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NASA NTRS2002

Development of Advanced Life Cycle Costing Methods for Technology Benefit/Cost/Risk Assessment

The overall objective of this three-year grant is to provide NASA Langley's System Analysis Branch with improved affordability tools and methods based on probabilistic cost assessment techniques. In order to accomplish this objective, the Aerospace Systems Design Laboratory (ASDL) needs to pursue more detailed affordability, technology impact, and risk prediction methods and to demonstrate them on variety of advanced commercial transports. The affordability assessment, which is a cornerstone of ASDL methods, relies on the Aircraft Life Cycle Cost Analysis (ALCCA) program originally developed by NASA Ames Research Center and enhanced by ASDL. This grant proposed to improve ALCCA in support of the project objective by updating the research, design, test, and evaluation cost module, as well as the engine development cost module. Investigations into enhancements to ALCCA include improved engine development cost, process based costing, supportability cost, and system reliability with airline loss of revenue for system downtime. A probabilistic, stand-alone version of ALCCA/FLOPS will also be developed under this grant in order to capture the uncertainty involved in technology assessments. FLOPS (FLight Optimization System program) is an aircraft synthesis and sizing code developed by NASA Langley Research Center. This probabilistic version of the coupled program will be used within a Technology Impact Forecasting (TIF) method to determine what types of technologies would have to be infused in a system in order to meet customer requirements. A probabilistic analysis of the CER's (cost estimating relationships) within ALCCA will also be carried out under this contract in order to gain some insight as to the most influential costs and the impact that code fidelity could have on future RDS (Robust Design Simulation) studies.

Yackovetsky, Robert↗
NASA NTRS2005

A model to assess the Mars Telecommunications Network relay robustness

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Mars Telecommunications Orbiter (MTO)↗
NASA NTRS2006

Impact of Probabilistic Weather on Flight Routing Decisions

Flight delays in the United States have been found to increase year after year, along with the increase in air traffic. During the four-month period from May through August of 2005, weather related delays accounted for roughly 70% of all reported delays, The current weather prediction in tactical (within 2 hours) timeframe is at manageable levels, however, the state of forecasting weather for strategic (2-6 hours) timeframe is still not dependable for long-term planning. In the absence of reliable severe weather forecasts, the decision-making for flights longer than two hours is challenging. This paper deals with an approach of using probabilistic weather prediction for Traffic Flow Management use, and a general method using this prediction for estimating expected values of flight length and delays in the National Airspace System (NAS). The current state-of-the-art convective weather forecasting is employed to aid the decision makers in arriving at decisions for traffic flow and flight planing. The six-agency effort working on the Next Generation Air Transportation System (NGATS) have considered weather-assimilated decision-making as one of the principal foci out of a list of eight. The weather Integrated Product Team has considered integrated weather information and improved aviation weather forecasts as two of the main efforts (Ref. 1, 2). Recently, research has focused on the concept of operations for strategic traffic flow management (Ref. 3) and how weather data can be integrated for improved decision-making for efficient traffic management initiatives (Ref. 4, 5). An overview of the weather data needs and benefits of various participants in the air traffic system along with available products can be found in Ref. 6. Previous work related to use of weather data in identifying and categorizing pilot intrusions into severe weather regions (Ref. 7, 8) has demonstrated a need for better forecasting in the strategic planning timeframes and moving towards a probabilistic description of weather (Ref. 9). This paper focuses on. specified probability in a local region for flight intrusion/deviation decision-making. The process uses a probabilistic weather description, implements that in a air traffic assessment system to study trajectories of aircraft crossing a cut-off probability contour. This value would be useful for meteorologists in creating optimum distribution profiles for severe weather, Once available, the expected values of flight path and aggregate delays are calculated for efficient operations. The current research, however, does not deal with the issue of multiple cell encounters, as well as echo tops, and will be a topic of future work.

