bluuebunny/crossref_metadata_2025 huggingface dataset:
A preliminary geochemical study of the archaeological site of the Battle of Jenipapo (Piauí state, Brazil)
Authors: Oliveira Antonio Leite , Meneses Lage Maria Conceição Soares , Farias Filho Benedito Batista , de Carvalho Maria do Amparo Alves
Published: February 2024

This paper presents, for the first time, geochemical data related to the Battle of Jenipapo, a significant event that occurred on March 13, 1823, marking a political activity favorable to Brazil's independence. The present work aimed to analyze the contents of lead and copper as markers of historical armaments in sediments for geochemical prospecting of the archaeological area in which the Battle of Jenipapo probably occurred. Nine sediment samples were collected: seven from the prospect area and two control samples. Sedimentological characteristics were evaluated, such as pH (6.1–6.9), acidity (0.19–3.00 mmol L−1), organic matter (0.86–3.37% w/w), and granulometric classification (sandy predominance). The metals (lead and copper) were determined by inductively coupled plasma mass spectrometry (ICP‐MS) after acid digestion of the sediments. Lead content ranged from 7.9 to 14.9 mg kg−1 for samples and 5.4 mg kg−1 for control samples. Copper content ranged from 14.2 to 29.3 mg kg−1. The results showed that only lead could be used for the geochemical prospecting of the area that presented strong evidence of anthropic intervention allowing the carrying out of excavation steps.
Unleashing virtuous cycles of sustainable development goals and well‐being
Authors: Nobre Farley Simon
Published: February 2024

This article advances sustainability towards a new logic that favors the flourishing of Sustainable Development Goals (SDGs) and well‐being from North to South. It presents a Global Dual‐Perspective (GDP) and a Dynamic Equilibrium Framework (DEF) that inform sustainability, management, and international business with a paradoxical view of the SDGs and a strengthened analysis that outlines the role of multinational enterprises (MNEs) in addressing the SDGs within and across the North–South. This article reveals that organizations will effectively unleash virtuous cycles of SDGs and well‐being when confronting and juxtaposing environmental, health, social, economic, and law‐oriented goals. Furthermore, virtuous cycles will be more successful when splitting and integrating short‐ and long‐term conflicting goals within and across the North–South to fuel systemic resilience and sustainable development.
Income inequality and party alternation: State‐level evidence from the United States
Authors: Calcagno Peter , Marsella Alexander , Zhou Yang
Published: February 2024

Using multiple measures of income inequality and political party alternation for US states we examine panel data from 1971 to 2015 to test whether income inequality affects party alternation for governors. We test various measures of party alternation at the state level. The evidence suggests that income inequality increases party alternation and has a nonlinear relationship with gubernatorial‐party alternation. Income inequality backlash is more evident for Republicans and favors Democrats when inequality is high, with the opposite being true. Results vary with the choice of inequality and party alternation measures, which suggests income inequality for states should be further examined.
Associations of sensory sensitivity, pain catastrophizing, and alexithymia with dental anxiety
Authors: Ogawa Mika , Saito Akihiro , Karukivi Max , Lahti Satu
Published: February 2024

We aimed to reveal interrelationships between alexithymia, catastrophic thinking, sensory processing patterns, and dental anxiety among 460 participants who were registrants of a Japanese research company. Measures used were the Modified Dental Anxiety Scale, the Adult Sensory Profile, the Pain Catastrophizing Scale, and the 20‐item Toronto Alexithymia Scale. The interrelationships among the constructs were analyzed using structural equation modeling, adjusting for age, gender, and negative dental treatment experience. Data from 428 participants were used in the analyses. Sensory sensitivity and pain catastrophizing were independently associated with anticipatory and treatment‐related dental anxiety, while difficulty identifying feelings was not. In the mediation model, sensory sensitivity and pain catastrophizing served as full mediators between difficulty identifying feelings and the dimensions of dental anxiety (indirect effects were between 0.13 and 0.15). The strength of the associations was 0.55 from difficulty identifying feelings to both pain catastrophizing and sensory sensitivity, and between 0.24 and 0.26 to anticipatory and treatment‐related dental anxiety. The association between trait‐like phenomena, such as alexithymia, and dental anxiety may be mediated by neurophysiological and cognitive factors such as sensory sensitivity and pain catastrophizing. These findings could be crucial for new and innovative interventions for managing dental anxiety.
Evaluation of tag attachment techniques for plunge‐diving terns
Authors: Fijn Ruben C. , van Bemmelen Rob S. A. , Collier Mark P. , Courtens Wouter , van Loon E. Emiel , Poot Martin J. M. , Shamoun‐Baranes Judy
Published: February 2024

