Reversible Machine Research Articles

Experimental characterization of a reversible heat pump – Organic Rankine cycle pilot plant as a thermally integrated Carnot battery

This paper reports a first detailed experimental characterization of a reversible heat pump – Organic Rankine Cycle pilot plant, presenting its potential as a thermally integrated Carnot battery for efficient energy storage. A total of 150 stationary operating points were investigated in both heat pump and ORC modes. The study analyzes and discusses the main trends and interactions on system and component level. As expected, heat pump and ORC operation show an opposing behavior. While heat pump operation yields a maximum COP of 9.5 at a 15 K temperature lift, the peak net ORC efficiency reaches 4.4 % with a 40 K temperature gradient. If additional storage losses are neglected, this leads to a power-to-power (round-trip) efficiency of up to 41.8 % for the Carnot battery. The absence of a liquid receiver in the ORC mode leads to notable subcooling and increased condensation pressures, impacting the expander’s power generation due to excess refrigerant accumulation. Moreover, the experimental results reveal that the reversible screw machine is the critical factor influencing efficiency. The screw machine’s maximum isentropic efficiencies are 78 % and 66 % in compressor and expander operation, respectively. The reliability of the results was verified by means of an energy balance and reference operating points, underlining high reproducibility. Additionally, potential system improvements were derived from the experimental results. Altogether, this paper successfully proofs the concept of reversible heat pump – ORC systems and provides experimental results for ongoing advancements in this field.

Design and performance evaluation of a reversible bladeless turbo machine

It is well known that bladeless or Tesla turbomachinery idea was proposed by Nikola Tesla over a century ago, and it has several distinct features, such as reversibility of operation, which includes expander and compressor operations obtained by reversing the rotational speed, provided that the statoric channels are purposely designed.The main objective of this paper is to design a reversible Tesla or bladeless machine with optimal design parameter choice that can operate both in compressor and expander modes. As a consequence of the high losses due to rotor and stator interactions, statorless (volute) configurations are investigated here, showing superior performance in both compressor and expander modes of operation. Numerical study has been conducted for both compressor and expander modes of this machine using air as working fluid. In compressor mode, numerical results have been validated with experimental data, while results for expander mode will be validated in the near future. The numerical results predict peak isentropic efficiencies of 57.5 % and 63.5 %, for the compressor and expander modes of operation excluding assembly losses, respectively, while rotor efficiency is quite high > 90 % at very low mass flow rates. Tests on Tesla machine prototype have shown an experimental isentropic efficiency of maximum 32.4 % when operating in compressor mode. The overall assembly efficiency is reduced, compared to numerical predictions, due to leakage and end wall losses. The CFD simulations could match the experimental results at high pressure ratio and low mass flow, with less than 2 % error on pressure ratio. Through the improvement of the sealing system and the reduction of losses (leakage and end wall friction), it is expected that overall prototype efficiency could improve by at least 10 percentage points.

Distribution in the geometrically growing system and its evolution.

Recently, we developed a theory of a geometrically growing system. Here, we show that the theory can explain some phenomena of power-law distribution, including classical demographic and economic and novel instances of the COVID-19 pandemic, without introduction of delicate economic or pandemic propagation models but only on a statistical way. A convexity in the low-size part of the distribution diagram is one peculiarity of the theory, which is absent in the power-law distribution. We found that the distribution of the geometrically growing system in the diagram could have a trend to flatten in the evolution of the system so that the relative ratio between the biggest and smallest sizes within the system increases. The system can act as a reverse machine to convert the diffusion in parametric space to a concentration in size.

Development of Micro Electrical Discharge Machine and Micro-hole Machining Using Multiple Micro Electrodes

Recent advances in microtechnology have highlighted the need for fabricating microscopic parts with complex three-dimensional structures. This, in turn, has increased the demand for innovative tools. Micro electrical discharge machining (MEDM) is one such technique that has proven to be effective in producing parts that are difficult to fabricate using traditional manufacturing methods, such as micro shafts and micro holes. Therefore, this study focuses on the development of a specialized MEDM system that incorporates an optimized wire electrical discharge grinding (WEDG) module. Additionally, the study successfully fabricates microelectrodes for use as cutting tools using the MEDM/WEDG system. These micro-electrodes are then utilized in the precise fabrication of various types of multiple micro electrodes through the use of reverse electrical discharge machining (REDM). These multiple micro electrodes are subsequently employed in precise hole machining operations using MEDM. The application of REDM signifies a significant technological advancement in the precision fabrication of microstructures. Furthermore, it is expected to greatly enhance the versatility and efficiency of microtechnology manufacturing processes. Overall, REDM represents a new approach to manufacturing that is capable of producing more complex and high-precision microstructures.

