Computational Time Requirements Research Articles

Introducing Interactions in Multi-Objective Optimization of Software Architectures

Software architecture optimization aims to enhance non-functional attributes like performance and reliability while meeting functional requirements. Multi-objective optimization employs metaheuristic search techniques, such as genetic algorithms, to explore feasible architectural changes and propose alternatives to designers. However, this resource-intensive process may not always align with practical constraints. This study investigates the impact of designer interactions on multi-objective software architecture optimization. Designers can intervene at intermediate points in the fully automated optimization process, making choices that guide exploration towards more desirable solutions. Through several controlled experiments as well as an initial user study (14 subjects), we compare this interactive approach with a fully automated optimization process, which serves as a baseline. The findings demonstrate that designer interactions lead to a more focused solution space, resulting in improved architectural quality. By directing the search towards regions of interest, the interaction uncovers architectures that remain unexplored in the fully automated process. In the user study, participants found that our interactive approach provides a better trade-off between sufficient exploration of the solution space and the required computation time.

Numerical investigation on hydrothermal characteristics of microchannel heat sinks with PCM inserts for effective thermal management applications

PurposeThe purpose of the paper is to develop an efficient thermal management system, which effectively dissipate the heat generated from the electronic devices. The present paper focuses at the modeling of microchannel heat sinks (MCHSs) with phase change materials (PCMs) insets to deal with the fluctuating heat generated from the electronic components.Design/methodology/approachIn this paper, a novel approach is introduced to enhance the thermal performance of MCHSs through the integration of conjugate heat transfer and energy storage. Numerical investigations were conducted on six novel models of PCM-based hybrid MCHSs using ANSYS-FLUENT. The hydrothermal characteristics of six PCM-based hybrid MCHS models were analyzed and compared with an MCHS model without PCM.FindingsThe numerical model used for this study exhibited a good agreement with existing experimental and simulation results documented in the literature. The hybrid MCHS models developed in the present analysis showed superior thermal characteristics compared to MCHS without PCM. About 12% improvement in the thermal performance factor and a 7.3% reduction in thermal resistance were observed in the proposed MCHS models. A negligible influence of the PCM channel shape and aspect ratio (AR) was observed on the MCHS performance.Research limitations/implicationsAs the present work is a numerical investigation, the computational time and computational cost requirements are the main implication for the research.Practical implicationsHigh pumping power requirement and expensive manufacturing methods of the microfluidic devices are the main practical implications. Leakage problem is also a challenge for development of these heat sinks.Originality/valueThe surge in the heat generated by electronic components is a limiting factor for the conventional MCHSs. To accommodate the surge, researchers have explored energy storage methods using PCM-based passive MCHS but these are effective only during the phase change process. To address this limitation, novel PCM-based hybrid MCHSs, which combine convective heat transfer with energy storage capabilities, have been modeled in the present work. There is an ample opportunity for further exploration of hybrid MCHSs with PCM.

Interference Analysis of Very Fast Transient Overvoltages in Gas-Insulated Substations Using a Hybrid Algorithm

The reliable and efficient computational approach for the cable interference in gas insulated substations (GIS) is presented. The hybrid domain decomposition method-multilevel fast multi-pole algorithm (DDM-MLFMA) is presented herein. This technique determines the specifications of GIS cables for RG58 and AWG23. Self and mutual interference are identified utilizing the very fast transient overvoltage (VFTO) transient interference signal. The hybrid technique offers a numerical simulation to replicate the impact of the VFTO interference signal. Utilizing the matrix-vector multiplication product (MVX) to address compression and approximation challenges, the hybrid approach proves reliable and pragmatic. Because of its computational nature and explicit factorization reduction, this strategy reduces computation time and memory requirements. Consequently, the system's complexity follows a linear trend under this proposed approach. The computed results are juxtaposed with traditional methodologies to validate the effectiveness of the proposed algorithm.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Probabilistic online learning framework for short-term wind power forecasting using ensemble bagging regression model

The increasing penetration of renewable energy sources, with a notable focus on wind power, within modern electricity grids requires computationally efficient and burden-free short-term wind power forecasting models. Traditional models generating prediction intervals are trained offline and thus deployed for prediction purposes. However, this approach cannot obtain interval forecasts from the most recent wind power observations. In contrast, combining multiple regression models through ensemble learning is recognised as a successful method for improving forecasting performance. By utilising the most recent observations and exploiting the strengths of multiple regression models, this article investigates an Online Ensemble Bagging Regression (OEBR) model for generating prediction intervals. Online gradient descent based optimisation algorithms capable of adaptive-depth calculation from a stream of observations are used here to address the problems with traditional batch learning frameworks. The proposed online learning framework is evaluated against other online learning frameworks using publicly accessible datasets. The results show the proposed model competes with the compared models regarding probabilistic metrics and energy estimations and outperforms computational time requirements for the same number of observations.

