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NASA NTRS2011

Ground Processing Affordability for Space Vehicles

Launch vehicles and most of their payloads spend the majority of their time on the ground. The cost of ground operations is very high. So, why so often is so little attention given to ground processing during development? The current global space industry and economic environment are driving more need for efficiencies to save time and money. Affordability and sustainability are more important now than ever. We can not continue to treat space vehicles as mere science projects. More RLV's (Reusable Launch Vehicles) are being developed for the gains of reusability which are not available for ELV's (Expendable Launch Vehicles). More human-rated vehicles are being developed, with the retirement of the Space Shuttles, and for a new global space race, yet these cost more than the many unmanned vehicles of today. We can learn many lessons on affordability from RLV's. DFO (Design for Operations) considers ground operations during design, development, and manufacturing-before the first flight. This is often minimized for space vehicles, but is very important. Vehicles are designed for launch and mission operations. You will not be able to do it again if it is too slow or costly to get there. Many times, technology changes faster than space products such that what is launched includes outdated features, thus reducing competitiveness. Ground operations must be considered for the full product Lifecycle, from concept to retirement. Once manufactured, launch vehicles along with their payloads and launch systems require a long path of processing before launch. Initial assembly and testing always discover problems to address. A solid integration program is essential to minimize these impacts, as was seen in the Constellation Ares I-X test rocket. For RLV's, landing/recovery and post-flight turnaround activities are performed. Multi-use vehicles require reconfiguration. MRO (Maintenance, Repair, and Overhaul) must be well-planned--- even for the unplanned problems. Defect limits and standard repairs need to be in-place as well as easily added. Many routine inspections and maintenance can be like an aircraft overhaul. Modifications and technology upgrades should be expected. Another factor affecting ground operations efficiency is trending. It is essential for RLV's, and also useful for ELV's which fly the same or similar models again. Good data analysis of technical and processing performance will determine fixes and improvements needed for safety, design, and future processing. Collecting such data on new or low-frequency vehicles is a challenge. Lessons can be learned from the Space Shuttle, or even the Concorde aircraft. For all of the above topics, efficient business systems must be established for comprehensive program management and good throughput. Drawings, specifications, and manuals for an entire launch vehicle are often in different formats from multiple vendors, plus they have proprietary constraints. Nonetheless, the integration team must ensure that all data needed is compatible and visible to each appropriate team member. Ground processing systems for scheduling, tracking, problem resolution, etc. must be well laid-out. The balance between COTS (commercial off the shelf) and custom software is difficult. Multiple customers, vendors, launch sites, and landing sites add to the complexity of efficient IT (Information Technology) tools.

Ingalls, John↗
NASA NTRS2016

Additive Manufacturing, Design, Testing, and Fabrication: A Full Engineering Experience at JSC

