Unraveling the role of dual Ti/Mg metals on the ignition and combustion behavior of HTPB-boron-based fuel

Metal additives play an essential role in explosive and propellant formulations. Boron(B) is widely used in propellant applications owing to its high energetic content. The addition of B to explosives and propellants increases their energy density, making them more efficient and powerful. Nevertheless, B forms oxide layers on its surface during combustion, slowing down the combustion rate and reducing rocket motor efficiency. To overcome this issue, other metal additives such as aluminum(Al), magnesium(Mg),and titanium(Ti) are revealed to be effective in boosting the combustion rate of propellants. These additives may improve the combustion rate and therefore enhance the rocket motor’s performance. The present study focused on preparing and investigating the ignition and combustion behavior of pure hydroxyl-terminated polybutadiene(HTPB)-B fuel supplemented with nano-titanium and nanomagnesium. The burn rates of HTPB-B fuel samples were evaluated on the opposed flow burner(OFB)under a gaseous oxygen oxidizer, for which the mass flux ranges from 22 kg/(m^(2)·s) to 86 kg/(m^(2)·s). The addition of Ti and Mg exhibited higher regression rates, which were attributed to the improved oxidation reaction of B due to the synergetic metal combustion effect. The possible combustion/oxidation reaction mechanism of B-Mg and B-Ti by heating the fuel samples at 900℃ and 1100℃ was also examined in a Nabertherm burnout furnace under an oxygen atmosphere. The post-combustion products were collected and further subjected to X-ray diffraction(XRD) and field emission scanning electron microscopy(FE-SEM) analyses to inspect the combustion behavior of B-Ti and B-Mg. It has been observed that the B oxide layer at the interface between B-Ti(B-Mg) is removed at lower temperatures, hence facilitating oxygen transfer from the surroundings to the core B. Additionally, Ti and Mg decreased the ignition delay time of B, which improved its combustion performance.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Application of Pomegranate Rind Extract for Improvement of Functional Properties of Various Materials—A Critical Review

Pomegranate rind is abundantly available as a waste material. Pomegranate Rind Extract (PRE) can be applied to cotton fabrics for its natural colours, as a mordanting agent and also for imparting certain functional properties such as fire retardancy and antimicrobial properties. This paper reviews the feasibility of Pomegranate Rind Extract to improve the functional properties of cellulosic fabrics. Studies show that varying concentrations and higher temperatures that were used to apply the extract on the fabric, resulted in enhanced functional properties. At a particular concentration, the treated fabric showed a 15 times lower burning rate in comparison with the control fabric. Also, antimicrobial efficacy has been observed against Gram-positive and Gram-negative bacteria. Due to the natural colouring material, it can be used as a natural dye on cotton material. The fire retardancy of pomegranate rind extract was tested on jute material under varying alkalinity. Research has indicated that pomegranate rind extract could be used to dye polyamide as well. The rubbing and wash fastness of the finished fabrics is good. The light fastness was fair, and its antibacterial efficiency against tested bacteria was good.

Numerical Study on the Behaviour of Hybrid FRPs Reinforced RC Slabs Subjected to Blast Loads

The safety of civilian and military infrastructure is a concern due to an increase in explosive risks,which has led to a demand for high-strength civil infrastructure with improved energy absorption capacity.In this study,a Finite Element(FE)numerical model was developed to determine the effect of hybrid Fibre Reinforced Polymer(FRP)as a strengthening material on full-scale Reinforced Concrete(RC)slabs.The reinforcing materials under consideration were Carbon(CFRP)and Glass(GFRP)fibres,which were subjected to blast loads to determine the structural response.A laminated composite fabric material model was utilized to model the failure of composite,which facilitates the consideration of strain rate effects.The damaged area of the laminate is determined in the FE model,and it is in good agreement with the corresponding experimental results in the literature.Models containing different stacking sequences were built to demonstrate their efficiency in resisting blast loads.In general,the damaged area was reduced when a hybrid reinforcement with CFRP as the top layer was used.

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable aerodynamic forces,lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade.It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine.In this paper,a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades.The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer.The statistical,histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.

Finite Element Modeling in Drilling of Nimonic C-263 Alloy Using Deform-3D

The paper proposes a simulated 3D Finite Element Model(FEM)for drilling of Nickel based super alloy known as Nimonic C-263.The Lagrangian finite element model-based simulations were performed to determine the thrust force,temperature generation,effective stress,and effective strain.The simulations were performed according to the L27 orthogonal array.A perfect plastic work piece was assumed,and the shape is considered to be cylindrical.The spindle speed,feed rate,and point angle were considered as the input parameters.The work piece was modeled by Johnson-Cook(JC)material model and tungsten carbide(WC)was chosen as the drill bit and the body was assumed to be rigid.The demonstrative results of the thrust force and the temperature at drill bit cutting edge were substantiated with the simulated results and a percentage error was observed within 10%.Further,simulated results of effective stress and strain were also observed.

