Computer Assisted Methods in Engineering and Science
Computer Assisted Methods in Engineering and Science (CAMES) is an open access, referred international journal, published quarterly, indexed by Scopus, EBSCO, and DOAJ, providing a scientific exchange forum and an authoritative source of information in the field of computational sciences and related areas of applied engineering. The objective of the journal is to support researchers and practitioners by offering them the means facilitating access to the newest research results reported by leading experts in the field, publication of own contributions, and dissemination of information relevant to the scope of the journal. Papers published in the journal will fall largely into three main categories: Contributions presenting new research methods of mathematical modeling and computer simulations in engineering and applied sciences, including traditional areas such as solid and structural mechanics, material science, fluid dynamics, acoustics and electromagnetics but going beyond them to account for application relevant issues in physics, chemistry, biology and mathematics, scientific computing, large scale optimization, intelligent systems as well as in multi-scale and multi-physics problems. Articles describing novel applications of computational techniques supporting engineering practice and education in areas like mechanical, aerospace, civil, naval, software, chemical and architectural engineering, materials science as well as demonstrations of their practical use in solving real life problems. State-of-the-art tutorials, providing the readership with a guidance on important research directions as observed in the current world literature on computer assisted methods in engineering and sciences. The journal will also publish book reviews and information on activities of the European Community on Computational Methods in Applied Sciences (ECCOMAS). This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. All articles are published on Creative Commons licence CC BY 4.0. Computer Assisted Methods in Engineering and Science is available in Diamond Open Access and no fees are charged to the authors or readers. Journal Metrics MNiSW (2023): 70 | CiteScore 2024: 2.3 (2023: 1.8) | SJR 2024: 0.225 (2023: 0.230) | SNIP 2024: 0.389 (2023: 0.663)
Institute of Fundamental Technological Research, Polish Academy of Sciences
Copyright © The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
Preface to the Special Issue on Computer Assisted Methods in Architecture and Urban Planning
We are pleased to present a special issue of Computer Assisted Methods in Engineering and Science (CAMES), covering papers focusing on selected aspects of computer-aided design methods in architecture, urban planning, and landscape architecture. The presented content is interdisciplinary in nature, merging many fields and research areas because of the usage of digital tools. The editors of this issue intended to highlight the beneficial role of computing methods in data acquisition and spatial analysis, while simultaneously considering the saturation of this space with cultural values. The papers included demonstrate the usefulness of these tools from both engineering and humanistic perspectives. We hope that addressing these matters in the CAMES journal will bridge the technological community with the historical and social aspects of space, thereby justifying the necessity for the development of digital tools to support the preservation of this context.
Copyright (c) 2026 dr hab. inż. arch. Agnieszka OZIMEK, prof. PK, dr hab. inż. arch. Paweł OZIMEK, prof. PK https://creativecommons.org/licenses/by/4.0
Multi-Input CNN for Vision-Based In Situ Analysis of Extraterrestrial Surface Composition
Earth’s natural resources are finite, which is why engineers and scientists are increasingly directing their attention to the extraction of materials from celestial bodies. Beyond the extraction itself, however, a major challenge remains the localization of valuable substances and the assessment of their quality in situ. In this article, we present a flexible and robust method for estimating the content of selected components in heterogeneous mixtures using RGB image processing. The proposed deep learning architecture achieves high prediction accuracy with root mean squared error (RMSE) of (0.190 ±0.024) %. The framework supports a variety of backbone architectures, including lightweight models, making it suitable for deployment on edge devices such as planetary rovers. Furthermore, the method is flexible, allowing for easy adaptation to other tasks, for example, the analysis of more complex mixtures or inference based on multi- or hyperspectral imagery.