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Energy management based on coalitionnal game subdivision applied to energy communities

The energy transition requires rethinking how we produce and consume energy. Energy communities (EC) provide a recent legal framework for sharing energy, aiming to reduce energy bills and the environmental footprint of their participants. One of the challenges is adapting economic models to this technological upheaval. In this context, cooperative games, based on game theory, are valuable tools for modeling energy management through cooperation. However, despite their promising characteristics, cooperative games are limited by their computational complexity. The required computation time to solve cooperative games increases exponentially with the number of participants, restricting their application in energy management. This paper aims to propose a solution to apply cooperative game theory tools to larger communities using a multidisciplinary approach. For this purpose, a game subdivision approach based on the specific properties of energy communities is proposed. This methodology will be shown to be efficient in terms of computation time. While the game theory concepts are depreciated by limiting computing time, the sub-games method can become an interesting tool in energy management. Advantages and drawbacks in terms of energy management and game theory are discussed in this paper.

Machine learning-based prediction of FeNi nanoparticle magnetization

This work proposes a computationally efficient approach for estimating the magnetization of Fe0.7Ni0.3 body-centered cubic (bcc) nanoparticles (NPs) at room temperature using machine-learning algorithms, in terms of the average magnetic moment per atom, ⟨μ⟩. The magnetization data of isolated NPs were generated using atomistic spin dynamics (ASD) simulations for various nanoparticle shapes (cubes, spheres, octahedra, cones, cylinders, ellipsoids, flakes, and pyramids, with or without nanovoids) and FeNi distributions (random, core-shell, onion, sandwich, and Janus with different boundary planes). More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. The best-performing models, CatBoost and RF, achieved R2 scores of up to 0.86, demonstrating their accuracy in predicting NP magnetization. Feature analysis highlighted the significance of the interface between Fe and Ni clusters, Fe–Fe interactions, and the presence of Fe on the surface as critical contributors to overall magnetization. Random alloy spherical NPs without porosity exhibited the highest ⟨μ⟩ ∼ 1.6μB due to reduced Ni–Ni interactions. Applying machine-learning methods significantly reduces computational time and memory requirements compared to traditional ASD simulations. This allows for rapid prediction of NPs with desired magnetic properties, making them suitable for various technological applications.

Cable Interference Analysis of Gas Insulated Substation Based on Domain Decomposition Method-Multilevel Fast Multipole Algorithm

To explore cable interference in gas insulated substations (GIS), this paper proposes a reliable and efficient computational approach. The hybrid domain decomposition method-multilevel fast multipole algorithm (DDM-MLFMA) is presented herein. This technique determines the specifications of GIS cables for RG58 and AWG23. Self and mutual interference are identified utilizing very fast transient overvoltage (VFTO) transient interference signal.This hybrid technique offers a numerical simulation to replicate the impact of the VFTO interference signal. Utilizing the matrix-vector multiplication (MVX) product to address compression and approximation challenges, the hybrid approach proves reliable and pragmatic. Because of its computational nature and explicit factorization reduction, this strategy reduces computation time and memory requirements. Consequently, the system’s complexity follows a linear trend under this proposed approach. The computed results are juxtaposed with traditional methodologies to validate the effectiveness of the proposed algorithm.

