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NASA NTRS2019

Evidence-based Approach to Establish Space Suit Carbon Dioxide Limits

A literature survey was conducted to assess if published data (evidence) could help inform a space suit carbon dioxide (CO2) limit. The search identified more than 120 documents about human interaction with elevated CO2. Until now, the guiding philosophy has been to drive space suit CO2 as low as reasonably achievable. NASA’s EVA Office requested an evidencebased approach to support a new generation of exploration-class extravehicular activity (EVA) space suits. Specific literature data about CO2 are not available for EVA in microgravity because EVA is an operational activity and not a research platform. However, enough data from groundbased research are available to facilitate a consensus of expert opinion on space suit CO2 limits. The compilation of data in this report can answer many but not all concerns about the consequences of hypercapnic exercise in a space suit. Inspired partial pressure of CO2 (PICO2) and not dry-gas partial pressure of CO2 (PCO2) is the appropriate metric for hypercapnic dose to establish space suit CO2 limits. The reduction of inspired gas partial pressures by saturation of the inspired gases with water vapor at 37°C is a significant factor under conditions of hypobaric space suit operation. Otherwise healthy EVA astronauts will exhibit wide variability in responses to acute hypercapnia while at rest and during exercise. What is clear from the literature is the absence of prospective (objective) accept or reject criteria for CO2 exposure in general, and no such criteria exist for operating a space suit. There is no absolute “Gold Standard” for an acceptable acute hypercapnic limit, just a gradual decrease in performance as CO2 increases. Acceptable CO2 exposure limits are occupation, situation (learned or novel tasks), and personspecific. Investigators who measured hypercapnic physiology rarely correlated those changes to neurocognitive symptoms, and those that measured hypercapnic neurocognition rarely correlated those changes with physiology. Some answers about changes in neurocognition and functional EVA performance during hypercapnic exercise in a space suit await new research.

Conkin, Johnny↗

NASA NTRS2022

Optimal Estimation Framework for Ocean Color Atmospheric Correction and Pixel-level Uncertainty Quantification

Ocean color remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance ( R rs )(λ) ; sr 1 ) at the ocean surface from the top-of-atmosphere reflectance. Often, the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (Optimal Estimation, OE) for ocean color AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a Neural Network (NN) RT forward model look-up-table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the MODerate resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua spacecraft. We compared the R rs )(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and Aerosol Robotic Network Ocean Color (AERONET-OC) datasets. The OE algorithm improved R rs )(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative R rs )(λ) , which often appear in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level R rs )(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Atmospheric correction↗

NASA NTRSDate not supplied

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗

NASA NTRS2014

The Present Habitability Potential of Gale Crater: What We Have Learned So Far From Mars Science Laboratory

The Mars Science Laboratory mission has comprehensively interrogated the surface environment of Mars as it explores Gale Crater. Both chemical and physical attributes of the present environment have been measured over the course of the mission, enabling us to compare the present state of the martian surface with the environmental requirements of prokaryotic microbes. While this approach does not exclude the possibility of martian life that may have evolved to adapt to the present conditions, it is advantageous in that it allows us to evaluate environmental requirements of known life and also provide insight into the likelihood of forward contamination by Earth organisms with the comparison of their environmental requirements with the measured attributes of the environment at Gale Crater. We have already modeled a paleoenvironment with high habitability potential (HP) based upon chemistry, mineralogy and other geological evidence such as sedimentary structures and larger scale geomorphology [1]. In this report, we turn our attention to the present HP of the Yellowknife Bay area, including the importance of the physical environmental metrics such as atmospheric pressure, air and ground temperature, ionizing radiation, wind speed and direction, slope, etc.

