The Binuangeun Fish Landing Port (PPI) plays a significant role in driving fisheries activities for fishermen in the Binuangeun area, Lebak, Banten. Despite being the largest fishing port in the region, the facilities owned by PPI Binuangeun have not been optimally utilized. This study aimed to explore the problems associated with the underutilization of operational facilities at PPI Binuangeun. The quantitative research design was carried out by conducting observations and interviews and distributing questionnaires to relevant stakeholders who played a role in the utilization of facilities at PPI Binuangeun. This research seeks to identify problems, constraints, and expected strategies for optimizing the PPI Binuangeun. All the results are presented descriptively. The findings show that the ability of facilities to utilize dock capacity reached 81.8%, that of port ponds reached 89.9%, and that of fish auction sites reached 50%, indicating that the operational facilities at PPI Binuangeun are at an adequate level of feasibility, but their utilization is not optimal. Field findings showed that during 2015-2022, there was a periodic increase in the number of vessels of various sizes, including the number of vessels that docked. Fish production has also increased with an increase in the number of fishing vessels, fishing gear, number of fishermen, production, and production value. In line with this, the manager of PPI Binuangeun must improve the operational efficiency of the port, including optimizing the port's contribution to improving the fishing industry and the economy of the port. Strategic steps are taken to improve the performance and competitiveness of PPI Binuangeun in the future.
{"title":"Facility Performance Evaluation at Fish Landing Port (PPI) Binuangeun Banten, Indonesia: What strategies can be applied?","authors":"Setiadi M Noor, Iin Solihin, Retno Muninggar","doi":"10.52783/dxjb.v36.134","DOIUrl":"https://doi.org/10.52783/dxjb.v36.134","url":null,"abstract":"The Binuangeun Fish Landing Port (PPI) plays a significant role in driving fisheries activities for fishermen in the Binuangeun area, Lebak, Banten. Despite being the largest fishing port in the region, the facilities owned by PPI Binuangeun have not been optimally utilized. This study aimed to explore the problems associated with the underutilization of operational facilities at PPI Binuangeun. The quantitative research design was carried out by conducting observations and interviews and distributing questionnaires to relevant stakeholders who played a role in the utilization of facilities at PPI Binuangeun. This research seeks to identify problems, constraints, and expected strategies for optimizing the PPI Binuangeun. All the results are presented descriptively. The findings show that the ability of facilities to utilize dock capacity reached 81.8%, that of port ponds reached 89.9%, and that of fish auction sites reached 50%, indicating that the operational facilities at PPI Binuangeun are at an adequate level of feasibility, but their utilization is not optimal. Field findings showed that during 2015-2022, there was a periodic increase in the number of vessels of various sizes, including the number of vessels that docked. Fish production has also increased with an increase in the number of fishing vessels, fishing gear, number of fishermen, production, and production value. In line with this, the manager of PPI Binuangeun must improve the operational efficiency of the port, including optimizing the port's contribution to improving the fishing industry and the economy of the port. Strategic steps are taken to improve the performance and competitiveness of PPI Binuangeun in the future.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid advancements in deep learning techniques have spurred a paradigm shift in materials research, revolutionizing the way we predict material properties, identify novel materials, and optimize material selection for mechanical components. This paper explores the integration of deep learning methodologies into materials science, presenting a comprehensive investigation into their efficacy and potential applications. The paper explores the development of deep learning models for predicting material properties.[1] Leveraging vast datasets containing information on diverse materials and their corresponding properties, we delve into the application of neural networks to establish robust predictive models. By extracting complex relationships within the data, deep learning facilitates the accurate estimation of material characteristics, enabling researchers and engineers to streamline the materials discovery process. In addition to property prediction, the study explores the role of deep learning in the identification of new materials with superior or tailored attributes. By training models on extensive databases encompassing known materials and their functionalities, we investigate the ability of deep learning algorithms to suggest novel materials with specific desired properties. This capability holds immense promise for accelerating the discovery of innovative materials, especially in fields where tailored material performance is critical. Furthermore, the paper examines the utilization of deep learning in optimizing material selection for mechanical components. By considering a holistic approach that factors in mechanical, thermal, and other relevant properties, we explore how neural networks can assist in selecting the most suitable materials for specific applications. This not only enhances the efficiency of the design process but also contributes to the development of more durable, efficient, and sustainable mechanical components. Through a systematic exploration of the integration of deep learning in materials research, this paper provides valuable insights into the transformative potential of these techniques. The findings contribute to the ongoing discourse on the intersection of artificial intelligence and materials science, paving the way for accelerated advancements in materials discovery, design, and application.
