Operating Points Research Articles

Analysis and Prediction of the Stability Limit for Centrifugal Compressors with Vaneless Diffusers

A numerical study was conducted to identify the mechanisms involved in the destabilisation of centrifugal compressors with vaneless diffusers. A stability analysis—carried out on the rotating and fixed parts of the studied machines—showed that the vaneless diffuser is a limiting component at a low mass flow rate. It was demonstrated that the reorganisation of stall patterns into recirculation in the inducer stabilises the impellers’ flow fields. As the destabilisation of vaneless diffusers has been a recurrent topic in the literature, many models have shown that it is the inlet-flow angle that drives the loss of stability. Models from the literature have estimated critical angle values using the geometry of the diffuser. Thus, for a given stage, expressing the diffuser inlet-flow angle as a function of the mass flow rate allows one to estimate its stability limit. However, this law needs to be calibrated to consider each compressor’s geometrical and aerodynamic specificities. This calibration can be achieved through single-passage steady simulations performed at stable operating points with high mass flow rates. With this methodology, a designer can estimate the stability limit of a centrifugal compressor with a vaneless diffuser from single-passage RANS calculations.

YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems.

Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model's stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection.

Fostc3net: A lightweight YOLOv5 based on the network structure optimization

Abstract Transmission line detection technology is crucial for automatic monitoring and ensuring the safety of electrical facilities. The YOLOv5 series is currently one of the most advanced and widely used methods for object detection. However, it faces inherent challenges, such as high computational load on devices and insufficient detection accuracy. To address these concerns, this paper presents an enhanced lightweight YOLOv5 technique customized for mobile devices, specifically intended for identifying objects associated with transmission lines. The C3Ghost module is integrated into the convolutional network of YOLOv5 to reduce floating point operations per second (FLOPs) in the feature channel fusion process and improve feature expression performance. In addition, a FasterNet module is introduced to replace the c3 module in the YOLOv5 Backbone. The FasterNet module uses Partial Convolutions to process only a portion of the input channels, improving feature extraction efficiency and reducing computational overhead. To address the imbalance between simple and challenging samples in the dataset and the diversity of aspect ratios of bounding boxes, the wIoU v3 LOSS is adopted as the loss function. To validate the performance of the proposed approach, Experiments are conducted on a custom dataset of transmission line poles. The results show that the proposed model achieves a 1% increase in detection accuracy, a 13% reduction in FLOPs, and a 26% decrease in model parameters compared to the existing YOLOv5. In the ablation experiment, it was also discovered that while the Fastnet module and the CSghost module improved the precision of the original YOLOv5 baseline model, they caused a decrease in the mAP@.5-.95 metric. However, the improvement of the wIoUv3 loss function significantly mitigated the decline of the mAP@.5-.95 metric.

Ultralow-phase-noise and broadband frequency-hopping coupled optoelectronic oscillator under quiet point operation

Advancements in microwave photonics have yielded novel approaches for generating high-purity microwave sources. Among these, optoelectronic oscillators (OEOs) and coupled optoelectronic oscillators (COEOs) have demonstrated the capability to generate frequency-independent microwaves with exceptionally low phase noise. Nonetheless, the tunability of the oscillators is rather limited due to the necessity for narrowband electronic bandpass filters, presenting challenges in achieving both wide and rapid tuning capabilities. Here, we present a COEO featuring ultralow phase noise, flexible tuning capability, and high robustness. This is achieved through a quiet point (QP)-operated harmonic mode-locked fiber laser, which effectively mitigates optical amplifier noise and supermode competition, thus significantly diminishing the necessity for ultra-narrow electronic filters. Due to the liberated tuning ability, we present an oscillator that can be tuned from 2 GHz to 18 GHz, with phase noise as low as −140 dBc/Hz at 10 kHz under the QP operation. We then illustrate the practical application of the proposed oscillator in generating frequency-hopping signals with consistent spurious modes less than −85 dBc, absolute phase noise below −135 dBc/Hz at 10 kHz, hopping resolution of 1.25 MHz, and fractional frequency stability below 6.1×10−12 at 1 s averaging time when locked to a reference. The presented COEO structure emerges as a compelling solution for agile and low-noise microwave sources in advanced wireless communication and radar systems.

Swin transformer adaptation into YOLOv7 for road damage detection

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.

