Turbulence modeling in human-in-the-loop simulation is important to assessing aircraft handling qualities and pilot performance and to provide additional realism for pilot training. In the simulation community, the Dryden turbulence spectra is a popular choice for modeling the linear turbulent gusts because its rational form is efficiently reproduced by passing white noise through linear filters. The MIL-F-8785 gust gradients similarly use additional linear filters to model the gradient of the turbulent gust over the wing, and it represents the gust gradients as perturbations to the air-relative rotational rates. The Cockpit Motion Facility at NASA Langley Research Center (LaRC) models continuous random turbulence using the Dryden one-dimensional spectra and MIL-F-8785 gust gradient. The facility recently reviewed and updated its verification of these models as part of an initiative to improve motion cueing under turbulence. This exercise introduced improved methods for verifying the turbulence models and led to rediscovery of model assumptions that informed improvements to implementation.
{"title":"Verifying Implementation of the Dryden Turbulence Model and MIL-F-8785 Gust Gradient","authors":"Michael M. Madden","doi":"10.2514/6.2018-3580","DOIUrl":"https://doi.org/10.2514/6.2018-3580","url":null,"abstract":"Turbulence modeling in human-in-the-loop simulation is important to assessing aircraft handling qualities and pilot performance and to provide additional realism for pilot training. In the simulation community, the Dryden turbulence spectra is a popular choice for modeling the linear turbulent gusts because its rational form is efficiently reproduced by passing white noise through linear filters. The MIL-F-8785 gust gradients similarly use additional linear filters to model the gradient of the turbulent gust over the wing, and it represents the gust gradients as perturbations to the air-relative rotational rates. The Cockpit Motion Facility at NASA Langley Research Center (LaRC) models continuous random turbulence using the Dryden one-dimensional spectra and MIL-F-8785 gust gradient. The facility recently reviewed and updated its verification of these models as part of an initiative to improve motion cueing under turbulence. This exercise introduced improved methods for verifying the turbulence models and led to rediscovery of model assumptions that informed improvements to implementation.","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126500117","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 paper adds data to establish fidelity criteria for the simulator motion system diagnostic test now required during commercial aircraft simulator approval in the United States. Nineteen airline transport pilots flew three tasks under six different motion conditions in an experiment on the NASA Vertical Motion Simulator. The motion conditions allowed refinement of the initial fidelity criteria developed in previous experiments. In line with these previous experiments, the motion condition significantly affected (1) false motion cue pilot ratings, and sink rate and longitudinal deviation at touchdown in the approach and landing task, (2) false motion cue pilot ratings, roll deviations, and maximum pitch rate in the stall task, and (3) false motion cue pilot ratings, heading deviation, and pedal reaction time after an engine failure in the take-off task. Combining data from three experiments, significant differences in pilot-vehicle performance were used to define objective motion cueing criteria boundaries. These fidelity boundaries suggest that some hexapod simulators can possibly produce motion cues with improved fidelity in several degrees of freedom.
{"title":"Objective Motion Cueing Criteria for Commercial Transport Simulators","authors":"P. Zaal, Jeffery A. Schroeder, W. Chung","doi":"10.2514/6.2018-2935","DOIUrl":"https://doi.org/10.2514/6.2018-2935","url":null,"abstract":"This paper adds data to establish fidelity criteria for the simulator motion system diagnostic test now required during commercial aircraft simulator approval in the United States. Nineteen airline transport pilots flew three tasks under six different motion conditions in an experiment on the NASA Vertical Motion Simulator. The motion conditions allowed refinement of the initial fidelity criteria developed in previous experiments. In line with these previous experiments, the motion condition significantly affected (1) false motion cue pilot ratings, and sink rate and longitudinal deviation at touchdown in the approach and landing task, (2) false motion cue pilot ratings, roll deviations, and maximum pitch rate in the stall task, and (3) false motion cue pilot ratings, heading deviation, and pedal reaction time after an engine failure in the take-off task. Combining data from three experiments, significant differences in pilot-vehicle performance were used to define objective motion cueing criteria boundaries. These fidelity boundaries suggest that some hexapod simulators can possibly produce motion cues with improved fidelity in several degrees of freedom.","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123148106","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}