Development and parametric study of reversible motion-wire electrochemical spark machining (RM-WECSM) setup for micro-width curve profile cutting on Al2O3 epoxy nanocomposites

The development of wire electrochemical spark machining setup is always challenging in the form of industrial viable for machining advanced engineering materials such as polymer nanocomposites, quartz, glass, ceramics, etc. Because of some drawbacks associated with it, like irregularity in kerf width, poor surface finish, no spark formed at high wire velocity, wire breaking at high voltage, etc. In the present work, a newly developed hybrid machining process that is reversible motion-wire electrochemical spark machining (RM-WECSM) has been developed for curve profile cutting, especially on non-conducting workpieces such as aluminum oxide epoxy nanocomposite of 3.5 mm thickness. The effect of NaOH concentration, applied voltage, reversible wire velocity, pulse on-and-off time, on material removal rate and average surface roughness have been studied using a one-parameter-at-a-time approach. Sparking occurred from the electrolyte surface to 10 mm above the electrolyte surface to cut the workpiece. The spark length is equal to the workpiece thickness. A continuous, stable spark has been formed at a high reversible wire velocity (19.8 m/min). Simultaneously, self-electrolyte flow has been generated, due to which melted materials have been washed out of the cut-surface and provided the smooth (1.987 µm) cut-surface of the workpiece. The maximum MRR is found as 1.63 mg/min. Thermal cracks, heat-affected zones and recast layers were not found on the curve cut-surface, and it was analyzed by a field emission scanning electron microscope.

SOFTWARE CAPABILITIES FOR TEXT IDENTIFICATION: COMPARISON FOR SIMILARITY, IDENTITY ESTABLISHMENT, UNIQUENESS CHECK

The study was carried out within the framework of identification linguistics and translational linguistics. The article describes some methods for determining the degree of similarity of texts: shingle algorithm, Levenshtein distance, systems for detecting plagiarism. The purpose of the work is to test the software capabilities of comparing texts for similarity, establishing their identity, and checking uniqueness. In a broad sense, these tasks fall within the area of text identification. In a qualitative (manual) assessment of the similarity of texts, identifying parameters are selected and selected specifically for the text under study. The use of electronic resources is determined by the desire for objectivity of the methods used to establish the identity of texts and the objectivity of the results obtained. Software products also make it possible to establish another, quantitative, characteristic — the degree of similarity of texts to each other or the degree of originality of the text. The work used services whose tasks include 1) comparing the similarity of two texts; 2) calculation of the Levenshtein distance; 3) detection of borrowing. The research material was an excerpt from an interview with Foreign Minister Sergei Lavrov. Reverse machine translation texts served as options for comparison with the source text. Reverse machine translation as a translation product is part of artificial intelligence and a model of the process of understanding and interpreting natural language. The results of using the proposed services made it possible to arrange five reverse machine translation options from the most unique text to the text that is most identical to the invariant. The study showed that the programs generally produce similar results, which can be applicable to solving research and applied problems related to establishing the identity and difference of texts. The prospect of the study is to identify lexical parameters that make it possible to classify reverse machine translation texts as the most or least identical with respect to the invariant.

Sustainable approach for machining of Ti6Al4V using micro-pillar textured turning tool insert