Conrad, P. G.↗

NASA NTRS2018

Performance Assessments and Comparisons of S-NPP and NOAA-20 (JPSS-1) VIIRS On-Orbit Calibration

The first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been in operation for more than 6 years on-board the S-NPP satellite and the second instrument, with the same design and performance requirements, was launched in November, 2017 on-board the JPSS-1 satellite (named NOAA-20 after reaching its orbit) and is currently in normal operation conditions. This paper provides a brief description of VIIRS on-orbit calibration and characterization activities and presents performance assessments and comparisons of S-NPP and NOAA-20 VIIRS using data collected from their on-board calibrators (OBC) and regularly scheduled lunar observations. Results show that NOAA-20 VIIRS is performing as well or better than S-NPP VIIRS in all of the key performance metrics. The NOAA-20 reflective solar bands, including the day-night band, have experienced less than 1% change in gain in the first 250 days since launch and did not suffer from the contamination related rapid degradation experienced by S-NPP VIIRS. Some of the NOAA20 thermal emissive bands had larger than expected gain degradation after launch due to ice buildup on the dewar window of the long-wave IR focal plane assembly but a mid-mission outgassing operation was able to restore their gains and maintain stable behavior. Though this study is focused on the sensor's key performance parameters, such as detector responses (gains), signal-to-noise ratios, and noise-equivalent temperature differences, challenges identified and lessons learned through different phases of on-orbit calibration and characterization are also discussed.

DNB↗

NASA NTRS2007

A Comprehensive Plan for the Long-Term Calibration and Validation of Oceanic Biogeochemical Satellite Data

The primary objective of this planning document is to establish a long-term capability and validating oceanic biogeochemical satellite data. It is a pragmatic solution to a practical problem based primarily o the lessons learned from prior satellite missions. All of the plan's elements are seen to be interdependent, so a horizontal organizational scheme is anticipated wherein the overall leadership comes from the NASA Ocean Biology and Biogeochemistry (OBB) Program Manager and the entire enterprise is split into two components of equal sature: calibration and validation plus satellite data processing. The detailed elements of the activity are based on the basic tasks of the two main components plus the current objectives of the Carbon Cycle and Ecosystems Roadmap. The former is distinguished by an internal core set of responsibilities and the latter is facilitated through an external connecting-core ring of competed or contracted activities. The core elements for the calibration and validation component include a) publish protocols and performance metrics; b) verify uncertainty budgets; c) manage the development and evaluation of instrumentation; and d) coordinate international partnerships. The core elements for the satellite data processing component are e) process and reprocess multisensor data; f) acquire, distribute, and archive data products; and g) implement new data products. Both components have shared responsibilities for initializing and temporally monitoring satellite calibration. Connecting-core elements include (but are not restricted to) atmospheric correction and characterization, standards and traceability, instrument and analysis round robins, field campaigns and vicarious calibration sites, in situ database, bio-optical algorithm (and product) validation, satellite characterization and vicarious calibration, and image processing software. The plan also includes an accountability process, creating a Calibration and Validation Team (to help manage the activity), and a discussion of issues associated with the plan's scientific focus.

Hooker, Stanford B.↗

NASA NTRS2014

Cost Analysis in a Multi-Mission Operations Environment

Spacecraft control centers have evolved from dedicated, single-mission or single mission-type support to multi-mission, service-oriented support for operating a variety of mission types. At the same time, available money for projects is shrinking and competition for new missions is increasing. These factors drive the need for an accurate and flexible model to support estimating service costs for new or extended missions; the cost model in turn drives the need for an accurate and efficient approach to service cost analysis. The National Aeronautics and Space Administration (NASA) Huntsville Operations Support Center (HOSC) at Marshall Space Flight Center (MSFC) provides operations services to a variety of customers around the world. HOSC customers range from launch vehicle test flights; to International Space Station (ISS) payloads; to small, short duration missions; and has included long duration flagship missions. The HOSC recently completed a detailed analysis of service costs as part of the development of a complete service cost model. The cost analysis process required the team to address a number of issues. One of the primary issues involves the difficulty of reverse engineering individual mission costs in a highly efficient multi-mission environment, along with a related issue of the value of detailed metrics or data to the cost model versus the cost of obtaining accurate data. Another concern is the difficulty of balancing costs between missions of different types and size and extrapolating costs to different mission types. The cost analysis also had to address issues relating to providing shared, cloud-like services in a government environment, and then assigning an uncertainty or risk factor to cost estimates that are based on current technology, but will be executed using future technology. Finally the cost analysis needed to consider how to validate the resulting cost models taking into account the non-homogeneous nature of the available cost data and the decreasing flight rate. This paper presents the issues encountered during the HOSC cost analysis process, and the associated lessons learned. These lessons can be used when planning for a new multi-mission operations center or in the transformation from a dedicated control center to multi-center operations, as an aid in defining processes that support future cost analysis and estimation. The lessons can also be used by mature service-oriented, multi-mission control centers to streamline or refine their cost analysis process.