Dynamic security constrained AC optimal power flow for microgrids

To ensure the continuous operation of a microgrid, proactive planning is essential, especially when contemplating possible dynamic events that alter the scheduled scenarios for the islanded operation or during its transition from connected to islanded mode. This paper introduces an innovative mathematical programming model for the AC optimal power flow (AC-OPF) with dynamic security constraints (DSCs), considering two scenarios: the islanded operation and the transition. This model uses positive-sequence equations to represent the inverter-based resources (IBRs) for grid-forming and grid-following roles, along with a fourth-order synchronous generator model equipped with excitation and frequency control systems. They assess the dynamic response to specific events such as short-circuits, decreases in PV generation, increases in load demand during the islanded operation, and the transition itself. The DSCs are applied to dispatchable distributed energy resources (DERs), which react to variations in the microgrid, considering generation and opportunity costs to minimize the discrepancy between planned and secure operating points. The mathematical programming model is implemented using AMPL, and solutions are obtained through the nonlinear optimization solver IPOPT. The tests are conducted in an adapted version of the microgrid being developed at the University of Campinas that includes a synchronous generator, photovoltaic (PV) generation, and a battery energy storage system (BESS). Results demonstrate the model’s effectiveness in adjusting generation dispatch to withstand defined events and optimizing generation resources, even when limits are not reached.

Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition

For series hybrid electric vehicles, due to the limitation of engine operating characteristics, speed regulation is usually performed using discrete operating points. This leads to the problem of mixed integer programming when solving energy management problem. In this paper, an analytical method with strong real-time performance and small computational load is proposed. First, an intelligent cycle recognition system is designed. It can cluster driving cycles offline based on the probability distribution of the required power and efficiently recognize unknown driving cycles online. Then the analytical expression of power distribution is derived, and the selection scheme of engine operating point is given. Finally, the required power information obtained from the clustering is substituted into the analytical expression. The specific values of the corresponding analytical solutions for each type are computed offline. When facing unknown conditions, this method can match the power distribution scheme quickly once the driving cycle is recognized. The results of the simulation and hardware-in-loop experiments demonstrate that this method can effectively control the SOC trajectory and allocate power more optimally. Compared to the benchmark method, the proposed strategy improves the fuel economy by 5.52 % and 7.49 % in two cases, and the computational efficiency is significantly enhanced.

Quantum key distribution in optical fibers: a comprehensive design view of the overall quantum layer beyond transmission

This paper reports the network operator point of view about the introduction of quantum key distribution (QKD) in optical networks to secure the data plane and/or specific applications, focusing on the design aspects that go beyond pure transmission. The functional architecture of a quantum key distribution network is depicted focusing on the integration in the existing telecommunications infrastructure. Some use cases of the utilization of the QKD layer, presenting results from in-field demonstrations, are reported together with a technology agnostic numerical model about resource sharing in a metropolitan area network environment.

Research note: A method for recognizing and evaluating typical behaviors of laying hens in a thermal environment

Automatically identifying abnormal behaviors of caged laying hens in a thermal environment improves manual management efficiency. It also provides reference indicators for breeding heat-tolerant hens. In this study, we propose a deep learning-based method for automatic recognition and evaluation of typical heat stress behaviors in hens. We developed a lightweight object detection algorithm, YOLO-HGP, based on the YOLOv8n as the baseline model. YOLO-HGP achieves Precision (P), Recall (R), and mean average precision (mAP) of 95.952%, 94.127%, and 97.667%, respectively, effectively detecting typical heat stress behaviors in hens. Compared to the original YOLO v8n, YOLO-HGP improves R, and mAP by 6.257%, and 1.963%, respectively. The FLOPs (floating point operations) and parameter count of YOLO-HGP are 4.3G and 1.729M, reducing by 47.56% and 42.58% compared to the original model. Additionally, we introduce the "ORC-ratio" (The ratio of the combined frequency of open-beak breathing and retching behaviors to the frequency of closed-beak behaviors.) as an evaluation indicator for the frequency of typical heat stress behaviors in hens and combine it with the Hybrid-SORT multiobject tracking algorithm to achieve tracking detection of individual hens. The study demonstrates that the proposed model effectively identifies and quantitatively evaluates typical behaviors of hens in a thermal environment, providing an effective approach for the automated recognition of heat stress behaviors in hens.

