B. Lv, Guotao Wang, Shuo Li, Shicheng Wang, X. Liang
Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data balancing effect than SMOTE, and our optimized RF algorithm achieves an accuracy of over 96% for identifying loose particle signals. The proposed method can also be popularized in the field of loose particle detection for large-scale sealing equipment and other various areas of fault diagnosis based on sound signals.
{"title":"Recognition Method for Electronic Component Signals Based on LR-SMOTE\u0000 and Improved Random Forest Algorithm","authors":"B. Lv, Guotao Wang, Shuo Li, Shicheng Wang, X. Liang","doi":"10.4271/01-17-01-0005","DOIUrl":"https://doi.org/10.4271/01-17-01-0005","url":null,"abstract":"Loose particles are a major problem affecting the performance and safety of\u0000 aerospace electronic components. The current particle impact noise detection\u0000 (PIND) method used in these components suffers from two main issues: data\u0000 collection imbalance and unstable machine-learning-based recognition models that\u0000 lead to redundant signal misclassification and reduced detection accuracy. To\u0000 address these issues, we propose a signal identification method using the\u0000 limited random synthetic minority oversampling technique (LR-SMOTE) for\u0000 unbalanced data processing and an optimized random forest (RF) algorithm to\u0000 detect loose particles. LR-SMOTE expands the generation space beyond the\u0000 original SMOTE oversampling algorithm, generating more representative data for\u0000 underrepresented classes. We then use an RF optimization algorithm based on the\u0000 correlation measure to identify loose particle signals in balanced data. Our\u0000 experimental results demonstrate that the LR-SMOTE algorithm has a better data\u0000 balancing effect than SMOTE, and our optimized RF algorithm achieves an accuracy\u0000 of over 96% for identifying loose particle signals. The proposed method can also\u0000 be popularized in the field of loose particle detection for large-scale sealing\u0000 equipment and other various areas of fault diagnosis based on sound signals.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":"1 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42042978","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 article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI. The results reported herein illustrate that researchers are generally using agreed-upon terms such as physical fidelity, but there is an emerging definition of functional fidelity that is being adopted to replace the concept of psychological fidelity for training AI instead of humans.
{"title":"A Literature Review of Simulation Fidelity for Autonomous-Vehicle\u0000 Research and Development","authors":"Christopher Johnson, Elan Graupe, Maxfield Kassel","doi":"10.4271/01-16-03-0021","DOIUrl":"https://doi.org/10.4271/01-16-03-0021","url":null,"abstract":"This article explores the value of simulation for autonomous-vehicle research and\u0000 development. There is ample research that details the effectiveness of\u0000 simulation for training humans to fly and drive. Unfortunately, the same is not\u0000 true for simulations used to train and test artificial intelligence (AI) that\u0000 enables autonomous vehicles to fly and drive without humans. Research has shown\u0000 that simulation “fidelity” is the most influential factor affecting training\u0000 yield, but psychological fidelity is a widely accepted\u0000 definition that does not apply to AI because it describes how well simulations\u0000 engage various cognitive functions of human operators. Therefore, this\u0000 investigation reviewed the literature that was published between January 2010\u0000 and May 2022 on the topic of simulation fidelity to understand\u0000 how researchers are defining and measuring simulation fidelity\u0000 as applied to training AI. The results reported herein illustrate that\u0000 researchers are generally using agreed-upon terms such as physical\u0000 fidelity, but there is an emerging definition of functional\u0000 fidelity that is being adopted to replace the concept of\u0000 psychological fidelity for training AI instead of\u0000 humans.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46887783","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}
Joseph K. Ausserer, M. Polanka, Paul J. Litke, K. D. Grinstead
