Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.9
Journals
Computation
Special Issues
Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025)
share announcement

Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025)

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (15 February 2026) | Viewed by 4626

Special Issue Editor

Prof. Dr. Demos T. Tsahalis

E-Mail Website
Guest Editor
Learning Foundation in Mechatronics (LFME), Irakleiou 17, GR-11141 Athens, Greece
Interests: mechatronics; unsteady boundary layers; separation; vortex-induced vibrations; active control; noise; vibrations; health monitoring; structures; human response; modeling; artificial neural networks; multiobjective optimization; genetic algorithms; expert systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 11th International Conference on Experiments/Process/System Modeling/Simulation/Optimization (11th IC-EpsMsO) was held in Athens, Greece, from July 2nd to July 5th, 2025. For more information about the conference, please visit the conference website at https://lfme.gr/ic-epsmso/.

Selected papers presented at the conference and included in the conference proceedings will be considered for inclusion in this Special Issue. The authors of the selected papers will be notified by the Conference Chairman, in due time, to submit their papers to this Special Issue of the journal Computation, at the latest by 31 December 2025, if they so wish. Submitted papers may be extended from their conference size to include new results, if any. All submitted papers will undergo the journal’s standard peer-review procedure. Accepted papers will be published in open access format in Computation and collected on the website of this Special Issue.

Please prepare and format your paper according to the Instructions for Authors using the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via the susy.mdpi.com editorial system.

Prof. Dr. Demos T. Tsahalis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • experiments
  • process
  • system Modeling
  • simulation
  • optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1467 KB  
Article
ECG Heartbeat Classification Using Echo State Networks with Noisy Reservoirs and Variable Activation Function
by Ioannis P. Antoniades, Anastasios N. Tsiftsis, Christos K. Volos, Andreas D. Tsigopoulos, Konstantia G. Kyritsi and Hector E. Nistazakis
Computation 2026, 14(2), 49; https://doi.org/10.3390/computation14020049 - 13 Feb 2026
Viewed by 582
Abstract
In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the [...] Read more.
In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the performance of ESN in a challenging task that involves classification of complex, unprocessed one-dimensional signals, distributed into five classes. Moreover, we investigate the performance of the ESN in the presence of (i) noise in the dynamics of the internal variables of the hidden (reservoir) layer and (ii) random variability in the activation functions of the hidden layer cells (neurons). The overall accuracy of the best-performing ESN, without noise and variability, exceeded 96% with per-class accuracies ranging from 90.2% to 99.1%, which is higher than previous studies using CNNs and more complex machine learning approaches. The top-performing ESN required only 40 min of training on a CPU (Intel i5-1235U@1.3 GHz) HP laptop. Notably, an alternative ESN configuration that matched the accuracy of a prior CNN-based study (93.4%) required only 6 min of training, whereas a CNN would typically require an estimated training time of 2–3 days. Surprisingly, ESN performance proved to be very robust when Gaussian noise was added to the dynamics of the reservoir hidden variables, even for high noise amplitudes. Moreover, the success rates remained essentially the same when random variability was imposed in the activation functions of the hidden layer cells. The stability of ESN performance under noisy conditions and random variability in the hidden layer (reservoir) cells demonstrates the potential of analog hardware implementations of ESNs to be robust in time-series classification tasks. Full article
(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))
►▼ Show Figures

Figure 1

21 pages, 4280 KB  
Article
Development of a Dashboard for Simulation Workflow Visualization and Optimization of an Ammonia Synthesis Reactor in the HySTrAm Project (Horizon EU)
by Eleni Douvi, Dimitra Douvi, Jason Tsahalis and Haralabos-Theodoros Tsahalis
Computation 2026, 14(2), 38; https://doi.org/10.3390/computation14020038 - 2 Feb 2026
Viewed by 952
Abstract
Although hydrogen plays a crucial role in the EU’s strategy to reduce greenhouse gas emissions, its storage and transport are technically challenging. If ammonia is produced efficiently, it can be a promising hydrogen carrier, especially in decentralized and flexible conditions. The Horizon EU [...] Read more.
Although hydrogen plays a crucial role in the EU’s strategy to reduce greenhouse gas emissions, its storage and transport are technically challenging. If ammonia is produced efficiently, it can be a promising hydrogen carrier, especially in decentralized and flexible conditions. The Horizon EU HySTrAm project addresses this problem by developing a small-scale, containerized demonstration plant consisting of (1) a short-term hydrogen storage container using novel ultraporous materials optimized through machine learning, and (2) an ammonia synthesis reactor based on an improved low-pressure Haber–Bosch process. This paper presents an initial version of a Python (v3.9)-based dashboard designed to visualize and optimize the simulation workflow of the ammonia synthesis process. Designed as a baseline for a future online, automated tool, the dashboard allows the comparison of three reactor configurations already defined through simulations and aligned with the upcoming experimental campaign: single tube, two reactors in parallel swing mode and two reactors in series. Pressures at the inlet/outlet, temperatures across the reactor, operation recipe and ammonia production over time are displayed dynamically to evaluate the performance of the reactor. Future versions will include optimization features, such as the identification of optimal operating modes, the reduction of production time, an increase of productivity, and catalyst degradation estimation. Full article
(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))
►▼ Show Figures

