Pub Date : 2022-05-01DOI: 10.1109/ietc54973.2022.9796883
F. Ramezani, Christopher M. Major, Colter Barney, Justin Williams, B. Lameres, Bradley M. Whitaker
Fault tolerant computers have been developed in recent years to operate in the harsh radiation environment of outer space. These computers employ multiple copies of soft processors in a reconfigurable hardware environment and can automatically repair faults caused by radiation strikes. However, during certain recovery procedures, data collection and processing can be halted, and valuable scientific data can be lost. In addition, current fault recovery procedures may inadvertently make the computer more susceptible to faults or errors, for example, by introducing voltage and temperature changes. Machine learning feature extraction algorithms have the potential to reduce data loss by identifying patterns related to computational fault mitigation and recovery techniques. In this work, we will gather telemetry data from RadPC: a reconfigurable, radiation tolerant computer that has been developed over the past 12 years by Montana State University to advance high performance space computing under varying environmental conditions. RadPC has recently been configured to provide regular telemetry data to measure and communicate the performance of the radiation-tolerant computing platform. Specifically, the telemetry data includes information about data memory integrity, faults experienced, and successful repairs; as well as various measurements including voltage, current, and temperature. While RadPC has been under development for some time, the developers have never searched the telemetry data for associations between fault recovery procedures and the physical state of the hardware itself (e.g., voltage and current levels of power supplies or internal temperature). In this work, the computer will be subject to synthetic faults—emulating radiation strikes that may occur in space—and perform standard recovery procedures. The tests will be performed with the RadPC on a high-altitude balloon flight as well as inside a temperature-controlled vacuum chamber, allowing for a range of controlled external environmental conditions. The collected telemetry data will be analyzed using PCA to detect patterns in the hardware status associated with fault recovery techniques. Identifying these patterns may lead to improved fault mitigation strategies that reduce the risk of subsequent faults by considering how recovery techniques affect the physical state of the hardware.
{"title":"Identifying Patterns in Fault Recovery Techniques and Hardware Status of Radiation Tolerant Computers Using Principal Components Analysis","authors":"F. Ramezani, Christopher M. Major, Colter Barney, Justin Williams, B. Lameres, Bradley M. Whitaker","doi":"10.1109/ietc54973.2022.9796883","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796883","url":null,"abstract":"Fault tolerant computers have been developed in recent years to operate in the harsh radiation environment of outer space. These computers employ multiple copies of soft processors in a reconfigurable hardware environment and can automatically repair faults caused by radiation strikes. However, during certain recovery procedures, data collection and processing can be halted, and valuable scientific data can be lost. In addition, current fault recovery procedures may inadvertently make the computer more susceptible to faults or errors, for example, by introducing voltage and temperature changes. Machine learning feature extraction algorithms have the potential to reduce data loss by identifying patterns related to computational fault mitigation and recovery techniques. In this work, we will gather telemetry data from RadPC: a reconfigurable, radiation tolerant computer that has been developed over the past 12 years by Montana State University to advance high performance space computing under varying environmental conditions. RadPC has recently been configured to provide regular telemetry data to measure and communicate the performance of the radiation-tolerant computing platform. Specifically, the telemetry data includes information about data memory integrity, faults experienced, and successful repairs; as well as various measurements including voltage, current, and temperature. While RadPC has been under development for some time, the developers have never searched the telemetry data for associations between fault recovery procedures and the physical state of the hardware itself (e.g., voltage and current levels of power supplies or internal temperature). In this work, the computer will be subject to synthetic faults—emulating radiation strikes that may occur in space—and perform standard recovery procedures. The tests will be performed with the RadPC on a high-altitude balloon flight as well as inside a temperature-controlled vacuum chamber, allowing for a range of controlled external environmental conditions. The collected telemetry data will be analyzed using PCA to detect patterns in the hardware status associated with fault recovery techniques. Identifying these patterns may lead to improved fault mitigation strategies that reduce the risk of subsequent faults by considering how recovery techniques affect the physical state of the hardware.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803885","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796776
Hollis Belnap, Samuel Lahti, A. Hawkins
There are no developed methods of pumping fluids on the nano-scale without contaminating the sample being pumped. This paper describes a nano-electromechanical system device intended for pumping fluids and trapping particles. This device can improve accuracy of fast viral testing, increase the capabilities of target drug delivery, and be used in Lab-On-a-Chip systems to transport fluids and concentrate samples. We detail its structure and important fabrication techniques, as well as present preliminary characterization test results.
