Inventions

26 pages, 5341 KiB

Open AccessReview

Systematic Review on Additive Friction Stir Deposition: Materials, Processes, Monitoring and Modelling

by Evren Yasa, Ozgur Poyraz, Anthony Molyneux, Adrian Sharman, Guney Mert Bilgin and James Hughes

Inventions 2024, 9(6), 116; https://doi.org/10.3390/inventions9060116 - 13 Nov 2024

Abstract

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology [...] Read more.

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts. Full article

(This article belongs to the Special Issue Revolutionizing Manufacturing: Advances in Additive Manufacturing Technologies)

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21 pages, 3242 KiB

Open AccessArticle

Information and Analytical System Monitoring and Assessment of the Water Bodies State in the Mineral Resources Complex

by Olga Afanaseva, Mikhail Afanasyev, Semyon Neyrus, Dmitry Pervukhin and Dmitry Tukeev

Inventions 2024, 9(6), 115; https://doi.org/10.3390/inventions9060115 - 12 Nov 2024

Abstract

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water [...] Read more.

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system. Full article

(This article belongs to the Special Issue Innovative Monitoring Techniques and Modeling Approaches for Natural Hazards)

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24 pages, 3192 KiB

Open AccessArticle

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

by Amarendra Alluri, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy and Debapriya Das

Inventions 2024, 9(6), 114; https://doi.org/10.3390/inventions9060114 - 12 Nov 2024

Abstract

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution [...] Read more.

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies. Full article

(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 2nd Edition)

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23 pages, 4732 KiB

Open AccessArticle

Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing

by Nuphar Avital, Idan Egel, Ido Weinstock and Dror Malka

Inventions 2024, 9(6), 113; https://doi.org/10.3390/inventions9060113 - 4 Nov 2024

Abstract

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been [...] Read more.

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices. Full article

(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)

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16 pages, 5378 KiB

Open AccessArticle

Results on the Use of an Original Burner for Reducing the Three-Way Catalyst Light-Off Time

by Adrian Clenci, Bogdan Cioc, Julien Berquez, Victor Iorga-Simăn, Robert Stoica and Rodica Niculescu

Inventions 2024, 9(6), 112; https://doi.org/10.3390/inventions9060112 - 29 Oct 2024

Abstract

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC [...] Read more.

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC starts to convert once an optimal temperature, usually known as the light-off temperature, is reached. There are many methods to reduce the warm-up period of the TWC, among which is using a burner. The initial question underlying this study was to see if the use of a relatively straightforward extra-combustion device mounted upstream the TWC, without complex elements, was able to serve the purpose of reducing the light-off time. Consequently, an original burner was designed and investigated numerically via the CFD method and experimentally via measurements of the temperature evolution within a TWC, along with the emissions specific to the burner’s operation. The main findings of this study are: (1) the CFD-based examination is a good way to decide on how to achieve the so-called fit-for-purpose internal aerodynamics of the burner (i.e., to obtain a homogeneous mixture) and (2) to reach the light-off temperature, conventionally taken as 500 K, the burner was operated for 5.2 s, i.e., 3.6 g of gasoline injected, 2.7 g of CO₂ and 1.351 g of CO, respectively, emitted. Moreover, this study identified measures for improving the burner’s design as well as an enhanced procedure for the burner’s operating control both aiming to produce a cleaner combustion during the TWC pre-heating. Full article

(This article belongs to the Special Issue Innovative Research and Applications in Hydrodynamics and Flow Control, 2nd Edition)

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23 pages, 4313 KiB

Open AccessArticle

Corrosion-Resistant Polymer Composite Tubes with Enhanced Thermal Conductivity for Heat Exchangers

by Jan-Hendrik Imholze and Heike Glade

Inventions 2024, 9(5), 111; https://doi.org/10.3390/inventions9050111 - 21 Oct 2024

Abstract

The heat transfer surfaces of heat exchangers are usually made of metals which may suffer from severe corrosion. When corrosive fluids are present, highly corrosion-resistant metals, graphite or ceramics are used, resulting in high costs. This study presents measured data on the thermophysical [...] Read more.

