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Volume 1 of the Final Study Report provides an Executive Summary of the Phase B study effort conducted under contract NAS8-36526. Space station Phase B implementation resulted in the timely establishment of preliminary design tasks, including trades and analyses. A comprehensive summary of project activities in conducting this study effort is included.
A high level robot command language is proposed for the autonomous mode of an advanced telerobotics system and a predictive display mechanism for the teleoperational model. It is believed that any such system will involve some mixture of these two modes, since, although artificial intelligence can facilitate significant autonomy, a system that can resort to teleoperation will always have the advantage. The high level command language will allow humans to give the robot instructions in a very natural manner. The robot will then analyze these instructions to infer meaning so that is can translate the task into lower level executable primitives. If, however, the robot is unable to perform the task autonomously, it will switch to the teleoperational mode. The time delay between control movement and actual robot movement has always been a problem in teleoperations. The remote operator may not actually see (via a monitor) the results of high actions for several seconds. A computer generated predictive display system is proposed whereby the operator can see a real-time model of the robot's environment and the delayed video picture on the monitor at the same time.
This analysis of the deformation of rotary combustion engine housings targeted the following objectives: (1) the development and verification of a finite element model of the trochoid housing, (2) the prediction of the stress and deformation fields present within the trochoid housing during operating conditions, and (3) the development of a specialized preprocessor which would shorten the time necessary for mesh generation of a trochoid housing's FEM model from roughly one month to approximately two man hours. Executable finite element models were developed for both the Mazda and the Outboard Marine Corporation trochoid housings. It was also demonstrated that a preprocessor which would hasten the generation of finite element models of a rotary engine was possible to develop. The above objectives are treated in detail in the attached appendices. The first deals with finite element modeling of a Wankel engine center housing, and the second with the development of a preprocessor that generates finite element models of rotary combustion engine center housings. A computer program, designed to generate finite element models of user defined rotary combustion engine center housing geometries, is also included.
This report describes a structural optimization procedure developed for use with the Engineering Analysis Language (EAL) finite element analysis system. The procedure is written primarily in the EAL command language. Three external processors which are written in FORTRAN generate equivalent stiffnesses and evaluate stress and local buckling constraints for the sections. Several built-up structural sections were coded into the design procedures. These structural sections were selected for use in aircraft design, but are suitable for other applications. Sensitivity calculations use the semi-analytic method, and an extensive effort has been made to increase the execution speed and reduce the storage requirements. There is also an approximate sensitivity update method included which can significantly reduce computational time. The optimization is performed by an implementation of the MINOS V5.4 linear programming routine in a sequential liner programming procedure.
To prepare for the challenge of handling the archiving and querying of terabyte-sized scientific spatial databases, the NASA Goddard Space Flight Center's Applied Information Sciences Branch (AISB, Code 935) developed a number of characterization algorithms that rely on supervised clustering techniques. The research reported upon here has been aimed at continuing the evolution of some of these supervised techniques, namely the neural network and decision tree-based classifiers, plus extending the approach to incorporating unsupervised clustering algorithms, such as those based on robust estimation (RE) techniques. The algorithms developed under this task should be suited for use by the Intelligent Information Fusion System (IIFS) metadata extraction modules, and as such these algorithms must be fast, robust, and anytime in nature. Finally, so that the planner/schedule module of the IlFS can oversee the use and execution of these algorithms, all information required by the planner/scheduler must be provided to the IIFS development team to ensure the timely integration of these algorithms into the overall system.
A finite element model of an ATR42-300 commuter-class aircraft was developed and a crash simulation was executed. Analytical predictions were correlated with data obtained from a 30-ft/s (9.14-m/s) vertical drop test of the aircraft. The purpose of the test was to evaluate the structural response of the aircraft when subjected to a severe, but survivable, impact. The aircraft was configured with seats, dummies, luggage, and other ballast. The wings were filled with 8,700 lb. (3,946 kg) of water to represent the fuel. The finite element model, which consisted of 57,643 nodes and 62,979 elements, was developed from direct measurements of the airframe geometry. The seats, dummies, luggage, fuel, and other ballast were represented using concentrated masses. The model was executed in LS-DYNA, a commercial code for performing explicit transient dynamic simulations. Predictions of structural deformation and selected time-history responses were generated. The simulation was successfully validated through extensive test-analysis correlation.