A wide variety of attachment techniques have been used to track birds with electronic tags, with glue, tape, leg rings, neck collars and harnesses being the most common methods. In general, the choice of attachment method should strive to minimize tagging effects, but ensure that sufficient data are collected to address the research question at hand. The aim of our study was to develop and evaluate tag attachment methods to track Sandwich Terns Thalasseus sandvicensis during the last part of the incubation and the chick‐rearing period of one breeding season. Tag attachments had to stay on for the duration of the chick‐rearing period (5–6 weeks) and be non‐restraining and flexible, but strong enough to withstand the forces and submersion associated with their plunge‐diving foraging technique. We first experimentally tested the durability of flexible material under various environmental conditions with the aim of developing a self‐releasing harness. Then, in field studies, we compared three different attachment methods on terns during the breeding seasons, attaching tags to dorsal feathers using (1) tape, (2) glue or (3) a newly developed harness made specifically for short‐term deployments of one chick‐rearing period and constructed from degradable material. Assessment of the performance of attachment methods was based on retention time of the loggers and on annual survival rates of tagged individuals in comparison with non‐tagged individuals. The use of tape and glue led to premature loss of tags (median minimum retention time (range) of 3 (1–4) days and 15 (5–26) days, respectively), whereas the self‐releasing harness had a median minimum retention time of 42 (18–91) days, which is sufficient to track Sandwich Terns during the entire chick‐rearing period. The apparent annual survival of birds tagged using glue or tape did not differ from that observed in non‐tagged control birds. In contrast, birds fitted with the self‐releasing harnesses might have experienced a lower survival rate than control birds. Entanglement of birds in the harness material was incidentally observed in three cases, which may have contributed to the lower survival rates observed in this group. The risk of entanglement can potentially be mitigated with a leg‐loop harness instead of a full‐body harness. Our results highlight the necessity of careful consideration when selecting appropriate attachment methods. Specifically, there is a need to address whether the research questions and desired tracking duration justify the use of a harness and the higher impact that it entails, or whether a tape or glue‐mount is sufficient. More broadly, sharing field expertise in tag attachments across studies is essential to successful deployments while minimizing the impact on animals.
Experimental investigation of integrated systems of precooling and plasma‐activated water‐based bacterial inactivation
Authors: Liu Ming , Li Peiru , Xu Hongbo , Tian Changqing , Zhang Hainan , Kong Fanchen , Zou Huiming
Published: February 2024

Plasma‐activated water (PAW) is produced by treating aqueous solution with cold plasma. PAW is rich in reactive oxygen and nitrogen species, making it a safe and efficient approach for the inactivation of pathogens. In this paper, two types of integrated systems of precooling and PAW‐based bacterial inactivation for fruits and vegetables were proposed. One is cold water precooling (CWP) and PAW, the other is differential pressure precooling (DPP) and PAW. The influencing factors of PAW preparation and the bacterial inactivation effect of PAW suspension were experimentally investigated. The results demonstrated that with the increase in preparation time, the pH decreased, with a concurrent increase in temperature, electrical conductivity, and redox potential. The physicochemical properties of PAW were affected by temperature, with the optimal temperature being about 17°C. As the preparation time and bacterial inactivation time increased, the decontamination effect of PAW against Escherichia coli increased in suspension bacterial inactivation. Conversely, with the decrease in PAW temperature, the bacterial inactivation effect weakened. Furthermore, PAW was applied through CWP and DPP combined with PAW for precooling snow peas. Soaking CWP showed a 2.1 log reduction of background bacteria after 10 days of storage. Spraying CWP showed a 1.1 log reduction of background bacteria after 5 days of storage. DPP showed a 0.8 log reduction of background bacteria after 10 days of storage, and the appearance of snow peas surpassed those of the control group. Integrated systems of precooling and PAW‐based bacterial inactivation indicated promising effects on maintaining the safety and quality of snow peas.

Practical applications

PAW is a sterilization method with the advantage of easy preparation, low cost, minimal chemical residue, and effective sterilization. PAW could be produced simply by discharge on the tap water, which is very practical for its up‐scale application. In this study, different parameters on the physicochemical properties of PAW were evaluated, which was helpful in designing industrial PAW‐producing equipment. Besides, integrated systems of precooling and PAW sterilization for fruits and vegetables were proposed, which showed prominent sterilization and good preservation effects. The integrated systems could maintain the freshness of fruits and vegetables and prevent bacterial contamination during precooling by combining PAW sterilization and precooling, which will help extend the shelf lives of fruits and vegetables. In addition, this combination will prevent agricultural product waste and maintain the safety of fruits and vegetables.
Feasible feeding strategies for sustainable management of serve heat stress conditions: Effect of Milk Thistle extract on growth performance and health status of newly weaned rabbits
Authors: Hassan Mahmoud A. E. , Ragab Mona A. , Shazly Soheir A. , Ahmed Mohamed E. , El‐Kholany Mohamed E. , El‐Raghi Ali Ali
Published: February 2024