The heat transfer surfaces of heat exchangers are usually made of metals which may suffer from severe corrosion. When corrosive fluids are present, highly corrosion-resistant metals, graphite or ceramics are used, resulting in high costs. This study presents measured data on the thermophysical and mechanical properties of recently developed corrosion-resistant polymer composite tubes for use in heat exchangers. Extruded polymer composite tubes based on polypropylene or polyphenylene sulfide filled with graphite flakes were investigated. The anisotropic thermal conductivities of the polymer composite tubes were measured at various temperatures. The through-wall thermal conductivity of the tubes made of polypropylene filled with 50 vol.% graphite is increased by a factor of 30 compared to pure polypropylene, resulting in a thermal conductivity of 6.5 W/(m K) at 25 °C. The tubes composed of polyphenylene sulfide filled with 50 vol.% graphite have a through-wall thermal conductivity of 4.5 W/(m K) at 25 °C. The mechanical properties of the polymer composites were measured using tensile and flexural tests at different temperatures. The composite materials are more rigid and keep their mechanical properties up to a higher temperature level compared to the unfilled polymers. Surface roughness measurements show the very smooth and sealed surface of the composite tubes. The results contribute to establishing the viability of using polymer composites for heat exchanger applications with corrosive fluids. Full article

(This article belongs to the Special Issue Innovations in Heat Exchangers)

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23 pages, 15644 KiB

Open AccessArticle

Effects of Perforated Plates on Shock Structure Alteration for NACA0012 Cascade Configurations

by Mihnea Gall, Oana Dumitrescu, Valeriu Drăgan and Daniel-Eugeniu Crunțeanu

Inventions 2024, 9(5), 110; https://doi.org/10.3390/inventions9050110 - 6 Oct 2024

Abstract

To alleviate the shock boundary layer interaction adverse effects, various active or passive flow control strategies have been investigated in the literature. This research sheds light on the behavior of perforated plates as passive flow control techniques applied to NACA0012 airfoils in cascade [...] Read more.

To alleviate the shock boundary layer interaction adverse effects, various active or passive flow control strategies have been investigated in the literature. This research sheds light on the behavior of perforated plates as passive flow control techniques applied to NACA0012 airfoils in cascade configurations. Two identical perforated plates with shallow cavities underneath are accommodated on the upper and lower surfaces of each airfoil in the cascade arrangement. Six different cascade arrangements, including a baseline configuration with no control applied, are additively manufactured, with different perforated plate orifice sizes in the range of 0.5–1.2 mm. A high-speed wind tunnel with Schlieren optical diagnosis and wall static pressure taps is used to investigate the changes in the shock waves pattern triggered by the perforated plates. Steady 3D density-based numerical simulations in Ansys FLUENT are conducted for further analysis and validation. In the cascade configuration, the perforated plates alter the shock structure, and the strong normal shock wave is replaced by a weaker X-type shock structure. Eventually, a 1% penalty in overall total pressure loss is induced by the perforated plates because of the negative loss balance between the reduced shock losses and the enhanced viscous losses. Further studies on perforated plate geometrical features are needed to improve this outcome in a cascade arrangement. Full article

(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)

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20 pages, 666 KiB

Open AccessArticle

Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models

by Stanislav Eroshenko, Evgeniy Shmakov, Dmitry Klimenko and Irina Iumanova

Inventions 2024, 9(5), 109; https://doi.org/10.3390/inventions9050109 - 4 Oct 2024

Abstract

This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir [...] Read more.

This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir inflows from multiple catchments in a steppe region, where spring runoff dominates the annual water volume and requires careful consideration of snowfall. Two well-known conceptual models, HBV and GR6J-CemaNeige, which incorporate snow-melting processes, were evaluated. The research also investigated the best approach to preprocessing historical data to enhance model accuracy. Furthermore, the study emphasizes the importance of accurately defining low-water periods to ensure reliable HPP operation through more accurate inflow forecasting. A hypothesis was proposed to explore the relationship between atmospheric circulation and the definition of low-water periods; however, the findings did not support this hypothesis. Overall, the results suggest that combining the conceptual models under consideration can lead to more accurate forecasts, underscoring the need for integrated approaches in managing HPP reservoirs and promoting sustainable energy production. Full article

(This article belongs to the Special Issue Emerging Trends and Innovations in Renewable Energy)

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29 pages, 6072 KiB

Open AccessArticle

Optimized Wavelet Transform for the Development of an Algorithm Designed for the Analysis of Digital Substation Electrical Equipment Parameters

by Alexander S. Efimov, Stanislav A. Eroshenko, Pavel V. Matrenin and Vladislav V. Popovtsev

Inventions 2024, 9(5), 108; https://doi.org/10.3390/inventions9050108 - 29 Sep 2024

Abstract

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the [...] Read more.

