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The EFCOG Human Performance Improvement (HPI) Task Team (TT) and the Work Planning & Control (WP&C) TT collaborated to develop a Best Practice that provides examples of how to integrate of HPI into WP&C tasks. This document is a collection of some best practices as determined by team members.
The G-Guidance software implements a guidance and control (G and C) algorithm for small-body, autonomous proximity operations, developed under the Small Body GN and C task at JPL. The software is written in Matlab and interfaces with G-OPT, a JPL-developed optimization package written in C that provides G-Guidance with guaranteed convergence to a solution in a finite computation time with a prescribed accuracy. The resulting program is computationally efficient and is a prototype of an onboard, real-time algorithm for autonomous guidance and control. Two thruster firing schemes are available in G-Guidance, allowing tailoring of the software for specific mission maneuvers. For example, descent, landing, or rendezvous benefit from a thruster firing at the maneuver termination to mitigate velocity errors. Conversely, ascent or separation maneuvers benefit from an immediate firing to avoid potential drift toward a second body. The guidance portion of this software explicitly enforces user-defined control constraints and thruster silence times while minimizing total fuel usage. This program is currently specialized to small-body proximity operations, but the underlying method can be generalized to other applications.
To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.
An advanced near term (1990's) space-based Orbit Transfer Vehicle Engine (OTVE) system was designed, and the technologies applicable to its construction, maintenance, and operations were developed under Tasks A through F of the Orbit Transfer Rocket Engine Technology Program. Task A was a reporting task. In Task B, promising OTV turbomachinery technologies were explored: two stage partial admission turbines, high velocity ratio diffusing crossovers, soft wear ring seals, advanced bearing concepts, and a rotordynamic analysis. In Task C, a ribbed combustor design was developed. Possible rib and channel geometries were chosen analytically. Rib candidates were hot air tested and laser velocimeter boundary layer analyses were conducted. A channel geometry was also chosen on the basis of laser velocimeter data. To verify the predicted heat enhancement effects, a ribbed calorimeter spool was hot fire tested. Under Task D, the optimum expander cycle engine thrust, performance and envelope were established for a set of OTV missions. Optimal nozzle contours and quick disconnects for modularity were developed. Failure Modes and Effects Analyses, maintenance and reliability studies and component study results were incorporated into the engine system. Parametric trades on engine thrust, mixture ratio, and area ratio were also generated. A control system and the health monitoring and maintenance operations necessary for a space-based engine were outlined in Task E. In addition, combustor wall thickness measuring devices and a fiberoptic shaft monitor were developed. These monitoring devices were incorporated into preflight engine readiness checkout procedures. In Task F, the Integrated Component Evaluator (I.C.E.) was used to demonstrate performance and operational characteristics of an advanced expander cycle engine system and its component technologies. Sub-system checkouts and a system blowdown were performed. Short transitions were then made into main combustor ignition and main stage operation.
This interim report is an update of the report Jove Colon et al. (2019; M4SF-19SN010301091) describing international collaboration activities pertaining to FEBEX-DP and DECOVALEX19 Task C projects. Although work on these two international repository science activities is no longer continuing by the international partners, investigations on the collected data and samples is still ongoing. Descriptions of these underground research laboratory (URL) R&D activities are given in Jové Colón et al. (2018; 2019) but will repeated here for completeness. The 2019 status of work conducted at Sandia National Laboratories (SNL) on these two activities is summarized along with other international collaboration activities in Birkholzer et al. (2019).
The following interim report describes updates to ongoing international collaboration activities pertaining the FEBEX-DP and DECOVALEX Task C projects. Descriptions of these underground research laboratory (URL) activities are given in Jové Coke et al. (2018) but will repeated here for completeness. The 2018 status of work conducted at Sandia National Laboratories (SNL) on these two activities has been described in Jové Coke et al. (2018) and were summarized along with other international collaboration activities in Birkholzer et al. (2018).
Thermal-Hydrologic-Mechanical (THM) modeling of DECOVALEX 2023, Task C has continued. In FY2022 the simulations have progressed to Step 1, which is on 3-D modeling of the full-scale emplacement experiment at the Mont Terri Underground Rock Laboratory (Nagra, 2019). This report summarizes progress in Thermal-Hydrologic (TH) modeling of Step 1. THM modeling will be documented in future reports.
Thermal-Hydrologic (TH) modeling of DECOVALEX 2023, Task C has continued in FY23. This report summarizes progress in TH modeling of Step 1c, with calibration modeling and the addition of shotcrete. The work involves 3-D modeling of the full-scale emplacement experiment at the Mont Terri Underground Rock Laboratory (Nagra, 2019). While Step 1 is focused on modeling the heating phase of the FE experiment with changes in pore pressure in the Opalinus clay resulting from heating, Step 1c is focused on calibration of models using available data.
G-Guidance is a robust G&C (guidance and control) algorithm developed under the small-body GN&C task. The G-Guidance scheme utilizes a model predictive control approach, along with a convexification of the governing dynamics, control constraints, and trajectory/state constraints. This report details an enhancement to the FY2005 G-Guidance algorithm; the addition of a fire-second approach helps to nullify velocity errors and hit desired final velocities much more precisely than the original Fire-first scheme developed in FY2005. The enhancement preserves the primary benefit of the algorithm, which is to ensure required thruster silent times during trajectory maneuvers. The fire-second scheme increases the versatility of the algorithm for missions employing G-Guidance. For instance, a landing sequence could employ the fire-second scheme to ensure a null final velocity at landing, whereas an ascent sequence could utilize a fire-first scheme to ensure there is no drift of the spacecraft toward the ground at the beginning of the maneuver. Examples are provided within to demonstrate a fire-first versus fire-second guidance scheme. As in the existing G-Guidance algorithm, the examples and schemes incorporate gravity models and thruster firing times into discrete dynamics that are solved as a optimal control problem to minimize fuel consumption or thruster energy expenditure.