Parallelizing software to execute on multi-core central processing units (CPUs) and graphics processing units (GPUs) can be challenging. For some fields outside of Computer Science, this transition comes with new issues. For example, memory limitations can require modifications to code not initially developed to run on GPUs. This work applies the Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) directive-based parallelization strategies on a Monte Carlo simulation approach for trajectory reconstruction enabling it to run on multi-core CPUs and GPUs. Large matrix operations are the most common use of GPUs, which are not present in this algorithm; however, the natural parallelism of independent trajectories in Monte Carlo simulations is exploited. Benchmarking data are presented comparing execution times of the software for single-thread CPUs, multi-thread CPUs with OpenMP, and multi-thread GPUs using OpenACC. These data were collected using nodes with Intel ® Xeon ® E5-2670 (Sandy Bridge) CPUs enhanced with NVIDIA ® Tesla ® K40 GPUs on the Pleiades Supercomputer cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) and a local Intel ® Xeon Phi ™ node at NASA Langley Research Center (LaRC). and orientation), and integrates the inertial measurement unit (IMU) data to determine the vehicle states throughout its flight. Lugo et al. 1 developed a Monte Carlo based approach for trajectory reconstruction that incorporated the vehicle’s final state information and introduces statistics. This method decreases uncertainties in the reconstruction results, which improves model validations and post-flight analysis. However, this Monte Carlo approach requires the integration of several thousand trajectories. These calculations are time consuming when executed serially, but the execution time can be decreased by utilizing concurrent computation. This paper examines the use of parallel programming techniques on an algorithm that applies inertial navigation to trajectory reconstruction in a Monte Carlo dispersion process. The two parallel programming techniques being utilized are OpenMP and OpenACC, which are used on multi-core CPUs and GPUs, respectively. Two studies are conducted to determine optimal performance based on thread count with OpenMP and register per thread for OpenACC. Additionally, comparisons are shown between three different compilers and three different types of hardware. or V100, will tested in future work.
{"title":"An Investigation of Parallel Programming Techniques Applied to Monte Carlo Simulations for Post-Flight Reconstruction of Spacecraft Trajectory","authors":"Robert A. Williams, Justin S. Green","doi":"10.2514/6.2018-3431","DOIUrl":"https://doi.org/10.2514/6.2018-3431","url":null,"abstract":"Parallelizing software to execute on multi-core central processing units (CPUs) and graphics processing units (GPUs) can be challenging. For some fields outside of Computer Science, this transition comes with new issues. For example, memory limitations can require modifications to code not initially developed to run on GPUs. This work applies the Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) directive-based parallelization strategies on a Monte Carlo simulation approach for trajectory reconstruction enabling it to run on multi-core CPUs and GPUs. Large matrix operations are the most common use of GPUs, which are not present in this algorithm; however, the natural parallelism of independent trajectories in Monte Carlo simulations is exploited. Benchmarking data are presented comparing execution times of the software for single-thread CPUs, multi-thread CPUs with OpenMP, and multi-thread GPUs using OpenACC. These data were collected using nodes with Intel ® Xeon ® E5-2670 (Sandy Bridge) CPUs enhanced with NVIDIA ® Tesla ® K40 GPUs on the Pleiades Supercomputer cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) and a local Intel ® Xeon Phi ™ node at NASA Langley Research Center (LaRC). and orientation), and integrates the inertial measurement unit (IMU) data to determine the vehicle states throughout its flight. Lugo et al. 1 developed a Monte Carlo based approach for trajectory reconstruction that incorporated the vehicle’s final state information and introduces statistics. This method decreases uncertainties in the reconstruction results, which improves model validations and post-flight analysis. However, this Monte Carlo approach requires the integration of several thousand trajectories. These calculations are time consuming when executed serially, but the execution time can be decreased by utilizing concurrent computation. This paper examines the use of parallel programming techniques on an algorithm that applies inertial navigation to trajectory reconstruction in a Monte Carlo dispersion process. The two parallel programming techniques being utilized are OpenMP and OpenACC, which are used on multi-core CPUs and GPUs, respectively. Two studies are conducted to determine optimal performance based on thread count with OpenMP and register per thread for OpenACC. Additionally, comparisons are shown between three different compilers and three different types of hardware. or V100, will tested in future work.","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487228","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}
Alicia Sudol, Seth E. Gordon, Hallie Ford, E. Inclan, D. Mavris, Michael Z. Miller
{"title":"Simulation of Radar Signal Propagation via Multipath","authors":"Alicia Sudol, Seth E. Gordon, Hallie Ford, E. Inclan, D. Mavris, Michael Z. Miller","doi":"10.2514/6.2018-4060","DOIUrl":"https://doi.org/10.2514/6.2018-4060","url":null,"abstract":"","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439261","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}
{"title":"Evaluation and Comparison of Sailing Flight Optimization Algorithms for a Stratospheric Dual Aircraft Platform Concept","authors":"Nolan Coulter, H. Moncayo, W. Engblom","doi":"10.2514/6.2018-3887","DOIUrl":"https://doi.org/10.2514/6.2018-3887","url":null,"abstract":"","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133722526","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}
Zero-pressure balloon ascent prediction is a critical issue for Swedish Space Corporation(SSC) as targeted test ights are important part of SSC activities. This paper introducesa data-driven approa ...