Linear optimization queries, which usually arise in various decision support and resource planning applications, are queries that retrieve top N data records (where N is an integer greater than zero) which satisfy a specific optimization criterion. The optimization criterion is to either maximize or minimize a linear equation. The coefficients of the linear equation are given at query time. Methods and apparatus are disclosed for constructing, maintaining and utilizing a multidimensional indexing structure of database records to improve the execution speed of linear optimization queries. Database records with numerical attributes are organized into a number of layers and each layer represents a geometric structure called convex hull. Such linear optimization queries are processed by searching from the outer-most layer of this multi-layer indexing structure inwards. At least one record per layer will satisfy the query criterion and the number of layers needed to be searched depends on the spatial distribution of records, the query-issued linear coefficients, and N, the number of records to be returned. When N is small compared to the total size of the database, answering the query typically requires searching only a small fraction of all relevant records, resulting in a tremendous speedup as compared to linearly scanning the entire dataset.
Automatic code generation can be used to convert software state diagrams into executable code, enabling a model- based approach to software design and development. The primary benefits of this process are reduced development time and continuous consistency between the system design (statechart) and its implementation. We used model-based design and code generation to produce software for the Electra UHF radios that is functionally equivalent to software that will be used by the Mars Reconnaissance Orbiter (MRO) and the Mars Science Laboratory to communicate with each other. The resulting software passed all of the relevant MRO flight software tests, and the project provides a useful case study for future work in model-based software development for flight software systems.
This software provides a new set of capabilities for the Mars Relay Operations Service (MaROS) in support of Strategic and Tactical relay, including a highly interactive relay request Web user interface, mission control over relay planning time periods, and mission management of allowed strategic vs. tactical request parameters. Together, these new capabilities expand the scope of the system to include all elements critical for Tactical relay operations. Planning of replay activities spans a time period that is split into two distinct phases. The first phase is called Strategic, which begins at the time that relay opportunities are identified, and concludes at the point that the orbiter generates the flight sequences for on board execution. Any relay request changes from this point on are called Tactical. Tactical requests, otherwise called Orbit - er Relay State Changes (ORSC), are highly restricted in terms of what types of changes can be made, and the types of parameters that can be changed may differ from one orbiter to the next. For example, one orbiter may be able to delay the start of a relay request, while another may not. The legacy approach to ORSC management involves exchanges of e-mail with "requests for change" and "acknowledgement of approval," with no other tracking of changes outside of e-mail folders. MaROS Phases 1 and 2 provided the infrastructure for strategic relay for all supported missions. This new version, 3.0, introduces several capabilities that fully expand the scope of the system to include tactical relay. One new feature allows orbiter users to manage and "lock" Planning Periods, which allows the orbiter team to formalize the changeover from Strategic to Tactical operations. Another major feature allows users to interactively submit tactical request changes via a Web user interface. A third new feature allows orbiter missions to specify allowed tactical updates, which are automatically incorporated into the tactical change process. This software update is significant in that it provides the only centralized service for tactical request management available for relay missions.
The Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) is a system that monitors the near-Earth asteroid (NEA) population to identify NEAs whose orbital characteristics may make them potential destinations for future round-trip human space flight missions. To accomplish this monitoring, Brent Barbee (GSFC) developed and automated a system that applies specialized trajectory processing to the orbits of newly discovered NEAs, and those for which we have updated orbit knowledge, obtained from the JPL Small Bodies Database (SBDB). This automated process executes daily and the results are distributed to the general public and the astronomy community. This aids in prioritizing telescope radar time allocations for obtaining crucial follow-up observations of highly accessible NEAs during the critical, because it is often fleeting, time period surrounding the time at which the NEAs are initially discovered.
Scanning Equipment supporting the Millimeter Wave Radiometer Instrument (MWRI) are flying in a sunsynchronized orbit of 850-km altitude with an inclination of 98.8 deg on the FY-3 meteorological satellite (FY = Feng Yun, Wind and Cloud). MWRI is a linearly polarized, ten-channel passive Radiometer; it measures precipitation and water clouds, sea ice, snow/water equivalent, drought and flood index, land temperature and soil moisture. Following the FY3-A, the FY3-B Satellite was launched in autumn 2010. Since that time, the Scanning Equipment was continuously operated. During the last three and a half years in orbit, the Scanning Mechanism has executed about 65 million revolutions, while the Scan Compensation Mechanism (SCM) - used for momentum compensation - has already successfully executed more than one billion revolutions. During the commissioning phase of the instrument and during the first operation phase, random torque spikes, which manifested themselves as a motor current increase, were observed in the Scan Drive Mechanism, whereas the Scan Compensation drive operated nominally from the beginning. The result of the root cause investigations performed in order to isolate the issue, and the consequences for the follow-on MWRI equipment which was successfully launched by end of September 2013 (now flying on the FY 3-C Spacecraft), are discussed.