This work describes an investigation of measurement techniques for the indicated mean effective pressure (IMEP) on a 55 cc single-cylinder, 4.4 kW, two-stroke, spark ignition (SI) engine intended for use on Group 1 and Group 2 remotely piloted aircraft (RPAs). Three different sensors were used: two piezoelectric pressure transducers (one flush mount and one measuring spark plug) for measuring in-cylinder pressure and one capacitive sensor for determining the top dead center (TDC) position of the piston. The effort consisted of three objectives: to investigate the merits of a flush mount pressure transducer compared to a pressure transducer integrated into the spark plug, to perform a parametric analysis to characterize the effect of the variability in the engine test bench controls on the IMEP, and to determine the thermodynamic loss angle for the engine. The results indicate that as a spark plug, the measuring spark plug is not statistically different from the stock spark plug at the 95% confidence level. The results indicate a statistically significant, 4% difference in the measured IMEP between the pressure transducer in the measuring spark plug and the flush mount transducer. The results also suggest a statistically significant difference in performance between the modified and unmodified engine heads, verifying the suppositions of other researchers who suggested that even a small modification to a combustion chamber this size could measurably affect the engine performance. While run-to-run variation resulted in a 2% to 5% variation in IMEP, a sensitivity analysis determined that 1% to 3% of that variation arose from variability in the control variables, while the remainder was caused by variation in other engine operating parameters. Between 1000 rpm and 2000 rpm, where the engine was typically motored to determine the TDC, the thermodynamic loss angle was 0.3 crank angle degrees (CAD) to 0.7 CAD, larger than loss angles observed in automotive-sized gasoline engines. The results indicate that using tabulated thermodynamic loss angles to set the TDC location of the engine using a mono-directional peak pressure method would lead to a −1% to −2.5% bias in the IMEP.
{"title":"Investigation of In-Cylinder Pressure Measurement Methods within a\u0000 Two-Stroke Spark Ignition Engine","authors":"Joseph K. Ausserer, M. Polanka, Paul J. Litke, K. D. Grinstead","doi":"10.4271/01-17-01-0004","DOIUrl":"https://doi.org/10.4271/01-17-01-0004","url":null,"abstract":"This work describes an investigation of measurement techniques for the indicated\u0000 mean effective pressure (IMEP) on a 55 cc single-cylinder, 4.4 kW, two-stroke,\u0000 spark ignition (SI) engine intended for use on Group 1 and Group 2 remotely\u0000 piloted aircraft (RPAs). Three different sensors were used: two piezoelectric\u0000 pressure transducers (one flush mount and one measuring spark plug) for\u0000 measuring in-cylinder pressure and one capacitive sensor for determining the top\u0000 dead center (TDC) position of the piston. The effort consisted of three\u0000 objectives: to investigate the merits of a flush mount pressure transducer\u0000 compared to a pressure transducer integrated into the spark plug, to perform a\u0000 parametric analysis to characterize the effect of the variability in the engine\u0000 test bench controls on the IMEP, and to determine the thermodynamic loss angle\u0000 for the engine. The results indicate that as a spark plug, the measuring spark\u0000 plug is not statistically different from the stock spark plug at the 95%\u0000 confidence level. The results indicate a statistically significant, 4%\u0000 difference in the measured IMEP between the pressure transducer in the measuring\u0000 spark plug and the flush mount transducer. The results also suggest a\u0000 statistically significant difference in performance between the modified and\u0000 unmodified engine heads, verifying the suppositions of other researchers who\u0000 suggested that even a small modification to a combustion chamber this size could\u0000 measurably affect the engine performance. While run-to-run variation resulted in\u0000 a 2% to 5% variation in IMEP, a sensitivity analysis determined that 1% to 3% of\u0000 that variation arose from variability in the control variables, while the\u0000 remainder was caused by variation in other engine operating parameters. Between\u0000 1000 rpm and 2000 rpm, where the engine was typically motored to determine the\u0000 TDC, the thermodynamic loss angle was 0.3 crank angle degrees (CAD) to 0.7 CAD,\u0000 larger than loss angles observed in automotive-sized gasoline engines. The\u0000 results indicate that using tabulated thermodynamic loss angles to set the TDC\u0000 location of the engine using a mono-directional peak pressure method would lead\u0000 to a −1% to −2.5% bias in the IMEP.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42235328","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}
