holzer method.pptx
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This document outlines Holzer's method, a technique for determining the natural frequencies and mode shapes of linear dynamical systems. The key points are: - Holzer's method involves assuming a trial solution for the mode shape and using the characteristic equation to solve for the natural frequency and mode shape coefficients. - It is an iterative, tabular method used to analyze multi-degree of freedom vibrational systems, both undamped and damped. - The steps involve writing the equation of motion, assuming a trial mode shape, substituting into the equation of motion, using the characteristic equation to find the natural frequency, and then solving for the mode shape coefficients.
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Data Driven Maintenance | UReason Webinar
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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AT MBB AIRPORT
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Applications of artificial Intelligence in Mechanical Engineering.pdf
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
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Design and optimization of ion propulsion drone
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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holzer method.pptx
- 1. Group 1 Name ID UBAID BILAL------------------MCEN51F20R001 MUHAMMAD HASSAN------MCEN51F20R002 MUHAMMAD HAZIQ---------MCEN51F20R006 HAMZA GHAFFAR------------MCEN51F20R010 MUHAMMAD AHMED-------MCEN51F20R027 KASHIF RIAZ-------------------MCEN51F19R034
- 2. • HOLZER’S METHOD It is named after Austrian engineer Franz Holzer, who developed the method in the early 20th century. • Holzer's method can be used to determine the natural frequency and mode shapes of a linear dynamical system. • The method involves assuming a trial solution for the mode shape and then using the characteristic equation to solve for the natural frequency and the coefficients of the mode shape. • It is a tabular method which is used for the analysis of MULT- DEGREE of FREEDOM SYSTEM in vibration
- 3. • Cont… It is useful for the analysis of free and forced vibrations and also for the system with or without damping. • Holzer's method can be used for fixing both linear and tortional natural frequencies.
- 4. Method for Analysis • • • • Holzer’s method is essentially a trial-and-error scheme to find the natural frequencies of undamped, damped, semi definite, fixed, or branched vibrating systems. It is a trail and error method and can be used to find the higher modes independent of fundamental mode. A trial frequency of the system is first assumed, and a solution is found when the assumed frequency satisfies the constraints of the system. This generally requires several trials.
- 5. Cont… • Depending on the trial frequency used, the fundamental as well as the higher frequencies of the system can be determined. • The method also gives the mode shapes.
- 6. Steps 1.Write the equation of motion for the system in terms of displacement, velocity, and acceleration. 2.Assume a trial solution for the mode shape of the system. The trial solution should satisfy the boundary conditions and have a general form of sin or cosine functions. 3.Substitute the trial solution into the equation of motion and simplify the resulting equation. 4.Use the characteristic equation to solve for the natural frequency of the system. The characteristic equation is obtained by assuming a solution of the form X = e^(st) and substituting it into the equation of motion.
- 7. Cont.. 5.Once the natural frequency is determined, substitute it back into the equation of motion to solve for the coefficients of the mode shape. 6.Normalize the mode shape by dividing each coefficient by the maximum value of the mode shape. 7.Repeat steps 2-6 for each mode shape of the system to determine all the natural frequencies and mode shapes.
- 9. • In Holzer s method, a trial frequency is assumed, and Next, 1 is arbitrarily chosen as unity.
- 10. For Mass spring system
- 11. Example (Torsional Prob.) 1. Calculate the first non zero natural frequency of the shaft-rotor system shown in fig using Holzer’s method. Take: J₁=J₂=J₃= 1kg. ㎡ Kt₁=Kt₂= 1Nm/rad
- 12. Solution
- 13. THANK YOU