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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System

Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.

Mental Models↗
NASA NTRS1993

KSC management training system project

The stated objectives for the summer of 1993 were: to review the Individual Development Plan Surveys for 1994 in order to automate the analysis of the Needs Assessment effort; and to develop and implement evaluation methodologies to perform ongoing program-wide course-to-course assessment. This includes the following: to propose a methodology to develop and implement objective, performance-based assessment instruments for each training effort; to mechanize course evaluation forms and develop software to facilitate the data gathering, analysis, and reporting processes; and to implement the methodology, forms, and software in at lease one training course or seminar selected among those normally offered in the summer at KSC. Section two of this report addresses the work done in regard to the Individual Development Plan Surveys for 1994. Section three presents the methodology proposed to develop and implement objective, performance-based assessment instruments for each training course offered at KSC.

Sepulveda, Jose A.↗
NASA NTRS1977

Optimal design and evaluation criteria for acoustic emission pulse signature analysis

Successful pulse recording and evaluation is strongly dependent on the instrumentation system selected and the method of analyzing the pulse signature. The paper studies system design, signal analysis techniques, and interdependences with a view toward defining optimal approaches to pulse signal analysis. For this purpose, the instrumentation system is modeled, and analytical pulses, representative of the types of acoustic emissions to be distinguished are passed through the system. Particular attention is given to comparing frequency spectrum analysis and deconvolution referred to as time domain reconstruction of the pulse or pulse train. The possibility of optimal transducer-filter system parameters is investigated. Deconvolution of a pulse is shown to be a superior approach for transient pulse analysis. Reshaping of a transducer output back to the original input pulse is possible and gives an accurate representation of the generating pulse in the time domain. Any definable transducer and filter system can be used for measurement of pulses by means of the deconvolution method. Selection of design variables for general usage is discussed.

Houghton, J. R.↗
NASA NTRS1991

Observations and analysis of oil spills using polarized imagery

On Saturday, July 28, 1990, a train of barges collided with the Greek tanker Shinoussa in Galveston Bay off Red Fish Island near Texas City, Texas. The first barge sank and the second began to leak while the third barge in the chain and the Shinoussa both escaped without damage. The NASA Flight Science Support Office sponsored a graduate student from SUNY - College of Environmental Science and Forestry and a student from Texas, to survey the damage. The purpose of these surveys was to correlate aircraft base data with orbital data obtained during the Space Shuttle Polarization Experiment and existing laboratory data to evaluate the potential for an application such as oil spill monitoring and mapping. NASA has no charter with the local response agencies to support oil spill monitoring and cleanup.

Israel, S. A.↗
NASA NTRSDate not supplied

Shoulder Postures in EVA Training in Reduced Gravity Analogues

Shoulder Postures in EVA Training in Reduced Gravity Analogues K. Guhl1, L. Vu2, H. Kim3, S. Rajulu4 1KBR Inc., Houston, TX, 2Aegis Aerospace Inc., Houston, TX, 3Leidos Innovations, Houston, TX, 4NASA Johnson Space Center, Houston, TX. During extravehicular activities (EVAs) and EVA training in both the Neutral Buoyancy Laboratory (NBL) and at the Active Response Gravity Offload System (ARGOS), crewmembers perform a variety of hand-intensive tasks with frequent arm/shoulder repositioning while wearing a pressurized spacesuit. As a result, crewmembers may experience ergonomic stressors such as awkward shoulder postures. The ergonomic shoulder risk is also compounded by limited or restricted shoulder mobility of the spacesuit, extreme work positions such as overhead tasks, and tasks with heavy tools and repetitive motions. Prolonged or frequent shoulder elevation and overhead work, in particular, can lead to excessive stresses and musculoskeletal injuries of the shoulder joints. Future EVA missions, specifically lunar surface EVAs, will also be longer in duration and thus increase the exposure to awkward shoulder postures. In this study, we aimed to assess the ergonomic risk of awkward shoulder postures in simulated lunar surface EVAs by quantifying when and how long the arms are raised above the chest level. We assessed video recordings of pilot lunar EVA simulations (3 lunar trials each in the NBL and at ARGOS and 2 microgravity EVA trials in the NBL). These runs consisted of both training and engineering test objectives. Actual demands for shoulder use varied for different EVA types and analogues, but many EVA runs have common tasks and require similar motion components. A video observation and event logging software was used to document the duration and occurrence of the subject’s arms being raised throughout the video recordings of each EVA run. An arm raised instance was classified as the arm being at 90 degrees or above with relation to gravity for lunar EVA training events and with relation to the body for microgravity EVA training events. Such events included EVA hardware maintenance or heavy geology sampling tool operations. Events where the arm load was partially supported by external objects, like climbing a ladder or leaning against a surface were separately identified and excluded. Statistical analysis was performed to summarize the timing, frequency, and durations of the arm raise events and compared across the different EVA tasks and analogue types. Preliminary observations indicated that the total duration and number of arm raised instances were surprisingly smaller for lunar surface EVA training as compared to microgravity EVA training. The observed difference may be attributed to differing task demands and unique environmental characteristics found in lunar EVAs in comparison to microgravity EVAs. A detailed statistical analysis will be performed between lunar surface EVA training events and microgravity EVA training events in the final submission. Overall, this analysis is expected to provide insight into how ergonomic recommendations can be refined for lunar EVA training with pressurized suits. It may also inform task design and influence suit padding design to better protect crewmembers during future lunar training.

