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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Off-line programming motion and process commands for robotic welding of Space Shuttle main engines

The off-line-programming software and hardware being developed for robotic welding of the Space Shuttle main engine are described and illustrated with diagrams, drawings, graphs, and photographs. The menu-driven workstation-based interactive programming system is designed to permit generation of both motion and process commands for the robotic workcell by weld engineers (with only limited knowledge of programming or CAD systems) on the production floor. Consideration is given to the user interface, geometric-sources interfaces, overall menu structure, weld-parameter data base, and displays of run time and archived data. Ongoing efforts to address limitations related to automatic-downhand-configuration coordinated motion, a lack of source codes for the motion-control software, CAD data incompatibility, interfacing with the robotic workcell, and definition of the welding data base are discussed.

Ruokangas, C. C.↗
NASA NTRS1992

NASA/DoD Aerospace Knowledge Diffusion Research Project. XXIII - Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowledge production, transfer, and use as equally important components of the process of knowledge diffusion. This article argues that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

Pinelli, Thomas E.↗
DOE OSTI2020

D and D Research on KM-IT Platform - 20494

The Knowledge Management Information Tool (KM-IT) is a web-based system developed to maintain and preserve the Department of Energy's (DoE's) knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the collaboration and support of the DoE's Energy Facility Contractors Group (EFCOG) and the former ALARA Centers at Hanford and Savannah. The KM-IT system is a community driven system tailored to serve the technical issues faced by the workforce across the DOE Complex. This KM-IT web-based interactive system is operational and available for use at www.dndkm.org and is currently comprised of the following modules: Web Crawler, Hotline, Technology, Hanford's ALARA Center Reports, Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors and Industry News. The Web Crawler dynamically searches through the KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, and Pictures/Videos to the D and D repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. KM-IT makes an excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. FIU has developed the dedicated module on KM-IT platform to publish D and D research being performed at FIU. This module highlights current EM research efforts and activities in support of D and D being performed at FIU/SRNL associated with fixatives and intumescent products. FIU will expand this module to include the recent and current D and D research being performed across multiple DOE EM sites, national labs and universities. FIU will collaborate with SRNL, INL, ANL and other universities to explore and publish D and D research activities on KM-IT. As a result, the user community will have a centralized location to review new D and D related research across the DOE EM complex. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Mercury immunotoxicity in the brown watersnake ( Nerodia taxispilota ): An in vitro study

Mercury (Hg) is a heavy metal that enters the environment through natural and anthropogenic means. Once in the environment, Hg can biomagnify in food webs and is known to cause immunotoxic effects to wildlife. Compared with other vertebrates, knowledge of the reptilian immune system is lacking, especially in snakes. Further, even less is known about the impact of environmental contaminants on snake immunity. This gap in knowledge is largely due to an absence of established immune-based assays or specific reagents for these species. In this study, brown watersnakes (Nerodia taxispilota; n = 23) were captured on the Savannah River (Augusta, Georgia, USA), weighed, measured, bled, and released. Peripheral blood leukocytes (24 h old) were enriched and evaluated with an established mammalian in vitro lymphocyte proliferation assay. Enriched leukocytes were then exposed to mercury chloride (HgCl2) at 3.75, 37.5, and 75 μM. Total mercury (THg) in whole blood was also quantified. Snake peripheral blood leukocyte enrichment yielded >90% lymphocytes with viabilities averaging >70%. Exposure to HgCl 2 resulted in significant dose-dependent suppression of proliferative responses relative to spontaneous proliferation at 37.5 and 75 μM (both p ≤ 0.01) but not 3.75 μM (p = 0.99). Mean ± 1 SE concentration of THg in whole blood was 0.127 ± 0.027 mg/kg (wet weight). Based on the in vitro findings with HgCl 2 , snakes in systems with heavy Hg pollution may be at risk of immunosuppression, but N. taxispilota at the site in this study appear to be at low risk.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Zhou, Doudou↗
NASA NTRS1992

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 23: Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowled reproduction, transfer, and use as equally important components of the process of knowledge diffusion. It is argued that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of the members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