{"title":"Investigating the use of Deep Learning, in Materials Research for Predicting Material Properties, Identifying new Materials, and Optimizing Material Selection for Mechanical Components","authors":"Et al. Mohan Raparthi","doi":"10.52783/dxjb.v36.124","DOIUrl":"https://doi.org/10.52783/dxjb.v36.124","url":null,"abstract":"The rapid advancements in deep learning techniques have spurred a paradigm shift in materials research, revolutionizing the way we predict material properties, identify novel materials, and optimize material selection for mechanical components. This paper explores the integration of deep learning methodologies into materials science, presenting a comprehensive investigation into their efficacy and potential applications. The paper explores the development of deep learning models for predicting material properties.[1] Leveraging vast datasets containing information on diverse materials and their corresponding properties, we delve into the application of neural networks to establish robust predictive models. By extracting complex relationships within the data, deep learning facilitates the accurate estimation of material characteristics, enabling researchers and engineers to streamline the materials discovery process. In addition to property prediction, the study explores the role of deep learning in the identification of new materials with superior or tailored attributes. By training models on extensive databases encompassing known materials and their functionalities, we investigate the ability of deep learning algorithms to suggest novel materials with specific desired properties. This capability holds immense promise for accelerating the discovery of innovative materials, especially in fields where tailored material performance is critical. Furthermore, the paper examines the utilization of deep learning in optimizing material selection for mechanical components. By considering a holistic approach that factors in mechanical, thermal, and other relevant properties, we explore how neural networks can assist in selecting the most suitable materials for specific applications. This not only enhances the efficiency of the design process but also contributes to the development of more durable, efficient, and sustainable mechanical components. Through a systematic exploration of the integration of deep learning in materials research, this paper provides valuable insights into the transformative potential of these techniques. The findings contribute to the ongoing discourse on the intersection of artificial intelligence and materials science, paving the way for accelerated advancements in materials discovery, design, and application.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tenacious development of innovation has pushed associations towards embracing inventive answers for explore the complicated scenes of hazard appraisal, nonstop improvement, and provider execution observing. This exploration examines the prospering field of man-made reasoning (simulated intelligence) and its application in creating powerful answers for these basic business areas. [1] As organizations work in a climate set apart by vulnerabilities, disturbances, and worldwide interdependencies, the joining of artificial intelligence offers a promising road to upgrade navigation, moderate dangers, and drive persistent improvement. The investigation starts with a top to bottom examination of customary ways to deal with risk appraisal, accentuating their limits and the squeezing need for additional versatile systems. Utilizing a thorough survey of existing writing, the review presents simulated intelligence driven arrangements, enveloping AI calculations, regular language handling, and prescient investigation, to change risk evaluation systems. Contextual analyses are analyzed to show fruitful executions across different ventures, revealing insight into the substantial advantages understood and examples learned. The paper examines the relationship between AI technologies and well-established methodologies like Lean Six Sigma in the context of continuous improvement. It digs into the use of man-made intelligence in prescient upkeep, underlying driver examination, and constant observing, showing how these progressions add to additional spry and responsive hierarchical designs. Difficulties and open doors related with the mix of simulated intelligence into persistent improvement processes are fundamentally inspected, giving a fair viewpoint on the groundbreaking capability of these innovations. As artificial intelligence keeps on reshaping business standards, this examination contributes a nuanced comprehension of its part in risk evaluation, ceaseless improvement, and provider execution observing. Businesses looking to take advantage of AI technologies' full potential while navigating the difficulties and ethical considerations associated with their adoption can benefit from the findings presented here.
{"title":"Investigating the Creation of AI-Driven Solutions for Risk Assessment, Continuous Improvement, and Supplier Performance Monitoring","authors":"Et al. Mohan Raparthi","doi":"10.52783/dxjb.v36.122","DOIUrl":"https://doi.org/10.52783/dxjb.v36.122","url":null,"abstract":"The tenacious development of innovation has pushed associations towards embracing inventive answers for explore the complicated scenes of hazard appraisal, nonstop improvement, and provider execution observing. This exploration examines the prospering field of man-made reasoning (simulated intelligence) and its application in creating powerful answers for these basic business areas. [1] As organizations work in a climate set apart by vulnerabilities, disturbances, and worldwide interdependencies, the joining of artificial intelligence offers a promising road to upgrade navigation, moderate dangers, and drive persistent improvement. The investigation starts with a top to bottom examination of customary ways to deal with risk appraisal, accentuating their limits and the squeezing need for additional versatile systems. Utilizing a thorough survey of existing writing, the review presents simulated intelligence driven arrangements, enveloping AI calculations, regular language handling, and prescient investigation, to change risk evaluation systems. Contextual analyses are analyzed to show fruitful executions across different ventures, revealing insight into the substantial advantages understood and examples learned. The paper examines the relationship between AI technologies and well-established methodologies like Lean Six Sigma in the context of continuous improvement. It digs into the use of man-made intelligence in prescient upkeep, underlying driver examination, and constant observing, showing how these progressions add to additional spry and responsive hierarchical designs. Difficulties and open doors related with the mix of simulated intelligence into persistent improvement processes are fundamentally inspected, giving a fair viewpoint on the groundbreaking capability of these innovations. As artificial intelligence keeps on reshaping business standards, this examination contributes a nuanced comprehension of its part in risk evaluation, ceaseless improvement, and provider execution observing. Businesses looking to take advantage of AI technologies' full potential while navigating the difficulties and ethical considerations associated with their adoption can benefit from the findings presented here.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":" 72","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139628051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research paper presents an in-depth bibliometric analysis of cloud computing literature, aiming to uncover underlying patterns, assess citation levels, and identify key trends shaping this rapidly evolving field. The study employs robust bibliometric techniques, analyzing data collected from reputable academic databases, including Scopus. The methodology involves comprehensive data acquisition, preprocessing, analysis, and interpretation, using advanced tools like VOSviewer and Biblioshiny for visualization and statistical analysis.Our findings reveal significant growth in cloud computing research from 2019 to 2023, with a fluctuating yet ascending trajectory of scholarly output. Key contributors include China and India, underscoring their roles in advancing cloud computing research. The analysis of keyword frequency highlights 'cloud', 'data', 'computing', 'security', and 'block chain' as dominant themes, indicating a strong emphasis on data integrity, security, and emerging technologies in cloud computing.Sentiment analysis of research abstracts reveals a predominantly positive yet cautiously optimistic tone, reflecting the field's optimistic outlook towards advancements and potential applications, balanced by an awareness of inherent challenges. The study also delves into the impact of authors and organizations, identifying influential contributors and collaborative networks within the academic community.In conclusion, this paper provide a comprehensive overview of the cloud computing research landscape, offering valuable insights for researchers, policymakers, and industry leaders. It underscores the significance of cloud computing in modern society and highlights the need for continued exploration and innovation in this critical technology domain.
{"title":"Mapping the Evolving Landscape of Cloud Computing Research: A Bibliometric Analysis","authors":"Deepak Hajoary, Raju Narzary, Rinku Basumatary","doi":"10.52783/dxjb.v35.115","DOIUrl":"https://doi.org/10.52783/dxjb.v35.115","url":null,"abstract":"This research paper presents an in-depth bibliometric analysis of cloud computing literature, aiming to uncover underlying patterns, assess citation levels, and identify key trends shaping this rapidly evolving field. The study employs robust bibliometric techniques, analyzing data collected from reputable academic databases, including Scopus. The methodology involves comprehensive data acquisition, preprocessing, analysis, and interpretation, using advanced tools like VOSviewer and Biblioshiny for visualization and statistical analysis.Our findings reveal significant growth in cloud computing research from 2019 to 2023, with a fluctuating yet ascending trajectory of scholarly output. Key contributors include China and India, underscoring their roles in advancing cloud computing research. The analysis of keyword frequency highlights 'cloud', 'data', 'computing', 'security', and 'block chain' as dominant themes, indicating a strong emphasis on data integrity, security, and emerging technologies in cloud computing.Sentiment analysis of research abstracts reveals a predominantly positive yet cautiously optimistic tone, reflecting the field's optimistic outlook towards advancements and potential applications, balanced by an awareness of inherent challenges. The study also delves into the impact of authors and organizations, identifying influential contributors and collaborative networks within the academic community.In conclusion, this paper provide a comprehensive overview of the cloud computing research landscape, offering valuable insights for researchers, policymakers, and industry leaders. It underscores the significance of cloud computing in modern society and highlights the need for continued exploration and innovation in this critical technology domain.