Study of the Simulation and Application of the PEEC Method for Modeling the Prediction of Emissions Radiated by the Onboard Electronic Wiring System

The coexistence of electronic power and control systems in the same box presents a serious threat to the proper functioning of electronic systems integrated into automobiles, aeronautics and space. Thus, managing the electromagnetic compatibility of these systems constitutes a challenge, particularly given electromagnetic phenomena. This paper describes a simulation study and application of the partial element equivalent circuit method to model the prediction of radiated emissions from the onboard electronic cable system. In this article, we first explain the use of the partial cell equivalent circuit modeling method, then describe its mathematical formulation and introduce the different electromagnetic phenomena it considers in connection with wiring, then propose radiation calculations electromagnetic as a function of the geometry of the discrete cells taking into account the increase in frequency. The goal is to get as close to the structure as possible. To balance the accuracy and speed of the proposed method, we replaced the partial capacities with the equivalent capacities so as to considerably minimize the number of capacities, the complexity of the system and the computational time requirement. This option adapts the partial element equivalent circuit method to the study of larger structures such as cable structures and provides an efficient and rapid way to simulate electromagnetic radiation. To achieve this objective, we have associated the physical laws of electromagnetic wave propagation with the method of modeling equivalent circuits using partial elements because it is easy to manipulate flat surfaces and cables. 2D simulations based on the proposed models were developed as well as the verification of the consistency of the different models, by comparing the fractal dimensions of the program results with those of the figures obtained experimentally.

Simultaneous design optimisation methodology for floating offshore wind turbine substructure and feedback-based control strategy

This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.

Dual Domain Decomposition Method for High-Resolution 3D Simulation of Groundwater Flow and Transport

The high-resolution 3D groundwater flow and transport simulation problem requires massive discrete linear systems to be solved, leading to significant computational time and memory requirements. The domain decomposition method is a promising technique that facilitates the parallelization of problems with minimal communication overhead by dividing the computation domain into multiple subdomains. However, directly utilizing a domain decomposition scheme to solve massive linear systems becomes impractical due to the bottleneck in algebraic operations required to coordinate the results of subdomains. In this paper, we propose a two-level domain decomposition method, named dual-domain decomposition, to efficiently solve the massive discrete linear systems in high-resolution 3D groundwater simulations. The first level of domain decomposition partitions the linear system problem into independent linear sub-problems across multiple subdomains, enabling parallel solutions with significantly reduced complexity. The second level introduces a domain decomposition preconditioner to solve the linear system, known as the Schur system, used to coordinate results from subdomains across their boundaries. This additional level of decomposition parallelizes the preconditioning of the Schur system, addressing the bottleneck of the Schur system solution while improving its convergence rates. The dual-domain decomposition method facilitates the partition and distribution of the computation to be solved into independent finely grained computational subdomains, substantially reducing both computational and memory complexity. We demonstrate the scalability of our proposed method through its application to a high-resolution 3D simulation of chromium contaminant transport in groundwater. Our results indicate that our method outperforms both the vanilla domain decomposition method and the algebraic multigrid preconditioned method in terms of runtime, achieving up to 8.617× and 5.515× speedups, respectively, in solving massive problems with approximately 108 million degrees of freedom. Therefore, we recommend its effectiveness and reliability for high-resolution 3D simulations of groundwater flow and transport.

An iterative approach to grid topology and redispatch optimization in congestion management

The selection of Remedial Actions (RA) to ensure system security is a highly complex task performed by Transmission System Operators (TSOs). Phase shifting transformer (PST) tap changes and active power changes of generation units (redispatch) are some RA available in Security Constrained Optimal Power Flow (SCOPF) simulations within the operational planning processes. Topological RA are not part of these SCOPF yet, as the optimization thereof adds high complexity to the existing optimization problem. To overcome this complexity, this paper introduces an iterative approach that decouples topology optimization from redispatch & PST optimization. Linearized models of RA are used to meet computation time requirements. Exemplary investigations of the presented method were performed based on modified IEEE 39-Bus, 118-Bus and PEGASE-1354-Bus grid models. Within the scope of these investigations, the method shows good results and a great potential for the reduction of congestions and required redispatch through topological RA. In order to eliminate inaccuracies of the approach and to further improve its suitability for use in grid operation, a need for future investigations and possible further developments were identified.

Fitting Monte Carlo simulation results with an empirical model of megavoltage x-ray beams for rapid depth dose calculations in water