The trail aimed to explore the effect of dietary supplementation of Milk Thistle (MT) extract on growth performance and health status of growing rabbits exposed to serve heat stress condition, considering the economic efficiency of supplementation. A total of 96 weaned male rabbits were divided into 4 groups (24 rabbits/group). The first group received the basal diet without any supplementation and served as a control (MT0), while 2nd, 3rd and 4th groups supplemented with MT at levels of 5 (MT5), 10 (MT10) and 15 (MT15) g/kg diet, respectively, for 10 consecutive weeks. Both of growth performance and feed utilisation were significantly enhanced by the dietary treatment, the optimum dose of MT was 12 g/kg diet for average daily gain, specific growth rate and performance index. However, it was 13 g/kg diet for feed conversation ratio. The polynomial regression analysis showed that the lowest values of rectal temperature and respiration rate were observed at doses of 11 and 13 g/kg diet respectively. The dressing percentage and the relative weights of liver and total edible giblets were significantly improved by the treatment (p = 0.0416, 0.0112 and 0.0032, respectively), maximising in the MT10 group. The MT10 and MT15 groups showed higher erythrocytes and leucocytes counts and lower levels of urea, creatinine and total cholesterol compared to the control (p < 0.05). Liver functions significantly enhanced in aforementioned two treated groups, the liver ultrastructure represented normal cytoplasmic organelles, and nucleus and mitochondria in MT10 group, while the MT15 group showed hepatocytes with dilated nucleus with most cytoplasmic organelles appeared well organised and normal except few small cytoplasms vacuolated. The levels glutathione, superoxide dismutase, catalase and total antioxidant capacity as well as immunoglobulin M, and immunoglobulin G significant improved in the MT‐Treated groups compared to the control (p < 0.05). Economically, MT supplemented diets improved the net revenue of fattened rabbits during the summer season. In conclusion, the supplementation of MT extract at levels of 10 or 15 g/kg diet enhanced growth performance, feed utilisation, dressing percentage, hemato‐biochemical attributes, immunity and redox balance of heat stressed growing rabbits during the hot season.
Impact of bee venom supplement on productive performance, health status and economics of weaned male rabbits: Considering breed and dosage factors
Authors: Abd El‐Aziz Ayman H. , El‐Kasrawy Nagwa I. , Abd El‐Hack Mohamed E. , Swelum Ayman A. , Suliman Gamaleldin , Tufarelli Vincenzo , Abo Ghanima Mahmoud M.
Published: February 2024

The objectives of the present study were to investigate the potential effects of purified bee venom (BV) on various aspects of growth, carcass, antioxidant, intestinal bacterial count and economic considerations in rabbits. A total of 240 male rabbits, comprising two distinct breeds (V‐Line and New Zealand White [NZW]), 5 weeks old, with an average live body weight (BW) of 680 ± 20 g, were randomly divided into six groups, each containing 30 rabbits. Each group had five replicates, with six rabbits in each replicate. The allocation of animals to the groups followed a fully factorial design, incorporating two factors: breed (V‐Line and NZW) and four levels of purified BV derived from Apis Mellifera. The control group (G1) received a basal diet without additives. The other three groups (G2, G3 and G4) received the basal diet with BV supplementation in their drinking water at 0.5, 1 and 2 mg/L respectively. The study results indicated that NZW rabbits significantly enhanced feed conversion ratio while maintaining consistent carcass attributes compared to the V‐Line breed. Despite variations in growth parameters being less pronounced, the supplementation of BV at levels of 1–2 mg/L demonstrated significant improvements in various other parameters. Notably, the interaction between the BV supplement and the breed factor (p < 0.001) yielded notable distinctions in most production metrics, encompassing BW, weight gain, feed conversion, carcass attributes and blood parameters. Increasing levels of BV supplementation, particularly at 1 mg/L, led to substantial improvements in serum and tissue metabolites. Moreover, the levels of total bacterial count and Escherichia coli in the jejunum and colon were significantly diminished, while the population of Lactobacilli in the colon was augmented (p < 0.001) in rabbits from both breeds receiving BV supplementation (1–2 mg/L) compared to the control group. The results underscore the potential of the BV supplement to enhance final weights, bolster antioxidant status and mitigate the presence of pathogenic bacteria, thereby contributing to enhanced economic efficiency in rabbits. Further inquiries are warranted to comprehensively investigate BV supplementation's potential advantages and limitations across different breeds and dosage levels.
The Application of Sesamoid Position in Diagnosing Thumb Metacarpophalangeal Joint Dorsal Dislocation: A Retrospective Study
Authors: Xu Jianhua , Han Li , Zhang Bo , Cao Shuming , Zhu Dake , Yin Zhonggang , Gong Ketong , Zhan Haihua
Published: February 2024

Objectives

The position of sesamoid of thumb metacarpophalangeal (MCP) joint changed clearly when the joint was dislocated dorsally. However, the significance of sesamoid location in diagnosing joint dislocation was unclear. The present study aimed to explore the positional relationship between sesamoid bone and thumb metacarpophalangeal joint in normal and dorsal dislocation joints.