Copyright (c) 2025 Kacper Marciniak, Michał Grzesiak, Igor Zaworski, Dominik Pawliszewski, Dominika Zygarlicka, Michał Wnuk, Adrian Zakrzewski https://creativecommons.org/licenses/by/4.0
Application of Neural Networks for Determine the Radiation Pressure in Two-Moment Radiation Hydrodynamics in Slab Geometry
In two-moment radiation transport, the closure is the constitutive relation that maps the energy and momentum to the radiation-pressure tensor. Among available closures, the maximum-entropy (ME) approach is the most reliable. However, it is associated with a high computational cost. In this paper, we propose a machine-learning approach for the rapid evaluation of the ME closure for bosonic, classical and fermionic radiation. We generate ME reference data using Gauss–Legendre angular quadrature combined with a robust bisection-based inversion of the moment constraints. Next, we train a small physics-constrained multilayer perceptron (MLP) with output restricted to the physically admissible range (between one third and one). Monotonicity in the reduced flux is enforced, and the derivative is matched to the ME reference. The neural network (NN)-based closure achieves a mean absolute error (MAE) of 9.0×10−4 over the range ϕ ∈ [0, 0.98], which yields a latency reduction of about ∼103× per closure evaluation. In the Marshak wave benchmark the full simulation runs about 247 × faster while the hyperbolicity indicator remains strictly positive (minimum 5.1 · 10−2). Compared with the analytic Kershaw closure for bosonic and classical radiation, our model is substantially more accurate and faster. For practical adoption, we also provide a lightweight rational approximation (MAE 1.01× ∼10−3), and in the bosonic and classical cases we confirm positivity of the hyperbolicity indicator for degeneracy parameters between −5 and 5.
Copyright (c) 2025 Agnieszka Pregowska, Wiesław Larecki, Janusz Szczepanski https://creativecommons.org/licenses/by/4.0
Combining Indoor and Outdoor Positioning for Navigation in AR Environments
This article presents a comparative analysis of augmented reality (AR) technologies – Vuforia, Immersal, MultiSet, and the ARCore Geospatial Application Programming Interface (API) – in terms of performance, accuracy, and interference tolerance for indoor and outdoor positioning and navigation. Two test environments were used: an indoor (laboratory) setup enabling detailed module testing, and a hybrid deployment on the Cracow University of Technology (CUT) campus to illustrate the feasibility of AR navigation in diverse environmental conditions. The research was conducted according to six scenarios. One involved outdoor GPS navigation, while the others concerned indoor navigation. Based on the measurements, recommendations are provided for selecting AR localization platforms for mixed navigation. As part of the detailed testing, an AR navigation system was implemented on the CUT campus as a combination of indoor and outdoor approaches. The final implementation was developed in the Unity environment. Software tests were conducted with particular emphasis on transitions between indoor and outdoor navigation.
Computer Assisted Methods in Architecture & Urban Planning
Copyright (c) 2025 Krzysztof Skabek, Dominika Rola, Wojciech Zamarski https://creativecommons.org/licenses/by/4.0
Reconstruction of Atomistic Models of Dislocations by Means of Finite Deformation Theory
The present paper discusses mathematical barriers in the development of software for preprocessing of atomistic models of dislocation networks. As a matter of fact, as yet, there are neither analytical nor numerical methods nor programs available which can be used for atomistic reconstruction of complex dislocation networks. Some of the problems to overcome are discussed in this paper. In the previous papers discussed below it was shown that a direct superposition of analytic formulae for displacements of atoms induced by single dislocations does not give possibility to hold the essential geometric properties of the resultant atomistic models. Namely, after the input of first dislocation, the lattice symmetry required to input the next dislocations is usually broken. These inaccuracies compose the mathematical barrier for atomistic reconstruction of advanced dislocation nets. A method developed here has been applied to reconstruction of the dislocation nodes localized in the copper/shaffire interface. In the present case, the partial dislocations are inserted by slips. For comparison, the junction corresponding to the stacking faults obtained by the rigid shifts of copper on the Burgers vector 1/6 <112> are discussed.
