{"project":{"acronym":"","projectId":94151,"title":"Intelligent Data Understanding for Architecture Analysis of Entry, Descent, and Landing","primaryTaxonomyNodes":[{"taxonomyNodeId":10775,"taxonomyRootId":8816,"parentNodeId":10770,"level":3,"code":"TX09.4.5","title":"Modeling and Simulation for EDL","definition":"Modeling and simulation for EDL refers to the computer codes, underlying physical models, and processes that enable configuration definition and design verification and validation for systems that—short of a full scale flight test—cannot be tested exactly in the configuration and environment for which it is intended to operate. The models cover both the environmental response to the presence of the system in operation, and the operational performance of the system in the environment. A key concern is understanding and modeling of interactions between rocket plumes and the ground.","exampleTechnologies":"Multi-disciplinary coupled analysis tools, aerothermodynamics modeling, ablative material response models, non-ablative material response models, TPS quantification models and processes, numerical methodologies and techniques, autonomous aerobraking, orbital debris entry and breakup modeling, meteor entry and breakup modeling, Fluid Structure Interaction (FSI) tools, SRP modeling tools, aerodynamic modeling tools, plume-surface interaction, multi-scale simulation tools","hasChildren":false,"hasInteriorContent":true}],"startTrl":2,"currentTrl":3,"endTrl":3,"benefits":"
The proposed research will develop the next generation of Intelligent Data Understanding (IDU) technologies and will develop capabilities for high-fidelity architecture analysis to evaluate EDL choices. This technology will address NASA's challenges for developing effective computational mechanisms to identify high value data, analyze, and communicate critical issues regarding the mission. Furthermore, these technologies could also be adapted to other aspects of a mission such as mixed-initiative landing of human spacecraft, mixed-initiative exploration of planetary bodies, and multi-spacecraft collaborative on-board event detection.
","description":"Because Entry, Descent and Landing (EDL) system validations are limited in Earth environments, these technologies rely heavily on models and analysis tools to evaluate system performance and capture uncertainties, which determine the success of a mission. The proposed research seeks to develop technologies that will provide top-level analysis capabilities for Entry, Descent, and Landing Architecture Analysis. The goal of this research is to advance the state of the art for offline Intelligent Data Understanding (IDU) technologies by incorporating an intelligent assistant that helps identify and analyze a complex data set and mine for interesting features and insight. These goals will be achieved by using adaptive operator selection algorithms to solve hard computational problems. This goal will also be met by exploring the explanation abilities of intelligent agents through visual and verbal interactions and provide critique. Secondly, this research will make use of machine learning techniques to incorporate knowledge into objective functions. These algorithms will be validated on a set of missions such as human landings on Mars and Europa. For each case study, extensive simulations will be run and sensitivity analysis and data mining will be performed to identify sensible factors that affect dependent variables during EDL. Nevertheless, the proposed research will develop the next generation of IDU technologies and will develop capabilities for high-fidelity architecture analysis to evaluate EDL choices. This technology will address NASA's challenges for developing effective computational mechanisms to identify high value data, analyze, and communicate critical issues regarding the mission. Furthermore, these technologies could also be adapted to other aspects of a mission such as mixed-initiative landing of human spacecraft, mixed-initiative exploration of planetary bodies, and multi-spacecraft collaborative on-board event detection.
","startYear":2017,"startMonth":8,"endYear":2021,"endMonth":7,"statusDescription":"Completed","principalInvestigators":[{"contactId":99066,"canUserEdit":false,"firstName":"Daniel","lastName":"Selva","fullName":"Daniel Selva","fullNameInverted":"Selva, Daniel","primaryEmail":"ds925@cornell.edu","publicEmail":false,"nacontact":false}],"programDirectors":[{"contactId":84634,"canUserEdit":false,"firstName":"Claudia","lastName":"Meyer","fullName":"Claudia M Meyer","fullNameInverted":"Meyer, Claudia M","middleInitial":"M","primaryEmail":"claudia.m.meyer@nasa.gov","publicEmail":true,"nacontact":false}],"programExecutives":[{"contactId":84634,"canUserEdit":false,"firstName":"Claudia","lastName":"Meyer","fullName":"Claudia M Meyer","fullNameInverted":"Meyer, Claudia M","middleInitial":"M","primaryEmail":"claudia.m.meyer@nasa.gov","publicEmail":true,"nacontact":false}],"programManagers":[{"contactId":183514,"canUserEdit":false,"firstName":"Hung","lastName":"Nguyen","fullName":"Hung D Nguyen","fullNameInverted":"Nguyen, Hung D","middleInitial":"D","primaryEmail":"hung.d.nguyen@nasa.gov","publicEmail":true,"nacontact":false}],"projectManagers":[{"contactId":113036,"canUserEdit":false,"firstName":"David","lastName":"Way","fullName":"David W Way","fullNameInverted":"Way, David W","middleInitial":"W","primaryEmail":"david.way@jpl.nasa.gov","publicEmail":true,"nacontact":false}],"coInvestigators":[{"contactId":420170,"canUserEdit":false,"firstName":"Samalis","lastName":"Santini De Leon","fullName":"Samalis Santini De Leon","fullNameInverted":"Santini De Leon, Samalis","primaryEmail":"ssantini@tamu.edu","publicEmail":false,"nacontact":false}],"website":"https://www.nasa.gov/strg#.VQb6T0jJzyE","libraryItems":[],"transitions":[{"transitionId":76009,"projectId":94151,"transitionDate":"2021-07-01","path":"Closed Out","details":"Overall, this research presented new approaches and tools to improve current knowledge discovery process in the EDL domain. The goal was to provide interactive analysis capabilities whilst automating common tasks conducted during the analysis of EDL simulations. The end goal was to extract high-value information and help reduce the cognitive load imposed by the data analysis process. This work also proposed a new framework address the issue of comprehensibility of association rule mining data products.
","infoText":"Closed out","infoTextExtra":"","dateText":"July 2021"}],"responsibleMd":{"acronym":"STMD","canUserEdit":false,"city":"","external":false,"linkCount":0,"organizationId":4875,"organizationName":"Space Technology Mission Directorate","organizationType":"NASA_Mission_Directorate","naorganization":false,"organizationTypePretty":"NASA Mission Directorate"},"program":{"acronym":"STRG","active":true,"description":"\tThe Space Technology Research Grants Program will accelerate the development of "push" technologies to support the future space science and exploration needs of NASA, other government agencies and the commercial space sector. Innovative efforts with high risk and high payoff will be encouraged. The program is composed of two competitively awarded components.
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