Previous artificial lightning strike direct effect research has examined a broad range of specimen design parameters. No works have studied how such specimen design parameters and electrical boundary conditions impact the dissipation of electric current flow through individual plies. This article assesses the influence of carbon fiber composite specimen design parameters (design parameters = specimen size, shape, and stacking sequence) and electrical boundary conditions on the dissipation of current and the spread of damage resulting from Joule heating. Thermal-electric finite element (FE) modelling is used and laboratory scale (<1 m long) and aircraft scale (>1 m long) models are generated in which laminated ply current dissipation is predicted, considering a fixed artificial lightning current waveform. The simulation results establish a positive correlation between the current exiting the specimen from a given ply and the amount of thermal damage in that ply. The results also establish that the distance to ground, from the strike location to the zero potential boundary conditions (ground), is the controlling factor which dictates the electric current dissipation in each ply. Significantly, this distance to ground is dependent on each of the specimen shape, dimensions, stacking sequence, and location of ground boundary conditions. Therefore, it is not possible to decouple current dissipation and damage from specimen design and boundary condition setup. However, it is possible to define a specimen size for a given specimen shape, stacking sequence, and waveform which limit the influence of specimen dimensions on the resulting current distribution and damage. For a rectangular specimen design which appears in literature multiple times, as 100 × 150 mm and with a stacking sequence of [45/0/−45/90]4s, a specimen design of greater than 300 × 200 mm is required to limit the influence of specimen dimensions on current distribution and damage.
{"title":"The Influence of Carbon Fiber Composite Specimen Design Parameters on\u0000 Artificial Lightning Strike Current Dissipation and Material Thermal\u0000 Damage","authors":"Scott L J Millen, Vipin Kumar, A. Murphy","doi":"10.4271/01-16-02-0017","DOIUrl":"https://doi.org/10.4271/01-16-02-0017","url":null,"abstract":"Previous artificial lightning strike direct effect research has examined a broad\u0000 range of specimen design parameters. No works have studied how such specimen\u0000 design parameters and electrical boundary conditions impact the dissipation of\u0000 electric current flow through individual plies. This article assesses the\u0000 influence of carbon fiber composite specimen design parameters (design\u0000 parameters = specimen size, shape, and stacking sequence) and electrical\u0000 boundary conditions on the dissipation of current and the spread of damage\u0000 resulting from Joule heating. Thermal-electric finite element (FE) modelling is\u0000 used and laboratory scale (<1 m long) and aircraft scale (>1 m long)\u0000 models are generated in which laminated ply current dissipation is predicted,\u0000 considering a fixed artificial lightning current waveform. The simulation\u0000 results establish a positive correlation between the current exiting the\u0000 specimen from a given ply and the amount of thermal damage in that ply. The\u0000 results also establish that the distance to ground, from the strike location to\u0000 the zero potential boundary conditions (ground), is the controlling factor which\u0000 dictates the electric current dissipation in each ply. Significantly, this\u0000 distance to ground is dependent on each of the specimen shape, dimensions,\u0000 stacking sequence, and location of ground boundary conditions. Therefore, it is\u0000 not possible to decouple current dissipation and damage from specimen design and\u0000 boundary condition setup. However, it is possible to define a specimen size for\u0000 a given specimen shape, stacking sequence, and waveform which limit the\u0000 influence of specimen dimensions on the resulting current distribution and\u0000 damage. For a rectangular specimen design which appears in literature multiple\u0000 times, as 100 × 150 mm and with a stacking sequence of\u0000 [45/0/−45/90]4s, a specimen design of greater than 300 × 200 mm\u0000 is required to limit the influence of specimen dimensions on current\u0000 distribution and damage.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49040266","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}
In view of the structural accidental events in the ongoing airworthiness stage of civil aircraft, it is necessary to conduct a risk assessment to ensure that the risk level is within an acceptable range. However, the existing models of risk assessment have not effectively dealt with the risk of accidental structural damage due to random failure. This article focuses on probabilistic risk assessment using the Transport Airplane Risk Assessment Methodology (TARAM) of accidental structural damage of civil aircraft. Based on the TARAM and probability reliability integral, a refined failure frequency probability calculation model is established to elaborate on composite structure failure frequency. A case study is analyzed for the outer wing plane of an aircraft having impact damage of composite materials. Finally, results of the risk assessment without correction and risk assessment with correction are presented for detailed visual inspection and general visual inspection.