Copyright (c) 2025 Paweł Dłużewski, Kinga Nalepka https://creativecommons.org/licenses/by/4.0
Characterization Method of the Damping in Harmonically Forced Two-State Piecewise Linear Elastic Structures
An equivalent viscous damping ratio is introduced to characterize the energy dissipation caused by the plastic impact in the periodic responses of piecewise linear elastic systems.
Copyright (c) 2025 Bilal Alzubaidi, Róbert K. Németh https://creativecommons.org/licenses/by/4.0
AI-Assisted Microscopy for Infection Biology: Advances in High-Content Imaging of Host-Pathogen Interactions
Advances in high-content microscopy and artificial intelligence (AI) are transforming the quantitative study of infection biology. Automated imaging platforms now enable rapid, large-scale acquisition of host-pathogen interactions across thousands of cells and multiple experimental conditions. When combined with AI-based segmentation, these workflows extract infection-relevant features such as pathogen load, intracellular localization, and host response markers at single-cell resolution. Deep-learning models have proven especially powerful, outperforming classical threshold-based methods under different imaging conditions, reducing reliance on manual annotation, and detecting rare infection outcomes. Beyond robust image analysis, these approaches generate scalable and reproducible datasets that can be integrated with computational modelling and systems biology, providing predictive insight into infection dynamics. This review highlights recent progress in AI-assisted microscopy for bacterial infection and outlines future directions toward multimodal integration, clinical translation, and open-source tool development.
Copyright (c) 2025 Juan Alfonso Redondo, Pawel Paszek https://creativecommons.org/licenses/by/4.0
Digital Tool Supporting the Documentation and Analysis of Cultural Heritage: The Case of the Analytical 3D Model of the Zamość Fortress
This paper presents a city-scale digital documentation and analysis workflow for cultural heritage assets, demonstrated on the example of the UNESCO-listed historic centre and fortress area of Zamość (Poland). The study integrates terrestrial laser scanning (TLS) with unmanned aerial vehicle (UAV) and terrestrial photogrammetry to produce a geometrically consistent 3D dataset covering over 100 buildings and key public-space elements. The processing pipeline includes scan registration, image-based reconstruction, and cross-sensor alignment, followed by the creation of an analytical 3D model segmented by address and parcel identifiers to enable linkage with municipal datasets. A semantic layer is implemented by assigning a structured set of building- and neighbourhood-level parameters and mapping them into building information modelling (BIM)/openBIM structures (Revit shared parameters and industry foundation classes (IFC) Property Sets), targeting a level of information adequate for conservation-oriented diagnostics and urban-scale assessments rather than detailed component-level historic building information modelling (HBIM). Geometric quality is verified using independent checkpoints and registration statistics (e.g., root mean square error (RMSE) where applicable), yielding a typical spatial agreement on the order of 4 cm to 5 cm for the integrated model in representative test areas. The resulting environment supports multi-criteria querying and visualisation, including functional categorisation, technical condition screening (e.g., moisture-related indicators), and energy-related attributes for prioritisation at the district scale. The main contribution is a reproducible integration of multi-source survey data with an explicit semantic/BIM mapping and verifiable accuracy reporting for a heritage city context, clarifying which outputs stem from the proposed method (data integration, segmentation, semantic schema, and validation) versus the standard capabilities of the employed software.
Copyright (c) 2025 Michał Wac, Bartosz Szostak https://creativecommons.org/licenses/by/4.0
Goal-Rriented Optimal Sensor Placement for PDE-Constrained Inverse Problems in Crisis Management
This paper presents a novel framework for goal-oriented optimal static sensor placement and dynamic sensor steering in PDE-constrained inverse problems, utilizing a Bayesian approach accelerated by low-rank approximations. The framework is applied to airborne contaminant tracking, extending recent dynamic sensor steering methods to complex geometries to improve computational efficiency. A C-optimal design criterion is employed to strategically place sensors, minimizing uncertainty in predictions. Numerical experiments validate the approach’s effectiveness for source identification and monitoring, highlighting its potential for real-time decision-making in crisis management scenarios.