Sheth, Kapil↗
NASA NTRS2007

NASA Tech Briefs, January 2007

Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.

Source record↗
NASA NTRS2013

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Roberts, J. Brent↗
NASA NTRS1985

Probabilistic computer model of optimal runway turnoffs

Landing delays are currently a problem at major air carrier airports and many forecasters agree that airport congestion will get worse by the end of the century. It is anticipated that some types of delays can be reduced by an efficient optimal runway exist system allowing increased approach volumes necessary at congested airports. A computerized Probabilistic Runway Turnoff Model which locates exits and defines path geometry for a selected maximum occupancy time appropriate for each TERPS aircraft category is defined. The model includes an algorithm for lateral ride comfort limits.

Schoen, M. L.↗
NASA NTRS2022

NASA's NMME-Based S2S Hydrologic Forecast System for Food Insecurity Early Warning in Southern Africa

In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA’s Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET’s Land Data Assimilation System (FLDAS). The FLDAS-Forecast’s dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa’s growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020.

Hydrologic forecasts↗
NASA NTRS2011

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

Kirschbaum, Dalia↗
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 NTRS2014

Integrated Medical Model Verification, Validation, and Credibility

The Integrated Medical Model (IMM) was designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic (stochastic process) model based on historical data, cohort data, and subject matter expert opinion. A probabilistic approach is taken since exact (deterministic) results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation. METHODS: In 2008, the IMM team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) was published, the IMM team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. RESULTS: IMM VVC updates are compiled recurrently and include 7009 Compliance and Credibility matrices, IMM VV Plan status, and a synopsis of any changes or updates to the IMM during the reporting period. Reporting tools have evolved over the lifetime of the IMM project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the NASA-STD-7009 Guidance Document. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including Operations, Science and Technology Planning, and Exploration Planning. CONCLUSIONS: The VVC approach established by the IMM project of combining the IMM VV Plan with 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Validation↗
NASA NTRS2015

Integrated Medical Model (IMM) Project Verification, Validation, and Credibility (VVandC)

The Integrated Medical Model (IMM) Project supports end user requests by employing the Integrated Medical Evidence Database (iMED) and IMM tools as well as subject matter expertise within the Project. The iMED houses data used by the IMM. The IMM is designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic model based on historical data, cohort data, and subject matter expert opinion. A stochastic approach is taken because deterministic results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation, as well as lay the foundation for external review and application. METHODS: In 2008, the Project team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) [1] was published, the Project team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. Construction of these tools included meeting documentation and evidence requirements sufficient to meet external review success criteria. RESULTS: IMM Project VVC updates are compiled recurrently and include updates to the 7009 Compliance and Credibility matrices. Reporting tools have evolved over the lifetime of the IMM Project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the HRP NASA-STD-7009 Guidance Document working group and the NASA-HDBK-7009 [2]. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including operations, science and technology planning, and exploration planning. IMM v4.0 is slated for operational release in the FY015 and current VVC assessments illustrate the expected VVC status prior to the completion of customer lead external review efforts. CONCLUSIONS: The VVC approach established by the IMM Project of incorporating Project-specific recommended practices and guidelines for implementing the 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM Project represented a critical communication tool in providing clear and concise suitability assessments to IMM customers. These processes have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Walton, M.↗
NASA NTRS2012

Comparison of TRMM 2A25 Products Version 6 and Version 7 with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem was addressed in a previous paper by comparison of 2A25 version 6 (V6) product with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25 version 7 (V7) products that were recently released as a replacement of V6. This new version is considered superior over land areas. Several aspects of the two versions are compared and quantified including rainfall rate distributions, systematic biases, and random errors. All analyses indicate V7 is an improvement over V6.