Kaitlyn Lea Guhl↗
NASA NTRS1989

Space station Simulation Computer System (SCS) study for NASA/MSFC. Volume 5: Study analysis report

The Simulation Computer System (SCS) is the computer hardware, software, and workstations that will support the Payload Training Complex (PTC) at the Marshall Space Flight Center (MSFC). The PTC will train the space station payload scientists, station scientists, and ground controllers to operate the wide variety of experiments that will be on-board the Freedom Space Station. The further analysis performed on the SCS study as part of task 2-Perform Studies and Parametric Analysis-of the SCS study contract is summarized. These analyses were performed to resolve open issues remaining after the completion of task 1, and the publishing of the SCS study issues report. The results of these studies provide inputs into SCS task 3-Develop and present SCS requirements, and SCS task 4-develop SCS conceptual designs. The purpose of these studies is to resolve the issues into usable requirements given the best available information at the time of the study. A list of all the SCS study issues is given.

Source record↗
NASA NTRS1971

Simulation of Mariner Mars 1971 spacecraft

Preparation for the Mariner Mars 1971 mission is reported, including an extensive training program for operations personnel during which the primary source of spacecraft data was a computer program simulating the spacecraft. The objectives of a simulation model for training purposes differed from objectives appropriate to a design or analysis model. Model subsystems were designed to provide realistic telemetry data reflecting changes due both to commands and environmental parameters affecting the spacecraft at various times during the mission. The spacecraft is modeled along two separate functional lines. Boolean operations are concentrated in the spacecraft logic model, which determines the spacecraft state or mode, while mathematical operations or algorithms are executed in computational subsystem models. Although logic parameters are interrogated as a part of each computational pass, actual logic model processing occurs only when a change-of-state input is generated by the operations organization. The program design, some of the special characteristics of each of the modeled subsystems, and how the model was used in support of mission operations training are presented.

Ausman, N. E., Jr.↗
NASA NTRS2016

A Strategic Approach to Medical Care for Exploration Missions

Exploration missions will present significant new challenges to crew health, including effects of variable gravity environments, limited communication with Earth-based personnel for diagnosis and consultation for medical events, limited resupply, and limited ability for crew return. Providing health care capabilities for exploration class missions will require system trades be performed to identify a minimum set of requirements and crosscutting capabilities, which can be used in design of exploration medical systems. Medical data, information, and knowledge collected during current space missions must be catalogued and put in formats that facilitate querying and analysis. These data are used to inform the medical research and development program through analysis of risk trade studies between medical care capabilities and system constraints such as mass, power, volume, and training. Medical capability as a quantifiable variable is proposed as a surrogate risk metric and explored for trade space analysis that can improve communication between the medical and engineering approaches to mission design. The resulting medical system design approach selected will inform NASA mission architecture, vehicle, and subsystem design for the next generation of spacecraft.