The use of cutting fluids (CF) in metal cutting is questioned due to environmental and biological impacts, encouraging the adoption of dry machining to minimize these effects. However, dry machining accelerates tool wear, particularly in difficult-to-machine materials like Titanium and Nickel alloys. Approaches, such as rake surface texturing, address this challenge and promote sustainability in metal cutting. This study explored the impact of an innovative micro-pillar texture pattern fabricated using a combination of Laser Beam Micro Machining (LBμM) and Reverse Micro Electrical Discharge Machining (RμEDM) for the dry machining of Ti6Al4V. The investigation considers various parameters, including tool wear, contact length, titanium adhesion, cutting forces, cutting temperature, and chip morphology. An indirect approach was adopted to estimate the temperature of the tool tip using the temperature measured at a distant location. Implementing the textured tool with 15 μm deep micro-pillars in the dry machining of Ti6Al4V exhibited the best overall performance for various responses. A notable reduction of more than 40% for both tool-chip contact length and titanium adhesion, compared to a plain tool, indicated a perceptible decrease in the seizure zone at the interface. Moreover, the textured tools demonstrated a remarkable maximum reduction of 56.4% in flank wear, ensuring the prolonged retention of a sharp cutting edge. The responses for contact length, adhesion, and flank wear contributed to an 18.8% decrease in cutting force and a substantial 45.7% reduction in thrust force. Force analysis further confirmed the directional independence of the texture pattern. Under the influence of surface texturing, chip morphology changed to shorter, untangled, and tightly curled, with a 30.8% reduction in curl radius. A theoretical estimation based on the minimum energy approach was also performed to demonstrate an increment in the shear plane angle.

Techno-Economic Analysis of Using Reversible Turbomachinery for Pumped Thermal Energy Storage Systems

Abstract The objective of this paper is to assess the techno-economic performance of different cycle configurations for pumped thermal energy storage (PTES), including the effects of charging electricity costs. Reversible turbomachinery was employed to reduce the capital cost of the system. Brayton cycles with different working fluids and a subcritical Rankine cycle operating with ammonia were compared. Both liquid and packed bed thermal storages were investigated. A new cost correlation for turbomachines, initially established for the turbines of organic Rankine cycles, was developed for compressors and reversible machines. This correlation is based on the number of stages and physical size of the machine, which were estimated considering thermodynamic as well as mechanical limitations. The results indicate that for a plant size of 50 MW and a discharge duration of 8 h, the Brayton system with liquid storage and helium as a working fluid has the lowest levelized cost of storage at 0.138 $/kWh, mainly due to the high thermal conductivity of the fluid. Packed bed thermal energy storage systems were found to be more expensive than liquid storage systems due to the large cost of the pressure vessels, with cost parity reached at a discharge duration of 4 h. However, at this duration, lithium-ion batteries are likely to be cheaper. The results suggest that the levelized cost of storage for the Rankine cycle-based system is slightly higher at 0.151 $/kWh.

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. In order to avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider: i) the complexity of human behavior when interacting with power conservation systems; and ii) realistic home-level power dynamics. As a consequence, this leads to approaches that are i) ineffective due to poor long-term user engagement; and ii) too abstract to be used in real-world settings. In this paper, we propose an auction-theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator (SO) sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine-learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine-learning-based Power Saving Predictions (PSP) algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial-time algorithm called Greedy Ranking Allocation (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Controlling recast layer and material removal in reverse µEDM for sustainable fabrication of microelectrode arrays

Microelectrode arrays (MEAs) are essential for researching neurons and heart tissue because they record and stimulate neuronal activity. For manufacturing MEAs on conductive materials, reverse microelectrical discharge machining, or reverse µEDM, presents a viable method. However, achieving precise MEAs requires control over recast layers generated during micromachining. This study focuses on recast layer thickness (RLT) and material removal rate (MRR) as key performance indicators for sustainable reverse µEDM fabrication of MEAs. RLT affects surface stability, while MRR influences energy consumption and cost. Optimising these responses depends on three input factors: feed rate, capacitance, and voltage. ANOVA analysis reveals that capacitance is crucial in both MRR and recast layer thickness (RLT) in reverse µEDM, contributing to 88.71% and 94.04% simultaneously. A hybrid artificial neural network-genetic algorithm for global optimisation was employed. The optimised parameters 46.5nF capacitance, 110 V voltage, and 5 µm/sec feed yielded a minimum RLT of 1.58 µm and a maximum MRR of 0.3 mm³/min. These advancements signify significant improvements in the precision and efficiency of MEAs fabrication through reverse µEDM, contributing to its sustainable implementation.

Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.