Methods

Between January 2018 and August 2023, we collected 60 isometric plain films from sixty outpatients and reviewed 56 anisometric plain films from twenty‐eight emergency patients with dorsal dislocation of thumb MCP joint at Tianjin Hospital, then took measurements on the hand X‐ray images. The sesamoid length on its longitudinal axis was defined as DP, the distance between the distal edge of sesamoid and thumb MCP joint was defined as DJ, and the ratio of DJ and DP was R. An independent‐samples t‐test and paired‐samples t‐test was utilized to analyze difference among data groups.

Results

The 60 isometric images were from 30 male and 30 female outpatients with normal bone structure in their hands, and the 56 anisometric images of the 28 emergency patients included both preoperative and postoperative materials. Among the outpatients, the actual distance between the distal edge of sesamoid and thumb MCP joint space (DJ) was 2.09 mm and 1.40 mm in males and females, respectively. The authentic average length of sesamoid (DP) was 4.46 mm in males and 4.22 mm in females. The average value of R (the ratio of DJ and DP) in males and females was 0.49 and 0.34, respectively. There were gender‐related statistical differences in DJ (p < 0.01) and R (p=0.01), but no statistical difference in DP (p > 0.05). For the 28 emergency patients, the mean value of R was –0.47 before joint reduction and 0.58 after joint reduction, with statistical difference between them (p < 0.01).

Conclusions

There was significant difference in the relative position between sesamoid and thumb MCP joint when joint dislocation and joint reduction. The distal edge of sesamoid beyond thumb MCP joint could be an evidence in diagnosing joint dorsal dislocation. The distal edge of sesamoid below thumb MCP joint could be an evidence of joint reduction.
Are public sector accounts trusted? Exploring the verdict of the Public Administration and Constitutional Affairs Committee in the United Kingdom
Authors: Ferry Laurence , Midgley Henry
Published: February 2024

Legislatures face difficult challenges holding modern bureaucratic democracies to account due to the scale, complexity, and diverse impacts on citizens' lives. One way that democracies bridge the gap between the legislature and executive is through financial accounts of government departments. This paper examines whether financial accounts are trusted by MPs in the UK Parliament for purposes of transparency in the service of accountability. The article does this through examination of two linked inquiries by the Public Administration and Constitutional Affairs Committee at the UK House of Commons. The article argues that transparency, accountability, and trust are involved in a rhizomatic relationship where each is related to the other without a hierarchy between them. The article uses a framework proposed by Oomsels and Bouckeart to show the accounts are not trusted, which has implications both for the accounts as a tool of accountability and for creating transparency.
Dual transcriptomic and metabolomic analyses provide novel insights into the role of vitamins A and B metabolism in ectomycorrhizal symbiosis between Pinus yunnanensis and Lactarius deliciosus
Authors: Su Kai , Yuan Jing , Wang Ran , Huang Lanlan , Zhan Jian , Zhang Xueqiong , Wan Shanping , He Xinhua , Lambers Hans , Yu Fuqiang , Wang Yanliang
Published: February 2024

Ectomycorrhizal (EM) fungi play important roles in nutrient cycling and plant community establishment in forest ecosystems. Effects of EM formation on global alterations of the transcriptome and metabolome during plant‐fungal interaction and the key metabolites involved in EM development are largely unknown. Here, dual RNA‐Seq and untargeted metabolomic analyses were used to reveal stage‐specific and core responses of Pinus yunnanensis and Lactarius deliciosus during mycorrhizal colonization. We found that L. deliciosus colonization in P. yunnanensis roots induced different transcriptional changes across three interaction stages, with a small core of genes consistently regulated at all stages. Concentrations of retinol (vitamin A) and retinoic acid increased while that of B group vitamins decreased during EM formation, which was coordinately regulated by these two plant‐fungus partners, with L. deliciosus possibly playing a dominant role. Exogenous retinol altered the diameter and mantle thickness of P. yunnanensis ‐ L. deliciosus EM tips and affected host plant growth and phosphorus acquisition. In the absence of L. deliciosus, exogenous retinol increased the root diameter and the number of root tips of P. yunnanensis. Furthermore, the concentration of auxin increased, but that of abscisic acid decreased during EM formation, and the genes involved in plant hormone signal transduction were gradually activated, and auxin and cytokinin signal transduction potentially played a positive role in this EM symbiosis. In conclusion, we propose that the interaction of P. yunnanensis and L. deliciosus alters vitamin metabolism, which may further affect plant hormone biosynthesis and signal transduction, modulating root morphology and EM traits.
Woldemar Voigt’s Alliance of Finite Reality with Infinite Fantasy
Authors: Cassella Antonio
Published: February 2024

