Dr Sridhar Akiri
@gitam.edu
assistant professor
GITAM Deemed to be University
EDUCATION
M.Sc, M.Phil., MBA, Ph.D
RESEARCH INTERESTS
Reliability Theory
Scopus Publications
Scopus Publications
Arun Kumar Saripalli, Sridhar Akiri, Rekha Sarode, B. V. Nagarjuna Vasili, and M. Ramanaiah
World Researchers Associations
This study investigates the suitability of three parameters continuous probability distributions-Burr Type XII 3P, Dagum Type I 3P and Log-Logistic 3P-in modeling secondary air pollutants: ozone (O₃), particulate matters (PM₁₀ and PM₂.₅) in Visakhapatnam, an urban region having rapid industrialization. By employing rigorous statistical techniques including maximum likelihood estimation (MLE) and bootstrapping, we estimate distribution parameters and validate model fit through diagnostic plots-skewness vs. kurtosis, P-P and Q-Q plots as well as goodness-of-fit test-statistics, such as Kolmogorov-Smirnov(KS), Anderson-Darling(AD) and Cramér von Mises(CvM) tests. Additional, performance metrics including Akaike information criterion(AIC), Bayesian information criterion(BIC), evaluation metrics like mean absolute error(MAE), mean absolute percentage error(MAPE), mean squared error(MSE), root mean squared error(RMSE) and coefficient of determination(R²) and cross-validation, were also applied to ensure model robustness. Results indicate that the Burr Type XII 3P distribution most effectively models the high variability and skewed nature of O₃ concentrations, while the Dagum Type I 3P distribution provides the best fit for PM₁₀ and both Burr Type XII 3P and Log-Logistic 3P distributions are suitable for PM₂.₅. These findings offer new insights into the behavior of secondary pollutants, supporting the development of robust air quality monitoring frameworks. R software facilitated all numerical analyses and visualizations of data suited to environmental data modeling.
Ramadevi Surapati, Sridhar Akiri, Srinivasa Rao Velampudi, Vasili B V Nagarjuna, Potluri S. S. Swethan, and Bhavani Kapu
New York Business Global LLC
In reliability theory, series-parallel configuration systems provide a fundamental framework for examining the relationship between individual component lives and the overall system durability. This research examines the influence of several constraints, namely weight, volume, dimensions, and spatial limitations, on enhancing system reliability, specifically regarding spare components for standard oil burners, including nozzle tubes and electrode brackets. An Integrated Redundant Reliability Series-Parallel configuration system is systematically designed and evaluated utilizing the Lagrangean multiplier method, yielding real-valued solutions for essential parameters such as component quantities, component reliability, stage reliability, and overall system reliability. The study used the dynamic programming method to seek integer solutions, hence improving the accuracy and relevance of the reliability analysis.
S. Rao Jammalamadaka, Kaushik Ghosh, and Sridhar Akiri
Elsevier BV
Bhavani Kapu
Science Research Society
The Integrated Redundant Reliability Model (IRRM) represents an innovative approach to reliability engineering, employing a Parallel-Series Configuration to enhance system dependability. The performance of the system hinges on the effectiveness of each component within the parallel-series structure, surpassing the efficiency of a single-system factor with a comparable setup. To address component efficiencies, factors in each phase, and existing constraints, this paper introduces a customized Integrated Reliability Model (IRM) specifically designed for the parallel-series scenario. In this model, redundant components are arranged in parallel within subsystems, providing immediate fallback for a specialized machine that is specifically designed for the assembly of an IC Engine. The interconnected series configuration ensures operational continuity in the event of one subsystem failure, thereby minimizing vulnerabilities associated with both parallel and series configurations. Particularly beneficial in critical systems, the integrated approach aims to enhance reliability levels. The model utilizes Lagrangean methods for computing variable quantities, effectiveness, and phase reliability, taking into account various criteria to improve overall system efficiency. Adjustments to simulation techniques and Integer Programming approaches guarantee integer outputs, contributing to the realism of obtained values. This research provides valuable insights into optimizing system reliability and efficiency through integrated redundancy strategies.
Sridhar Akiri, P. Sasikala, Pavan Kumar Subbara, and V.S.S Yadavalli
Elsevier
Sridhar Akiri, Pavan Kumar Subbara, P. Sasikala, and Venkata S. Sarma Yadavalli
De Gruyter