State-Based Offloading Model for Improving Response Rate of IoT Services

The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive manner.Task and computation offloading in the IoT helps to improve the response rate and the availability of resources.Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing.In this paper,the state-based task offloading method(STOM)is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities.The proposed method is designed using the Markov decision-making model to improve the rate of requests processed.By defining optimal states and filtering the actions based on the probability of response and request analysis,this method achieves less response time.Based on the defined states,request processing and resource allocations are performed to reduce the backlogs in handling multiple requests.The proposed method is verified for the response rate and time for the varying requests and processing servers through an experimental analysis.From the experimental analysis,the proposed method is found to improve response rate and reduce backlogs,response time,and offloading factor by 11.5%,20.19%,20.31%,and 8.85%,respectively.

Performance analysis of thermal storage unit with possible nano enhanced phase change material in building cooling applications

The heat transfer performance of the phase change materials used in free cooling and air conditioning applications is low,due to the poor thermal conductivity of the materials.The recent phenomenal advancement in nano technology provides an opportunity for an appreciable enhancement in the thermal conductivity of the phase change materials.In order to explore the possibilities of using nano technology for various applications,a detailed parametric study is carried out,to analyse the heat transfer enhancement potential with the thermal conductivity of the conventional phase change materials and nano enhanced phase change materials under various flow conditions of the heat transfer fluid.Initially,the theoretical equation,used to determine the time for outward cylindrical solidification of the phase change material,is validated with the experimental results.It is inferred from the parametric studies,that for paraffinic phase change materials with air as the heat transfer fluid,the first step should be to increase the heat transfer coefficient to the maximum extent,before making any attempt to increase the thermal conductivity of the phase change materials,with the addition of nano particles.When water is used as the phase change material,the addition of nano particles is recommended to achieve better heat transfer,when a liquid is used as the heat transfer fluid.

Comparative Study on Tree Classifiers for Application to Condition Monitoring ofWind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources.The wind turbine is an essential system used to change kinetic energy into electrical energy.Wind turbine blades,in particular,require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine cost.The main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach through histogram features.In this study,blade bend,hub-blade loose connection,blade erosion,pitch angle twist,and blade cracks were simulated on the blade.This problem is formulated as a machine learning problem which consists of three phases,namely feature extraction,feature selection and feature classification.Histogram features are extracted from vibration signals and feature selection was carried out using the J48 decision tree algorithm.Feature classification was performed using 15 tree classifiers.The results of the machine learning classifiers were compared with respect to their accuracy percentage and a better model is suggested for real-time monitoring of a wind turbine blade.

Combustion performance of hybrid rocket fuels loaded with MgB_(2)and carbon black additives

Paraffin-based fuel has a great potential for several innovative missions,including space tourism,due to its safety,low environmental impact,high performance and low cost.Despite the fact that liquefying solid fuels increases the regression rate of hybrid rocket motors,incorporating energetic materials into solid fuel can still improve the performance.The objective and scope of this study is to increase the performance characteristics of the paraffin-based fuel by using magnesium diboride(MgB_(2))and carbon black(CB)additives.The cylindricalport fuel grains were manufactured with various additives percentages in mass(wt%:CB-2%and MgB_(2)-10%)and tested using a laboratory-scale ballistic hybrid motor under gaseous oxygen.The mechanical performance results revealed that adding CB and MgB_(2) improved the ultimate strength and elastic modulus of paraffin-based fuels.The addition of these fillers increased the hardness of fuel by developing a strong interaction in the paraffin matrix.Thermogravimetry(TG)results showed that CB inclusion improved the thermal stability of the paraffin matrix.The average regression rates of fuels loaded with CB and MgB_(2) were 32%and 52%higher than those of unmodified paraffin wax,respectively.The characteristic velocity efficiency was found in the range of 68%e79%at an O/F ratio of 1.5e2.6.The MgB_(2) oxidation/combustion in the paraffin matrix was described by a four-step oxidation process ranging from 473 K to 1723 K.Finally,a combustion model of MgB_(2) in the paraffin matrix was proposed,and four-step oxidation processes were discussed in detail.