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The pervasive integration of Internet of Things (IoT) devices across industries has ushered in a new era of data-driven operational efficiency. However, the reliability and uninterrupted functionality of these interconnected devices necessitate innovative approaches to maintenance. This research focuses on the development and implementation of a predictive maintenance framework for IoT devices, leveraging the synergies between Time Series Analysis (TSA) and Deep Learning (DL) techniques. The primary objective of this study is to enhance the accuracy and efficiency of predictive maintenance processes, ultimately minimizing downtime and optimizing resource utilization. The research methodology involves the collection of diverse data types from IoT devices, encompassing sensor readings, error logs, and historical maintenance records. A meticulous data preprocessing stage follows, involving cleaning, normalization, and feature extraction to prepare the dataset for analysis. The core analytical components of the proposed framework include Time Series Analysis for uncovering temporal patterns in the IoT data. Statistical methods and time series decomposition are applied to identify trends and seasonality, providing valuable insights into the device's performance over time. Concurrently, Deep Learning models, specifically recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are employed to predict maintenance needs based on historical patterns. Results obtained from the application of the predictive maintenance framework to real-world IoT datasets demonstrate promising accuracy and efficiency in anticipating maintenance requirements. The paper identifies existing challenges in predictive maintenance for IoT devices and suggests future research directions. These include the exploration of edge computing, federated learning, and the integration of explainable AI to enhance model interpretability. In conclusion, the study underscores the significance of predictive maintenance in ensuring the reliability of IoT devices, offering a roadmap for industries seeking to harness the full potential of data analytics and artificial intelligence for operational excellence.
{"title":"Predictive Maintenance in IoT Devices using Time Series Analysis and Deep Learning","authors":"Et al. Mohan Raparthy","doi":"10.52783/dxjb.v35.113","DOIUrl":"https://doi.org/10.52783/dxjb.v35.113","url":null,"abstract":"The pervasive integration of Internet of Things (IoT) devices across industries has ushered in a new era of data-driven operational efficiency. However, the reliability and uninterrupted functionality of these interconnected devices necessitate innovative approaches to maintenance. This research focuses on the development and implementation of a predictive maintenance framework for IoT devices, leveraging the synergies between Time Series Analysis (TSA) and Deep Learning (DL) techniques. The primary objective of this study is to enhance the accuracy and efficiency of predictive maintenance processes, ultimately minimizing downtime and optimizing resource utilization. The research methodology involves the collection of diverse data types from IoT devices, encompassing sensor readings, error logs, and historical maintenance records. A meticulous data preprocessing stage follows, involving cleaning, normalization, and feature extraction to prepare the dataset for analysis. The core analytical components of the proposed framework include Time Series Analysis for uncovering temporal patterns in the IoT data. Statistical methods and time series decomposition are applied to identify trends and seasonality, providing valuable insights into the device's performance over time. Concurrently, Deep Learning models, specifically recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are employed to predict maintenance needs based on historical patterns. Results obtained from the application of the predictive maintenance framework to real-world IoT datasets demonstrate promising accuracy and efficiency in anticipating maintenance requirements. The paper identifies existing challenges in predictive maintenance for IoT devices and suggests future research directions. These include the exploration of edge computing, federated learning, and the integration of explainable AI to enhance model interpretability. In conclusion, the study underscores the significance of predictive maintenance in ensuring the reliability of IoT devices, offering a roadmap for industries seeking to harness the full potential of data analytics and artificial intelligence for operational excellence.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"55 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-05DOI: 10.5772/INTECHOPEN.82822
C. Osheku, O. Babayomi, Oluwaseyi T. Olawole
This chapter proposes the application of Newtonian particle mechanics for the derivation of predictive equations for burn time, burning and unburnt area propagation for the case of a core propellant grain. The grain is considered to be inhibited in a solid rocket combu- stion chamber subject to the assumption that the flame propagation speed is constant for the particular solid fuel formulation and formation chemistry in any direction. Here, intricacies surrounding reaction chemistry and kinetic mechanisms are not of interest at the moment. Meanwhile, the physics derives from the assumption of a regressive solid fuel pyrolysis in a cylindrical combustion chamber subject to any theoretical or empirical burn rate characterization law. Essential parametric variables are expressed in terms of the propellant geometrical configuration at any instantaneous time. Profiles from simulation studies revealed the effect of modulating variables on the burning propagation arising from the kinematics and ordinary differential equations models. In the meantime, this mathematical exercise explored the tendency for a tie between essential kernels and mat- ching polynomial approximations. In the limiting cases, closed form expressions are couched in terms of the propellant grain geometrical parameters. Notably, for the fuel burn time, a good agreement is observed for the theoretical and experimental results.