{"title":"Particle Concentration Using Electroactuated Nanopumps","authors":"Hollis Belnap, Samuel Lahti, A. Hawkins","doi":"10.1109/ietc54973.2022.9796776","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796776","url":null,"abstract":"There are no developed methods of pumping fluids on the nano-scale without contaminating the sample being pumped. This paper describes a nano-electromechanical system device intended for pumping fluids and trapping particles. This device can improve accuracy of fast viral testing, increase the capabilities of target drug delivery, and be used in Lab-On-a-Chip systems to transport fluids and concentrate samples. We detail its structure and important fabrication techniques, as well as present preliminary characterization test results.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124376781","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796741
T. Moon, Randy Christensen, J. Gunther
Best linear unbiased estimator (BLUE) theory is well established for discrete, finite-dimensional vectors, where methods of vector gradients can be used on a constrained optimization problem. However, when the observation is infinite-dimensional (e.g., continuous-time functions), the gradient-based approach can be problematic. We pose the BLUE problem as an instance of a dual approximation problem, which recasts the problem into finite dimensional space employing the principle of orthogonality, requiring no gradients for solution. To demonstrate the ideas, they are first developed on a finite-dimensional problem, then extended to infinite dimensional problems. We present an example application of phase estimation from continuous-time observations.
{"title":"Using Dual Approximation for Best Linear Unbiased Estimators in Continuous Time, with Application to Continuous-Time Phase Estimation","authors":"T. Moon, Randy Christensen, J. Gunther","doi":"10.1109/ietc54973.2022.9796741","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796741","url":null,"abstract":"Best linear unbiased estimator (BLUE) theory is well established for discrete, finite-dimensional vectors, where methods of vector gradients can be used on a constrained optimization problem. However, when the observation is infinite-dimensional (e.g., continuous-time functions), the gradient-based approach can be problematic. We pose the BLUE problem as an instance of a dual approximation problem, which recasts the problem into finite dimensional space employing the principle of orthogonality, requiring no gradients for solution. To demonstrate the ideas, they are first developed on a finite-dimensional problem, then extended to infinite dimensional problems. We present an example application of phase estimation from continuous-time observations.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117200316","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796956
Jingpeng Tang, Qianwen Bi, Ian Beal, Eric Stauffer, Yashwanth Kotha, Smita Gupta
There are challenges to analyzing huge volumes of data in the financial sector. How to handle big financial data intelligently is among one of the most important topics faced by researchers and practitioners. The stock market data are too large or complex to be dealt with by traditional data-processing application software. In this research, we propose using the orthogonal array to systematically generate pairs of input data fields for the Machine Learning model developed in our previous works. Trials in the automated wealth management industry (e.g. Robo-Advisors) have increased with the introduction of newer data analysis tools and technology applications. This has resulted in new methods, variables, and ideations being considered for optimal predictive analysis in the stock, bond, and cryptocurrency markets. Large data sets used in conjunction with machine learning are telling and predictive for different points in time. Our research attempts to understand which input factors will affect the stock market the most. As a result, we are expecting to reduce the volume of data needed to supply our machine learning model.
{"title":"Stock Market Feature Selection Using Orthogonal Array","authors":"Jingpeng Tang, Qianwen Bi, Ian Beal, Eric Stauffer, Yashwanth Kotha, Smita Gupta","doi":"10.1109/ietc54973.2022.9796956","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796956","url":null,"abstract":"There are challenges to analyzing huge volumes of data in the financial sector. How to handle big financial data intelligently is among one of the most important topics faced by researchers and practitioners. The stock market data are too large or complex to be dealt with by traditional data-processing application software. In this research, we propose using the orthogonal array to systematically generate pairs of input data fields for the Machine Learning model developed in our previous works. Trials in the automated wealth management industry (e.g. Robo-Advisors) have increased with the introduction of newer data analysis tools and technology applications. This has resulted in new methods, variables, and ideations being considered for optimal predictive analysis in the stock, bond, and cryptocurrency markets. Large data sets used in conjunction with machine learning are telling and predictive for different points in time. Our research attempts to understand which input factors will affect the stock market the most. As a result, we are expecting to reduce the volume of data needed to supply our machine learning model.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123525529","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796730
Isaac P. Boyd, David Hedges, Benjamin T. Carter, Bradley M. Whitaker
The spread of the novel coronavirus across the world in 2020 exposed the tenuous nature of hospital capacity and medical resource supply lines. Being able to anticipate surge events days before they hit an area would allow healthcare workers to pivot and prepare, critically expanding capacity and adjusting to resource loads. This work aims to enable advanced healthcare planning by providing adaptive forecasts into short range COVID-19 outbreaks and surge events. Here, we present a novel method to predict the spread of COVID-19 by using creative neural network architectures, especially convolutional and LSTM layers. Our goal was to create a generalizable method or model to predict disease spread on a county-level granularity. Importantly, we found that by using an adaptive neural network model with a frequent refresh rate, we were able to outperform simple feed forward estimation methods to predict county level new case counts on a daily basis. We also show the capabilities of neural network architectures by comparing performance on different sizes of training data and geographic inputs. Our results indicate that neural networks are well suited to dynamically modeling the spread of COVID-19 on a county-level basis, but that cultural and/or geographic differences in regions prevent the portability of fully-trained models.