I worked on several projects this term. While most projects involved additive manufacturing, I was also involved with two design projects, two testing projects, and a fabrication project. The primary mentor for these was Richard Hagen. Secondary mentors were Hai Nguyen, Khadijah Shariff, and fabrication training from James Brown. Overall, my experience at JSC has been successful and what I have learned will continue to help me in my engineering education and profession long after I leave. My 3D printing projects ranged from less than a 1 cubic centimeter to about 1 cubic foot and involved several printers using different printing technologies. It was exciting to become familiar with printing technologies such as industrial grade FDM (Fused Deposition Modeling), the relatively new SLA (Stereolithography), and PolyJet. My primary duty with the FDM printers was to model parts that came in from various sources to print effectively and efficiently. Using methods my mentor taught me and the Stratasys Insight software, I was able to minimize imperfections, hasten build time, improve strength for specific forces (tensile, shear, etc...), and reduce likelihood of a print-failure. Also using FDM, I learned how to repair a part after it was printed. This is done by using a special kind of glue that chemically melts the two faces of plastic parts together to form a fused interface. My first goal with SLA technology was to bring the printer back to operational readiness. In becoming familiar with the Pegasus SLA printer, I researched the leveling, laser settings, and different vats to hold liquid material. With this research, I was successfully able to bring the Pegasus back online and have successfully printed multiple sample parts as well as functional parts. My experience with PolyJet technology has been focused on an understanding of the abilities/limits, costs, and the maintenance for daily use. Still upcoming will be experience with using a composite printer that uses FDM technology to print plastic while laying an internal filament of Kevlar or carbon-fiber inside the printed material. It has been incredible being exposed to this range of technologies and I feel very fortunate to be ready for virtually any kind of printing technology I come across in the future. Design work played a part in my internship this term as well. Working with Hai Nguyen, I was able to design a set of testing tips and a test frame for use with an Arcjet. The testing tips will be made of several different materials that will possibly be used in the heat shield of the Orion space craft. These designs included technical drawings that were presented to the fabrication shop. The frame design was created from 80/20 (a popular brand of frame construction equipment) and included an order form with pricing for fabrication. An independent design was also done for the virtual reality lab. This design was to create a hand-controller based on a previous design. This final design was sent directly to a 3D printer without technical drawings. Overall, my design work has given me experience with using 80/20, helped improve my CAD (Computer Aided Design) proficiency, and increased my knowledge of how to set up technical drawings for fabrication. The final role I have played in this internship has been to assist with testing of the inflatable technology materials working Khadijah Shariff. I began the internship assisting with permeability testing with the initial plan to continue the testing independently after training. Unfortunately, the testing apparatus suffered a technical failure and had funding pulled which cancelled that portion of the project. Further testing with inflatable technology continued with tensile testing of various stitching methods. This testing took place over a two-days and concluded successfully. Final testing was to be more tensile testing but of straps used to connect various inflatable sections. Unfortunately, the needed grips for the tensile tests could not be located and put the testing on hold. It is possible this round of testing will still take place by the end of the internship if the grips can be found. Overall, this portion of the internship has helped me become familiar with one kind of permeability test as well as a popular tensile/compression testing machine. Finally, I also had the chance to be trained using a metal lathe for making very small tips for a soldering iron. These tips will be used to melt threaded brass inserts into 3D printed plastic. Working with James Brown, I was able to successfully machine a brass rod down to as little as 0.064 inches plus or minus 0.001 inches. It was very rewarding to learn how to best use the machine and become familiar with a skill that will undoubtedly be used again in the future. I have been told by several professional engineers that learning to use a lathe and mill will be invaluable skills in the field. This knocks 50 percent of that goal off and I look forward to learning the mill at some point in the future. As is apparent with this list of projects, my internship was not focused on a single over-arching goal. Instead, I was able to gain experience in a myriad of very different areas. I feel like my time here was spent bouncing from one project to the next. Though sometimes difficult to switch gears, it was very rewarding to be a part of so much in so little time. My career and education will be positively impacted by what I have learned at JSC. My experience with 3D printing has improved my ability to handle many issues that may come up in the future with multiple different technologies. The design work I took part in, especially creating technical drawings, will help me better present designs to any engineer or shop I will encounter. My testing experience has helped me become familiar with a popular kind of tensile test machine that will likely be similar to the kinds I will encounter in the future. Finally, my experience with fabrication has given me a rare opportunity, as an engineer, to take part in the fabrication of a part. This experience will help me better tailor my future designs for the manufacturing process and has given me an appreciation for detailed/delicate machining work. My experience at JSC has been successful and will continue to assist me for a long time within the engineering field.

Zusack, Steven↗
NASA NTRS1991

A PC-based hardware implementation of the maximum-likelihood classifier for the Shuttle Ice Detection System

A PC-based near-real time implementation of a two-channel maximum-likelihood classifier is described. The statistical distribution of the reflectance characteristics of ice, frost, and water formation on spray-on-foam-insulation, which covers the External Tank surface of the Space Shuttle, is acquired. The classification technique is based on these statistics. The computer, set in either a training or a classifying mode, learns the statistics of the various classes, or produces a color-coded image denoting the respective categories of classification. The classified results are memory-mapped for efficiency. The speed of the classification process is only limited by the speed of the digital frame grabber and the software that interfaces the frame grabber to the monitor. The process took 4 seconds for a 512 x 480 pixel image.

Jaggi, S.↗
NASA NTRS1992

Multilayer perceptron, fuzzy sets, and classification

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Pal, Sankar K.↗
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↗
NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

The NASA Passive Thermal Control Engineering Guidebook provides recommendations, including best practices and lessons learned, related to the passive thermal control engineering discipline. Topics include analysis (including documentation and review), thermal hardware (design/selection, vendors, and integration), testing, and flight operations. The passive thermal discipline includes thermal control and thermal protection systems. The passive thermal control discipline, which is addressed in this Guidebook, is broad and covers internal and external systems, component passive thermal analysis, vehicle on-orbit attitude timeline analysis, integrated thermal analysis, various thermal control apparatus (heaters and controls, coatings, blankets and insulations, isolators, geometric design for view factors, materials emissivity/absorptivity properties), passive cooling of avionics, purge, vent, and drain for vehicle cavities and compartments, thermal model development and correlation, thermal cycle and thermal-vacuum testing. Topics related to thermal protection systems (TPS) are not within the scope of this document. The Guidebook provides a consolidated reference for early career as well as experienced engineers embarking on a new task. Leveraging the experience of the group minimizes the learning curve that exists at the start of new projects, reduces the risk of repeating mistakes, and improves the organization’s ability to deliver high-quality products efficiently. The intent of this document is to provide guidance based on collective experience, and is not intended impose constraints on project work. Best practices can vary substantially and it is important to apply judgment wisely based on careful consideration of the specific design and requirements. Implementation of the methods described will vary from project to project and should be applied in conjunction with the judgment of the thermal engineers performing and reviewing the work. The recommendations in this document are not intended to replace program, project, branch, Center, or NASA requirements and/or policies.