Design update of DEMO BOP for HCPB BB concept with an energy storage system

An option for the Intermediate Coupling Design (ICD) of the European Demonstration Fusion Reactor (DEMO) Balance of Plant (BOP) configuration for Helium-Cooled Pebble Bed (HCPB) Breeding Blanket (BB) concept consists of the Primary Heat Transport System (PHTS), the Intermediate Heat Transfer System (IHTS), using HITEC salt as coolant, equipped with a thermal Energy Storage System (ESS), and the Power Conversion System (PCS). The aforementioned BOP configuration is capable of producing electricity continuously during the pulse time (2 h) and dwell time (10 min) of DEMO plant operation.The present study reports on the current (2023) thermal system design of DEMO HCPB ICD BOP for 12 operational states defined by the DEMO Design Central Team (DCT) in 2021. It was shown that the PCS is able to accommodate all of the 12 operational points defined based on the uncertainties of the energy map, provided that an additional bypass line at the outlet of the Divertor Cassettes (DIV-CAS) Heat Exchanger (HX) towards the low-pressure part of the feedwater line (to the condenser in this investigation) is installed in the system. This line is activated only in the cases, where the feedwater flow to the low temperature DEMO heat sources HXs is needed to be higher than that necessary to remove BB power at the Steam Generators (SGs). In all the other cases, the bypass line remains unused. Additionally, perspective improvements of DEMO HCPB BOP regarding thermal ESS, as well as PCS are discussed.

Plasma performance and operational space with an RMP-ELM suppressed edge

The operational space and global performance of plasmas with edge-localized modes (ELMs) suppressed by resonant magnetic perturbations (RMPs) are surveyed by comparing AUG, DIII-D, EAST, and KSTAR stationary operating points. RMP-ELM suppression is achieved over a range of plasma currents, toroidal fields, and RMP toroidal mode numbers. Consistent operational windows in edge safety factor are found across devices, while windows in plasma shaping parameters are distinct. Accessed pedestal parameters reveal a quantitatively similar pedestal-top density limit for RMP-ELM suppression in all devices of just over 3×1019 m−3. This is surprising given the wide variance of many engineering parameters and edge collisionalities, and poses a challenge to extrapolation of the regime. Wide ranges in input power, confinement time, and stored energy are observed, with the achieved triple product found to scale like the product of current, field, and radius. Observed energy confinement scaling with engineering parameters for RMP-ELM suppressed plasmas are presented and compared with expectations from established H and L-mode scalings, including treatment of uncertainty analysis. Different scaling exponents for individual engineering parameters are found as compared to the established scalings. However, extrapolation to next-step tokamaks ITER and SPARC find overall consistency within uncertainties with the established scalings, finding no obvious performance penalty when extrapolating from the assembled multi-device RMP-ELM suppressed database. Overall this work identifies common physics for RMP-ELM suppression and highlights the need to pursue this no-ELM regime at higher magnetic field and different plasma physical size.

International Law Enforcement Observer XIV.