Copyright (c) 2026 Marco Mattuschka, Noah An der Lan, Max von Danwitz, Daniel Wolff, Popp Alexander https://creativecommons.org/licenses/by/4.0
The Impact of Point Cloud Simplification on the Accuracy of the Viewshed
Contemporary visibility analyses, particularly relevant in environmental and landscape studies, require the processing of very large datasets derived from point clouds. While such data provide high accuracy, they also involve substantial computational demands and long processing times, which limit their practical applicability. This article presents a detailed analysis of the impact of point cloud simplification on the accuracy of viewshed. The viewshed diagrams were generated using the ray tracing method, and the analysis included an evaluation of discrepancies between results obtained from simplified datasets and reference outcomes based on the complete, unprocessed dataset. In addition, the computation time required to generate viewshed under different levels of simplification was investigated. The findings made it possible to identify the maximum acceptable levels of simplification as well as the potential computational gains in terms of the number of processed points. The results demonstrate that properly selected simplification levels can significantly enhance the efficiency of ray tracing-based visibility analyses while preserving their practical reliability.
Copyright (c) 2025 Jerzy Orlof, Adrian Widłak https://creativecommons.org/licenses/by/4.0
Isogeometric Topology Optimization Based on Topological Derivatives
Topology optimization is a valuable tool in engineering, facilitating the design of optimized structures. However, topological changes often require a remeshing step, which can become challenging. In this work, we propose an isogeometric approach to topology optimization driven by topological derivatives. The combination of a level-set method together with an immersed isogeometric framework allows seamless geometry updates without the necessity of remeshing. At the same time, topological derivatives provide topological modifications without the need to define initial holes [7]. We investigate the influence of higher-degree basis functions in both the level-set representation and the approximation of the solution. Two numerical examples demonstrate the proposed approach, showing that employing higher-degree basis functions for approximating the solution improves accuracy, while linear basis functions remain sufficient for the level-set function representation.
Copyright (c) 2025 Guilherme Henrique Teixeira, Nepomuk Krenn, Peter Gangl, Benjamin Marussig https://creativecommons.org/licenses/by/4.0
Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study
In the era of Industry 4.0, one of the key challenges facing underground mines is the real-time tracking of both the production process and machinery movements. Significant emphasis is placed on comprehensive monitoring to achieve situational awareness to ensure informational continuity of operations in dispersed organizations. This knowledge is fundamental for safe and efficient extraction, current production reconciliation, and all operational and planning activities, particularly when considering specialized simulation environments for production optimization. So far, implementations of such solutions on an industrial scale have primarily been encountered in open-pit mines or smaller underground mines. This article presents a solution for machine monitoring and tracking based on data from a collision avoidance system, specifically designed for multi-site underground mining enterprises, where the scale of implementation is incomparably more challenging. This anti-collision system was originally designed for detecting machine-to-machine or machine-to-worker collisions. Consequently, the development of validation algorithms, including error correction and adaptive filtering, was imperative. This also required integration with enterprise resource planning (ERP) systems. Moreover, it was also essential to enhance the system infrastructure with additional sensors to enable the registration of machine localization in specified mining zones (e.g., heavy machinery chamber, mining area, loading and unloading point). As part of this study, several analytical models (enhanced by machine learning techniques) were developed to identify movement patterns and cooperation among wheeled transport machinery, as well as the entire course of ore logistics within the mining area. Finally, the process of implementing the system in the target environment is presented, along with a description of the user interface, which features manager dashboards for production visualization.