Felton, Larry↗

NASA NTRS2014

Cost Analysis In A Multi-Mission Operations Environment

Spacecraft control centers have evolved from dedicated, single-mission or single missiontype support to multi-mission, service-oriented support for operating a variety of mission types. At the same time, available money for projects is shrinking and competition for new missions is increasing. These factors drive the need for an accurate and flexible model to support estimating service costs for new or extended missions; the cost model in turn drives the need for an accurate and efficient approach to service cost analysis. The National Aeronautics and Space Administration (NASA) Huntsville Operations Support Center (HOSC) at Marshall Space Flight Center (MSFC) provides operations services to a variety of customers around the world. HOSC customers range from launch vehicle test flights; to International Space Station (ISS) payloads; to small, short duration missions; and has included long duration flagship missions. The HOSC recently completed a detailed analysis of service costs as part of the development of a complete service cost model. The cost analysis process required the team to address a number of issues. One of the primary issues involves the difficulty of reverse engineering individual mission costs in a highly efficient multimission environment, along with a related issue of the value of detailed metrics or data to the cost model versus the cost of obtaining accurate data. Another concern is the difficulty of balancing costs between missions of different types and size and extrapolating costs to different mission types. The cost analysis also had to address issues relating to providing shared, cloud-like services in a government environment, and then assigning an uncertainty or risk factor to cost estimates that are based on current technology, but will be executed using future technology. Finally the cost analysis needed to consider how to validate the resulting cost models taking into account the non-homogeneous nature of the available cost data and the decreasing flight rate. This paper presents the issues encountered during the HOSC cost analysis process, and the associated lessons learned. These lessons can be used when planning for a new multi-mission operations center or in the transformation from a dedicated control center to multi-center operations, as an aid in defining processes that support future cost analysis and estimation. The lessons can also be used by mature serviceoriented, multi-mission control centers to streamline or refine their cost analysis process.

Newhouse, M.↗

NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗

NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

NASA NTRS2015

Technology Readiness Level Assessment Process as Applied to NASA Earth Science Missions

Technology assessments of fourteen science instruments were conducted within NASA using the NASA Technology Readiness Level (TRL) Metric. The instruments were part of three NASA Earth Science Decadal Survey missions in pre-formulation. The Earth Systematic Missions Program (ESMP) Systems Engineering Working Group (SEWG), composed of members of three NASA Centers, provided a newly modified electronic workbook to be completed, with instructions. Each instrument development team performed an internal assessment of its technology status, prepared an overview of its instrument, and completed the workbook with the results of its assessment. A team from the ESMP SEWG met with each instrument team and provided feedback. The instrument teams then reported through the Program Scientist for their respective missions to NASA's Earth Science Division (ESD) on technology readiness, taking the SEWG input into account. The instruments were found to have a range of TRL from 4 to 7. Lessons Learned are presented; however, due to the competition-sensitive nature of the assessments, the results for specific missions are not presented. The assessments were generally successful, and produced useful results for the agency. The SEWG team identified a number of potential improvements to the process. Particular focus was on ensuring traceability to guiding NASA documents, including the NASA Systems Engineering Handbook. The TRL Workbook has been substantially modified, and the revised workbook is described.

Systems Engineering↗

NASA NTRS2010

Coronal Mass Ejections and Solar Radio Emissions

Coronal mass ejections (CMEs) have important connections to various types of radio emissions from the Sun. The persistent noise storm radiation (type I storm at metric wavelengths, type III storms at longer wavelengths) can be clearly interrupted by the occurrence of a CME in the active region that produces the storm. Sometimes the noise storm completely disappears and other times, it reappears in the active region. Long-lasting type III bursts are associated with CME eruption, thought to be due to the reconnection process taking place beneath the erupting CME. Type II bursts are indicative of electron acceleration in the CME-driven shocks and hence considered to be the direct response of the CME propagation in the corona and interplanetary medium. Finally type IV bursts indicate large-scale post-eruption arcades containing trapped electrons that produce radio emission. This paper summarizes some key results that connect CMEs to various types of radio emission and what we can learn about particle acceleration in the corona) and interplanetary medium. Particular emphasis will be placed on type If bursts because of their connection to interplanetary shocks detected in situ.