{"title":"Fuzzy Modelling of Zero-pressure Balloon Ascent","authors":"Kanika Garg, R. Emami","doi":"10.2514/6.2018-3753","DOIUrl":"https://doi.org/10.2514/6.2018-3753","url":null,"abstract":"Zero-pressure balloon ascent prediction is a critical issue for Swedish Space Corporation(SSC) as targeted test ights are important part of SSC activities. This paper introducesa data-driven approa ...","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122473043","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}
Y. Beyer, Alexander Kuzolap, M. Steen, J. H. Diekmann, N. Fezans
In this paper an adaptive control system for a passenger aircraft with active high-lift system is presented. Failures in the high-lift system parts of such aircraft are critical and consequently need to be handled automatically. An adaptive controller is proposed which consists of incremental nonlinear dynamic inversion (INDI) with a reference model and linear controller. As the INDI is adaptive against uncertainties or system failures, no additional adaptive element like a neural network is needed. The implementation of the INDI requires a nonlinear system model which is permanently linearized during the runtime in order to obtain the current input matrix which here basically consists of the control surfaces effectiveness. It also requires feedback of the translational and rotational acceleration measurements which usually suffer from noise. In order to test the adaptivity of the INDI, a partial failure of the high-lift system during the landing approach is regarded. It shows that the INDI is capable of compensating the error by only using the conventional control surfaces.
{"title":"Adaptive Nonlinear Flight Control of STOL-Aircraft Based on Incremental Nonlinear Dynamic Inversion","authors":"Y. Beyer, Alexander Kuzolap, M. Steen, J. H. Diekmann, N. Fezans","doi":"10.2514/6.2018-3257","DOIUrl":"https://doi.org/10.2514/6.2018-3257","url":null,"abstract":"In this paper an adaptive control system for a passenger aircraft with active high-lift system is presented. Failures in the high-lift system parts of such aircraft are critical and consequently need to be handled automatically. An adaptive controller is proposed which consists of incremental nonlinear dynamic inversion (INDI) with a reference model and linear controller. As the INDI is adaptive against uncertainties or system failures, no additional adaptive element like a neural network is needed. The implementation of the INDI requires a nonlinear system model which is permanently linearized during the runtime in order to obtain the current input matrix which here basically consists of the control surfaces effectiveness. It also requires feedback of the translational and rotational acceleration measurements which usually suffer from noise. In order to test the adaptivity of the INDI, a partial failure of the high-lift system during the landing approach is regarded. It shows that the INDI is capable of compensating the error by only using the conventional control surfaces.","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122943838","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}
Automatic Dependent Surveillance – Broadcast (ADS-B) is being employed in numerous peer-to-peer initiatives attempting to expand the capacity of the National Airspace System (NAS) or enable mixed operations of manned and unmanned vehicles. Safety assessments of these initiatives rely, in part, on modeling the accuracy of ADS-B in reporting the position and direction of an ownship and surrounding traffic. Frequently, these initiatives utilize a position uncertainty model that applies a reported ADS-B estimation position uncertainty (EPU) value to a Rayleigh distribution and uses a Gauss-Markov random walk to add error to the ADS-B output of a vehicle. This model of ADS-B state error is easy to implement and apply to numerous problems. However, it has a couple of drawbacks. First, the ADS-B state errors are equally probable in all directions. This is a good assumption in situations where aircraft maneuvering is not constrained. However, in situations where the aircraft maneuvering is constrained such as landing, the error distribution is likely to exhibit directionality and the non-directional model may skew results especially when assessing very low probabilities (e.g., 10-9) of catastrophic encounters. Second, the model does not account for processing latency in the receiving aircraft. NASA Langley Research Center (LaRC) recently examined the feasibility of decreasing the spacing of aircraft on parallel approaches to runways separated by as little as 700 feet [Perry2013]. For Monte-Carlo analysis using a high-fidelity simulation of a large