Copyright (c) 2020 Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński https://creativecommons.org/licenses/by/4.0
Parametric Analysis of Damage in Cancellous Bone and in Materials with Bone-like Microstructure
A systematic approach to the macroscopic damage analysis of bone-like cellular materials is presented in which damage conditions are expressed as tabularized functions of microstructure geometry parameters. Based on three different strain-based microscopic damage criteria, a large number of cellular microstructures, characterized by different values of geometric parameters, are analysed by the finite element method to determine damage factor values for a number of macroscopic strain states. As a result, an exhaustive database is prepared in which macroscopic damage conditions for a variety of microstructures are presented as tabularized parametric functions of both geometric parameters and strain states. A numerical procedure of data interpolation is proposed as a tool to predict parameterized damage surfaces for any bone-like microstructure. The results are made publicly available in an open data repository to enable further research on their characterization and analytical approximation.
Copyright (c) 2025 Piotr Kowalczyk https://creativecommons.org/licenses/by/4.0
Application of the Finite Pointset Method to Numerical Modeling of Heat Transferin Two-dimensional Tissue Subjected to a Moving Laser Beam
In this article, a numerical analysis of thermal processes occurring in biological tissue during laser irradiation is presented. The mathematical model is based on the two-dimensional Pennes equation. The analyzed tissue is exposed to the laser irradiation of a moving beam with constant velocity along the tissue surface. The upper face of the skin tissue is subjected to the vertical laser beam, and it is assumed that heat dissipation through convection and radiation from the surface is negligible compared to the heat delivered by the laser beam. Thus, the surface is treated as thermally insulated surface. The effect of the laser beam’s transitional speed and power on the temperature distribution within the skin tissue are investigated. Moreover, the perfusion rate and the effective scattering coefficient are treated as variables dependent on tissue damage. In the computational part of this study, the finite pointset method (FPM) is applied. The temperature distribution computed with FPM is compared with an analytical one obtained for a three-dimensional problem by analyzing a relevant cross-section under the same conditions. This modeling of the dynamic thermal processes within biological tissue subjected to laser irradiation supports the evaluation of biological tissue damage and provides a basis for determining the time and intensity of laser irradiation. In the last part of the article, numerical examples and conclusions are presented.
Copyright (c) 2025 Anna Korczak, Felix R. Saucedo-Zendejo https://creativecommons.org/licenses/by/4.0
Pattern-Based Approach to Topology and Geometry Optimization of Steel Building Structures
This research primarily focuses on evaluating the effectiveness of various methodologies for the topological and geometrical optimization of steel building structures through parametric descriptions. The study specifically addresses steel trusses, frames, and beams, emphasizing their integration within the broader structural system. Initial investigations have highlighted the benefits of an innovative pattern-based approach that segments the structure into distinct patterns, namely groups of structural elements subjected to localized optimization. This method effectively overcome the challenges of global parameter optimization, by providing enhanced control over local criteria and enabling a more detailed assessment of each pattern’s contribution to global optimization objectives. Building on these insights, the research seeks to advance and refine the concept of patterns, aiming to further enhance their applicability and efficiency in structural optimization.
Copyright (c) 2025 Artur Lax, Sławomir Milewski https://creativecommons.org/licenses/by/4.0
A Study on the Extraction of Training Dataset from Fine-Tuned Language Models
Large language models (LLMs) excel at various natural language tasks, even those beyond their explicit training. Fine-tuning these models on smaller datasets enhances their performance for specific tasks but it can also lead to risk of training data memorization, raising privacy concerns. This study explores the extraction of private training data from fine-tuned LLMs through a series of experiments. The focus is on assessing the ease of data extraction using various techniques and examining how factors such as the size of training data, number of epochs, training sample length and content, and fine-tuning parameters influence this process. Our results indicate that data extraction is relatively straightforward with direct model access, especially when training loss is computed over entire prompts. Models with higher precision (8-bit and 16-bit) demonstrate increased memorization capabilities compared to 4-bit quantized models. Even without direct access, insights into training data can be obtained by comparing output probability scores across multiple queries. Furthermore, the study also reveals that the proportion of extractable data increases with training dataset size, given a fixed number of epochs. These findings highlight the privacy risks faced by individuals whose data is used in fine-tuning, as well as for organizations deploying fine-tuned models in public applications.