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored. Full article

(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 2nd Edition)

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26 pages, 5160 KiB

Open AccessArticle

Operating Characteristics of a Wave-Driven Plasma Thruster for Cutting-Edge Low Earth Orbit Constellations

by Anna-Maria Theodora Andreescu, Daniel Eugeniu Crunteanu, Maximilian Vlad Teodorescu, Simona Nicoleta Danescu, Adrian Stoicescu, Alexandru Cancescu and Alexandru Paraschiv

Inventions 2024, 9(5), 107; https://doi.org/10.3390/inventions9050107 - 29 Sep 2024

Abstract

This paper outlines the development phases of a wave-driven Helicon Plasma Thruster for cutting-edge Low Earth Orbit (LEO) constellations. The two-stage ambipolar electric propulsion (EP) system combines the efficient ionization of an ultra-compact helicon reactor with plasma acceleration based on an ambipolar electric [...] Read more.

This paper outlines the development phases of a wave-driven Helicon Plasma Thruster for cutting-edge Low Earth Orbit (LEO) constellations. The two-stage ambipolar electric propulsion (EP) system combines the efficient ionization of an ultra-compact helicon reactor with plasma acceleration based on an ambipolar electric field provided by a magnetic nozzle. This paper reveals maturation challenges associated with an emerging EP system in the hundreds-watt class, followed by outlook strategies. A 3 cm diameter helicon reactor was operated using argon gas under a time-modulated RF power envelope ranging from 250 W to 500 W with a fixed magnetic field strength of 400 G. Magnetically enhanced inductively coupled plasma reactor characteristics based on half-wavelength right helical and Nagoya Type III antennas under capacitive (E-mode), inductive (W-mode), and wave coupling (W-mode) were systematically investigated based on Optical Emission Spectroscopy. The operation characteristics of a wave-heated reactor based on helicon configuration were investigated as a function of different operating parameters. This work demonstrates the ability of two-stage HPT using a compact helicon reactor and a cusped magnetic field to outperform today’s LEO spacecraft propulsion. Full article

(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)

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22 pages, 3783 KiB

Open AccessArticle

Energy Analysis of Standardized Shipping Containers for Housing

by Elena Arce Fariña, Mirela Panait, José María Lago-Cabo and Raquel Fernández-González

Inventions 2024, 9(5), 106; https://doi.org/10.3390/inventions9050106 - 27 Sep 2024

Abstract

Shipping containers that remain in ports after exporting or importing products cause an environmental and logistical problem. Transporting them to the port of origin is costly; therefore, some of them are stored in the regions of destination. Recycling or reusing them in an [...] Read more.

Shipping containers that remain in ports after exporting or importing products cause an environmental and logistical problem. Transporting them to the port of origin is costly; therefore, some of them are stored in the regions of destination. Recycling or reusing them in an efficient and sustainable way represents a clean alternative. The purpose of this article is to analyze the feasibility and impact of implementing different insulating configurations on the energy demands required by a house based on a construction with standardized shipping containers. More specifically, it assesses the impact of the different orientations in which the dwelling can be arranged, depending on the location and its meteorological data. To this aim, a construction model will be developed in which first, the geometrical parameters are defined, and second, the energy characteristics are identified. The results show that, in Southwest Europe, the western orientation generates a saving of 10% of the energy demand compared to the less favourable orientation, which is the southern one. Full article

(This article belongs to the Special Issue Thermodynamic and Technical Analysis for Sustainability (Volume 3))

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18 pages, 6025 KiB

Open AccessArticle

Innovative Design of a Continuous Ultrasound Bath for Effective Lignocellulosic Biomass Pretreatment Based on a Theorical Method

by Paula Andrea Ramirez Cabrera, Alejandra Sophia Lozano Pérez and Carlos Alberto Guerrero Fajardo

Inventions 2024, 9(5), 105; https://doi.org/10.3390/inventions9050105 - 26 Sep 2024

Abstract

Ultrasonic pretreatment is a crucial step in the bioconversion of lignocellulosic biomass, such as peapods, into valuable products. Ultrasonic pretreatment is a highly effective physical method that utilizes ultrasonic waves to enhance various processes. Biomass pretreatment is achieved through physical effects such as [...] Read more.

