Fire survivability depends on the detection of and response to a fire before it has produced an unacceptable environment in the vehicle. This detection time is the result of interplay between the fire burning and growth rates; the vehicle size; the detection system design; the transport time to the detector (controlled by the level of mixing in the vehicle); and the rate at which the life support system filters the atmosphere, potentially removing the detected species or particles. Given the large differences in critical vehicle parameters (volume, mixing rate and filtration rate) the detection approach that works for a large vehicle (e.g. the ISS) may not be the best choice for a smaller crew capsule. This paper examines the impact of vehicle size and environmental control and life support system parameters on the detectability of fires in comparison to the hazard they present. A lumped element model was developed that considers smoke, heat, and toxic product release rates in comparison to mixing and filtration rates in the vehicle. Recent work has quantified the production rate of smoke and several hazardous species from overheated spacecraft polymers. These results are used as the input data set in the lumped element model in combination with the transport behavior of major toxic products released by overheating spacecraft materials to evaluate the necessary alarm thresholds to enable appropriate response to the fire hazard.

An accidental fire inside a spacecraft is an unlikely, but very real emergency situation that can easily have dire consequences. While much has been learned over the past 25+ years of dedicated research on flame behavior in microgravity, a quantitative understanding of the initiation, spread, detection and extinguishment of a realistic fire aboard a spacecraft is lacking. Virtually all combustion experiments in microgravity have been small-scale, by necessity (hardware limitations in ground-based facilities and safety concerns in space-based facilities). Large-scale, realistic fire experiments are unlikely for the foreseeable future (unlike in terrestrial situations). Therefore, NASA will have to rely on scale modeling, extrapolation of small-scale experiments and detailed numerical modeling to provide the data necessary for vehicle and safety system design. This paper presents the results of parallel efforts to better model the initiation, spread, detection and extinguishment of fires aboard spacecraft. The first is a detailed numerical model using the freely available Fire Dynamics Simulator (FDS). FDS is a CFD code that numerically solves a large eddy simulation form of the Navier-Stokes equations. FDS provides a detailed treatment of the smoke and energy transport from a fire. The simulations provide a wealth of information, but are computationally intensive and not suitable for parametric studies where the detailed treatment of the mass and energy transport are unnecessary. The second path extends a model previously documented at ICES meetings that attempted to predict maximum survivable fires aboard space-craft. This one-dimensional model implies the heat and mass transfer as well as toxic species production from a fire. These simplifications result in a code that is faster and more suitable for parametric studies (having already been used to help in the hatch design of the Multi-Purpose Crew Vehicle, MPCV).

Under the Advanced Exploration Systems (AES) Spacecraft Fire Safety Demonstration Project (SFSDP), as a risk mitigation activity in support of the development of a large-scale fire demonstration experiment in microgravity, flame-spread tests were conducted in normal gravity on thin, cellulose-based fuels in a sealed chamber. The primary objective of the tests was to measure pressure rise in a chamber as sample material, burning direction (upward/downward), total heat release, heat release rate, and heat loss mechanisms were varied between tests. A Design of Experiments (DOE) method was imposed to produce an array of tests from a fixed set of constraints and a coupled response model was developed. Supplementary tests were run without experimental design to additionally vary select parameters such as initial chamber pressure. The starting chamber pressure for each test was set below atmospheric to prevent chamber overpressure. Bottom ignition, or upward propagating burns, produced rapid acceleratory turbulent flame spread. Pressure rise in the chamber increases as the amount of fuel burned increases mainly because of the larger amount of heat generation and, to a much smaller extent, due to the increase in gaseous number of moles. Top ignition, or downward propagating burns, produced a steady flame spread with a very small flat flame across the burning edge. Steady-state pressure is achieved during downward flame spread as the pressure rises and plateaus. This indicates that the heat generation by the flame matches the heat loss to surroundings during the longer, slower downward burns. One heat loss mechanism included mounting a heat exchanger directly above the burning sample in the path of the plume to act as a heat sink and more efficiently dissipate the heat due to the combustion event. This proved an effective means for chamber overpressure mitigation for those tests producing the most total heat release and thusly was determined to be a feasible mitigation strategy to incorporate into the microgravity experiment.