Figure 1

22 pages, 1706 KB  
Article
A Replication Study for Consumer Digital Twins: Pilot Sites Analysis and Experience from the SENDER Project (Horizon 2020)
by Eleni Douvi, Dimitra Douvi, Jason Tsahalis and Haralabos-Theodoros Tsahalis
Computation 2026, 14(2), 31; https://doi.org/10.3390/computation14020031 - 1 Feb 2026
Viewed by 507
Abstract
The SENDER (Sustainable Consumer Engagement and Demand Response) project aims to develop an innovative interface that engages energy consumers in Demand Response (DR) programs by developing new technologies to predict energy consumption, enhance market flexibility, and manage the exploitation of Renewable Energy Sources [...] Read more.
The SENDER (Sustainable Consumer Engagement and Demand Response) project aims to develop an innovative interface that engages energy consumers in Demand Response (DR) programs by developing new technologies to predict energy consumption, enhance market flexibility, and manage the exploitation of Renewable Energy Sources (RES). The current paper presents a replication study for consumer Digital Twins (DTs) that simulate energy consumption patterns and occupancy behaviors in various households across three pilot sites (Austria, Spain, Finland) based on six-month historical and real-time data related to loads, sensors, and relevant details for every household. Due to data limitations and inhomogeneity, we conducted a replication analysis focusing only on Austria and Spain, where available data regarding power and motion alarm sensors were sufficient, leading to a replication scenario by gradually increasing the number of households. In addition to limited data and short time of measurements, other challenges faced included inconsistencies in sensor installations and limited information on occupancy. In order to ensure reliable results, data was filtered, and households with common characteristics were grouped together to improve accuracy and consistency in DT modeling. Finally, it was concluded that a successful replication procedure requires sufficient continuous, frequent, and homogeneous data, along with its validation. Full article
(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))
►▼ Show Figures

Graphical abstract

14 pages, 2141 KB  
Communication
A Consumer Digital Twin for Energy Demand Prediction: Development and Implementation Under the SENDER Project (HORIZON 2020)
by Dimitra Douvi, Eleni Douvi, Jason Tsahalis and Haralabos-Theodoros Tsahalis
Computation 2026, 14(1), 9; https://doi.org/10.3390/computation14010009 - 3 Jan 2026
Cited by 1 | Viewed by 609
Abstract
This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative [...] Read more.
This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article
(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))
►▼ Show Figures

Graphical abstract

19 pages, 4586 KB  
Article
Heat Losses in the Exhaust Manifold of a 4-Stoke DI Diesel Engine Subjected to Pulsating Flow
by Grigorios Spyrounakos and Georgios Mavropoulos
Computation 2025, 13(9), 223; https://doi.org/10.3390/computation13090223 - 15 Sep 2025
Cited by 1 | Viewed by 1155
Abstract
This paper presents a study aiming to provide insight into the complex flow and heat transfer processes in the exhaust manifold of a four-stroke, compression ignition engine. An experimental system has been constructed capable of capturing temperature and heat flux high-frequency signals as [...] Read more.
This paper presents a study aiming to provide insight into the complex flow and heat transfer processes in the exhaust manifold of a four-stroke, compression ignition engine. An experimental system has been constructed capable of capturing temperature and heat flux high-frequency signals as they develop in the exhaust pipe wall during the engine cycle, under its steady-state operation. The values of the Heat Transfer Coefficient obtained by applying the classic convection relations have been correlated in the form of a Nusselt–Reynolds number relationship for local and spatially averaged steady-state heat transfer and compared with available experimental data obtained at the same position of the exhaust manifold. It has been shown that the use of conventional steady-state heat transfer relationships for fully developed steady-state turbulent flow in pipes underpredicts heat transfer rates when compared with those experimentally observed. Periodic flow of high frequency and geometrical effects at the exhaust entrance are expected to affect the validity of the application of the classic steady-state correlations for the exhaust manifold. To overcome this problem it is developed and presented a new correlation for the time-averaged heat transfer rates. To verify the heat transfer mechanism, the thermal field of the whole engine cylinder head, including the intake and exhaust manifolds, was analyzed using FEA (Finite Element Analysis), and the results are compared and verified with available experimental data. Full article
(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop