Variational-Based Reduced-Order Model in Dynamic Substructuring of Coupled Structures Through a Dissipative Physical Interface: Recent Advances

This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative substructures perfectly connected through interfaces by a linking substructure. The linking substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present recent advances adapted to such a situation, which is positioned with respect to an appropriate review that we carry out on the different methods used in dynamic substructuring. It consists in constructing a reduced-order model using the free-interface elastic modes of the two main substructures and, for the linking substructure, an appropriate frequency-independent elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.     

Determination of catalytic active sites in (Ce, Ca)-supported Y zeolite by Fourier transform IR spectroscopy

Catalytic active sites in an aluminosilicate matrix Y zeolite were generated by exchange with calcium and cerium chloride solutions, followed by calcination. Acid properties of the (Ce, Ca)^ex-NaY zeolite were studied by semi quantitative (Fourier transform) IR spectroscopy of the pyridine adsorption. The molecular probe interacts with Brönsted active sites (BASs) and bonds coordinatively to Lewis active sites (LASs). The main observed frequencies were 1459 cm⁻¹ (BAS) and 1443 cm⁻¹ (LAS). It has been found that the BAS:LAS optical density ratio was ca. 1.4. The observed protonic active sites may be due to cerium cation-hydroxyl supported on the Y zeolite matrix under specific ion exchange and thermal conditions.     

Improving robustness of industrial object detection by automatic generation of synthetic images from CAD models

Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non-expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process.     

Edge Fracture Prediction Using Uncoupled Ductile Fracture Models for DP780 Sheet

This paper is a contribution to the prediction of edge fracture behavior using uncoupled ductile fracture models. A fully integrated simulation framework for the edge fracture prediction is proposed with the shear-induced pre-damage considered. User-defined material subroutines are coded with uncoupled ductile fracture models (Lou-Huh, Oh, Brozzo) incorporated, which are calibrated using the fracture strains of various loading paths. A series of 3D numerical simulations are performed and compared with the results of hole-expansion tests. The effects of pre-damage field and fracture models are analyzed and discussed.

     

Quantum-inspired design of resilient substitution boxes: From coding to hardware implementation

S-boxes constitute a cornerstone component in symmetric-key cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and non-correlated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a twofold objective: first, we evolve regular S-boxes with high non-linearity and low auto-correlation properties; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use a quantum-inspired evolutionary algorithm to optimize regularity, non-linearity and auto-correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit the same algorithm to automatically generate the evolvable hardware designs of substitution boxes that minimize hardware space and encryption/decryption time, which form the two main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through genetic algorithm. We will show that our approach provides higher quality S-boxes coding as well as circuits.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.     

A New Distribution System Reconfiguration Approach Using Optimum Power Flow and Sensitivity Analysis for Loss Reduction

This paper presents a new approach for distribution system reconfiguration (DSR) based on optimum power flow (OPF) in which the branch statuses (open/close) are represented by continuous functions. In the proposed approach, all branches are initially considered closed, and from the OPF results, a heuristic technique is used to determine the next loop to be broken by opening one switch. Then the list of switches that are candidates to be opened is updated, and the above process is repeated until all loops are broken, making the distribution system radial. This paper includes results and comparisons on test systems utilized in three classical papers published in the technical literature, as well as in a previous paper by the authors. Results obtained on a real large-scale distribution system are also presented     

NAROAS: a neural network-based advanced operator support system for the assessment of systems reliability

We have developed and implemented a computerized reliability monitoring system for nuclear power plant applications, based on a neural network. The developed computer program is a new tool related to operator decision support systems, in case of component failures, for the determination of test and maintenance policies during normal operation or to follow an incident sequence in a nuclear power plant. The NAROAS (Neural Network Advanced Reliability Advisory System) computer system has been developed as a modularized integrated system in a C++ Builder environment, using a Hopfield neural network instead of fault trees, to follow and control the different system configurations, for interventions as quickly as possible at the plant. The observed results are comparable and similar to those of other computer system results. As shown, the application of this neural network contributes to the state of the art of risk monitoring systems by turning it easier to perform online reliability calculations in the context of probabilistic safety assessments of nuclear power plants.     

A new approach to designing reduced scale thermal-hydraulic experiments

Reduced scale experiments are often employed in engineering because they are much cheaper than real scale testing. Unfortunately, though, it is difficult to design a thermal-hydraulic circuit or equipment in reduced scale capable of reproducing, both accurately and simultaneously, all the physical phenomena that occur in real scale and operating conditions. This paper presents a methodology to designing thermal-hydraulic experiments in reduced scale based on setting up a constrained optimization problem that is solved using genetic algorithms (GAs). In order to demonstrate the application of the methodology proposed, we performed some investigations in the design of a heater aimed to simulate the transport of heat and momentum in the core of a pressurized water reactor (PWR) at 100% of nominal power and non-accident operating conditions. The results obtained show that the proposed methodology is a promising approach for designing reduced scale experiments.