Ultrasonic pretreatment is a crucial step in the bioconversion of lignocellulosic biomass, such as peapods, into valuable products. Ultrasonic pretreatment is a highly effective physical method that utilizes ultrasonic waves to enhance various processes. Biomass pretreatment is achieved through physical effects such as acoustic cavitation, which disrupts the biomass structure, and chemical effects like radical formation, which breaks down complex molecules. This article focuses on the characteristics, types, and applications of ultrasonic pretreatment in peapods, with a particular emphasis on its role in lignin removal and ultrasound design. An innovative mechanical design in a CAD application of a continuous ultrasound treatment with a capacity of 5 L and an FEA analysis of the equipment are presented as results, providing insights for the design and optimization of ultrasonic pretreatment processes. Full article

(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)

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24 pages, 14746 KiB

Open AccessArticle

Hydraulic Design of an Ultracompact Liquid Methane–Liquid Oxygen Turbopump for a Mid-Scale Thruster for Upper Stage Application

by Alexandru-Claudiu Cancescu, Daniel-Eugeniu Crunteanu, Anna-Maria Theodora Andreescu, Simona-Nicoleta Danescu and Valeriu Dragan

Inventions 2024, 9(5), 104; https://doi.org/10.3390/inventions9050104 - 25 Sep 2024

Abstract

As space missions proliferate and the payload requirements increase, the environmental impact of thrusters can no longer be considered negligible. Therefore, less impactful fuels such as methane are starting to be considered for launchers. In this paper we present a design case study [...] Read more.

As space missions proliferate and the payload requirements increase, the environmental impact of thrusters can no longer be considered negligible. Therefore, less impactful fuels such as methane are starting to be considered for launchers. In this paper we present a design case study for such a turbopump. Using both analytical models and Computational Fluid Dynamics techniques, we were able to reduce the size and weight of the turbopump assembly. Also, due to the elimination of some auxiliary systems, the overall efficiency was enhanced. This paper’s findings and methods can be transferred not only to launchers in its own class, but also to larger scale engines with a similar construction. Full article

(This article belongs to the Special Issue Thermodynamic and Technical Analysis for Sustainability (Volume 3))

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2 pages, 130 KiB

Open AccessCorrection

Correction: Freddi et al. Reverse Engineering of a Racing Motorbike Connecting Rod. Inventions 2023, 8, 23

by Marco Freddi, Patrich Ferretti, Giulia Alessandri and Alfredo Liverani

Inventions 2024, 9(5), 103; https://doi.org/10.3390/inventions9050103 - 24 Sep 2024

Abstract

In the original publication [...] Full article

(This article belongs to the Collection Feature Innovation Papers)

25 pages, 3047 KiB

Open AccessArticle

Hierarchical Dynamic Spatio-Temporal Graph Convolutional Networks with Self-Supervised Learning for Traffic Flow Forecasting

by Siwei Wei, Yanan Song, Donghua Liu, Sichen Shen, Rong Gao and Chunzhi Wang

Inventions 2024, 9(5), 102; https://doi.org/10.3390/inventions9050102 - 20 Sep 2024

Abstract

It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on [...] Read more.

It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on local geographic correlations, ignoring cross-region interdependencies in a global context, which is insufficient to extract comprehensive semantic relationships, thereby limiting prediction accuracy. Additionally, most GCN-based models rely on pre-defined graphs and unchanging adjacency matrices to reflect the spatial relationships among node features, neglecting the dynamics of spatio-temporal features and leading to challenges in capturing the complexity and dynamic spatial dependencies in traffic data. To tackle these issues, this paper puts forward a fresh approach: a new self-supervised dynamic spatio-temporal graph convolutional network (SDSC) for traffic flow forecasting. The proposed SDSC model is a hierarchically structured graph–neural architecture that is intended to augment the representation of dynamic traffic patterns through a self-supervised learning paradigm. Specifically, a dynamic graph is created using a combination of temporal, spatial, and traffic data; then, a regional graph is constructed based on geographic correlation using clustering to capture cross-regional interdependencies. In the feature learning module, spatio-temporal correlations in traffic data are subjected to recursive extraction using dynamic graph convolution facilitated by Recurrent Neural Networks (RNNs). Furthermore, self-supervised learning is embedded within the network training process as an auxiliary task, with the objective of enhancing the prediction task by optimising the mutual information of the learned features across the two graph networks. The superior performance of the proposed SDSC model in comparison with SOTA approaches was confirmed by comprehensive experiments conducted on real road datasets, PeMSD4 and PeMSD8. These findings validate the efficacy of dynamic graph modelling and self-supervision tasks in improving the precision of traffic flow prediction. Full article

(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)

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22 pages, 1281 KiB

Open AccessReview

Review of Existing Tools for Software Implementation of Digital Twins in the Power Industry

by Irina F. Iumanova, Pavel V. Matrenin and Alexandra I. Khalyasmaa

Inventions 2024, 9(5), 101; https://doi.org/10.3390/inventions9050101 - 19 Sep 2024

Abstract

Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization [...] Read more.

Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin’s development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented. Full article

(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications, 2nd Edition)

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19 pages, 4765 KiB

Open AccessArticle

Design of a Fiber Temperature and Strain Sensor Model Using a Fiber Bragg Grating to Monitor Road Surface Conditions

by Gulzhan Kashaganova, Ainur Kozbakova, Timur Kartbayev, Kulzhan Togzhanova, Zhuldyz Alimseitova and Gani Sergazin

Inventions 2024, 9(5), 100; https://doi.org/10.3390/inventions9050100 - 13 Sep 2024

Abstract

In this paper, the types and principles of operation of fiber sensors based on fiber Bragg gratings (FBGs) are investigated. The influence of strain and temperature on the characteristics of FBGs is considered, and a method for the simultaneous measurement of these parameters [...] Read more.

In this paper, the types and principles of operation of fiber sensors based on fiber Bragg gratings (FBGs) are investigated. The influence of strain and temperature on the characteristics of FBGs is considered, and a method for the simultaneous measurement of these parameters is presented. Laboratory studies were carried out in the temperature range from +18 °C to +135 °C with an incremental step of 5 °C, with the actual temperature not deviating by more than ±0.5 °C. From the data obtained, the Bragg wavelength–temperature relationships were plotted, which showed a linear increase in wavelength with increasing temperature. This study shows that the use of two FBGs with a different sensitivity to temperature and strain allowed for the simultaneous measurement of both parameters. Numerical models created in the MATLAB R2022b environment confirmed the high accuracy and precision of the measurements. The FBG-based sensors demonstrated a robust performance in harsh environments, withstanding temperatures of up to 160 °C and high humidity, making them applicable in various industries and sciences. This work confirms that FBGs are a promising tool for accurate temperature and strain measurements, providing reliable results in harsh environments. Full article

(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)

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3 pages, 146 KiB

Open AccessEditorial

Innovative Research and Applications in Hydrodynamics and Flow Control

by Peng Du, Haibao Hu and Xiaopeng Chen

Inventions 2024, 9(5), 99; https://doi.org/10.3390/inventions9050099 - 13 Sep 2024

Abstract

This work aims to collect cutting-edge developments in the area of hydrodynamics and flow control, including both fundamental and engineering research [...] Full article

(This article belongs to the Special Issue Innovative Research and Applications in Hydrodynamics and Flow Control)

13 pages, 5890 KiB

Open AccessArticle

Composite Modified Graphite Felt Anode for Iron–Chromium Redox Flow Battery

by Sheng Wu, Haotian Zhu, Enrui Bai, Chongyang Xu, Xiaoyin Xie and Chuanyu Sun

Inventions 2024, 9(5), 98; https://doi.org/10.3390/inventions9050098 - 9 Sep 2024

Abstract

The iron–chromium redox flow battery (ICRFB) has a wide range of applications in the field of new energy storage due to its low cost and environmental protection. Graphite felt (GF) is often used as the electrode. However, the hydrophilicity and electrochemical activity of [...] Read more.

The iron–chromium redox flow battery (ICRFB) has a wide range of applications in the field of new energy storage due to its low cost and environmental protection. Graphite felt (GF) is often used as the electrode. However, the hydrophilicity and electrochemical activity of GF are poor, and its reaction reversibility to Cr³⁺/Cr²⁺ is worse than Fe²⁺/Fe³⁺, which leads to the hydrogen evolution side reaction of the negative electrode and affects the efficiency of the battery. In this study, the optimal composite modified GF (Bi-Bio-GF-O) electrode was prepared by using the optimal pomelo peel powder modified GF (Bio-GF-O) as the matrix and further introducing Bi³⁺. The electrochemical performance and material characterization of the modified electrode were analyzed. In addition, using Bio-GF-O as the positive electrode and Bi-Bio-GF-O as the negative electrode, the high efficiency of ICRFB is realized, and the capacity attenuation is minimal. When the current density is 100 mA·cm⁻², after 100 cycles, the coulomb efficiency (CE), voltage efficiency (VE), and energy efficiency (EE) were 97.83%, 85.21%, and 83.36%, respectively. In this paper, the use of pomelo peel powder and Bi³⁺ composite modified GF not only promotes the electrochemical performance and reaction reversibility of the negative electrode but also improves the performance of ICRFB. Moreover, the cost of the method is controllable, and the process is simple. Full article

(This article belongs to the Special Issue Advanced Electrode Material for Electrochemical Production Conversion and Storage of Energy)

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