{"title":"Analytical Prediction for Grain Burn Time and Burning Area Kinematics in a Solid Rocket Combustion Chamber","authors":"C. Osheku, O. Babayomi, Oluwaseyi T. Olawole","doi":"10.5772/INTECHOPEN.82822","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82822","url":null,"abstract":"This chapter proposes the application of Newtonian particle mechanics for the derivation of predictive equations for burn time, burning and unburnt area propagation for the case of a core propellant grain. The grain is considered to be inhibited in a solid rocket combu- stion chamber subject to the assumption that the flame propagation speed is constant for the particular solid fuel formulation and formation chemistry in any direction. Here, intricacies surrounding reaction chemistry and kinetic mechanisms are not of interest at the moment. Meanwhile, the physics derives from the assumption of a regressive solid fuel pyrolysis in a cylindrical combustion chamber subject to any theoretical or empirical burn rate characterization law. Essential parametric variables are expressed in terms of the propellant geometrical configuration at any instantaneous time. Profiles from simulation studies revealed the effect of modulating variables on the burning propagation arising from the kinematics and ordinary differential equations models. In the meantime, this mathematical exercise explored the tendency for a tie between essential kernels and mat- ching polynomial approximations. In the limiting cases, closed form expressions are couched in terms of the propellant grain geometrical parameters. Notably, for the fuel burn time, a good agreement is observed for the theoretical and experimental results.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"229 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82975378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-05DOI: 10.5772/intechopen.82292
Aliyu Bhar Kisabo, Aliyu Funmilayo Adebimpe
This chapter is the first of two others that will follow (a three-chapter series). Here we present the derivation of the mathematical model for a rocket ’ s autopilots in state space. The basic equations defining the airframe dynamics of a typical six degrees of freedom (6DoFs) are nonlinear and coupled . Separation of these nonlinear coupled dynamics is presented in this chapter to isolate the lateral dynamics from the longitudinal dynamics. Also, the need to determine aerodynamic coefficients and their derivative components is brought to light here. This is the crux of the equation. Methods of obtaining such coeffi- cients and their derivatives in a sequential form are also put forward. After the aerodynamic coefficients and their derivatives are obtained, the next step is to trim and linearize the decoupled nonlinear 6DoFs. In a novel way, we presented the linearization of the decoupled 6DoF equations in a generalized form. This should provide a lucid and easy way to implement trim and linearization in a computer program. The longitudinal model of a rocket presented in this chapter will serve as the main mathematical model in two other chapters that follow in this book.
{"title":"State-Space Modeling of a Rocket for Optimal Control System Design","authors":"Aliyu Bhar Kisabo, Aliyu Funmilayo Adebimpe","doi":"10.5772/intechopen.82292","DOIUrl":"https://doi.org/10.5772/intechopen.82292","url":null,"abstract":"This chapter is the first of two others that will follow (a three-chapter series). Here we present the derivation of the mathematical model for a rocket ’ s autopilots in state space. The basic equations defining the airframe dynamics of a typical six degrees of freedom (6DoFs) are nonlinear and coupled . Separation of these nonlinear coupled dynamics is presented in this chapter to isolate the lateral dynamics from the longitudinal dynamics. Also, the need to determine aerodynamic coefficients and their derivative components is brought to light here. This is the crux of the equation. Methods of obtaining such coeffi- cients and their derivatives in a sequential form are also put forward. After the aerodynamic coefficients and their derivatives are obtained, the next step is to trim and linearize the decoupled nonlinear 6DoFs. In a novel way, we presented the linearization of the decoupled 6DoF equations in a generalized form. This should provide a lucid and easy way to implement trim and linearization in a computer program. The longitudinal model of a rocket presented in this chapter will serve as the main mathematical model in two other chapters that follow in this book.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"os-17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87198655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.78315
C. Pîrvu, L. Deleanu
Industry and market of ballistic protection materials and systems are characterized by a dynamic and competing succession of inventions for projectiles and protective systems. The requirements for the ballistic panels are many and complex, varying depending on the threat type, the required mobility in the tactical theater, and protection level. The safety degree, the price, and the dynamics of research in the field are also taken into account. This chapter underlines the necessity of testing ballistic protection panels made of LFT SB1 plus (multidirectional fiber fabrics, supplied by Teijin) against a certain threat in order to assess their resistance to this specific threat and the investigation of failure mechanisms in order to improve their behavior at ballistic impact. The models for ballistic impact are useful when they are particularly formulated for resembling the actual system projectile, target, and can be validated through laboratory experiments. Tests made on panels made of LFT SB1plus, according to NIJ Standard-0101.06-2008 gave good results for the panels made of 12 layers of this fabric, and the backface signature (BFS) was measured. The BFS upper tolerance limit of 24,441 mm recommends this system for protection level IIA, according to the abovementioned standard.