thermal↗
NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

The NASA Passive Thermal Control Engineering Guidebook provides recommendations, including best practices and lessons learned, related to the passive thermal control engineering discipline. Topics include analysis (including documentation and review), thermal hardware (design/selection, vendors, and integration), testing, and flight operations. The passive thermal discipline includes thermal control and thermal protection systems. The passive thermal control discipline, which is addressed in this Guidebook, is broad and covers internal and external systems, component passive thermal analysis, vehicle on-orbit attitude timeline analysis, integrated thermal analysis, various thermal control apparatus (heaters and controls, coatings, blankets and insulations, isolators, geometric design for view factors, materials emissivity/absorptivity properties), passive cooling of avionics, purge, vent, and drain for vehicle cavities and compartments, thermal model development and correlation, thermal cycle and thermal-vacuum testing. Topics related to thermal protection systems (TPS) are not within the scope of this document. The Guidebook provides a consolidated reference for early career as well as experienced engineers embarking on a new task. Leveraging the experience of the group minimizes the learning curve that exists at the start of new projects, reduces the risk of repeating mistakes, and improves the organization’s ability to deliver high-quality products efficiently. The intent of this document is to provide guidance based on collective experience, and is not intended impose constraints on project work. Best practices can vary substantially and it is important to apply judgment wisely based on careful consideration of the specific design and requirements. Implementation of the methods described will vary from project to project and should be applied in conjunction with the judgment of the thermal engineers performing and reviewing the work. The recommendations in this document are not intended to replace program, project, branch, Center, or NASA requirements and/or policies.

thermal↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
NASA NTRS2016

Promoting a Culture of Tailoring for Systems Engineering Policy Expectations

NASA's Marshall Space Flight Center (MSFC) has developed an integrated systems engineering approach to promote a culture of tailoring for program and project policy requirements. MSFC's culture encourages and supports tailoring, with an emphasis on risk-based decision making, for enhanced affordability and efficiency. MSFC's policy structure integrates the various Agency requirements into a single, streamlined implementation approach which serves as a "one-stop-shop" for our programs and projects to follow. The engineers gain an enhanced understanding of policy and technical expectations, as well as lesson's learned from MSFC's history of spaceflight and science missions, to enable them to make appropriate, risk-based tailoring recommendations. The tailoring approach utilizes a standard methodology to classify projects into predefined levels using selected mission and programmatic scaling factors related to risk tolerance. Policy requirements are then selectively applied and tailored, with appropriate rationale, and approved by the governing authorities, to support risk-informed decisions to achieve the desired cost and schedule efficiencies. The policy is further augmented by implementation tools and lifecycle planning aids which help promote and support the cultural shift toward more tailoring. The MSFC Customization Tool is an integrated spreadsheet that ties together everything that projects need to understand, navigate, and tailor the policy. It helps them classify their project, understand the intent of the requirements, determine their tailoring approach, and document the necessary governance approvals. It also helps them plan for and conduct technical reviews throughout the lifecycle. Policy tailoring is thus established as a normal part of project execution, with the tools provided to facilitate and enable the tailoring process. MSFC's approach to changing the culture emphasizes risk-based tailoring of policy to achieve increased flexibility, efficiency, and effectiveness in project execution, while maintaining appropriate rigor to ensure mission success.

Blankenship, Van A.↗
NASA NTRS1982

Helicopter simulation technology: An Ames Research Center perspective

The total experience for evidence regarding the levels of motion and visual cueing fidelity required for handling-qualities research in ground-based simulators is reviewed. Positive contributions of cockpit motion were identified, but much remains to be learned regarding the sensitivities of individual control modes to cueing attenuation. A firmer understanding of the pilot's utilization of visual and motion cues is the key to more efficient use of simulation in helicopter control-systems research.

Bray, R. S.↗
NASA NTRS1985

NASA image-based geological expert system development project for hyperspectral image analysis

The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, it is shown that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. A very powerful natural language system was incorporated as the user-friendly front end, and the concurrent processing efficiency of the frame-based knowledge representation in the hypercube microsupercomputer simulation was tested.