Copyright (c) 2025 Raja Vavekanand, Aybek Kalandarov Ruzimbaevich, Muhabbat Jumaniyozova https://creativecommons.org/licenses/by/4.0
Transient heat conduction by different versions of the Method of Fundamental Solutions - a comparison study
The computational accuracy of three versions of the method of fundamental solutions (MFS) is compared. The first version of MFS is based on the Laplace transformation of the governing differential equations and of the boundary conditions. The second version of MFS is based on the fundamental solution of the governing differential equation and discretization in time. The third method approximates the temperature time derivative by finite difference scheme. As the test problems the 2D boundary-initial-value problems (2D_BIVP) in square rectangular region Ω with known exact solutions are considered. Our numerical experiments show that all discussed methods achieve relatively accurate approximate solution but the third one offers less computational complexity and better efficiency.
Identification of solidification process parameters
In the paper, the identification problems connected with estimation of cast iron and mould thermophysical parameters are discussed. The additional information necessary to solve the problem results from the knowledge of cooling (heating) curves at the set of points from casting (mould) domain. The course of cooling (heating) curves results from the temperature measurements done in the real conditions of technological process, but at the present stage of research the numerical solution of direct problem plays the role of measured temperatures. In this place the problem of optimal sensors position in a system castingmould appears. Both the choice of measuring points and also the solution of inverse problem, using the gradient methods, require the application of sensitivity analysis methods. The theoretical considerations are illustrated by the examples of computations. The numerical algorithms presented base on the finite difference method (2D problems are considered).
Evolutionary identification of laminates' granular parameters
The paper deals with the identification of material constants in simple and hybrid laminates. It is assumed that identified constants are non-deterministic and can be described by means of different forms of the information granularity represented by interval numbers, fuzzy numbers or random variables. The Two-Stage Granular Strategy combining global (Evolutionary Algorithm) and local (gradient method supported by an Artificial Neural Network) optimization techniques is used to solve the identification problems. Finite Element Method in the granular form is used to solve the direct problem for laminates. Modal analysis methods are employed to collect measurement data for the identification process. Numerical examples presenting effectiveness of the strategy are enclosed.
Multiobjective evolutionary optimization of MEMS structures
The paper is devoted to the shape optimization of piezoelectric and electro-thermo-mechanical devices by the use of multiobjective evolutionary algorithm. In this paper, special implementation of multiobjective evolutionary algorithm is applied (MOOPTIM). Several test problems are solved in order to test efficiency of the algorithm. The results are compared with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The objective function values are calculated for each chromosome in every generation by solving a boundary value problem for the piezoelectricity and electro-thermal-mechanical analysis. In order to solve the boundary value problems, the finite element method is used. Different functionals based on the results derived from coupled field analyses are formulated. The aim of the multiobjective problem is to determine the specific dimensions of the optimized structures. Numerical examples for multiobjective shape optimization are enclosed.
Root finding method for problems of elastodynamics
This paper presents a simple and efficient method for finding complex roots of dispersion equations occurring in many problems of elastodynamics. The method is characterized by high accuracy in root finding and absence of restrictions on function representation. The essence of the method is explained geometrically; initial guesses are found as the solutions to the appropriate problems of elastostatics. Numerical solutions to dispersion equations are obtained for two elastic isotropic waveguides: a plate of infinite cross-section and a rod of rectangular cross-section. For an infinite plate, the calculated results are in full conformity with those obtained by Newton-Raphson and bisection methods. For a waveguide of rectangular cross-section, the earlier unsolved problem of finding complex roots of dispersion equations is solved by the proposed method.
Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study
This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SHM). A self-referencing setup, employing FBG sensors with edge filtering method and remote bonding, enables a baseline-free damage detection approach. The methodology is validated as a proof-of-concept numerical model. The simulation framework incorporates a three-dimensional spectral element method for precise and efficient modelling of GW propagation and interactions with structural anomalies. Three different machine learning (ML) techniques are employed to detect and localize damages, demonstrating effectiveness of ML methods compared to traditional methods. The three techniques employed are decision tree, logistic model tree and random forest. Key findings highlight the effectiveness of random forest models in classifying damage states with a 98.67% accuracy. Different feature selection methods, are used to identify critical features. The proposed methodology reduces sensor requirements, lowers system complexity and cost, and enables efficient SHM solutions in extreme or large-scale environments. This work underscores the potential of ML techniques to perform detection and localization where traditional techniques fail.
machine learning (ML) referencing method
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
Modeling of Tissue Laser Irradiation in Cylindrical Coordinates Using the Finite Pointset Method
This study focuses on a numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation under transient conditions within a two-dimensional (2D) cylindrical domain. The tissue undergoes laser irradiation, with internal heat sources determined based on the Beer–Lambert law. Moreover, key parameters, including the perfusion rate and effective scattering coefficient, are modeled as functions dependent on tissue damage. Numerical computations are performed using the finite pointset method (FPM). The findings, discussed in the final section, indicate that the FPM approach is a viable and effective tool for analyzing thermal processes in biological tissues.
Homogenization of composites with interfacial debonding using duality-based solver and micromechanics
One of the key aspects governing the mechanical performance of composite materials is debonding: the local separation of reinforcing constituents from matrix when the interfacial strength is exceeded. In this contribution, two strategies to estimate the overall response of particulate composites with rigid- brittle interfaces are investigated. The first approach is based on a detailed numerical representation of a composite microstructure. The resulting problem is discretized using the Finite Element Tearing and Interconnecting method, which, apart from computational efficiency, allows for an accurate representation of interfacial tractions as well as mutual inter-phase contact conditions. The candidate solver employs the assumption of uniform fields within the composite estimated using the Mori-Tanaka method. A set of representative numerical examples is presented to assess the added value of the detailed numerical model over the simplified micromechanics approach.
FE based structural reliability analysis using STAND environment
An assessment of structural reliability requires multiple evaluations of the limit state function for various realizations of random parameters of the structural system. In the majority of industrial applications the limit state functions cannot be expressed explicitly in terms of the random parameters but they are specified using selected outcomes of the FE analysis. In consequence, in order to be useful in practice, a structural reliability analysis program should be closely integrated with a FE module or it should be interfaced with an advanced external FE program. When the FE source code is not available, which is usually the case, the only option is to establish a communication between the reliability analysis program and an external FE software through the batch mechanism of data modification, job submission and results extraction. The main subject of this article is to present the reliability analysis capabilities of STAND software, which is being developed in the Institute of Fundamental Technological Research of Polish Academy of Sciences. A special emphasis is put on the issues related to its interfacing with external general purpose FE codes. It is shown that when shape type random variables are used, leading to modifications of the FE mesh, or when the limit state function contains numerical noise, standard algorithms for localizing the design point often fail to converge and a special method based on some response surface approximation is needed. A proposition of such a strategy that employs an adaptive response surface approximation of the limit state function is presented in this article. Development of a reliability analysis program is a challenging project and calls for such a code organization, which would facilitate a simultaneous work of many programmers and allow for easy maintenance and modifications. The so-called object-oriented programming seems to provide a convenient framework to realize these objectives. The object-oriented approach is used in STAND development. The advantages of this programming paradigm and a short description of the STAND's class hierarchy are presented in the text. The study is concluded with two numerical examples of interfacing STAND with state of the art commercial FE programs.
The method of the fundamental solutions and its modifications for electromagnetic field problems
The paper presents the method of fundamental solutions (MFS) for solving electromagnetic problems. We compare the MFS with the method of boundary integral equations in solution of potential problems. We demonstrate the MFS technique together with the Laplace transform in application to the problem of scattering of electromagnetic pulses. A modification of the MFS - the method of approximate fundamental solutions (MAFS) is also considered in the paper. The method is applied to axisymmetric field problems. Numerical examples justifying the methods are presented.