Introduction: The Integrated Medical Model (IMM) utilizes historical data, cohort data, and external simulations as input factors to provide estimates of crew health, resource utilization and mission outcomes. The Cervical Spine Injury Module (CSIM) is an external simulation designed to provide the IMM with parameter estimates for 1) a probability distribution function (PDF) of the incidence rate, 2) the mean incidence rate, and 3) the standard deviation associated with the mean resulting from injury/trauma of the neck. Methods: An injury mechanism based on an idealized low-velocity blunt impact to the superior posterior thorax of an ISS crewmember was used as the simulated mission environment. As a result of this impact, the cervical spine is inertially loaded from the mass of the head producing an extension-flexion motion deforming the soft tissues of the neck. A multibody biomechanical model was developed to estimate the kinematic and dynamic response of the head-neck system from a prescribed acceleration profile. Logistic regression was performed on a dataset containing AIS1 soft tissue neck injuries from rear-end automobile collisions with published Neck Injury Criterion values producing an injury transfer function (ITF). An injury event scenario (IES) was constructed such that crew 1 is moving through a primary or standard translation path transferring large volume equipment impacting stationary crew 2. The incidence rate for this IES was estimated from in-flight data and used to calculate the probability of occurrence. The uncertainty in the model input factors were estimated from representative datasets and expressed in terms of probability distributions. A Monte Carlo Method utilizing simple random sampling was employed to propagate both aleatory and epistemic uncertain factors. Scatterplots and partial correlation coefficients (PCC) were generated to determine input factor sensitivity. CSIM was developed in the SimMechanics/Simulink environment with a Monte Carlo wrapper (MATLAB) used to integrate the components of the module. Results: The probability of generating an AIS1 soft tissue neck injury from the extension/flexion motion induced by a low-velocity blunt impact to the superior posterior thorax was fitted with a lognormal PDF with mean 0.26409, standard deviation 0.11353, standard error of mean 0.00114, and 95% confidence interval [0.26186, 0.26631]. Combining the probability of an AIS1 injury with the probability of IES occurrence was fitted with a Johnson SI PDF with mean 0.02772, standard deviation 0.02012, standard error of mean 0.00020, and 95% confidence interval [0.02733, 0.02812]. The input factor sensitivity analysis in descending order was IES incidence rate, ITF regression coefficient 1, impactor initial velocity, ITF regression coefficient 2, and all others (equipment mass, crew 1 body mass, crew 2 body mass) insignificant. Verification and Validation (V&V): The IMM V&V, based upon NASA STD 7009, was implemented which included an assessment of the data sets used to build CSIM. The documentation maintained includes source code comments and a technical report. The software code and documentation is under Subversion configuration management. Kinematic validation was performed by comparing the biomechanical model output to established corridors.

INTRODUCTIONNASA's Human Research Program is using a probabilistic risk assessment approach to identify acute and chronic medical risks to manned spaceflight. The objective of this project was to estimate the likelihood of a neurological head injury to a crewmember severe enough to require medical assessment, treatment, or evacuation during a typical International Space Station (ISS) increment.METHODSA 2 degree-of-freedom analytical model of the human head was created to allow for analysis of the impact response. The output of the model is acceleration of the head, which was used to determine the probability that the simulated impact resulted in a head injury with an Abbreviated Injury Scale (AIS) score of 3 or greater. These data were then integrated into a probabilistic risk assessment, which outputs a likelihood of injury with a representative measure of the uncertainty.RESULTSA Monte Carlo simulation was performed to vary input parameters over their defined distributions. The mean probability of a moderate neurological injury (AIS 3 or greater) occurring due to a head impact by a crewmember translating through the ISS is 1.16 x 10(-4) per 6-mo mission increment (2.32 x 10(-4) per year).DISCUSSIONOur head injury prediction model has shown that there is a low, yet not insignificant, probability of neurological head injury of AIS score 3 or greater. The results from this simulation will be input into the parent Integrated Medical Model, which incorporates the risks of over 80 different medical events in order to inform mission planning scenarios.