Kirstetter, Pierre-Emmanuel↗
NASA NTRS2011

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Kirstettier, Pierre-Emmanual↗
NASA NTRS2018

Sensitivity Analysis of the Change of Renal Stone Occurrence Rates in Astronauts Using Urine Chemistries

Changes in urine chemistry, during and post-flight, potentially alter the likelihood of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidences of renal stones in US astronauts exist and the question How much does this risk change with space flight? remains difficult to accurately quantify. Previous work by our group has illustrated the application of multi-factor deterministic and probabilistic modeling to assess the change in predicted likelihood of renal stone. Utilizing 1517 astronaut urine chemistries to inform the renal stone occurrence rate forecasting model, we performed a sensitivity analysis on urine chemistry components for their influence on predictions of renal stone size and rate of renal stone occurrence.

physiological response↗
NASA NTRS2017

Sensitivity Analysis of the Bone Fracture Risk Model

Introduction: The probability of bone fracture during and after spaceflight is quantified to aid in mission planning, to determine required astronaut fitness standards and training requirements and to inform countermeasure research and design. Probability is quantified with a probabilistic modeling approach where distributions of model parameter values, instead of single deterministic values, capture the parameter variability within the astronaut population and fracture predictions are probability distributions with a mean value and an associated uncertainty. Because of this uncertainty, the model in its current state cannot discern an effect of countermeasures on fracture probability, for example between use and non-use of bisphosphonates or between spaceflight exercise performed with the Advanced Resistive Exercise Device (ARED) or on devices prior to installation of ARED on the International Space Station. This is thought to be due to the inability to measure key contributors to bone strength, for example, geometry and volumetric distributions of bone mass, with areal bone mineral density (BMD) measurement techniques. To further the applicability of model, we performed a parameter sensitivity study aimed at identifying those parameter uncertainties that most effect the model forecasts in order to determine what areas of the model needed enhancements for reducing uncertainty. Methods: The bone fracture risk model (BFxRM), originally published in (Nelson et al) is a probabilistic model that can assess the risk of astronaut bone fracture. This is accomplished by utilizing biomechanical models to assess the applied loads; utilizing models of spaceflight BMD loss in at-risk skeletal locations; quantifying bone strength through a relationship between areal BMD and bone failure load; and relating fracture risk index (FRI), the ratio of applied load to bone strength, to fracture probability. There are many factors associated with these calculations including environmental factors, factors associated with the fall event, mass and anthropometric values of the astronaut, BMD characteristics, characteristics of the relationship between BMD and bone strength and bone fracture characteristics. The uncertainty in these factors is captured through the use of parameter distributions and the fracture predictions are probability distributions with a mean value and an associated uncertainty. To determine parameter sensitivity, a correlation coefficient is found between the sample set of each model parameter and the calculated fracture probabilities. Each parameters contribution to the variance is found by squaring the correlation coefficients, dividing by the sum of the squared correlation coefficients, and multiplying by 100. Results: Sensitivity analyses of BFxRM simulations of preflight, 0 days post-flight and 365 days post-flight falls onto the hip revealed a subset of the twelve factors within the model which cause the most variation in the fracture predictions. These factors include the spring constant used in the hip biomechanical model, the midpoint FRI parameter within the equation used to convert FRI to fracture probability and preflight BMD values. Future work: Plans are underway to update the BFxRM by incorporating bone strength information from finite element models (FEM) into the bone strength portion of the BFxRM. Also, FEM bone strength information along with fracture outcome data will be incorporated into the FRI to fracture probability.

mathematical models↗
NASA NTRS2015

Integrated Medical Model Project - Overview and Summary of Historical Application