This article tries to explain why we should ally the cerebral and asymmetrical finiteness spared in autism with the cerebellar and symmetrical infinity impaired in it to avoid global warming, a super-wicked problem. Woldemar Voigt implied the alliance of detectible finiteness and lying infinity in 1887 at the University of Göttingen through the transformations of his “Shining Sphere,” or Riemann Sphere. Since they cannot lie, autistics cannot face problems, rooted in the maybe of dreaming and fantasy. The author posits that our problem is ignoring the alliance of classical asymmetry and quantum symmetry in our brainstem, Stonehenge’s builders, artless discourse, and nature’s nature. The author posits that recalling nature’s roots will save about eight billion nonautistic liars (rooted in the “tree of knowledge”) from a fate worse than Lisbon’s Earthquake in 1755. The ruin of life on Earth can wane by willfully grasping the union of vertical and finite asymmetry with horizontal and infinite symmetry in the “tree of life.” Asymmetrical finiteness (e.g., from front to back in a dog tick) hugs symmetrical infinity in living beings (e.g, the legs and eyes of a dog tick) as relativity hugs quantum physics; and Descartes’ cosines hug Euler’s sines in a complex plane.
Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery
Authors: Liu Wei , Gao Songchen , Yan Wendi
Published: February 2024

Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. This paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. First, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge–discharge cycles of the lithium-ion battery, facilitating input to the model. Second, a comparison-transfer network using the Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small-sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.
Enhancing CO2 Water-to-Water Heat Pump Performance Through the Application of a Multi-Objective Evolutionary Algorithm
Authors: Soleimani Shima , Schaefer Laura , Liaqat Kashif , Cole Aaron , Temming Jörg , Kösters Heiner
Published: February 2024

Due to growing concerns about the environmental impact of refrigerants, carbon dioxide (CO2) heat pumps have been increasingly evaluated as efficient alternatives for conventional heat pumps. Performance analyses of CO2 heat pump water heaters (HPWHs) have been the subject of many studies, but these are typically limited to parametric analyses of air-source HPWHs. The interrelated behavior of the supercritical and subcritical thermodynamic properties, component operation, and efficiency means that a parametric study cannot adequately capture the inherent nonlinearity. Therefore, this paper, for the first time, aims to perform a multi-objective optimization on CO2 water-sourced HPWH performance in order to minimize the total component costs, maximize gas cooler (GC) heating capacity, and maximize the coefficient of performance (COP) using two different optimization scenarios. The decision variables are defined as GC pressure (75–140 bar), evaporator temperature (−19.5–0.2 °C), and GC outlet temperature for CO2 (16–36 °C). The model performance is constrained by the practical ranges of the GC and evaporator inlet and outlet temperatures for water. A coupled simulation-optimization model through python is developed using Engineering Equation Solver (EES) software and the non-dominated sorting genetic algorithm II (NSGA-II). The result of the optimal Pareto front showed that the optimal GC heating capacity changes from 19.2 to 56.7 kW, with a lowest cost of $7771 to a highest cost of $9742, respectively. When the lower bound of the GC outlet temperature was set to 32 °C, the Pareto front showed a maximum COP of 3.23, with a corresponding GC heating capacity of 44.36 kW.
An Innovative Approach for the Validation of Computational Structural Outcomes of Octocopter’s Connection Arms Through Advanced Finite Element Methods
Authors: Muniswaran Sabareesh , Raja Vijayanandh , Rajapandi Rajkumar , Rajendran Parvathy , Mageswaran Ramesh , Madasamy Senthil Kumar , Stanislaus Arputharaj Beena
Published: February 2024

Reliability testing on computational structural outcomes is necessary because they are relays approximations. This study uses the connecting arm finite element method to validate computational structural results. The connecting arm’s displacement has been predicted using ANSYS Structural 17.2, a sophisticated analysis tool. This study uses the Multirotor Unmanned Aerial Vehicle (MUAV) connecting arm as a test platform. At various working situations like heavy gust loading, low thermal conditions, etc., ANSYS Fluent 17.2 computes the aerodynamic loads on the sophisticated Octocopter’s connection arms. These conditions compute connection arm weight-related structural outcomes. Through ANSYS Structural 17.2, structural displacements have been obtained due to aerodynamic loads and/or connection arm weight. The typical numerical approach validates the displacements analytically. This novel method has increased the computational results’ credibility. The connecting arms are then computed for equivalent elastic stress, strain energy, equivalent elastic strain, and stress intensity level. This Octocopter survives agricultural, foggy, and dust spraying. The modified convergent-divergent passage-based spraying tank has been mounted to an Octocopter to project fertilizers, insecticides, and chemicals correctly on working domains. Thus, the Octocopter with a convergent-divergent passage-based spraying tank may require additional push to finish the operation. These increased thrusts directly impact propeller performance and connecting arm longevity. Thus, computational structural results of the sophisticated cantilever construction (connecting arm) for lightweight materials are essential. This computational structural simulation imposes over 25 lightweight materials. After that, connection arms for different working situations were found. The Octocopter’s propeller diameter and connection arm design relationship are also unique. Thus, this confirmed computational inquiry will help future researchers build and deploy large payload-based drone connection arms.
Tuning of Natural Frequencies of Aeroengine Compressor Blades Using Spatial Distribution of Ceramic Reinforced Particles Composites Using Multi-Scale Modelling
Authors: Kari Sreedhar , C. P. Mahesha
Published: February 2024