{"title":"Probabilistic Risk Assessment of Accidental Damage to Civil Aircraft\u0000 Composite Structures","authors":"B. Jia, Jiachen Fang, Xiang Lu, Yijie Xiong","doi":"10.4271/01-16-02-0016","DOIUrl":"https://doi.org/10.4271/01-16-02-0016","url":null,"abstract":"In view of the structural accidental events in the ongoing airworthiness stage of\u0000 civil aircraft, it is necessary to conduct a risk assessment to ensure that the\u0000 risk level is within an acceptable range. However, the existing models of risk\u0000 assessment have not effectively dealt with the risk of accidental structural\u0000 damage due to random failure. This article focuses on probabilistic risk\u0000 assessment using the Transport Airplane Risk Assessment Methodology (TARAM) of\u0000 accidental structural damage of civil aircraft. Based on the TARAM and\u0000 probability reliability integral, a refined failure frequency probability\u0000 calculation model is established to elaborate on composite structure failure\u0000 frequency. A case study is analyzed for the outer wing plane of an aircraft\u0000 having impact damage of composite materials. Finally, results of the risk\u0000 assessment without correction and risk assessment with correction are presented\u0000 for detailed visual inspection and general visual inspection.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48010853","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}
P. Bartlett, Lyle Chamberlain, Sanjiv Singh, Lauren Coblenz
Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced air mobility (AAM) applications ranging from cargo delivery to structure inspection to passenger transport, across multiple sectors. In addition to guiding the UAS, autonomy will ensure that they stay safe in a large number of off-nominal situations without requiring the operator to intervene. While the addition of autonomy enables the safety case for the overall operation, there is a question as to how we can assure that the autonomy itself will work as intended. Specifically, we need assurable technical approaches, operational considerations, and a framework to develop, test, maintain, and improve these capabilities. We make the case that many of the key autonomy functions can be realized in the near term with readily assurable, even certifiable, design approaches and assurance methods, combined with risk mitigations and strategically defined concepts of operations. We present specific autonomy functions common to many civil beyond visual line of sight (BVLOS) operations and corresponding design assurance strategies, along with their contributions to an overall safety case. We provide examples of functions that can be certified under existing standards, those that will need runtime assurance (RTA) and those that will need to be qualified with statistical evidence.
{"title":"A Near-Term Path to Assured Aerial Autonomy","authors":"P. Bartlett, Lyle Chamberlain, Sanjiv Singh, Lauren Coblenz","doi":"10.4271/01-16-03-0020","DOIUrl":"https://doi.org/10.4271/01-16-03-0020","url":null,"abstract":"Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced\u0000 air mobility (AAM) applications ranging from cargo delivery to structure\u0000 inspection to passenger transport, across multiple sectors. In addition to\u0000 guiding the UAS, autonomy will ensure that they stay safe in a large number of\u0000 off-nominal situations without requiring the operator to intervene. While the\u0000 addition of autonomy enables the safety case for the overall operation, there is\u0000 a question as to how we can assure that the autonomy itself will work as\u0000 intended. Specifically, we need assurable technical approaches, operational\u0000 considerations, and a framework to develop, test, maintain, and improve these\u0000 capabilities.\u0000\u0000 \u0000We make the case that many of the key autonomy functions can be realized in the\u0000 near term with readily assurable, even certifiable, design approaches and\u0000 assurance methods, combined with risk mitigations and strategically defined\u0000 concepts of operations. We present specific autonomy functions common to many\u0000 civil beyond visual line of sight (BVLOS) operations and corresponding design\u0000 assurance strategies, along with their contributions to an overall safety case.\u0000 We provide examples of functions that can be certified under existing standards,\u0000 those that will need runtime assurance (RTA) and those that will need to be\u0000 qualified with statistical evidence.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43186387","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 aerodynamic equation of state is introduced in Part I and applies to selected aerospace systems. Part II applies it to the flapping of hovering and forward-flying biological fliers. This last Part III expands the aerodynamic equation of state by adding the potential energy term, assumed up to this point to be zero as the system and its trajectory is placed coplanar with an arbitrary reference potential plane. Part III applies the expanded equation of state to familiar and well-trodden fluid-static and fluid-dynamic cases selected from fluid mechanic textbooks.