Pinelli, Thomas E.↗
NASA NTRS2007

Adaptive Modeling of the International Space Station Electrical Power System

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Thomas, Justin Ray↗
DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗
NASA NTRSDate not supplied

Medical System Requirements Development for Lunar Operations

The major health hazards of spaceflight include higher levels of damaging radiation, altered gravity, extended periods of isolation and confinement, a closed and potentially hostile living environment, and the stress associated with being a long distance from Earth. As we increase the duration of lunar stays with foreseeable communication latencies and disruptions, there will be a progressive need for crew to maintain their own health and independently respond to critical medical events. The Exploration Medical Capability element of the NASA Human Research Program is developing a set of Medical System requirements for lunar transit and surface operations. These requirements specify the capabilities, processes and procedures of a habitat Medical System needed for a range of conditions known to occur during spaceflight. Requirement text is written so as not to constrain innovative design solutions necessary for a resilient system. The requirement set includes attributes and functions the Medical System imposes on eight additional habitat systems. A key property of the Medical System is the provision of medical knowledge that will be stored, updated, analyzed, and secured within a Habitat Data System. A Task Performance Support System will aid in medical data acquisition and interpretation, crew training, medical condition prevention, diagnosis and treatment, provide interactive procedures, and track medical inventory. A Wellness System will focus on the provision of countermeasures to prevent, mitigate or treat adverse physical and behavioral health effects while the Medical System recommends adjustments to these countermeasures to maintain crew health. An Environmental Monitoring System will share out-of-bounds readings of air and water quality, acoustics, and radiation exposure levels with the Medical System to help identify issues before they affect crew health and performance. A Communications System will provide secured and private consultations between crew and the ground medical team and their loved ones on Earth. The Medical System also imposes requirements on a Research & Testbed System, fostering advanced medical science such as human research. A Waste Management System provides biohazard waste containment and waste disposal options (recycle and reuse). An Extravehicular Activity System supports crew health during lunar surface activities. And finally, a Maintenance Support System ensures that medical equipment is performing as expected. These requirements are being specifically developed for lunar surface operations but could help to identify Medical System requirements for any space habitat (e.g., I-Hab, commercial endeavors, etc.).

technology↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
NASA NTRS2002

NASA Remote Sensing Data for Epidemiological Studies

In response to the need for improved observations of environmental factors to better understand the links between human health and the environment, NASA has established a new program to significantly improve the utilization of NASA's diverse array of data, information, and observations of the Earth for health applications. This initiative, lead by Goddard Space Flight Center (GSFC) has the following goals: (1) To encourage interdisciplinary research on the relationships between environmental parameters (e.g., rainfall, vegetation) and health, (2) Develop practical early warning systems, (3) Create a unique system for the exchange of Earth science and health data, (4) Provide an investigator field support system for customers and partners, (5) Facilitate a system for observation, identification, and surveillance of parameters relevant to environment and health issues. The NASA Environment and Health Program is conducting several interdisciplinary projects to examine applications of remote sensing data and information to a variety of health issues, including studies on malaria, Rift Valley Fever, St. Louis Encephalitis, Dengue Fever, Ebola, African Dust and health, meningitis, asthma, and filariasis. In addition, the NASA program is creating a user-friendly data system to help provide the public health community with easy and timely access to space-based environmental data for epidemiological studies. This NASA data system is being designed to bring land, atmosphere, water and ocean satellite data/products to users not familiar with satellite data/products, but who are knowledgeable in the Geographic Information Systems (GIS) environment. This paper discusses the most recent results of the interdisciplinary environment-health research projects and provides an analysis of the usefulness of the satellite data to epidemiological studies. In addition, there will be a summary of presently-available NASA Earth science data and a description of how it may be obtained.