Fuzzy control based on “true and false” philosophy for mechatronics systems
For man, possessing the ability to recognize the specific situation that has arisen at any instant of time and taking the appropriate decision without using a mathematical model is what ?adaptation? means. Adaptive principles are being extended to more complex systems in widely different areas, in which we need to replace the traditional metaphor of a fixed environment with a dynamic and constantly changing one. Each mechatronic system, for example, is usually, faced with a multiplicity of choices and objectives of the system change with it. Its modeling requires more than a mathematical model that is based on clear definitions and axioms using the rules of logic deduction theory. To solve this problem, the new concept of system integration by software control, such as real-time multi-tasking operating system using fuzzy logic, is emphasized in the education of modem mechatronics engineering. In this paper, I would like to return, firstly, to the fundamental element of truth measure, which we can use as basis for constructing a way to spread the binary philosophy rather than its rejecting and to increase our ability to describe the real world. Next, in order to explanation into details one aspect of real-time software using fuzzy logic, applied in control of mechatronic systems, we present a fuzzy control technology that combines artificial intelligence and control methodologies. It achieves control purposes based on expert knowledge and experience expressed in the form of IF-THEN rules (Sugeno-type) or neural networks using neuro- fuzzy based intelligent control scheme in order to create a real-time-adaptive control process. Application of this technology is presented in numerical example of trajectory control of PUMA 560 robot manipulators using Fuzzy Artificial Neural Network, FANN, rather than using Artificial Neural Network, ANN, only.
A time-step insensitive recurrent approach to analyze non-stationary random responses
The recurrent approach constructed via the stochastic central difference (SCD) is a very fast method for analyzing non-stationary random responses. However, the computational results depend to a great extent upon the discrete time-step size. A new recurrent approach is proposed in this report. It is based on the theory of linear differential equations. Theoretical analysis shows that this algorithm is unconditionally stable for all damped systems. Two examples show that the proposed approach is not sensitive to the time-step.
Application concept of artificial neural networks for turbomachinery design
This paper presents the results of an extensive investigation evaluating and improving the development of artificial neural network (ANN) models for turbomachinery design purposes. A set of 1100 differing axial compressor geometries based on 5 single-stage compressor rigs was prepared. Computations with the mean line analysis tool AXIAL™ took place to determine the according compressor maps defined by 15 operating points each. The challenge of ANN model development in terms of dimensionality reduction (feature selection), data normalization, defining the networks necessary plasticity, and network training is discussed using the example of three different models. As a result, the first model is able to predict the total pressure loss of the rotor blade row with a mean magnitude of the relative error (MMRE) of 3.6%. The second model predicts the total pressure ratio with an average accuracy of 0.8%. The third and last model was trained to predict basic geometrical parameters by presenting the load level and the performance data as an input. The achieved MMRE varied between 2.4% and 5.6% in respect of the particular output variable. The results show that ANNs are applicable to develop efficient models for turbomachinery design and analysis purposes, respectively.
A method of identification of kinematic chains and distinct mechanisms
A new method is proposed to identify the distinct mechanisms derived from a given kinematic chain in this paper. The kinematic chains and their derived mechanisms are presented in the form of a flow matrix. Two structural invariants, sum of the absolute values of the characteristic polynomial coefficients (SCPC) and maximum absolute value of the characteristic polynomial coefficient (MCPC) are determined using the software MATLAB. These invariants are used as the composite identification number of a kinematic chain and mechanisms and clearly identify the distinct mechanisms derived from the family of 1-F, 8-links and 10-links KC as well as 2-F, 9-links simple joined KC. This study will help the designer to select the best possible mechanism to perform the specified task at the conceptual stage of design. The proposed method does not require any test for isomorphism separately. Some examples are provided to demonstrate the effectiveness of this method.