DESIGN OF HIGHLY EFFICIENT WATER PASSAGE OF PUMP-TURBINE

The problem of the need to develop renewable energy sources as a way to save the energy supply and energy independence of Ukraine by changing the consumption of carbohydrates is considered. It is shown that the development of Ukrainian renewable energy must occur in parallel with the development of energy storage systems and balancing of the energy system. The most effective system for storing energy and balancing the energy system is pumped storage power stations (PSPS). The work shows that the design of a highly effective equipment of a hydroelectric power plant depends on the correct selection of the element geometry of the pump-turbine water passage. Therefore, it is possible to ensure the necessary level of energy characteristics of hydraulic equipment. A method of dimensionless average parameters was established, which allows even at the initial stages of designing new reversible hydraulic machines to determine the optimal geometry of the watter passage elements. This method showed good results during the numerical research of Francis hydraulic turbines at a wide range of heads, as well as reversible hydromachines at heads of 300–500 m. Using expressions that establish the connection of hydrodynamic characteristics with dimensionless complexes, three variants of the flow part of the high-pressure pump-turbines (ORO500) were investigated. Based on the obtained results, a significant influence of the geometry on the hydraulic indicators of the machine was noted. The distribution of energy losses in the inlet, blade system and outlet was analyzed. The greatest energy losses occur in the inlet of the pump-turbines. To increase the energy and kinematic indicators of the ORO500 pump-turbines, the geometry of the feed elements, as well as the spiral casing and stator, was changed. Variants for improving the parameters in the elements of the supply of the water passage of the pump-turbine are proposed.

Discourse factor of semantic uncertainty in reverse machine translation of media texts

The article is devoted to the study of semantic uncertainty in the texts of legal and media discourses using online programs. The object of the study is conflict factors caused by semantic uncertainty in problem areas. An assessment and description of problem areas in machine translation of media texts was carried out, taking into account lexical ambiguity. The prerequisites for possible conflict situations when translating texts on legal and environmental topics are described. In achieving this goal, both traditional linguistic and innovative methods of analysis (use of artificial intelligence technologies) were used. The characteristics of the philosophical category of uncertainty are given according to the following methodology: 1) assessment of the semantic uncertainty of reverse machine translation texts, taking into account the discursive factor; 2) comparison of legislative and media texts for their similarity to the original after machine translation. The comparative analysis took into account the following parameters: the total number of words in the texts; similarity of texts in percentage terms; presence of various fragments. It is shown that discursive and linguistic means of realizing semantic uncertainty have a significant impact on the reader’s perception of the text. A comparison of two discourses of the same thematic focus on environmental management revealed the influence of the context (discourse) and the intermediary language on the degree of translatability of the text. It has been established that semantic uncertainty and tension zones as conflict-generating factors depend on the quality of translation programs. The study took into account possible errors associated with the limited capabilities of the online programs used. Prospects for studying problem areas in reverse machine translation include expanding sources of empirical material, involving other computer-assisted translation systems, and the possibility of applying research results in an expanded context of “potential conflict”.

FACTORS AFFECTING CITIZENS’ INTENTIONS TO REDUCE PLASTIC WASTE

The necessity of protecting the ecosystem is of crucial importance and requires immediate attention. The issues of pollution, waste, and the need to modify both individual and business practices have become dominant, especially in developing countries and disadvantaged nations. Identifying the key drivers that facilitate a shift towards circular economic processes and a significant transformation in citizens' environmentally-friendly behaviors is imperative. The aim of this study is to examine the recycling practices of residents in Bitola and Prilep, located in the Pelagonia region of the Republic of North Macedonia and to identify the main motivating factors for changing habits related to plastic recycling. Specifically, the study aims to increase plastic waste sorting practices and encourage the use of reverse vending machines (RVM). A survey was conducted, collecting data from 842 citizens to gain insights into their habits and intentions. Analyses of the study confirm that the most significant motivators for changing citizens' recycling practices are financial incentives, such as rewards or discounts. Additionally, the need to increase public awareness and provide greater education on recycling remains essential elements in initiatives aimed at changing citizens' behavior.

Feasibility of Reverse Vending Machine for PET Bottle Recycling in Case of ABC Hypermarket

The prevalence of polyethylene terephthalate (PET) in beverage packaging has increased microplastic (MP) accumulation in the environment. MP can become a component of air pollution, especifically of particulate matter with a diameter of less than 2.5 microns (PM2.5). Unfortunately, Thailand has a low recycling rate for plastic bottles, with only 4.5% being recycled. Reverse Vending Machines (RVMs) were introduced in the early 2010s as a potential solution to this problem. RVMs incentivize recycling by providing rewards or refunds for each bottle deposited, thereby reducing the amount of PET waste and MP in the environment. This study analyzed usage and waste collection data from RVMs located at 15 locations of a hypermarket chain in Thailand from June 2020 to December 2022. The results showed that the average PET bottles collected from each machine was approximately 670 kg per year, which was lower than the break-even point of 3,200 kg per year. Economic feasibility indicators also suggested that the use of RVMs might not be economically sound. This study proposed suggestions to improve the business model of RVMs and offered policy recommendations to the government on how to enhance the effectiveness of RVMs.