{"title":"An Aerodynamic Equation of State—Part III: Applications to Fluid\u0000 Statics and Dynamics","authors":"Phillip Burgers","doi":"10.4271/01-17-01-0003","DOIUrl":"https://doi.org/10.4271/01-17-01-0003","url":null,"abstract":"The aerodynamic equation of state is introduced in Part I and applies to selected\u0000 aerospace systems. Part II applies it to the flapping of hovering and\u0000 forward-flying biological fliers. This last Part III expands the aerodynamic\u0000 equation of state by adding the potential energy term, assumed up to this point\u0000 to be zero as the system and its trajectory is placed coplanar with an arbitrary\u0000 reference potential plane. Part III applies the expanded equation of state to\u0000 familiar and well-trodden fluid-static and fluid-dynamic cases selected from\u0000 fluid mechanic textbooks.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45907697","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}
In subsonic aircraft design, the aerodynamic performance of aircraft is compared meaningfully at a system level by evaluating their range and endurance, but cannot do so at an aerodynamic level when using lift and drag coefficients, CL and CD , as these often result in misleading results for different wing reference areas. This Part I of the article (i) illustrates these shortcomings, (ii) introduces a dimensionless number quantifying the induced drag of aircraft, and (iii) proposes an aerodynamic equation of state for lift, drag, and induced drag and applies it to evaluate the aerodynamics of the canard aircraft, the dual rotors of the hovering Ingenuity Mars helicopter, and the composite lifting system (wing plus cylinders in Magnus effect) of a YOV-10 Bronco. Part II of this article applies this aerodynamic equation of state to the flapping flight of hovering and forward-flying insects. Part III applies the aerodynamic equation of state to some well-trodden cases in fluid mechanics found in fluid-mechanics textbooks.
{"title":"An Aerodynamic Equation of State—Part I: Introduction and Aerospace\u0000 Applications","authors":"Phillip Burgers","doi":"10.4271/01-17-01-0001","DOIUrl":"https://doi.org/10.4271/01-17-01-0001","url":null,"abstract":"In subsonic aircraft design, the aerodynamic performance of aircraft is compared\u0000 meaningfully at a system level by evaluating their range and\u0000 endurance, but cannot do so at an aerodynamic level when using\u0000 lift and drag coefficients, CL\u0000 and CD\u0000 , as these often result in misleading results for different wing\u0000 reference areas. This Part I of the article (i) illustrates these shortcomings,\u0000 (ii) introduces a dimensionless number quantifying the induced drag of aircraft,\u0000 and (iii) proposes an aerodynamic equation of state for lift,\u0000 drag, and induced drag and applies it to evaluate the aerodynamics of the canard\u0000 aircraft, the dual rotors of the hovering Ingenuity Mars\u0000 helicopter, and the composite lifting system (wing plus cylinders in Magnus\u0000 effect) of a YOV-10 Bronco. Part II of this article applies\u0000 this aerodynamic equation of state to the flapping flight of hovering and\u0000 forward-flying insects. Part III applies the aerodynamic equation of state to\u0000 some well-trodden cases in fluid mechanics found in fluid-mechanics\u0000 textbooks.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45093962","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}
Part I introduced the aerodynamic equation of state. This Part II introduces the aerodynamic equation of state for lift and induced drag of flapping wings and applies it to a hovering and forward-flying bumblebee and a mosquito. Two- and three-dimensional graphical representations of the state space are introduced and explored for engineered subsonic flyers, biological fliers, and sports balls.