A mathematical representation of the human head was created to allow for analysis of the impact response. The output of the model is acceleration of the head, which was used to determine the probability that the simulated impact resulted in a head injury with an Abbreviated Injury Scale (AIS) score of three or greater. Final injury rates were estimated by combining the head impact response with a relevant mission scenario and likely head impact situations. These data were then tied together in a probabilistic risk assessment, which output a likelihood of injury with a representative measure of the uncertainty. A sensitivity analysis to identify the input parameters that had the most influence on the uncertainty of the output was performed using Partial Rank Correlation Coefficients. A Monte Carlo simulation was performed to vary to input parameters over their defined distributions, and the model was run 100,000 times. From this analysis, the mean probability of a moderate neurological injury (AIS three or greater) occurring due to a head impact by a crewmember translating through the ISS is 1.16 × 10 −4 per six-month mission increment (2.32 × 10 −4 per year). The 95 th percentile value for head injury is 4.94 × 10 −4 per six-month increment (9.88 × 10 −4 per year). The sensitivity analysis revealed that the parameters that the output uncertainty was most dependent on were the impact velocity (64.8% contribution to variance), a coefficient that defines the shape of the probability of injury curve (23.6% contribution to variance), and the incidence rate (10.7% contribution to variance). The model was executed to evaluate the probability of a head injury occurring due to crewmembers translating through the International Space Station. Our head injury prediction module has shown that there is a low probability of neurological head injury of AIS score three or greater, and the velocity of impact is the most important factor in the model. The results from this simulation will be input into the parent Integrated Medical Model, which incorporates the risks of over 80 different medical events in order to inform mission planning scenarios. The model did not evaluate less severe injuries that may require treatments such as bandages, antibiotic ointments, and other common medical kit items. It was felt that these less severe injuries would not result in the need for an immediate return to earth for medical treatment, and that the rate of occurrence of these injuries is known from previous data [1].

The NASA Integrated Medical Model (IMM) assesses the risk, including likelihood and impact of occurrence, of all credible in-flight medical conditions. Fatigue due to sleep disruption is a condition that could lead to operational errors, potentially resulting in loss of mission or crew. Pharmacological consumables are mitigation strategies used to manage the risks associated with sleep deficits. The likelihood of medical intervention due to sleep disruption was estimated with a well validated sleep model and a Monte Carlo computer simulation in an effort to optimize the quantity of consumables. METHODS: The key components of the model are the mission parameter program, the calculation of sleep intensity and the diagnosis and decision module. The mission parameter program was used to create simulated daily sleep/wake schedules for an ISS increment. The hypothetical schedules included critical events such as dockings and extravehicular activities and included actual sleep time and sleep quality. The schedules were used as inputs to the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model (IBR Inc., Baltimore MD), which calculated sleep intensity. Sleep data from an ISS study was used to relate calculated sleep intensity to the probability of sleep medication use, using a generalized linear model for binomial regression. A human yes/no decision process using a binomial random number was also factored into sleep medication use probability. RESULTS: These probability calculations were repeated 5000 times resulting in an estimate of the most likely amount of sleep aids used during an ISS mission and a 95% confidence interval. CONCLUSIONS: These results were transferred to the parent IMM for further weighting and integration with other medical conditions, to help inform operational decisions. This model is a potential planning tool for ensuring adequate sleep during sleep disrupted periods of a mission.

Despite over half a century of manned space flight, the space flight community is only now coming to fully assess the short and long term medical dangers of exposure to reduced gravity environments. Further, as new manned spacecraft are de signed and with the advent of commercial flight capabilities to the general public, a full understanding of medical risk becomes even more critical for maintaining and understanding mission safety and crew health. To address these critical issues, the National Aeronautics and Space Administration (NASA) Human Research Program (HRP) has begun to address the medical hazards with a formalized risk management approach by effectively identifying and attempting to mitigate acute and chronic medical risks to manned space flight. This paper describes NASA Glenn Research Center's (GRC) efforts to develop a systematic methodology to assess the likelihood of in-flight medical conditions. Using a probabilistic approach, medical risks are assessed using well established and accepted biomedical and human performance models in combination with fundamentally observed data that defines the astronauts' physical conditions, environment and activity levels. Two different examples of space flight risk are used to show the versatility of our approach and how it successfully integrates disparate information to provide HRP decision makers with a valuable source of information which is otherwise lacking.