The Aeroengine compressor blades design and weight play vital role in overall performance of the gas turbine engine efficiency. Often a particular blade profile will give more engine performance efficiency but may not have enough strength to withstand the vibration loads. Further if the natural frequencies of the blade coincide with high excitation frequency regions, then it will lead to high cycle fatigue (HCF) failures. In this scenario, it is required to increase the stiffness and weight of the blade by compromising on the aerodynamic performance.

In this paper, it is proposed to use the spatially distributed ceramic particle reinforced composites in local areas of the aeroengine compressor blades without changing the blade outer geometry to move the critical and fundamental frequencies away from the high excitation/vibration loads frequency regions. The typical aeroengine compressor blade geometry with titanium material (Ti-6AL-4V) and Silicon Carbide (SiC) particles as reinforcement have been considered for this case study. The multi-scaling modelling approach has been used using micro-macro scale modelling of randomly distributed particle reinforced composites. The effective material properties like stiffness and density of the composite have been calculated using a micro-scale Unit Cell approach for different volume fractions of the SiC particles. The effective material properties obtained from the micro-scale modelling have been used at local regions of the compressor blade (macro-scale) to simulate the effect of SiC particle reinforcement on the overall macro-scale compressor blade behavior. The natural frequencies of the baseline Titanium blade have been compared with the SiC reinforced composites with different volume fractions. The vibratory stresses and the tip displacements have been compared between the baseline blade and the ceramic particle reinforced blade at different critical frequencies. Using this approach, it is possible to tune the frequencies of the Aeroengine compressor blades design to achieve the improved performance and reduced vibratory stresses which will helps to improve the overall efficiency of the aeroengines with reduced Specific Fuel Consumption (SFC).
A Deep Learning Approach to Build Surrogate Model for Steady State and Transient Metal Temperature Prediction in Gas Turbine
Authors: Soni Raj Kumar , Ojha Avyakt , Verma Vishal , Niu Yi
Published: February 2024

This research paper explores the use of machine learning (ML) to create a surrogate model for steady state and transient metal temperature prediction in industrial gas turbines. This work leverage measured performance parameters from a development test engine to build a surrogate model using ML-based approach. The resulting surrogate model significantly reduces the effort required to derive empirical correlations to predict the tie-bolt temperature of the industrial gas turbine which operates in various environmental conditions and power requirements.

The development engine test involves approximately 10,000 measured signals which includes major performance parameters like speed, fuel flow rate, power, IGV angle, station temperature and pressure along with others testing requirements from different disciplines. Hence various statistical techniques such as heatmap, correlation map, auto-lag, and principal component analysis were employed to identify the key influencing performance parameters to train and test the surrogate model.

Several time-series models were studied for this research, starting from simple regression models such as ARIMA, ARIMAX, and VAR to more complex deep learning models like LSTM. The benefits and drawbacks of different machine learning models were assessed, and their relative performance was compared in terms of temperature prediction accuracy.

The LSTM model was found to be the most accurate and better fit for this kind of sequential problem (time series), with temperature prediction accuracy within 2% for steady-state and 4% for transient conditions. This surrogate model forecasting approach can potentially estimate the real-time remaining useful life of critical parts in the fleet engine.

Furthermore, the paper proposes that the machine learning framework developed for predicting metal temperature in industrial gas turbines can be extended to forecast temperatures for other regions using fleet measured data. This can lead to more accurate critical part lifing calculations based on real-time fleet measured data, which can be used to optimize part replacement, inventory management, servicing, and overhaul scheduling. In essence, this research presents a comprehensive analysis of the benefits and drawbacks of different machine learning models and demonstrates the effectiveness of using an LSTM model for metal temperature prediction in industrial gas turbines.