Gopalswamy, Nat↗

NASA NTRS2014

An Approach for Performance Assessments of Extravehicular Activity Gloves

The Space Suit Assembly (SSA) Development Team at NASA Johnson Space Center has invested heavily in the advancement of rear‐entry planetary exploration suit design but largely deferred development of extravehicular activity (EVA) glove designs, and accepted the risk of using the current flight gloves, Phase VI, for unique mission scenarios outside the Space Shuttle and International Space Station (ISS) Program realm of experience. However, as design reference missions mature, the risks of using heritage hardware have highlighted the need for developing robust new glove technologies. To address the technology gap, the NASA Game‐Changing Technology group provided start‐up funding for the High Performance EVA Glove (HPEG) Project in the spring of 2012. The overarching goal of the HPEG Project is to develop a robust glove design that increases human performance during EVA and creates pathway for future implementation of emergent technologies, with specific aims of increasing pressurized mobility to 60% of barehanded capability, increasing the durability by 100%, and decreasing the potential of gloves to cause injury during use. The HPEG Project focused initial efforts on identifying potential new technologies and benchmarking the performance of current state of the art gloves to identify trends in design and fit leading to establish standards and metrics against which emerging technologies can be assessed at both the component and assembly levels. The first of the benchmarking tests evaluated the quantitative mobility performance and subjective fit of two sets of prototype EVA gloves developed ILC Dover and David Clark Company as compared to the Phase VI. Both companies were asked to design and fabricate gloves to the same set of NASA provided hand measurements (which corresponded to a single size of Phase Vi glove) and focus their efforts on improving mobility in the metacarpal phalangeal and carpometacarpal joints. Four test subjects representing the design‐to hand anthropometry completed range of motion, grip/pinch strength, dexterity, and fit evaluations for each glove design in pressurized conditions, with and without thermal micrometeoroid garments (TMG) installed. This paper provides a detailed description of hardware and test methodologies used and lessons learned.

Aitchison, Lindsay↗

NASA NTRSDate not supplied

The Evolution of the Impact Evidence Library Methods – What We Learned

During fiscal years 2020-2022 NASA’s Human Research Program, Exploration Medical Capability Element has worked to source this evidence for the model. During the process several lessons learned have been identified and drive recommendations for future efforts. The ICL 1.0: Spaceflight Medical Operational expertise should be consulted regarding the addition of future conditions. A clinician experienced with the nuances of spaceflight is recommended to have a minimum of 20 hours per condition to adequately source the evidence. Condition definitions should be carefully crafted to maintain mutual exclusivity. Common side effects from medications, common and consequential incidental findings in laboratory studies, and consequential side effects of diagnostic and procedural capabilities should be considered for inclusion as unique conditions in future iterations of the ICL 1.0. Incidence: Spaceflight incidence, although based on small population sizes, is the gold standard. Terrestrial incidence should be applied cautiously. Probability of minor vs. severe cases of the condition: Data informing severity probability of the medical condition are nearly always terrestrial. Special attention should be paid to this metric as it drives outcomes in the model and terrestrial data often overestimate severity of expected cases in spaceflight. Condition duration: Three clinical phases currently exist in the model. They represent the time necessary for diagnosis, treatment/convalescence/recurrence, and the remaining duration of the mission after maximal recovery, respectively. Further subdivision of acute and convalescent treatment/recurrence is recommended. Probability of need for evacuation: Surrogates for “Return to Definitive Care” were chosen prior to data collection. In most cases these surrogates were “need for hospitalization” or “need for surgery.” For many conditions these may not be realistic drivers of “Return to Definitive Care” and should be re-examined. Probability of Crew Mortality: Terrestrial mortality data is difficult to apply to astronauts who, invariably, have less comorbidities than terrestrial subjects. It must be applied judiciously. The proportion of mission tasks impaired by the condition: “Task Impairment” was assigned to each condition prior to the condition duration data becoming available. Future efforts should assign Task Impairment as the final step in sourcing the evidence.

