluating explanations based on their compliance with the decision-making process in an intelligent system. Conclusions. The article structures the evaluation of explanations according to their level of detail. The levels of associative dependencies, precedents, causal dependencies and interactive dependencies are identified, which determine different levels of detail of explanations. It is shown that the associative and causal levels of detail of explanations can be assessed using numerical, probabilistic, or possibilistic indicators. The precedent and interactive levels require a subjective assessment based on a survey of users of the artificial intelligence system. The article develops a method for the possible assessment of the relevance of explanations for the decision-making process in an intelligent system, taking into account the dependencies between the input data and the decision of the intelligent system. The method includes the stages of assessing the sensitivity, correctness and complexity of the explanation based on a comparison of the values and quantity of the input data used in the explanation. The method makes it possible to comprehensively evaluate the explanation in terms of resistance to insignificant changes in the input data, relevance of the explanation to the result obtained, and complexity of the explanation calculation. In terms of practical application, the method makes it possible to minimize the number of input variables for the explanation while satisfying the sensitivity constraint of the explanation, which creates conditions for more efficient formation of the interpretation based on the use of a subset of key input variables that have a significant impact on the decision obtained by the intelligent system.

Interpreting a virtual reconstruction from different levels of detail: 3D modeling approaches combined with a phenomenological exploratory study

Despite offering new possibilities for the education, interpretation, preservation, and management of cultural assets, virtual heritage still demands more consistent theoretical-practical debates. Under the scope of developing historical buildings' virtual models, the level of detail (LOD) is defined as a parameter that directly affects workflows, requiring time, investments, and specialized professionals. This work investigates how different LOD configurations influence the construction and interpretation of three-dimensional models of historic buildings. To this end, five 3D models with different LODs of a historic building in Brazil were set in an exploratory study that addressed users' perceptions when experimenting with them from a phenomenological perspective. The goal was to identify which elements and LOD configurations of the model were preponderant. The experiment was performed through an online workshop, where first-year architecture students could virtually experience the 3D models created, comparing them to a movie of the existing building. Participants were divided into five groups, one for each proposed LOD, and described their experiences through reports that were then analyzed, considering a list of themes emerging from their speeches. The findings underwent statistical analysis to understand how different themes and videos were linked. The experiment findings showed that (1) the 3D models with higher LODs led to more comprehensive comments on the architecture of the building and (2) elements such as the building's surroundings, the humanization of the scenery, and the temporal depth of the 3D models should be considered in the virtual reconstruction process.

Tabulated Chemistry Combustion Model for Cost-Effective Numerical Simulation of Dual-Fuel Combustion Process

This study is dedicated to improving the efficiency of the flamelet-generated manifold (FGM) tabulated chemistry combustion modeling approach for predicting the combustion process in diesel-ignited internal combustion (IC) engines. The primary focus is on reducing table generation time and memory requirements. To accurately predict dual-fuel combustion processes, it is important to model both premixed and non-premixed combustion regimes. However, attempting to include both regimes in a single FGM lookup table leads to significant increases in the table size and generation time. In response, this work proposes a dual-table configuration, with each table dedicated to a specific regime. The solution is then interpolated from these tables based on the calculated combustion regime indicator during the computational fluid dynamics (CFD) simulation. This approach optimizes computational efficiency while ensuring an accurate representation of dual-fuel combustion. Additionally, to establish a cost-effective and accurate 3D CFD simulation workflow, the dual-table FGM methodology is coupled with the partially averaged Navier–Stokes (PANS) turbulence model. The feasibility of the proposed FGM methodology is tested utilizing six chemical kinetics mechanisms with different levels of detail. The results of this study demonstrated that the dual-table approach significantly accelerates table generation time and reduces memory requirements compared to a single table that includes both combustion regimes. Furthermore, 3D CFD simulation results of the dual-fuel combustion process are validated against available experimental data for three engine operating points. The in-cylinder pressure traces and rate of heat release obtained from the 3D CFD simulations employing the FGM PANS methodology show good agreement with experimental measurements, confirming the accuracy and reliability of this modeling approach.

Sustainable Material Selection Framework: Taxonomy and Systematisation of Design Approaches to Sustainable Material Selection

Design can play a fundamental role in addressing the climate crisis and preserving the planet’s finite resources. Through design, it is possible to reduce the environmental impact of products and services right from concept stage. The elements that concur within a project are diverse and often have an impact on each other. The material is one of them, being able to influence the product, but also the business model, company relations, etc. To help the designer keep all these aspects under control, various methodologies and tools have been developed, among them design strategies and guidelines. To date, several authors have dealt with the topic, offering different perspectives and generating a critical mass of information, which differs in the level of depth and operability of the suggestions, often differing only in terminology rather than content. This inhomogeneity can confuse both professionals and students. This study proposes an ordered taxonomy of the different levels of detail and a unified terminology of the strategies and guidelines in the literature. To test taxonomy and systematisation, this article focuses on guidelines for material choice, resulting in a framework to guide the selection of materials with a view to sustainability.

BIM-based framework of automatic tunnel segment assembly and deviation control

The design of universal segments and deviation control of segment assembly are essential for robust and low-risk tunnel construction. A building information modeling (BIM)-based framework was proposed for parametric modeling, automatic assembly, and deviation control of universal segments. First, segment models of different levels of detail (LoDs) were built based on BIM visual programming language (VPL) for different project life cycles. Then, the geometric constraints, requirements, and procedures for parametric segment assembly were distilled to develop a program that combines a novel typesetting algorithm with a 3D path replanning algorithm. Typesetting is implemented by introducing a point indication matrix, characterizing segments by sides, and manipulating geometries in a VPL. Simultaneously, 3D path replanning, with non-uniform rational B-splines (NURBS) and arcs as basic shapes, was used to resolve unacceptable deviation situations after typesetting. Finally, the proposed framework was validated on a water diversion line and was found to be more effective and accurate than the previous method.

Flame Structure at Elevated Pressure Values and Reduced Reaction Mechanisms for the Combustion of CH4/H2 Mixtures

Understanding and controlling the combustion of clean and efficient fuel blends, like methane + hydrogen, is essential for optimizing energy production processes and minimizing environmental impacts. To extend the available experimental database on CH4 + H2 flame speciation, this paper reports novel measurement data on the chemical structure of laminar premixed burner-stabilized CH4/H2/O2/Ar flames. The experiments cover various equivalence ratios (φ = 0.8 and φ = 1.2), hydrogen content amounts in the CH4/H2 blend (XH2 = 25%, 50% and 75%), and different pressures (1, 3 and 5 atm). The flame-sampling molecular-beam mass spectrometry (MBMS) technique was used to detect reactants, major products, and several combustion intermediates, including major flame radicals. Starting with the detailed model AramcoMech 2.0, two reduced kinetic mechanisms with different levels of detail for the combustion of CH4/H2 blends are reported: RMech1 (30 species and 70 reactions) and RMech2 (21 species and 31 reactions). Validated against the literature data for laminar burning velocity and ignition delays, these mechanisms were demonstrated to reasonably predict the effect of pressure and hydrogen content in the mixture on the peak mole fractions of intermediates and adequately describe the new data for the structure of fuel-lean flames, which are relevant to gas turbine conditions.