Overall, this research presents a significant step towards improving the reliability of industrial gas turbines through the use of machine learning approach. The proposed framework has the potential to revolutionize the way in which industrial gas turbines are maintained and serviced, leading to significant cost savings and provide valuable insights for the engineers and researchers working in this field.
Development of Machine Learning Model for Maximum Creep Strain Prediction in Gas Turbine Blades
Authors: Shrivastava Rishabh , Mehrotra Vidit , Singh Amit , Yadav Amit , Das Debdulal
Published: February 2024

Gas turbines blades are subjected to significantly high temperature, pressure, and centrifugal load for prolong period. To withstand this severe operating condition, turbines blades are made up of nickel base superalloy. However, still we observe that creep is one of the dominant failure modes for turbine blade and could cause significant damage to the turbine section. Creep is the progressive time-dependent in elastic deformation resulting from high stress and temperature. Because of above reasons, creep evaluation becomes an imperative part of the turbine design. Creep analysis involves multiple discipline such as aerodynamic, heat-transfer and mechanical integrity. Additionally, conventional creep analysis using finite element model requires defeaturing of models, meshing of geometry, boundary condition application, material definition, solving the model and post processing. The process involves a numerical solution of differential equations that governed these physical phenomena. The above steps need significant time, computational resource, and manpower. The mentioned limitation could be overcome by leveraging data driven techniques for calculating maximum creep strain using system level parameters. In this work, machine learning techniques are explored to predict maximum creep strain in gas turbine blades. Machine learning models will use FEA results as input to predict the maximum creep strain values. Since creep strain is a continuous variable, hence regression-based ML model will be studied. The input parameter used for building the machine learning models are coolant temperature, coolant mass flow rate, hot side mass flow rate, turbine inlet temperature along with blade material. Among all the data, 75% data were used for training purpose and 25% data were used for evaluating the machine learning model performance. With the developed model, time required for creep prediction is reduced significantly. This provides greater flexibility to the designer in terms of evaluating multiple boundary conditions, materials option, coating, and cooling optimization. A comparative study is presented between linear & polynomial regression, random forest, and support vector regressor models. To reduce the prediction error, hyperparameter optimization is carried out for all the studied models. Additionally, a feature importance study is performed to assess the impact of each parameter on the creep strain prediction. Findings from the study show that linear regression model generates the least root mean squared error. Study also shows that Turbine inlet temperature has the maximum impact on creep strain, which also aligns with our physics-based understanding of creep phenomenon.
Investigation of High Thermal Intensity Porous Media Combustion in a Reverse Flow Combustor
Authors: Karad Vinay , Arghode Vaibhav
Published: February 2024

A small-scale (6.25 kW) reverse flow combustor with different fillings of porous media is explored to examine the effect on combustion behaviour, lean operational limit, and NOx, CO emissions. Premixed mixture of reactants is supplied to the combustor. Silicon carbide foam is used as the porous media. The investigation parameters include pore density of the porous media, reactant mixture injection diameter, and the volume occupied by porous media in the combustor. Combustor without porous media is also included in the study for comparison. The combustor is operated at high thermal intensity of 39 MW/m3-atm. Combustion is stabilized in the porous media as observed from its glow. Similar NOx and lower CO emission levels were obtained for the case with porous media. The results highlight the effectiveness of ceramic porous media in achieving stable combustion and low pollutant emissions at high thermal intensity applicable to gas turbine engines.
Spray Combustion Study of Biofuel-Blended Aviation-Grade Fuel in an Annular Air-Swirl Burner
Authors: Balasubramaniyan Manikandan , Pandurangan Nandhakumar , Sahu Srikrishna
Published: February 2024

Growing concern over global warming due to carbon pollutant emissions such as CO2 and CO has led the aviation industry to take numerous steps to reduce carbon emissions. One viable option is to use biofuels instead of conventional hydrocarbon fuels. The net carbon emission produced while using biofuel is still neutral, and therefore, the usage of biofuels has significantly increased over the past decade. However, the physical and chemical properties of biofuels are drastically different from those of hydrocarbon fuels, which can significantly affect the spray characteristics. For example, biofuels generally have a higher viscosity, which can affect the atomization and vaporization process, resulting in lower combustion efficiency and higher pollutant emissions compared to hydrocarbon fuels. Biofuels also have higher cetane numbers and lower calorific values, which can increase the ignition delay time and release lower net heat energy. Despite these parameters being crucial to spray characteristics in a reactive environment, past studies have underrated their importance. The present study aims to investigate the spray characteristics of a commercially available biofuel, particularly under reactive swirl flow conditions.

In the current study, a simplex swirl atomizer housed in a spray burner rig is used to generate the fuel spray. The rig has provision to incorporate an axial swirler in the annular air pathway such that the swirling airflow interacts with the fuel spray at the burner exit. To understand the effect of air swirl on the spray characteristics, experiments are conducted for three different conditions in the presence and absence of air co-low and air swirl. The air and the liquid mass flow are maintained the same. Different optical diagnostics are employed to characterize the spray combustion process. The primary breakup length of the liquid sheet (near the nozzle exit) and the spray cone angle are measured by the shadow imaging technique. The spray flame photographs are captured using a SLR camera. The size of the spray droplets is measured using Interferometric Laser Imaging for Droplet Sizing (ILIDS). In this technique, the spray field is illuminated by a high-power laser sheet. Each droplet is imaged as a rectangular fringe pattern that is generated on a defocused plane due to the interference of the reflected and first-order refracted light scattered from the droplet. The number of fringes formed in an interference pattern is proportional to the droplet size. The ILIDS measurements are obtained at different axial and radial measurement stations. The measured droplet and flame characteristics for different air swirl strengths provide insight into the practical spray combustion behavior of the biofuel, which has not gained much attention in the past.
The Effect of H2 Addition on CNG/H2-Air Flame Shape Topology in a Lean-Premixed Swirl Stabilized Gas Turbine Combustor
Authors: Huda Noorul , Soni Surendra Kumar , De Santanu
Published: February 2024