{"title":"Using Neural Networks to Model the Spread of COVID-19","authors":"Isaac P. Boyd, David Hedges, Benjamin T. Carter, Bradley M. Whitaker","doi":"10.1109/ietc54973.2022.9796730","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796730","url":null,"abstract":"The spread of the novel coronavirus across the world in 2020 exposed the tenuous nature of hospital capacity and medical resource supply lines. Being able to anticipate surge events days before they hit an area would allow healthcare workers to pivot and prepare, critically expanding capacity and adjusting to resource loads. This work aims to enable advanced healthcare planning by providing adaptive forecasts into short range COVID-19 outbreaks and surge events. Here, we present a novel method to predict the spread of COVID-19 by using creative neural network architectures, especially convolutional and LSTM layers. Our goal was to create a generalizable method or model to predict disease spread on a county-level granularity. Importantly, we found that by using an adaptive neural network model with a frequent refresh rate, we were able to outperform simple feed forward estimation methods to predict county level new case counts on a daily basis. We also show the capabilities of neural network architectures by comparing performance on different sizes of training data and geographic inputs. Our results indicate that neural networks are well suited to dynamically modeling the spread of COVID-19 on a county-level basis, but that cultural and/or geographic differences in regions prevent the portability of fully-trained models.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115466049","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796745
H. Nieto-Chaupis
With the advent of novel Internet technologies it is clearly expected that most of them will have practical applications such as the recent Internet called Internet of Space Things. When this new technologies are running it is strongly expected the creation of private networks among satellites in order to optimize security when sensitive information is either uploading or downloading. In this paper a concrete idea to implement the information of solar photons to increase the quality of encryption processes is presented. Thus when photons energy are measured in space, this information enters as input at a BB84 quantum mechanics encryption scheme to maximize the security at the transference of information. Simulations are presented and discussed.
{"title":"Scheme of Secure Satellite Intercommunications Based at Solar Photons","authors":"H. Nieto-Chaupis","doi":"10.1109/ietc54973.2022.9796745","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796745","url":null,"abstract":"With the advent of novel Internet technologies it is clearly expected that most of them will have practical applications such as the recent Internet called Internet of Space Things. When this new technologies are running it is strongly expected the creation of private networks among satellites in order to optimize security when sensitive information is either uploading or downloading. In this paper a concrete idea to implement the information of solar photons to increase the quality of encryption processes is presented. Thus when photons energy are measured in space, this information enters as input at a BB84 quantum mechanics encryption scheme to maximize the security at the transference of information. Simulations are presented and discussed.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116306847","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796894
Gordon Fjeldsted, John Edwards
Understanding and measuring the work a student has put into a homework assignment is a metric that is not easy to calculate. This is because the output of a student’s work is not necessarily the sum of their efforts as lots of effort is lost along the way when coding. In this paper we attempt to demonstrate the effort a student puts into their homework through the creation of a novel algorithm based around keystroke data. With this algorithm we pass the results from the algorithm to a heat map generator to help show where a student is spending most of their time working on their code.
{"title":"Quantifying Student Struggles using Heatmaps and Keystroke Data","authors":"Gordon Fjeldsted, John Edwards","doi":"10.1109/ietc54973.2022.9796894","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796894","url":null,"abstract":"Understanding and measuring the work a student has put into a homework assignment is a metric that is not easy to calculate. This is because the output of a student’s work is not necessarily the sum of their efforts as lots of effort is lost along the way when coding. In this paper we attempt to demonstrate the effort a student puts into their homework through the creation of a novel algorithm based around keystroke data. With this algorithm we pass the results from the algorithm to a heat map generator to help show where a student is spending most of their time working on their code.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123779718","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796915
Kellie Wilson, M. Schoen, Ji-chao Li
Modeling of compressors and other complex thermodynamic structures is an important process in the design of these systems. Finding models that not only accurately describe the system, but also are convenient to create and modify is still considered a challenge. Many models have been created using the Moore-Greitzer model. This model has been used in many works throughout the years with some of those being detailed here. A model of a small experimental compressor rig was developed in Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). To verify the accuracy and capabilities of this toolbox, a comparison between actual experimental test data and data generated through simulation using the model generated. These two outcomes are compared and it is found that they have a good correlation with each other. The error is small between the experimental data and the simulation data which indicates utility of such simulation models for use in further research.