Maynard, Nancy G.↗
NASA NTRS2007

An IEEE 1451.1 Architecture for ISHM Applications

The IEEE 1451.1 Standard for a Smart Transducer Interface defines a common network information model for connecting and managing smart elements in control and data acquisition networks using network-capable application processors (NCAPs). The Standard is a network-neutral design model that is easily ported across operating systems and physical networks for implementing complex acquisition and control applications by simply plugging in the appropriate network level drivers. To simplify configuration and tracking of transducer and actuator details, the family of 1451 standards defines a Transducer Electronic Data Sheet (TEDS) that is associated with each physical element. The TEDS contains all of the pertinent information about the physical operations of a transducer (such as operating regions, calibration tables, and manufacturer information), which the NCAP uses to configure the system to support a specific transducer. The Integrated Systems Health Management (ISHM) group at NASA's John C. Stennis Space Center (SSC) has been developing an ISHM architecture that utilizes IEEE 1451.1 as the primary configuration and data acquisition mechanism for managing and collecting information from a network of distributed intelligent sensing elements. This work has involved collaboration with other NASA centers, universities and aerospace industries to develop IEEE 1451.1 compliant sensors and interfaces tailored to support health assessment of complex systems. This paper and presentation describe the development and implementation of an interface for the configuration, management and communication of data, information and knowledge generated by a distributed system of IEEE 1451.1 intelligent elements monitoring a rocket engine test system. In this context, an intelligent element is defined as one incorporating support for the IEEE 1451.x standards and additional ISHM functions. Our implementation supports real-time collection of both measurement data (raw ADC counts and converted engineering units) and health statistics produced by each intelligent element. The handling of configuration, calibration and health information is automated by using the TEDS in combination with other electronic data sheets extensions to convey health parameters. By integrating the IEEE 1451.1 Standard for a Smart Transducer Interface with ISHM technologies, each element within a complex system becomes a highly flexible computation engine capable of self-validation and performing other measures of the quality of information it is producing.

Morris, Jon A.↗
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
NASA NTRS1995

A method for diagnosing time dependent faults using model-based reasoning systems

This paper explores techniques to apply model-based reasoning to equipment and systems which exhibit dynamic behavior (that which changes as a function of time). The model-based system of interest is KATE-C (Knowledge based Autonomous Test Engineer) which is a C++ based system designed to perform monitoring and diagnosis of Space Shuttle electro-mechanical systems. Methods of model-based monitoring and diagnosis are well known and have been thoroughly explored by others. A short example is given which illustrates the principle of model-based reasoning and reveals some limitations of static, non-time-dependent simulation. This example is then extended to demonstrate representation of time-dependent behavior and testing of fault hypotheses in that environment.

Goodrich, Charles H.↗
NASA NTRS2000

A Regional-Scale Assessment of Satellite Derived Precipitable Water Vapor Across The Amazon Basin

Atmospheric water vapor is widely recognized as a key climate variable, linking an assortment of poorly understood and complex processes. It is a major element of the hydrological cycle and provides a mechanism for energy exchange among many of the Earth system components. Reducing uncertainty in our current knowledge of water vapor and its role in the climate system requires accurate measurement, improved modeling techniques, and long-term prediction. Satellites have the potential to satisfy these criteria, as well as provide high resolution measurements that are not available from conventional sources. The focus of this paper is to examine the temporal and mesoscale variations of satellite derived precipitable water vapor (PW) across the Amazon Basin. This region is pivotal in the functioning of the global climate system through its abundant release of latent heat associated with heavy precipitation events. In addition, anthropogenic deforestation and biomass burning activities in recent decades are altering the conditions of the atmosphere, especially in the planetary boundary layer. A physical split-window (PSW) algorithm estimates PW using images from the GOES satellites along with the NCEP/NCAR Reanalysis data that provides the first guess information. Retrievals are made at a three-hourly time step during daylight hours in the Amazon Basin and surrounding areas for the months of June and October in 1988 (dry year) and 1995 (wet year). Spatially continuous fields are generated 5 times daily at 12Z, 15Z, 18Z, 21Z, and 00Z. These fields are then averaged to create monthly and 3 hourly monthly grids. Overall, the PSW estimates PW reasonable well in the Amazon with MAE ranging from 3.0 - 9.0 mm and MAE/observed mean around 20% in comparison to radiosonde observations. The distribution of PW generally mimics that of precipitation. Maximum values (42 - 52 mm) are located in the Northwest whereas minimum values (18 - 27 mm) are found along Brazil's East coast. Aside from the East coast, PW has a stronger north-south gradient than that of rainfall. As for the temporal variation of PW, June (1988 and 1995) experiences a peak about 1400 local time, corresponding to a maximum in air temperature. In contrast, October (1988 and 1995) experiences a maximum early in the day, 1100 local time, with a gradual decrease toward nighttime.