Wind energy feasibility and wind turbine selection studies for the city Surat,India

Wind energy represents a clean,abundant and cost-effective power source,fostering job growth and environmental mitigation.Although wind energy harnesses several gigawatts today,its availability hinges on diverse factors,with geographical location standing out.Commercial turbines,with varying capacity ranges,saturate the market.Locating site-specific suitability and matching the appropriate turbine to meet specific requirements are of paramount importance.This study aims to assess the feasibility of wind energy in Surat,Gujarat,India and select an optimal small commercial turbine for residential use.The research involves Rayleigh and Weibull probability distribution functions based on yearlong velocity data.These distributions are fitted with actual data,revealing the most probable velocity(v_(mf)=3 m/s)and velocity at maximum power(v_(pmax)=5 m/s).The power availability of the site has been assessed as 42.6 W/m^(2)using both graphical and analytical methods.Several commercial turbines have been shortlisted based on on-site power criteria and their specifications are evaluated against site power availability.A comparative analysis culminates in identifying the most suitable turbine for the location.The best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential applications.

Experimental,analytical and numerical studies on concrete encased trapezoidally web profiled cold-formed steel beams by varying depth-thickness ratio

Concrete encased with trapezoidally corrugated web profiled cold-formed steel beams are used worldwide to improve resistance toward fire and corrosion,higher load carrying capacity as well as significant increase in the bending stiffness by encasing concrete on the beam portion.The present work gives a detailed description on the experimental,analytical and numerical investigation on the flexural behavior of concrete encased trapezoidally corrugated web profiled cold-formed steel beams which were simply supported at both ends and subjected to two point symmetric loading.The flexural behavior of such structure has been experimentally tested to failure under pure bending.To find the effct of concrete encasement in the web,12 experiments were conducted by two different series.Beams having three different web corrugation angles of 0°,30°,and 45°with two different web depth-thickness(dw/tw)ratios of 60 and 80 were tested.Experimental results such as load-deflection relationship,ultimate capacity,load-strain relationship,moment-curvature curves,ductility and failure mode indices of the specimens are presented.From the static bending tests the concrete encased trapezoidally corugated web beam showed improved moment carrying capacity,ductility bchavior and the resistance to transverse deflections in comparison to concrete encased with plain web beam.Especially for the beams with concrete encased 30°trapezoidally corrugated web having(dw/tw)ratio 60 and 80,the loading capacity was improved about 54%and 67.3%and the ductility also increased about l.6 and 3.6 times,when compared to concrete encased beams with plain web.This research should contribute to the future engineering applications on seismic resistant structures and efficient usage of concrete encased with cold-formed steel beams by exhibiting its super elasto-plastic property.The analytical and numerical results showed good agreement with the experimental results at yield load,which indicates that the proposed analytical equations can be applied in predicting flexural strength accurately for such concrete encased trapezoidally corrugated web profiled cold-formed steel beams.

Intelligent algorithm for trapezoidal interval valued neutrosophic network analysis

The shortest path problem has been one of the most fundamental practical problems in network analysis.One of the good algorithms is Bellman-Ford,which has been applied in network,for the last some years.Due to complexity in the decision-making process,the decision makers face complications to express their view and judgment with an exact number for single valued membership degrees under neutrosophic environment.Though the interval number is a special situation of the neutrosophic,it did not solve the shortest path problems in an absolute manner.Hence,in this work,the authors have introduced the score function and accuracy function of trapezoidal interval valued neutrosophic numbers with their illustrative properties.These properties provide important theoretical base of the trapezoidal interval valued neutrosophic number.Also,they proposed an intelligent algorithm called trapezoidal interval valued neutrosophic version of Bellman’s algorithm to solve neutrosophic shortest path problem in network analysis.Further,comparative analysis has been made with the existing algorithm.

State,Society and Environmental Security in International Relations Theory

As emerging non-traditional security issues in global politics such as environmental crises offer new avenues of research on the state-society behaviour in non-western political orders,this study applies an integrated conceptual framework of Copenhagen School’s securitization,constructivism and green theory in international relations theory(IRT).One of the key objectives of the study includes to provide a theoretical explanation on the role of non-state agencies in ameliorating the environmental condition.With the support of an illustrative case study,the researcher argues in line with the finding of Acharya and Buzan(2007)that there is no need to replace Western IRTs,but they should be enriched with more voices from non-western political orders.In other words,the emerging non-traditional issues in global politics are not“noises”to weaken existing IRTs but“voices”to strengthen their theoretical foundation.

Necessary and sufficient condition for modified Nevanlinna-Pick interpolation for closed-loop pole placement

Nevanlinna-Pick interpolation theory has sevaral applications, in particular in robust control. In this paper, we derive necessary and sufficient condition so that a modification of the Nevanlinna-Pick theory can place the closed-loop poles inside a circular region in the left half of the complex plane in addition to the control system design being robust and internally stable. Numerical examples illustrate the theory.