Canga, Michael A.↗
NASA NTRS2008

Entry, Descent, and Landing Operations Analysis for the Stardust Entry Capsule

On the morning of January 15, 2006, the Stardust capsule successfully landed at the Utah Test and Training range in northwest Utah returning cometary samples from the comet Wild-2. An overview of the entry, descent, and landing (EDL) trajectory analysis that was performed for targeting during the mission operations phase upon final approach to Earth is described. The final orbit determination solution produced an inertial entry flight-path angle of -8.21 deg (the desired nominal value) with a 3-sigma uncertainty of +/-0.0017 deg (2% of the requirement). The navigation and EDL operations effort accurately delivered the entry capsule to the desired landing site. The final landing location was 8.1 km from the target, which was well within the allowable landing area. Overall, the Earth approach operation procedures worked well and there were no issues (logistically or performance based) that arose. As a result, the process of targeting a capsule from an interplanetary trajectory and accurately landing it on Earth was successfully demonstrated.

Desai, Prasun N.↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS2003

Spiral-Bevel-Gear Damage Detected Using Decision Fusion Analysis

Helicopter transmission integrity is critical to helicopter safety because helicopters depend on the power train for propulsion, lift, and flight maneuvering. To detect impending transmission failures, the ideal diagnostic tools used in the health-monitoring system would provide real-time health monitoring of the transmission, demonstrate a high level of reliable detection to minimize false alarms, and provide end users with clear information on the health of the system without requiring them to interpret large amounts of sensor data. A diagnostic tool for detecting damage to spiral bevel gears was developed. (Spiral bevel gears are used in helicopter transmissions to transfer power between nonparallel intersecting shafts.) Data fusion was used to integrate two different monitoring technologies, oil debris analysis and vibration, into a health-monitoring system for detecting surface fatigue pitting damage on the gears.

Dempsey, Paula J.↗
NASA NTRS1971

Pyrotechnic hazards classification and evaluation program. Electrostatic vulnerability of the E8 and XM15/XM165 clusters, phase 2

An investigation into the electrostatic phenomena associated with the manufacturing and handling of explosives is discussed. The testing includes measurement of the severity of the primary charge generation mechanism, triboelectric effects between dissimilar surfaces; refinement of equivalent circuits of the XM15/XM165 and E8 fuse trains; evaluation of the electrostatic spark discharge characteristics predicted by an equivalent circuit analysis; and determination of the spark ignition sensitivity of materials, components, junctions, and subassemblies which compose the XM15/XM165 and E8 units. Special studies were also performed. These special tests included ignition sensitivity of the complete XM15 fuse train when subjected to discharges through its entire length, measurement of electrostatic potentials which occur during the E8 foaming operation during fabrication, and investigation of the inadvertent functioning of an XM15 cluster during manufacturing. The test results are discussed and related to the effectiveness of suggested modification to reduce the electrostatic ignition sensitivity.

Source record↗
NASA NTRS1999

Ninth Thermal and Fluids Analysis Workshop Proceedings

The Ninth Thermal and Fluids Analysis Workshop (TFAWS 98) was held at the Ohio Aerospace Institute in Cleveland, Ohio from August 31 to September 4, 1998. The theme for the hands-on training workshop and conference was "Integrating Computational Fluid Dynamics and Heat Transfer into the Design Process." Highlights of the workshop (in addition to the papers published herein) included an address by the NASA Chief Engineer, Dr. Daniel Mulville; a CFD short course by Dr. John D. Anderson of the University of Maryland; and a short course by Dr. Robert Cochran of Sandia National Laboratories. In addition, lectures and hands-on training were offered in the use of several cutting-edge engineering design and analysis-oriented CFD and Heat Transfer tools. The workshop resulted in international participation of over 125 persons representing aerospace and automotive industries, academia, software providers, government agencies, and private corporations. The papers published herein address issues and solutions related to the integration of computational fluid dynamics and heat transfer into the engineering design process. Although the primary focus is aerospace, the topics and ideas presented are applicable to many other areas where these and other disciplines are interdependent.