The NASA JAO/ES-MCAT (Eugene Stansbery Meter Class Autonomous Telescope) Facility is nearing Full Operational Capability, or FOC. ES-MCAT is now fully capable of autonomously running all observations, including: (a) monitoring weather and closing when conditions are not safe, as well as halting observations when conditions are not suitable (e.g. too cloudy) for operations, (b) start-up/shut-down nightly tasking, (c) collecting calibration data and survey or TLE-tracked data, and (d) processing all collected data, including on-chip photometry and astrometry calibrations using the GAIA star catalogue. The processed data are then further analyzed at NASA Johnson Space Center to correlate detections with known objects in the Space Surveillance Network (SSN) catalogue. MCAT can collect data of specific objects with known orbits or can search for objects with orbits similar to those of spacecraft or rocket bodies that have recently broken up. However, the primary goal for ES-MCAT is to survey the geosynchronous (GEO) belt to provide a statistical sample of the GEO debris environment for both engineering models for spacecraft designers and long-term environment evolutionary purposes. The approach for sweeping the sky to statistically survey GEO has been investigated and updated from past surveys taken by NASA and will be reported, herein referred to as the Candy Cane method. ES-MCAT’s optical performance and the limiting magnitude for the full optical system will be discussed. An analysis used to determine which filter to use for GEO surveys (SDSS r′) includes combining the reflectivity of the primary and secondary mirrors, transmission of the field corrector and CCD window, and the quantum efficiency of the CCD detector, resulting in throughput of the full optical path. This throughput is then combined with the expected typical transparency of the atmosphere at ES-MCAT’s altitude/location for the Sloan Digital Sky Survey (SDSS) g′r′i′z′ and Johnson/Kron-Cousins BVRI filters to yield expected relative throughput.
Abstract not provided.
Lightweight C++11 multithreading tasking system featuring thread-pool, task-groups, and lock-free task queue.
A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.
During FY92, USBI performed many programmatic related tasks. These programmatic tasks have been categorized as follows: (1) acquisition; (2) project engineering/program planning; and (3) cost. The reports associated with these tasks follow in paragraphs 1.1.1 through 1.3.3. Proceeding each task report is a brief description of the contents contained within.
Identify simple fully validated cost models that provide estimation uncertainty with cost estimate. Based on COCOMO variable set. Use machine learning techniques to determine: a) Minimum number of cost drivers required for NASA domain based cost models; b) Minimum number of data records required and c) Estimation Uncertainty. Build a repository of software cost estimation information. Coordinating tool development and data collection with: a) Tasks funded by PA&E Cost Analysis; b) IV&V Effort Estimation Task and c) NASA SEPG activities.
Automated human factor task data handling system
The Flight Telerobotic Servicer (FTS) Project at the Goddard Space Flight Center is developing an advanced telerobotic system to assist in and reduce crew extravehicular activity (EVA) for Space Station) Freedom (SSF). The FTS will provide a telerobotic capability to the Freedom Station in the early assembly phases of the program and will be employed for assembly, maintenance, and inspection applications throughout the lifetime of the space station. Appropriately configured elements of the FTS will also be employed for robotic manipulation in remote satellite servicing applications and possibly the Lunar/Mars Program. In mid-1989, the FTS entered the flight system design and implementation phase (Phase C/D) of development with the signing of the FTS prime contract with Martin Marietta Astronautics Group in Denver, Colorado. The basic FTS design is now established and can be reported on in some detail. This paper will describe the FTS flight system design and the rationale for the specific design approaches and component selections. The current state of space technology and the nature of the FTS task dictate that the FTS be designed with sophisticated teleoperation capabilities for its initial primary operating mode. However, there are technologies, such as advanced computer vision and autonomous planning techniques currently in research and advanced development phases which would greatly enhance the FTS capabilities to perform autonomously in less structured work environments. Therefore, a specific requirement on the initial FTS design is that it has the capability to evolve as new technology becomes available. This paper will describe the FTS design approach for evolution to more autonomous capabilities. Some specific task applications of the FTS and partial automation approaches of these tasks will also be discussed in this paper.
A cyclotron (C-80) designed to produce protons with an energy of 40–80 MeV and beam intensity of 100–200 μA has been launched at the Petersburg Nuclear Physics Institute, National Research Center Kurchatov Institute. The main task of C-80 is the production of a wide range of medical radionuclides for diagnosis and therapy. A complex named Radioactive Isotopes on the C-80 Cyclotron (RIC-80) and intended to function on the C-80 beam was developed for this purpose. A brief description of the RIC-80 complex is given, and the results of the use of new methods and studies of target devices for the production of the generator radioisotope {sup 82}Sr; {sup 223}Ra, {sup 224}Ra, and {sup 225}Ac radioisotopes that undergo alpha-decay; {sup 67}Cu; and other medical radionuclides are presented. The use of a mass separator in the “on-line” mode to obtain ion beams of high-purity radioisotopes is a distinctive feature of the complex used in the project, and it is especially important for medical applications.