Aim: Despite the fact that this time, the Observer provides insight into a wide range of topics in the international law enforcement literature – from counter-terrorism to football security – the editorial purpose that influenced the selection is still recognisable. The aim is to focus readers’ interest on methods that focus on facts and require publicity. Examples will be given that demonstrate that unjustified secrecy can be an obstacle not only to scientific research but also to effective professional work. Methodology: Tamás Drusza’s review shows that in the United Kingdom, a tactic of combining intelligence and criminal investigation is used to counter terrorism effectively, which also means that the flow of information between organisations is a critical point in the system's operation. Secrecy between authorities can lead to inefficiency. ‘The drawbacks of applying the principle of restricting the transfer of information have been confirmed by a survey of Europol staff. The general view is that proactively sharing information is as important as obtaining it.’ Of course, in order for such a counterproductive application of the principle of confidentiality to come to light, it was necessary for police academics to carry out the necessary research in a sensitive area of service and then publish the results in a way that would allow even Hungarian colleagues interested in the subject to be informed by means of a review. Findings: Beyond the threat of terror, all forms of violent radicalism in the broader sense need to be countered by social cohesion, as András Hunor Lehel illustrates with the Swedish example. ‘According to a 2016 report by the Swedish Security Service, approximately 300 people have left Sweden to join various violent Islamist groups abroad and fight alongside their organisations. Many of these men have returned to Sweden and represent a potential threat to the community.’ The study concludes that local authorities play a key role in detecting and preventing radicalisation, alongside central government. It is no coincidence that resilience has emerged as a key concept. Swedish municipalities are expected to develop plans that include measures to increase resilience at local and community level. Andrea Mészáros gives an insight into research conducted at the Tempe Police Department in Arizona. Here, they sought to understand how measures were affected by the pandemic in March 2020, and then by the national outcry over the death of George Floyd, a victim of police violence. The study analysed 474 measures, selected at random and all of which were recorded on body camera. Researchers said a review of the body-worn camera footage highlighted that despite the extreme circumstances, police officers handled encounters with citizens in a professional manner. Tamás Pressburger reports on new methods of policing football matches. The experience in England suggests that police officers responsible for the security of football grounds have successfully improved the effectiveness of their work through a change of approach, with sufficient training and practical experience. Police officers also need to work closely with the cultural and emotional dynamics of football fans, and need empathic skills to intervene effectively and constructively in escalating situations. Value: The reviews presented a number of solutions that have found new ways to challenge the traditional approach to policing. These include replacing the myth of secrecy with openness that emphasises cooperation, consensus based on empathy rather than confrontation, resilience that leads to the resolution of the trauma that accompanies violence, and the dissolution of centralisation with the principle of self-government.

DESIGN OF PUMPING INSTALLATIONS: DEVELOPMENT OF AN EXCEL WORKBOOK FOR HYDRAULIC MACHINES LECTURES

Nowadays, pumps are used worldwide in a very wide range of facilities and for countless purposes, including HVAC, domestic and commercial buildings, district energy, industrial processes and water treatment, municipal wastewater and water supply, agriculture and irrigation, among others. Accordingly, the number of applications is also very diverse. The purpose of present contribution is to introduce an Excel Workbook that represents a friendly easy to use tool that enables the design of pumping systems. It was first thought for Hydraulic Machines Master lectures, but its use might be looked at in a wider perspective. The Workbook includes 17 worksheets (Figure 1), all linked to each other, addressing different aspects of the design. Special attention is paid to the major and minor head losses calculation, the cavitation phenomenon, the use of dimensionless coefficients to determine the rotation speed to obtain a specific operating point and to the calculation of the system curve. Energy efficiency represents today an important goal in every pumping facility; therefore, one of the objectives of this tool is to enable the user to quantify both the shaft power and the efficiency of different operating points thus allowing a sustained definition of the best solution.

Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18

Objective: To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. Methods: A cross-sectional study. Twenty-five narcolepsy type 1 patients who were first diagnosed and never received treatment and 25 healthy controls recruited by advertisement in the Second Affiliated Hospital of Nanchang University from 2020 to 2023.After image preprocessing, a total of 1 180 images were obtained, including 583 cataplexy faces and 597 normal faces.90% were selected as the training set and validation set, and then expanded the data by 5 times.80% of the expanded data set was extracted as the training set and 20% as the validation set, that is, the number of the training set was (583+597)×0.9×0.8×5=4 248, the number of the validation set was (583+597)×0.9×0.2×5=1 062. The data sets for training and validation were used train parameters to establish the model and were trained through the five-fold cross-validation method, to establish the ResNet-18 cataplexy face recognition model via transfer learning.10% (118 images) of the original non-amplified images were extracted as the test set. The test set data did not participate in data enhancement and model training, and was only used to evaluate the final performance of the model. Finally, ResNet-18 was compared with VGG-16, ResNet-34 and Inception V3 deep learning models, and the receiver operating characteristic curve was used to evaluate the value of ResNet-18 image recognition network in cataplexy face recognition. Results: Among 25 patients with narcolepsy type 1, 15 were males and 10 were females, aged [M (Q1, Q3)] of 14.0(11.0, 20.5) years.Among 25 healthy controls, 14 were males and 11 were females, with a median age of 16.0(14.4, 23.0) years.The overall accuracy of ResNet-18 image recognition network in the test set was 90.9%, the sensitivity was 96.4% and the specificity was 85.2%. The area under the ROC curve was 0.99(95%CI:0.96-1.00). The ResNet-18 model parameter amount was 11.69 M, the floating point operation amount was 1 824.03 M, and the single image recognition time was 5.9 ms. Conclusions: The cataplexy face prediction model built based on the deep learning image recognition network ResNet-18 has a high accuracy in identifying cataplexy faces.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

Abstract The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.