Chiou, W. C., Sr.↗
NASA NTRS1992

Abstraction and Problem Reformulation

In work done jointly with Toby Walsh, the author has provided a sound theoretical foundation to the process of reasoning with abstraction (GW90c, GWS9, GW9Ob, GW90a). The notion of abstraction formalized in this work can be informally described as: (property 1), the process of mapping a representation of a problem, called (following historical convention (Sac74)) the 'ground' representation, onto a new representation, called the 'abstract' representation, which, (property 2) helps deal with the problem in the original search space by preserving certain desirable properties and (property 3) is simpler to handle as it is constructed from the ground representation by "throwing away details". One desirable property preserved by an abstraction is provability; often there is a relationship between provability in the ground representation and provability in the abstract representation. Another can be deduction or, possibly inconsistency. By 'throwing away details' we usually mean that the problem is described in a language with a smaller search space (for instance a propositional language or a language without variables) in which formulae of the abstract representation are obtained from the formulae of the ground representation by the use of some terminating rewriting technique. Often we require that the use of abstraction results in more efficient .reasoning. However, it might simply increase the number of facts asserted (eg. by allowing, in practice, the exploration of deeper search spaces or by implementing some form of learning). Among all abstractions, three very important classes have been identified. They relate the set of facts provable in the ground space to those provable in the abstract space. We call: TI abstractions all those abstractions where the abstractions of all the provable facts of the ground space are provable in the abstract space; TD abstractions all those abstractions wllere the 'unabstractions' of all the provable facts of the abstract space are provable in the ground space; and TC abstractions all those abstractions where a fact is provable in the ground space if and only if its abstraction is provable in the abstract space.

Giunchiglia, Fausto↗
NASA NTRS1997

Autogenic-Feedback Training Exercise (AFTE) Method and System

The Autogenic-Feedback Training Exercise (AFTE) method of the present invention is a combined application of physiologic and perceptual training techniques. such as autogenic therapy and biofeedback. This combined therapy approach produces a methodology that is appreciably more effective than either of the individual techniques used separately. The AFTE method enables sufficient magnitude of control necessary to significantly reduce the behavioral and physiologic reactions to severe environmental stressors. It produces learned effects that are persistent over time and are resistant to extinction and it can be administered in a short period of time. The AFTE method may be used efficiently in several applications, among which are the following: to improve pilot and crew performance during emergency flying conditions; to train people to prevent the occurrence of nausea and vomiting associated with motion and sea sickness, or morning sickness in early pregnancy; as a training method for preventing or counteracting air-sickness symptoms in high-performance military aircraft; for use as a method for cardiovascular training, as well as for multiple other autonomic responses, which may contribute to the alleviation of Space Motion Sickness (SMS) in astronauts and cosmonauts; training people suffering from migraine or tension headaches to control peripheral blood flow and reduce forehead and/or trapezius muscle tension; training elderly people suffering from fecal incontinence to control their sphincter muscles; training cancer patients to reduce the nauseagenic effects of chemotherapy; and training patients with Chronic Intestinal Pseudo-obstruction (CIP).

Cowings, Patricia S.↗
NASA NTRS2000

Crew/Automation Interaction in Space Transportation Systems: Lessons Learned from the Glass Cockpit

The progressive integration of automation technologies in commercial transport aircraft flight decks - the 'glass cockpit' - has had a major, and generally positive, impact on flight crew operations. Flight deck automation has provided significant benefits, such as economic efficiency, increased precision and safety, and enhanced functionality within the crew interface. These enhancements, however, may have been accrued at a price, such as complexity added to crew/automation interaction that has been implicated in a number of aircraft incidents and accidents. This report briefly describes 'glass cockpit' evolution. Some relevant aircraft accidents and incidents are described, followed by a more detailed description of human/automation issues and problems (e.g., crew error, monitoring, modes, command authority, crew coordination, workload, and training). This paper concludes with example principles and guidelines for considering 'glass cockpit' human/automation integration within space transportation systems.

Rudisill, Marianne↗
NASA NTRS2006

Automated Knowledge Discovery from Simulators

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

landscapes↗
NASA NTRS2013

Robotic Technology Development at Ames: The Intelligent Robotics Group and Surface Telerobotics

Future human missions to the Moon, Mars, and other destinations offer many new opportunities for exploration. But, astronaut time will always be limited and some work will not be feasible for humans to do manually. Robots, however, can complement human explorers, performing work autonomously or under remote supervision from Earth. Since 2004, the Intelligent Robotics Group has been working to make human-robot interaction efficient and effective for space exploration. A central focus of our research has been to develop and field test robots that benefit human exploration. Our approach is inspired by lessons learned from the Mars Exploration Rovers, as well as human spaceflight programs, including Apollo, the Space Shuttle, and the International Space Station. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and likely operational and functional risks. These assumptions, however, are not grounded by actual experimental data. Moreover, no crew-controlled surface telerobotic system has yet been fully tested, or rigorously validated, through flight testing. During Summer 2013, we conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover across short time delays. The tests simulated portions of a proposed human-robotic Lunar Waypoint mission, in which astronauts in lunar orbit remotely operate a planetary rover on the lunar Farside to deploy a radio telescope array. We used these tests to obtain baseline-engineering data.

robotics↗