{"title":"An Aerodynamic Equation of State—Part II: Applications to Flapping\u0000 Flight","authors":"Phillip Burgers","doi":"10.4271/01-17-01-0002","DOIUrl":"https://doi.org/10.4271/01-17-01-0002","url":null,"abstract":"Part I introduced the aerodynamic equation of state. This Part II introduces the\u0000 aerodynamic equation of state for lift and induced drag of flapping wings and\u0000 applies it to a hovering and forward-flying bumblebee and a mosquito. Two- and\u0000 three-dimensional graphical representations of the state space are introduced\u0000 and explored for engineered subsonic flyers, biological fliers, and sports\u0000 balls.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42641712","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}
An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly. The trained Support Vector Regression (SVR) predicted the FDM for the entire flight envelope. Although the SVR-FDM showed excellent performance, it remained vulnerable to adversarial attacks. Hence, we modified it using an adversarial retraining defense algorithm by transforming it into a Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was evaluated based on the Root Locus diagram. The closeness of predicted eigenvalues to the original eigenvalues confirmed the high accuracy of the UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight conditions, for different numbers of neighbors, and a variety of kernel functions were also considered. In addition, the regression performance was analyzed based on the step response of state variables in the closed-loop control architecture. The SVR-FDM provided the shortest rise time and settling time, but it failed when adversarial attacks were imposed on the SVR. The Robust-SVR-FDM step response properties showed that it could provide more accurate results than the LLS-FDM approach while protecting the controller from adversarial attacks.
{"title":"A Novel Flight Dynamics Modeling Using Robust Support Vector\u0000 Regression against Adversarial Attacks","authors":"S. Hashemi, R. Botez","doi":"10.4271/01-16-03-0019","DOIUrl":"https://doi.org/10.4271/01-16-03-0019","url":null,"abstract":"An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to\u0000 design its efficient controller in early development phases and to increase\u0000 safety while reducing costs. Flight tests are normally conducted for a\u0000 pre-established number of flight conditions, and then mathematical methods are\u0000 used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl,\u0000 216 local FDMs corresponding to different flight conditions were utilized to\u0000 create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial\u0000 flight envelope data containing 216 local FDMs was further augmented using\u0000 interpolation and extrapolation methodologies, thus increasing the number of\u0000 trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the\u0000 Support Vector Machine (SVM) methodology was used as a benchmarking regression\u0000 algorithm due to its excellent performance when training samples could not be\u0000 separated linearly. The trained Support Vector Regression (SVR) predicted the\u0000 FDM for the entire flight envelope. Although the SVR-FDM showed excellent\u0000 performance, it remained vulnerable to adversarial attacks. Hence, we modified\u0000 it using an adversarial retraining defense algorithm by transforming it into a\u0000 Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was\u0000 evaluated based on the Root Locus diagram. The closeness of predicted\u0000 eigenvalues to the original eigenvalues confirmed the high accuracy of the\u0000 UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight\u0000 conditions, for different numbers of neighbors, and a variety of kernel\u0000 functions were also considered. In addition, the regression performance was\u0000 analyzed based on the step response of state variables in the closed-loop\u0000 control architecture. The SVR-FDM provided the shortest rise time and settling\u0000 time, but it failed when adversarial attacks were imposed on the SVR. The\u0000 Robust-SVR-FDM step response properties showed that it could provide more\u0000 accurate results than the LLS-FDM approach while protecting the controller from\u0000 adversarial attacks.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46852182","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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