transport, LaRC started with a Gauss-Markov model of ADS-B error but then developed a component level model of ADS-B error to increase the fidelity of results. This LaRC assessment of parallel approaches had a forward looking time frame of five to ten years. Therefore, the component level model assumes that the ADS-B system is fed directly from and synchronized with an autonomous Global Positioning System (GPS) receiver. This model covers the end-to-end reporting and consumption of ADS-B state from the GPS receiver on the transmitting aircraft to processing of the ADS-B report on the receiving aircraft. The model essentially divides the ADS-B path into four systems: the GPS receiver, the ADS-B OUT system, the ADS-B IN system, and the target application. A common error source in each system is latency and each system may vary in the duration of its processing latency and how much of that latency the system attempts to https://ntrs.nasa.gov/search.jsp?R=20190000876 2020-05-07T22:03:10+00:00Z
{"title":"A Component-Level Model of Automatic Dependent Surveillance - Broadcast (ADS-B)","authors":"Michael M. Madden","doi":"10.2514/6.2018-4061","DOIUrl":"https://doi.org/10.2514/6.2018-4061","url":null,"abstract":"Automatic Dependent Surveillance – Broadcast (ADS-B) is being employed in numerous peer-to-peer initiatives attempting to expand the capacity of the National Airspace System (NAS) or enable mixed operations of manned and unmanned vehicles. Safety assessments of these initiatives rely, in part, on modeling the accuracy of ADS-B in reporting the position and direction of an ownship and surrounding traffic. Frequently, these initiatives utilize a position uncertainty model that applies a reported ADS-B estimation position uncertainty (EPU) value to a Rayleigh distribution and uses a Gauss-Markov random walk to add error to the ADS-B output of a vehicle. This model of ADS-B state error is easy to implement and apply to numerous problems. However, it has a couple of drawbacks. First, the ADS-B state errors are equally probable in all directions. This is a good assumption in situations where aircraft maneuvering is not constrained. However, in situations where the aircraft maneuvering is constrained such as landing, the error distribution is likely to exhibit directionality and the non-directional model may skew results especially when assessing very low probabilities (e.g., 10-9) of catastrophic encounters. Second, the model does not account for processing latency in the receiving aircraft. NASA Langley Research Center (LaRC) recently examined the feasibility of decreasing the spacing of aircraft on parallel approaches to runways separated by as little as 700 feet [Perry2013]. For Monte-Carlo analysis using a high-fidelity simulation of a large transport, LaRC started with a Gauss-Markov model of ADS-B error but then developed a component level model of ADS-B error to increase the fidelity of results. This LaRC assessment of parallel approaches had a forward looking time frame of five to ten years. Therefore, the component level model assumes that the ADS-B system is fed directly from and synchronized with an autonomous Global Positioning System (GPS) receiver. This model covers the end-to-end reporting and consumption of ADS-B state from the GPS receiver on the transmitting aircraft to processing of the ADS-B report on the receiving aircraft. The model essentially divides the ADS-B path into four systems: the GPS receiver, the ADS-B OUT system, the ADS-B IN system, and the target application. A common error source in each system is latency and each system may vary in the duration of its processing latency and how much of that latency the system attempts to https://ntrs.nasa.gov/search.jsp?R=20190000876 2020-05-07T22:03:10+00:00Z","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015354","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}
Vincent E. Houston, B. Barrows, Walter J. Manuel, L. L. Vie
{"title":"Machine Learning Algorithms To Improve Model Accuracy and Latency, and Human-Autonomy Teaming","authors":"Vincent E. Houston, B. Barrows, Walter J. Manuel, L. L. Vie","doi":"10.2514/6.2018-4063","DOIUrl":"https://doi.org/10.2514/6.2018-4063","url":null,"abstract":"","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129705380","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}
{"title":"A Mission Optimization Tool for Air to Ground Tactical Operations","authors":"M. Akgul, G. Aydin","doi":"10.2514/6.2018-3752","DOIUrl":"https://doi.org/10.2514/6.2018-3752","url":null,"abstract":"","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967687","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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