As NASA and its International Partners prepare to expand human space exploration beyond the International Space Station (ISS) to the Moon (and ultimately Mars) with the Artemis missions, the importance of evolving space medical operations in an Earth independent fashion becomes increasingly clear. Continuous human presence on the ISS for the past 20 years has demonstrated the successful development and execution of medical operations in low Earth orbit. However, this paradigm of ISS medical operations is dependent upon continuous real-time communications, frequent resupply missions, and the availability of rapid evacuation options. As the distance and time away from Earth increases, all of these dependencies will no longer be available. As a result, the paradigm of space medicine operations will need to become progressively more autonomous and less dependent upon mission support personnel on Earth
We describe a software emulator of the MIL-STD-1750A architecture that executes 1750A code at a rate of about 4 MIPS on a Sun 200MHz Ultra2 work-station, and effectively several times faster than that when application-specific optimizations are used.
NASA’s Artemis campaign is making heavy use of simulation to help return humans to the lunar surface by the end of the decade. There are several aspects of the lunar surface and its environment which must be accurately modeled before these simulations can be relied upon to influence decisions being made under these programs. Digital Lunar Exploration Sites, a paper submitted to the 2022 IEEE Aerospace Conference, outlined the process used to generate the lunar surface in a digital environment. This paper will expand upon this topic and delve into the steps being taken by the NASA Exploration Systems Simulations (NExSyS) team at NASA’s Johnson Space Center (JSC) to properly verify and validate these simulations, with a focus on the visual aspects of the environment. Natural lighting validation relies in part on the wealth of data generated during the Apollo program. Many images taken by Apollo astronauts on the lunar surface have been replicated in the simulated environments to gain confidence in the accuracy of terrain and lighting models. However, because the environment the Artemis astronauts will experience at the Lunar South Pole (LSP) is dissimilar from the near-equatorial Apollo sites, other validation techniques must be applied. At the LSP, the sun crests only about three degrees above the horizon and when combined with the lack of a lunar atmosphere, lighting in this region is often very different than what a human would experience on Earth. Solar illumination, earthshine, human eye response, solar blooming, lunar regolith optical properties, and shadows cast by rocks and crater walls will play a significant role in an astronaut’s ability to safely conduct an Extra-Vehicular Activity (EVA) or perform a traverse with a lunar rover. Approaches for validation of these aspects of the rendered LSP environment are considered in this paper. In addition to natural lighting, approaches for the validation of artificial lighting models at the LSP are discussed. The JSC Lighting Lab has been studying the illumination profile of the Exploration Infomatics Subsystem (xINFO) lighting on the Exploration EVA Mobility Unit (xEMU). How these lights interact with the solar illumination and the shadows being cast on the lunar surface is of particular interest, so the validity of models representing these lights in a human-in-the-loop virtual reality environment becomes very important. This paper also touches on some of the simulation performance considerations when a Human in the Loop (HITL) is present, which drives the need for real time rendering of the environment. Natural and artificial lighting will play a crucial role to decisions being made when planning and executing missions at the Lunar South Pole (LSP) and it is vitally important to understand the LSP environment before we return.
Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.
Optimization of a complex system involves the determination of optimum values for a set of design parameters to meet a specific set of objectives on the quantities of interest (QOI) wherein, the design parameters are a subset of the input parameters, and the QOIs are determined from the output parameters. Particularly, when the parameter space is large, optimization necessitates a significant number of executions of the simulator to obtain a desired solution in tolerance limits. When the simulations are expensive in terms of computation time, an emulator, based on regression methods is useful for predictions. This work presents a novel methodology that uses an iterative hybrid global optimization method (GOM) using genetic algorithms (GA), and simulated annealing (SA) model coupled (HYBGASA) with a Gaussian process regression method based emulator (GPMEM) to optimize a set of input parameters based on a set of defined objectives in a nuclear reactor power system. Hereafter this iterative hybrid method comprising of HYBGASA, and GPMEM would be called as 'IHGOM'. In addition to optimization, IHGOM iteratively updates the trial data, obtained from the neighborhood of the near optimal solution, used to train the GPMEM in order to reduce regression errors. The objective is to develop, model and analyze IHGOM and apply it to an optimization problem in the design of a nuclear reactor. Development and analysis of IHGOM and its implementation in a nuclear reactor power system problem is a significant contribution to the optimization and the nuclear engineering communities. The objective of this work is to develop IHGOM and apply it to an optimization problem in the design of a nuclear reactor. The research involves the development and analysis of a novel optimization strategy, IHGOM. The feasibility analysis involves the optimization of a set of defined parameters in the design and development of a complex system. A GCFBR reactor power system with a set of defined objectives and parameters based on criticality and depletion studies have been characterized as the complex system. Development and analysis of IHGOM and its implementation in an optimization problem in the design of a GCFBR is a significant contribution to the optimization and the nuclear engineering communities. In particular based on the literature review, a coupled GOM and GPMEM method for optimization and regression error reduction in nuclear engineering domain has never been explored. (authors)
NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.
The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.