{"title":"Ballistic Testing of Armor Panels Based on Aramid","authors":"C. Pîrvu, L. Deleanu","doi":"10.5772/INTECHOPEN.78315","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78315","url":null,"abstract":"Industry and market of ballistic protection materials and systems are characterized by a dynamic and competing succession of inventions for projectiles and protective systems. The requirements for the ballistic panels are many and complex, varying depending on the threat type, the required mobility in the tactical theater, and protection level. The safety degree, the price, and the dynamics of research in the field are also taken into account. This chapter underlines the necessity of testing ballistic protection panels made of LFT SB1 plus (multidirectional fiber fabrics, supplied by Teijin) against a certain threat in order to assess their resistance to this specific threat and the investigation of failure mechanisms in order to improve their behavior at ballistic impact. The models for ballistic impact are useful when they are particularly formulated for resembling the actual system projectile, target, and can be validated through laboratory experiments. Tests made on panels made of LFT SB1plus, according to NIJ Standard-0101.06-2008 gave good results for the panels made of 12 layers of this fabric, and the backface signature (BFS) was measured. The BFS upper tolerance limit of 24,441 mm recommends this system for protection level IIA, according to the abovementioned standard.","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79993737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.75550
W. Alshanti
From theoretical standpoint, it is difficult to analytically build a general theory and physical principles that critically describe the mechanical behaviour of granular systems. There are many substantial gaps in understanding the mechanical principles that govern these particulate systems. In this chapter, based on a two-dimensional soft particle discrete element method (DEM), a numerical approach is developed to investigate the vertical penetration of a non-rotating and rotating projectile into a granular system. The model outcomes reveal that there is a linear proportion between the projectile ’ s impact velocity and its penetration downward displacement. Moreover, depending on the rotation direc- tion, there is a significant deviation of the x -coordinate of the final stopping point of a rotating projectile from that of its original impact point. For negative angular velocities, a deviation to the right occurs while a left deviation has been recorded for positive angular velocities. to
{"title":"Discrete Element Modeling of a Projectile Impacting and Penetrating into Granular Systems","authors":"W. Alshanti","doi":"10.5772/INTECHOPEN.75550","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75550","url":null,"abstract":"From theoretical standpoint, it is difficult to analytically build a general theory and physical principles that critically describe the mechanical behaviour of granular systems. There are many substantial gaps in understanding the mechanical principles that govern these particulate systems. In this chapter, based on a two-dimensional soft particle discrete element method (DEM), a numerical approach is developed to investigate the vertical penetration of a non-rotating and rotating projectile into a granular system. The model outcomes reveal that there is a linear proportion between the projectile ’ s impact velocity and its penetration downward displacement. Moreover, depending on the rotation direc- tion, there is a significant deviation of the x -coordinate of the final stopping point of a rotating projectile from that of its original impact point. For negative angular velocities, a deviation to the right occurs while a left deviation has been recorded for positive angular velocities. to","PeriodicalId":35288,"journal":{"name":"Dandao Xuebao/Journal of Ballistics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89874122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-02-09DOI: 10.5772/INTECHOPEN.73511
R. D. Celis, Luis Cadarso
Accuracy and precision are the cornerstone for ballistic projectiles from the earliest days of this discipline. In the beginnings, impact point precision in artillery devices deteriorated when range were extended, particularly for non-propelled artillery rockets and shells. Later, inertial navigation and guidance systems are introduced and precision was unlinked from range increases. In the last 30 years, hybridization between inertial systems and GNSS devices has improved precision enormously. Unfortunately, during the last stages of flight, inertial and GNSS methods (hybridized or not) feature big errors on attitude and position determination. Low cost devices, which are precise on terminal guidance and do not feature accumulative error, such as quadrant photo-detector, seem to be appropriate to be included on the guidance systems. Hybrid algorithms, which combine GNSSs, IMUs and photodetectors, and a novel technic of attitude determination, which avoids the use of gyroscopes, are presented in this chapter. Hybridized measurements are implemented on modified proportional navigation law and a rotatory force control method. A realistic non-linear flight dynamics model has been developed to perform simulations to prove the accuracy of the presented algorithms.
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