Sakowski, Barbara↗
NASA NTRS2022

“Just Do It” Mission Operations Training in a COVID World

“No”- “not”- “can’t do it” – these words don’t fly in M2020 Mission Operations. The M2020 Surface Mission Operations Team trained for landing the Perseverance Rover under COVID-19 remote work/mandatory stay-at-home conditions. Training activities included presenting Flight Schools to the team via video conferencing, training COVID personal safety requirements to on-premises staff, and constantly updating and communicating COVID restrictions to the team as safety requirements changed. This paper explores the impact of COVID on the Mission System Training for M2020 Mission Operations. The layers of COVID, aptly named the “COVID Tax” by team management, affected project roles, communications, personnel interactions, operations facility usage and training exercises practiced by the team. Flight Schools and Operational Readiness Tests (ORTs) are driving forces behind the surface mission operations training. These activities work hand-in-hand to prepare the team for landing day, surface operations that transition from cruise to nominal operations, and nominal operations. Under normal training conditions, Flight Schools and ORTs are only concerned with tactical operations for the Uplink (Command) Downlink (Analysis) and Campaign Implementation (Planning) Teams of scientists and system engineers. Due to COVID, and the necessity to maintain physical distance between people, training for the landing team needed to include the new category of COVID personal safety. COVID also necessitated remote teams and video conferencing of the entirety of Flight Schools. This reliance on distance learning had not been done on previous missions. We will explore the advantages and disadvantages of video conferencing as a training platform; training effectiveness in communicating and practicing the multiple changes to the COVID safety protocols; and the timing in which we received and responded to COVID directives. Additionally, this paper will review the practical measures taken by the on-premises team and how the team adapted during the readiness tests, landing, and early mission operations, as well as how the team responded to the post-vaccine ramping down of COVID Protocols. The M2020 Surface Mission Operations Team responded very well to the challenges of COVID. All pre-landing operational and COVID-related training was completed. Post-landing COVID Training was provided as needed to new on-premises personnel. Training presented on COVID for the first operational readiness test (ORT) consisted of 2.5 hours of training. Each ORT had COVID Training. A person who had participated in all of the ORTs from September 2020 – February 2021 would have received over 7 hours of COVID Training. By ORT-12 (approximately 8 weeks after the first ORT), the Training team consolidated the original COVID training to a one-hour COVID Basic Training course, with additional recommended training. The Basic training course was updated after landing, as vaccines became available. COVID Training had multiple Flight Schools in the self-directed Blackboard Learning system, as well as a Quick Reference Wiki for Onboarding of new on-premises personnel and keeping on-premises personnel up-to-date. COVID Training covered many topics including practical methods of 6-foot distancing, how to interact with an IT professional when help was needed at a workstation, and challenging indoor meal-eating protocols. COVID Training continued to be updated, as the lab and the project respond to the virus variants and federal, state and local ordinances.

Rosette, Theresa↗
NASA NTRS1993

Forecasting the impact of virtual environment technology on maintenance training

To assist NASA and the Air Force in determining how and when to invest in virtual environment (VE) technology for maintenance training, we identified possible roles for VE technology in such training, assessed its cost-effectiveness relative to existing technologies, and formulated recommendations for a research agenda that would address instructional and system development issues involved in fielding a VE training system. In the first phase of the study, we surveyed VE developers to forecast capabilities, maturity, and estimated costs for VE component technologies. We then identified maintenance tasks and their training costs through interviews with maintenance technicians, instructors, and training developers. Ten candidate tasks were selected from two classes of maintenance tasks (seven aircraft maintenance and three space maintenance) using five criteria developed to identify types of tasks most likely to benefit from VE training. Three tasks were used as specific cases for cost-benefit analysis. In formulating research recommendations, we considered three aspects of feasibility: technological considerations, cost-effectiveness, and anticipated R&D efforts. In this paper, we describe the major findings in each of these areas and suggest research efforts that we believe will help achieve the goal of a cost-effective VE maintenance training system by the next decade.

Schlager, Mark S.↗
NASA NTRS1993

A preliminary empirical evaluation of virtual reality as an instructional medium for visual-spatial tasks

We explored the training potential of Virtual Reality (VR) technology. Thirty-one adults were trained and tested on spatial skills in a VR. They learned a sequence of button and knob responses on a VR console and performed flawlessly on the same console. Half were trained with a rote strategy; the rest used a meaningful strategy. Response times were equivalent for both groups and decreased significantly over five test trials indicating that learning continued on VR tests. The same subjects practiced navigating through a VR building, which had three floors with four rooms on each floor. The dependent measure was the number of rooms traversed on routes that differed from training routes. Many subjects completed tests in the fewest rooms possible. All subjects learned configurational knowledge according to the criterion of taking paths that were significantly shorter than those predicted by a random walk as determined by a Monte Carlo analysis. The results were discussed as a departure point for empirically testing the training potential of VR technology.

Regian, J. Wesley↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