DeLiberty, Tracy↗
NASA NTRS1994

CLIPS, AppleEvents, and AppleScript: Integrating CLIPS with commercial software

Many of today's intelligent systems are comprised of several modules, perhaps written in different tools and languages, that together help solve the user's problem. These systems often employ a knowledge-based component that is not accessed directly by the user, but instead operates 'in the background' offering assistance to the user as necessary. In these types of modular systems, an efficient, flexible, and eady-to-use mechanism for sharing data between programs is crucial. To help permit transparent integration of CLIPS with other Macintosh applications, the AI Research Branch at NASA Ames Research Center has extended CLIPS to allow it to communicate transparently with other applications through two popular data-sharing mechanisms provided by the Macintosh operating system: Apple Events (a 'high-level' event mechanism for program-to-program communication), and AppleScript, a recently-released scripting language for the Macintosh. This capability permits other applications (running on either the same or a remote machine) to send a command to CLIPS, which then responds as if the command were typed into the CLIPS dialog window. Any result returned by the command is then automatically returned to the program that sent it. Likewise, CLIPS can send several types of Apple Events directly to other local or remote applications. This CLIPS system has been successfully integrated with a variety of commercial applications, including data collection programs, electronics forms packages, DBMS's, and email programs. These mechanisms can permit transparent user access to the knowledge base from within a commercial application, and allow a single copy of the knowledge base to service multiple users in a networked environment.

Compton, Michael M.↗
NASA NTRS2009

Integrated Risk and Knowledge Management Program -- IRKM-P

The NASA Exploration Systems Mission Directorate (ESMD) IRKM-P tightly couples risk management and knowledge management processes and tools to produce an effective "modern" work environment. IRKM-P objectives include: (1) to learn lessons from past and current programs (Apollo, Space Shuttle, and the International Space Station); (2) to generate and share new engineering design, operations, and management best practices through preexisting Continuous Risk Management (CRM) procedures and knowledge-management practices; and (3) to infuse those lessons and best practices into current activities. The conceptual framework of the IRKM-P is based on the assumption that risks highlight potential knowledge gaps that might be mitigated through one or more knowledge management practices or artifacts. These same risks also serve as cues for collection of knowledge particularly, knowledge of technical or programmatic challenges that might recur.

Lengyel, David M.↗
NASA NTRSDate not supplied

Defining Pupil Knowledge Requirements for Roman Space Telescope Integrated Payload Assembly Testing

This thesis examines the use of phase retrieval to characterize an optical system under testing conditions with pupil knowledge error. The purpose of this investigation is to define pupil knowledge requirements for the integrated payload testing of the Roman Space Telescope. We have conducted a Monte Carlo study to determine how location uncertainty of the ground support equipment relative to the payload caused by the temperature deformation in the Space Environment Simulator will affect our ability to use image-based wavefront sensing during the test to characterize system alignment. This test will be conducted with significant wavefront error (WFE) present due to gravity sag on the primary mirror. The study incrementally decenters two pieces of ground support equipment and evaluates how the phase retrieval algorithm attempts to fit the wavefront. We conducted this investigation in two steps. First, we used an ideal optical model of the test configuration without the gravity sag on the primary mirror to see the fitting effects related only to pupil knowledge error introduced by the decentering. Then, we used perturbed and gravity sag models to investigate the effects of decentering dependent wavefront effects in addition to pupil knowledge error. The results from the ideal model provided a clear limit of the amount of pupil knowledge error that can be present before seeing significant WFE in the phase retrieval. For the second study, we used the decentering tolerances from the ideal model results to determine the WFE effects of decentering in the presence of gravity sag and system misalignments. The second part of this study will be factored into the overall integrated payload assembly testing WFE budget.

Roman Space Telescope↗