{"title":"Jet Engine Modeling Using T-MATS with Experimental Verification","authors":"Kellie Wilson, M. Schoen, Ji-chao Li","doi":"10.1109/ietc54973.2022.9796915","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796915","url":null,"abstract":"Modeling of compressors and other complex thermodynamic structures is an important process in the design of these systems. Finding models that not only accurately describe the system, but also are convenient to create and modify is still considered a challenge. Many models have been created using the Moore-Greitzer model. This model has been used in many works throughout the years with some of those being detailed here. A model of a small experimental compressor rig was developed in Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). To verify the accuracy and capabilities of this toolbox, a comparison between actual experimental test data and data generated through simulation using the model generated. These two outcomes are compared and it is found that they have a good correlation with each other. The error is small between the experimental data and the simulation data which indicates utility of such simulation models for use in further research.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303435","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796919
Colton Seegmiller, Blake Chamberlain, Jordan Miller, Mohammed A.S. Masoum, Mohammad Shekaramiz
Rapid and accurate identification of faults on wind turbine blades is important to ensure the continued operation of wind power generation systems. This paper explores the implementation of Support Vector Machines (SVM) combined with fuzzy logic for image recognition and fault classification of wind turbine blades. We discuss the concept, ideas, and implementation of SVM for image recognition, and the final result is to implement these features into a system for detecting the various cracks and damages on the blades of wind turbines using a scale model. The final system will be tested on a scale model of a wind turbine blade. We will focus on what SVM is, what the crossover between SVM and fuzzy may look like, and how it will effectively be able to detect cracks in the blades of wind turbines.
{"title":"Wind Turbine Fault Classification Using Support Vector Machines with Fuzzy Logic","authors":"Colton Seegmiller, Blake Chamberlain, Jordan Miller, Mohammed A.S. Masoum, Mohammad Shekaramiz","doi":"10.1109/ietc54973.2022.9796919","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796919","url":null,"abstract":"Rapid and accurate identification of faults on wind turbine blades is important to ensure the continued operation of wind power generation systems. This paper explores the implementation of Support Vector Machines (SVM) combined with fuzzy logic for image recognition and fault classification of wind turbine blades. We discuss the concept, ideas, and implementation of SVM for image recognition, and the final result is to implement these features into a system for detecting the various cracks and damages on the blades of wind turbines using a scale model. The final system will be tested on a scale model of a wind turbine blade. We will focus on what SVM is, what the crossover between SVM and fuzzy may look like, and how it will effectively be able to detect cracks in the blades of wind turbines.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131924517","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 : 2022-05-01DOI: 10.1109/ietc54973.2022.9796911
Shishir Khanal, Cooper Dastrup, Andrew Anderson, Anish Sebastian, M. Schoen
Axial compressor systems are one of the highly complex engineering systems that mechanical engineers deal with. Compression systems are a crucial section of jet engines which a play pivotal role in the determination of the efficiency of a jet engine. As such it is highly desired to have a test bench version of an axial compressor that can provide for an interface to investigate the effects of the compressor instabilities and remedial control action with at a low cost. Hence, a design procedure of a test bench compressor is proposed. This paper provides a detailed explanation of the design approach behind each of the components of a Moore-Grietzer Single-Stage axial-based compressor and provides the results of tests with regard to the maximum pressure rise coefficient on a proposed stator using velocity triangle calculation. Finally, future research goals utilizing the proposed axial compressor testbed is detailed.
{"title":"Design and Development of a Single-Stage Axial Compressor Testbench","authors":"Shishir Khanal, Cooper Dastrup, Andrew Anderson, Anish Sebastian, M. Schoen","doi":"10.1109/ietc54973.2022.9796911","DOIUrl":"https://doi.org/10.1109/ietc54973.2022.9796911","url":null,"abstract":"Axial compressor systems are one of the highly complex engineering systems that mechanical engineers deal with. Compression systems are a crucial section of jet engines which a play pivotal role in the determination of the efficiency of a jet engine. As such it is highly desired to have a test bench version of an axial compressor that can provide for an interface to investigate the effects of the compressor instabilities and remedial control action with at a low cost. Hence, a design procedure of a test bench compressor is proposed. This paper provides a detailed explanation of the design approach behind each of the components of a Moore-Grietzer Single-Stage axial-based compressor and provides the results of tests with regard to the maximum pressure rise coefficient on a proposed stator using velocity triangle calculation. Finally, future research goals utilizing the proposed axial compressor testbed is detailed.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133687436","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}
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