The present study investigates the flame shape topology in a lean premixed swirl stabilized combustor for various hydrogen thermal powers (Pth.H2) and equivalence ratios (ϕ) using CNG/H2-air mixtures at atmospheric conditions. High-speed OH*-chemiluminescence (OH*-CL) imaging is employed to study flame dynamics. It is found that at a fixed value of hydrogen mole fraction (XH2), an intense and short flame is found for a higher value of ϕ. Also, at a fixed value of ϕ, flame length shortens, and an enhanced heat release is observed near the burner exit with an increase in XH2. A V-shaped flame with the flame tip pointing toward the exhaust port is witnessed at low values of ϕ and XH2. Moreover, the V-shaped flame transitioned into an M-shaped flame with a flickering flame tip near the wall with an increase in ϕ and XH2. Further, the heat release zone gets extended and distributed over the entire length of the combustion chamber for small values of ϕ and XH2. Finally, the stable flame topology is correlated to the non-dimensional parameters Damköhler number (Da), and the wall temperatures (Tw).
Packed Bed Storage Systems and its Modelling Approaches: A Review of Navier- Stokes Equation and Turbulence Equations in Porous Media
Authors: Tiamiyu Nurayn , Hoffmann Jaap
Published: February 2024

Pressure drop prediction in packed bed storage systems is important for the overall cost and performance of solarized gas turbines. There is a need to accurately estimate the pressure drop across the packed beds to size the fans and estimate the system storage efficiency and the associated costs (capital and operating costs) accurately. This study reviews different empirical correlations and turbulence modelling approaches for pressure drop prediction. Flow through packed beds relies on properties such as flow velocity, orientation, particle packing density, and particle shape. Most pressure drop correlations were developed based on some assumptions such as isotropic packaging arrangement, laminar flow, and spherical particle shapes. This does not reflect the transport phenomena of packed beds. This paper provides fundamental insights into the modelling approaches necessary to accurately determine the pressure drop across the packed bed. Also, the limitations of the turbulence modelling approaches for porous media are highlighted in this review.
System Identification Approach to Ocular Tactile Tonometry
Authors: Zhang Qiuchen , Enikov Eniko T.
Published: February 2024

Frequent intraocular pressure (IOP) measurements are necessary for the diagnosis and management of glaucoma. Most current tonometers utilize some form of corneal deformation in order to estimate the IOP. Trans-corneal tonometers require sterile parts, or are complex and expensive (air-puff tonometer). Therefore, their use at home is not practical. Trans-scleral and trans-palpebral tonometry on the other hand, could be utilized at home but is less sensitive and prone to measurement errors. One such method is based on tactile palpation of the eye (palpation tonometry). During tactile palpation, the IOP increases significantly. The goal of tactile tonometry is to estimate the initial (starting) IOP, based on contact forces and displacements obtained during the palpation.

This paper describes the development of two phenomenological models relating the contact forces, displacement, and intraocular pressure during instrumented mechanical palpation of porcine eyes. The first model predicts the changes of intraocular pressure (IOP) and corresponding forces as a function of the applied displacements and initial IOP. The model was build in two parts. First, a pressure vs force and displacement was developed and subsequently a force vs displacement and pressure was added. It is then used to derive a second model that predicts the initial IOP from the applied forces and displacements. Least-squares techniques have been used to extract parameter values of the two models. The first model (14 parameters) produced a maximum error of 1.65 mmHg, while the second model (17 parameters) produced a maximum error of 0.85 mmHg over the test range of 10 to 35 mmHg. The second model leads to an explicit solution for the initial IOP as a function of the applied forces and displacements. When calibrated, this model can be used to interpret palpation data and estimate the starting IOP.

Most commercial tonometers are certified to be accurate within 2–3 mmHg against Goldmann applanation tonometry. They also suffer from larger errors for low (IOP < 11 mmHg) and high (IOP > 21 mmHg). Both models presented here demonstrate sufficient accuracy for the development of a tactile tonometer. Through simulations, it has been shown that accurate prediction of IOP from tactile data requires data from multiple displacements, i.e. an active palpation system is needed in order to realize a tactile tonometer. The ability to predict changes of IOP during palpation tonometery can also be useful in the development of mechanical eye stress tests, replacing the drug-induced Priscol provocative test.