Feasibility of Reverse Vending Machine for PET Bottle Recycling in Case of ABC Hypermarket

The prevalence of polyethylene terephthalate (PET) in beverage packaging has increased microplastic (MP) accumulation in the environment. MP can become a component of air pollution, especifically of particulate matter with a diameter of less than 2.5 microns (PM2.5). Unfortunately, Thailand has a low recycling rate for plastic bottles, with only 4.5% being recycled. Reverse Vending Machines (RVMs) were introduced in the early 2010s as a potential solution to this problem. RVMs incentivize recycling by providing rewards or refunds for each bottle deposited, thereby reducing the amount of PET waste and MP in the environment. This study analyzed usage and waste collection data from RVMs located at 15 locations of a hypermarket chain in Thailand from June 2020 to December 2022. The results showed that the average PET bottles collected from each machine was approximately 670 kg per year, which was lower than the break-even point of 3,200 kg per year. Economic feasibility indicators also suggested that the use of RVMs might not be economically sound. This study proposed suggestions to improve the business model of RVMs and offered policy recommendations to the government on how to enhance the effectiveness of RVMs.

A Novel Partitioned-Stator Flux Reversal Machine With Two Sets of Permanent Magnet

A Novel Partitioned-Stator Flux Reversal Machine With Two Sets of Permanent Magnet

Distortion element in the automorphism group of a full shift

Abstract We show that there is a distortion element in a finitely generated subgroup G of the automorphism group of the full shift, namely an element of infinite order whose word norm grows polylogarithmically. As a corollary, we obtain a lower bound on the entropy dimension of any subshift containing a copy of G, and that a sofic shift’s automorphism group contains a distortion element if and only if the sofic shift is uncountable. We obtain also that groups of Turing machines and the higher-dimensional Brin–Thompson groups $mV$ admit distortion elements; in particular, $2V$ (unlike V) does not admit a proper action on a CAT $(0)$ cube complex. In each case, the distortion element roughly corresponds to the SMART machine of Cassaigne, Ollinger, and Torres-Avilés [A small minimal aperiodic reversible Turing machine. J. Comput. System Sci.84 (2017), 288–301].

Building a Deposit–Refund System (DRS) for Closed-Loop Recycling of Water Bottles in the United Arab Emirates

Water bottles are widely used in the Gulf countries. One estimate indicates that the water bottle usage in the United Arab Emirates (UAE) may reach up to 250 L of water per person annually. Generally, the water bottles are made of polyethylene terephthalate (PET), a recyclable material. Because of the non-availability of a powerful incentive system, these recyclable water bottles are often disposed of in landfills. This paper proposes a feasibility study of building a Deposit–Refund System (DRS) to encourage the closed-loop recycling of 0.5 L PET water bottles in the UAE waste disposal system. Water bottles are collected by a reverse vending machine (RVM) and recycled to produce PET bottles, and the proposed system will reward consumers with 0.04 United Arab Emirates Dirham (AED) per deposited water bottle. Additionally, this study calculates the cost of 100% virgin polyethylene terephthalate (vPET) and 60% recycled polyethylene terephthalate (rPET) bottles based on the UAE population, data obtained from local water bottle companies, and existing research. Adopting this DRS will cut down on waste, protect the environment, improve the manufacturing process of water bottles, and boost the local economy.

Development of a low-cost reverse vending machine for clear plastic bottles and aluminum cans

The trend of environmental conservation has become an interesting topic over the past decade; a lot of waste has been recycled to preserve the environment. The objective of this research was to design and develop a low-cost reverse vending machine to support solid waste management in Khao Rup Chang Municipality, Songkhla province, Thailand. For the first step, clear plastic bottles and aluminum cans were classified using a color sensor and a proximity sensor. Next, coupon slips and earned points could be printed out for customers to exchange for money or rewards under the project. All of the mentioned steps were controlled by a microcontroller. The experiments were conducted using 1280 samples. The results showed that the accuracy of sorting three types of materials: clear plastic bottles, aluminum cans, and both materials, was 96.50%, 100%, and 95.25%, respectively. The processing time for each container was 5.52 s on average.