A.J. Kreykes↗

NASA NTRS1996

An Overview of Controls and Flying Qualities Technology on the F/A-18 High Alpha Research Vehicle

The NASA F/A-18 High Alpha Research Vehicle (HARV) has been the flight test bed of a focused technology effort to significantly increase maneuvering capability at high angles of attack. Development and flight test of control law design methodologies, handling qualities metrics, performance guidelines, and flight evaluation maneuvers are described. The HARV has been modified to include two research control effectors, thrust vectoring, and actuated forebody strakes in order to provide increased control power at high angles of attack. A research flight control system has been used to provide a flexible, easily modified capability for high-angle-of-attack research controls. Different control law design techniques have been implemented and flight-tested, including eigenstructure assignment, variable gain output feedback, pseudo controls, and model-following. Extensive piloted simulation has been used to develop nonlinear performance guide-lines and handling qualities criteria for high angles of attack. This paper reviews the development and evaluation of technologies useful for high-angle-of-attack control. Design, development, and flight test of the research flight control system, control laws, flying qualities specifications, and flight test maneuvers are described. Flight test results are used to illustrate some of the lessons learned during flight test and handling qualities evaluations.

Pahle, Joseph W.↗

NASA NTRS2008

Space Transportation Systems Life Cycle Cost Assessment and Control

Civil and military applications of space transportation have been pursued for just over 50 years and there has been, and still is, a need for safe, dependable, affordable, and sustainable space transportation systems. Fully expendable and partially reusable space transportation systems have been developed and put in operation that have not adequately achieved this need. Access to space is technically achievable, but presently very expensive and will remain so until there is a breakthrough in the way we do business. Since 1991 the national Space Propulsion Synergy Team (SPST) has reviewed and assessed the lessons learned from the major U.S. space programs of the past decades focusing on what has been learned from the assessment and control of Life Cycle Cost (LCC) from these systems. This paper presents the results of a selected number of studies and analyses that have been conducted by the SPST addressing the need, as well as the solutions, for improvement in LCC. The major emphasis of the SPST processes is on developing the space transportation system requirements first (up front). These requirements must include both the usual system flight performance requirements and also the system functional requirements, including the infrastructure on Earth's surface, in-space and on the Moon and Mars surfaces to determine LCC. This paper describes the development of specific innovative engineering and management approaches and processes. This includes a focus on flight hardware maturity for reliability, ground operations approaches, and business processes between contractor and government organizations. A major change in program/project cost control is being proposed by the SPST to achieve a sustainable space transportation system LCC - controlling cost as a program metric in addition to the existing practice of controlling performance and weight. Without a firm requirement and methodically structured cost control, it is unlikely that an affordable and sustainable space transportation system LCC will ever be achieved. '

Robinson, John W.↗

NASA NTRS2011

The Use of the Integrated Medical Model for Forecasting and Mitigating Medical Risks for a Near-Earth Asteroid Mission

Introduction The Integrated Medical Model (IMM) is a decision support tool that is useful to space flight mission managers and medical system designers in assessing risks and optimizing medical systems. The IMM employs an evidence-based, probabilistic risk assessment (PRA) approach within the operational constraints of space flight. Methods Stochastic computational methods are used to forecast probability distributions of medical events, crew health metrics, medical resource utilization, and probability estimates of medical evacuation and loss of crew life. The IMM can also optimize medical kits within the constraints of mass and volume for specified missions. The IMM was used to forecast medical evacuation and loss of crew life probabilities, as well as crew health metrics for a near-earth asteroid (NEA) mission. An optimized medical kit for this mission was proposed based on the IMM simulation. Discussion The IMM can provide information to the space program regarding medical risks, including crew medical impairment, medical evacuation and loss of crew life. This information is valuable to mission managers and the space medicine community in assessing risk and developing mitigation strategies. Exploration missions such as NEA missions will have significant mass and volume constraints applied to the medical system. Appropriate allocation of medical resources will be critical to mission success. The IMM capability of optimizing medical systems based on specific crew and mission profiles will be advantageous to medical system designers. Conclusion The IMM is a decision support tool that can provide estimates of the impact of medical events on human space flight missions, such as crew impairment, evacuation, and loss of crew life. It can be used to support the development of mitigation strategies and to propose optimized medical systems for specified space flight missions. Learning Objectives The audience will learn how an evidence-based decision support tool can be used to help assess risk, develop mitigation strategies, and optimize medical systems for exploration space flight missions.

Kerstman, Eric↗

NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