A simple and effective deep neural network based QRS complex detection method on ECG signal.

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device.

Recent progress in the operational flexibility of 1 MW circulating fluidized bed combustion

In this study, various experimental tests were carried out with a 1 MWth circulating fluidized bed pilot plant at the Technical University of Darmstadt to investigate the combustion and co-combustion behaviour of different fuels under load change conditions in air and oxyfuel mode. The combustion tests were carried out with low-calorific lignite and hard coal as reference cases for co-combustion. In the co-combustion experimental tests, straw pellets, and refuse-derived fuels (high and low calorific) were used as co-fuels for coal. In addition, a comprehensive dynamic 1.5-D process simulation model was established for a 1 MWth circulating fluidized bed pilot plant. The model created with the “APROS” software precisely reproduces both the gas/solids and the water/steam side of the 1 MWth pilot plant. The dynamic process simulation model was first validated at various steady-state operation points and, in a second step, at various dynamic load changes. The numerical results, such as the pressure drop and temperature profiles along the fluidized bed riser as well as the flue gas concentrations at the cyclone outlet, were compared with the measurements and showed agreement during the long-term tests of the combustion and co-combustion processes.

A crystal capping layer for formation of black-phase FAPbI3 perovskite in humid air.

Black-phase formamidinium lead iodide (α-FAPbI3) perovskites are the desired phase for photovoltaic applications, but water can trigger formation of photoinactive impurity phases such as δ-FAPbI3. We show that the classic solvent system for perovskite fabrication exacerbates this reproducibility challenge. The conventional coordinative solvent dimethyl sulfoxide (DMSO) promoted δ-FAPbI3 formation under high relative humidity (RH) conditions because of its hygroscopic nature. We introduced chlorine-containing organic molecules to form a capping layer that blocked moisture penetration while preserving DMSO-based complexes to regulate crystal growth. We report power conversion efficiencies of >24.5% for perovskite solar cells fabricated across an RH range of 20 to 60%, and 23.4% at 80% RH. The unencapsulated device retained 96% of its initial performance in air (with 40 to 60% RH) after 500-hour maximum power point operation.

Investigation of aerodynamic effects and dominant design variables of cavity squealer-tip in three-dimensional performance of a centrifugal compressor

Enhancing the performance of turbomachines has been a longstanding area of interest, with numerous geometry modification methods proposed in the literature. One such method, historically prevalent in turbines, is the cavity squealer tip. However, its application in compressors, particularly of the centrifugal type, remains relatively underdeveloped. This study employs the Taguchi method's L-9 orthogonal array as a sensitivity analysis (utilizing Design of Experiments) to investigate the impact of cavity squealer design variables on the performance of the NASA-CC3 compressor at three key operational points: choke, design, and near stall regions. Computational fluid dynamics is utilized to derive performance parameters, and various sets of design variables result in improvements across pressure ratio, isentropic efficiency, mass flow rate, and surge margin. Notably, a significant 20 percent enhancement in surge margin is achieved in one instance. The performance at the remaining operating points varies among the nine cases, with improvements observed in some areas and declines in others. Processing the data yields three optimal geometries, with the best configuration enhancing all performance parameters (0.59% for design mass flow rate and 2.20% for efficiency, maintaining a constant pressure ratio compared to the base). As anticipated, the optimal impellers exhibit reduced flow leakage compared to the baseline.

Perceptions And Manifestations Of Magic Among Some Peoples Of The Old World And In The Hittite Environment

In the framework of the article, the perception of magic in the ancient world, especially in the Hittite environment, has been focused on, considering magic as a phenomenon in comparative parallels with the perceptions of the ancient Egyptians, Jews, Armenians and other ancient Near Eastern peoples. Within the framework of the study, the origin and perception of magic will be presented, considering it from an etymological, ritualistic, operational point of view, as well as the role of magic as a negative and positive phenomenon for ancient societies, etc. will be presented. In addition, in the context of the question, an attempt is made to present the role of the sorcerer in the ancient society.