Objective. The purpose of this study was to assess a method of accelerating Monte Carlo simulations for modeling depth dose distributions from megavoltage x-ray beams by fitting them to an empirically-derived function. Approach. Using Geant4, multiple simulations of a typical medical linear accelerator beam in water and in water with an air cavity were conducted with varying numbers of initial electrons. The resulting percent depth dose curves were compared to published data from actual linear accelerator measurements. Two methods were employed to reduce computation time for this modeling process. First, an empirical function derived from measurements at a particular linear accelerator energy, source-to-surface distance, and field size was used to directly fit the simulated data. Second, a linear regression was performed to predict the empirical function’s parameters for simulations with more initial electrons. Main results. Fitting simulated depth dose curves with the empirical function yielded significant improvements in either accuracy or computation time, corresponding to the two methods described. When compared to published measurements, the maximum error for the largest simulation was 5.58%, which was reduced to 2.01% with the best fit of the function. Fitting the empirical function around the air cavity heterogeneity resulted in errors less than 2.5% at the interfaces. The linear regression prediction modestly improved the same simulation with a maximum error of 4.22%, while reducing the required computation time from 66.53 h to 43.75 h. Significance. This study demonstrates the effective use of empirical functions to expedite Monte Carlo simulations for a range of applications from radiation protection to food sterilization. These results are particularly impactful in radiation therapy treatment planning, where time and accuracy are especially valuable. Employing these methods may improve patient outcomes by ensuring that dose delivery more accurately matches the prescription or by shortening the preparation time before treatment in Monte Carlo-based treatment planning systems.

Implementation and optimization of Burg’s method for real-time packet loss concealment in networked music performance applications

In networked music performance (NMP) applications, which entail real-time audio streaming over the Internet, strict latency requirements are needed to ensure a realistic interaction between geographically dispersed musicians. Thus, NMP applications typically leverage uncompressed audio and unreliable transport protocols to avoid unnecessary processing and re-transmission delays. Given that no guarantee on packet delivery is offered, NMP applications must deal with late/lost audio packets to mitigate the impact of the resulting audio artifacts on the quality of the playback audio stream. This paper explores an audio packet loss concealment (PLC) technique based on autoregressive (AR) models. In particular, it investigates the algorithmic implementation of Burg’s method and the parameters configuration that offers the best trade-off between prediction error and computational time requirements. The purpose is to find the most suitable solution capable of running on a Raspberry Pi 4B within the real-time audio boundaries imposed by NMP applications. Additionally, we analyze the computational time required to fit the model and predict future samples by considering six implementations and various compilation flags. Results confirm that AR models can predict future audio samples more accurately than traditional PLC approaches, which consist of filling audio gaps with silence or repeating the last received audio segment. Furthermore, results demonstrate the effectiveness of the proposed solution in meeting the strict latency requirements when deployed on a Raspberry Pi 4B.

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

Existing studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.

Low fidelity data driven machine learning based optimisation method for box-wing configuration

Wing design optimization traditionally involves computationally expensive high-fidelity simulations, limiting the exploration of design spaces. In this study, we propose a methodology that combines low-fidelity numerical models with machine learning algorithms to efficiently navigate the complex parameter space of box-wing configurations. Through the utilisation of a surrogate model trained on a limited dataset derived from low-fidelity simulations, our method strives to predict results within an acceptable range, significantly curtailing computational costs and time. The effectiveness of this methodology is demonstrated through a series of case studies, involving the Onera M6 and NASA CRM wing as test cases and Bionica box-wing optimization as an application case. The initial application of the proposed methodology to the box-wing case successfully achieved an almost 9.82 % increase in overall aerodynamic efficiency. Its competitive performance compared to conventional optimization methods, along with its substantial reduction in computational time and resource requirements, is evident. This efficient methodology holds promise for enhancing the design optimization process for aviation start-ups by efficiently exploring complex design spaces with reduced computational burden.

Adaptive variational low-rank dynamics for open quantum systems

We introduce a model-independent method for the efficient simulation of low-entropy systems, whose dynamics can be accurately described with a limited number of states. Our method leverages the time-dependent variational principle to efficiently integrate the Lindblad master equation, dynamically identifying and modifying the low-rank basis over which we decompose the system's evolution. By dynamically adapting the dimension of this basis, and thus the rank of the density matrix, our method maintains optimal representation of the system state, offering a substantial computational advantage over existing adaptive low-rank schemes in terms of both computational time and memory requirements. We demonstrate the efficacy of our method through extensive benchmarks on a variety of model systems, with a particular emphasis on multiqubit bosonic codes, a promising candidate for fault-tolerant quantum hardware. Our results highlight the method's versatility and efficiency, making it applicable to a wide range of systems characterized by arbitrary degrees of entanglement and moderate entropy throughout their dynamics. We provide an implementation of the method as a Julia package, making it readily available to use. Published by the American Physical Society 2024