Introduction: The Integrated Medical Model (IMM) Project represents one aspect of NASA's Human Research Program (HRP) to quantitatively assess medical risks to astronauts for existing operational missions as well as missions associated with future exploration and commercial space flight ventures. The IMM takes a probabilistic approach to assessing the likelihood and specific outcomes of one hundred medical conditions within the envelope of accepted space flight standards of care over a selectable range of mission capabilities. A specially developed Integrated Medical Evidence Database (iMED) maintains evidence-based, organizational knowledge across a variety of data sources. Since becoming operational in 2011, version 3.0 of the IMM, the supporting iMED, and the expertise of the IMM project team have contributed to a wide range of decision and informational processes for the space medical and human research community. This presentation provides an overview of the IMM conceptual architecture and range of application through examples of actual space flight community questions posed to the IMM project. Methods: Figure 1 [see document] illustrates the IMM modeling system and scenario process. As illustrated, the IMM computational architecture is based on Probabilistic Risk Assessment techniques. Nineteen assumptions and limitations define the IMM application domain. Scenario definitions include crew medical attributes and mission specific details. The IMM forecasts probabilities of loss of crew life (LOCL), evacuation (EVAC), quality time lost during the mission, number of medical resources utilized and the number and type of medical events by combining scenario information with in-flight, analog, and terrestrial medical information stored in the iMED. In addition, the metrics provide the integrated information necessary to estimate optimized in-flight medical kit contents under constraints of mass and volume or acceptable level of mission risk. Results and Conclusions: Historically, IMM simulations support Science and Technology planning, Exploration mission planning, and ISS program operations by supplying simulation support, iMED data information, and subject matter expertise to Crew Health and Safety and the HRP. Upcoming release of IMM version 4.0 seeks to provide enhanced functionality to increase the quality of risk decisions made using the IMM through a more accurate representation of the real world system.

Myers, J.↗
NASA NTRS1972

Mariner Venus-Mercury 1973 mission solar proton environment: fluence and dose

A derivation is presented for proton fluence over the duration of the Mariner Venus-Mercury 1973 (MVM73) mission in terms of a relatively constant, low energy component, the solar wind, and a probabilistic high energy component from discrete solar events. An updated correlation of yearly energetic proton fluence with yearly average sunspot number is presented. This correlation and sunspot cycle forecasts for the period of the MVM73 mission (late 1973 through early 1975) form the basis for the high energy proton fluence estimates with various confidence levels. Uncertainties in the probability estimates and in calculation of the scaling with distance from the Sun are discussed. Selection of a particular 95-percentile model as the design constraint is recommended, and reasons are presented for not using the worst-case model. Interior fluences were calculated and expressed in terms useful for spacecraft design.

Thomas, J. R.↗
NASA NTRS1995

A Probabilistic Tool that Aids Logistics Engineers in the Establishment of High Confidence Repair Need-Dates at the NASA Shuttle Logistics Depot

The NASA Shuttle Logistics Depot (NSLD) is tasked with the responsibility for repair and manufacture of Line Replaceable Unit (LRU) hardware and components to support the Space Shuttle Orbiter. Due to shrinking budgets, cost effective repair of LRU's becomes a primary objective. To achieve this objective, is imperative that resources be assigned to those LRU's which have the greatest expectation of being needed as a spare. Forecasting the times at which spares are needed requires consideration of many significant factors including: failure rate, flight rate, spares availability, and desired level of support, among others. This paper summarizes the results of the research and development work that has been accomplished in producing an automated tool that assists in the assignment of effective repair start-times for LRU's at the NSLD. This system, called the Repair Start-time Assessment System (RSAS), uses probabilistic modeling technology to calculate a need date for a repair that considers the current repair pipeline status, as well as, serviceable spares and projections of future demands. The output from the system is a date for beginning the repair that has significantly greater confidence (in the sense that a desired probability of support is ensured) than times produced using other techniques. Since an important output of RSAS is the longest repair turn-around time that will ensure a desired probability of support, RSAS has the potential for being applied to operations at any repair depot where spares are on-hand and repair start-times are of interest. In addition, RSAS incorporates tenants of Just-in-Time (JIT) techniques in that the latest repair start-time (i.e., the latest time at which repair resources must be committed) may be calculated for every failed unit This could reduce the spares inventory for certain items, without significantly increasing the risk of unsatisfied demand.

Bullington, J. V.↗