Terrestrial fire safety technology has been developed from the careful study of the phenomena involved in the fire sciences. These include the ignitability of materials and fire behavior in relevant environments and scales, quantification of fire signatures required for early fire detection, development of instruments to measure those signatures, requirements for responding to and extinguishing a fire, and finally, identification of any special requirements for cleanup of the fire scene. This same overall approach is being applied to the development of spacecraft fire safety technologies through NASA’s Fire Prevention, Detection, and Suppression project and the requirements for infusion of these technologies into various space vehicles. In spite of the experience gained in terrestrial applications, we are limited by a general lack of specific information regarding the progress of fire development in relevant spacecraft environments. In this paper, we discuss the process that is being implemented to obtain the data to develop a coherent spacecraft fire safety strategy. This includes a material flammability strategy in low- and partial-gravity in elevated oxygen, low-pressure environments and the development of fire detection technologies. Fire suppression, response, and cleanup are presented as a single contingency response system that must work seamlessly with the rest of the spacecraft environmental control and life support system. Throughout the discussion, technology gaps and research needs for the continued development of fire safety technologies for NASA’s exploration missions are identified.

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

Smoke transport and detection were modeled numerically in the ISS Destiny module using the NIST, Fire Dynamics Simulator code. The airflows in Destiny were modeled using the existing flow conditions and the module geometry included obstructions that simulate the currently installed hardware on orbit. The smoke source was modeled as a 0.152 by 0.152 m region that emitted smoke particulate ranging from 1.46 to 8.47 mg/s. In the module domain, the smoke source was placed in the center of each Destiny rack location and the model was run to determine the time required for the two smoke detectors to alarm. Overall the detection times were dominated by the circumferential flow, the axial flow from the intermodule ventilation and the smoke source strength.

SUMMARY AND CONCLUSIONS The performance of five thermal mass flow meters were tested on the KC-135 Reduced Gravity Aircraft in orthogonal, coparallel, and counterparallel orientations relative to the g vector. Data was taken throughout the parabolic trajectory where the g-level varied from 0.01 to 1.8 times normal gravity. Each meter was calibrated in normal gravity in the orthogonal position prior to flight followed by ground testing at seven different flow conditions to establish a baseline operation. Departure functions relative to an orifice flow reference was developed to quantify the results. A three-factor analysis of variance was performed to uncover the main and interaction effects of the inlet gas pressure, gravity level, and flow meter orientation. A detailed uncertainty analysis was also performed to quantify these effects relative to the systematic and random errors of the experiment. All of these flow meters performed within manufacturers specifications when operated in normal gravity in the orientation at which they were calibrated. This, of course, is how they were designed and is recommended by the manufacturer. Gravitational acceleration and orientation had a unique effect on the performance of each meter. Specific conclusions from this study are as follows: 1. For only three of the five flow meters did the departure functions for the different orientations converge in reduced gravity. This implies that more complex heat transfer mechanisms exist between the internal components of these instruments than could be evaluated in this experiment. Explanations for this behavior include (1) a transient thermal response such that the meter did not reach steady state during the 25 seconds of reduced gravity on the aircraft or (2) sensitivity to the high-frequency, low amplitude fluctuations in g-level that occurs during the low-gravity portions of parabolic flight. 2. For four of the five meters, the variation in the departure function with orientation at normal gravity was similar to that experienced at elevated gravity. Therefore, meter performance in normal gravity gives a reasonable indication of its performance at elevated gravity levels. 3. The ANOVA analysis indicated that for four of the five meters, variations in the orientation had a greater contribution to the departure function than variations in gravity level. This indicates that if a flow meter will be used in a non-zero gravity environment, the meter should be mounted in the attitude at which it was calibrated. 4. There is no single test that can be performed in normal gravity that will conclusively indicate the reduced gravity performance of a thermal flow meter. Based on this evaluation of five flow meters, the variation of departure function with inlet pressure at reduced gravity may be the same, better, or worse than the behavior in normal gravity. 5. In reduced gravity, the performance of all meters evaluated was within about ±5% of full scale for any orientation, independent of its performance in normal or elevated gravity levels. 6. Even though the flow meters operate on similar principles, their performance was quite different, presumably because of variations in the internal design and details of operation. This emphasizes the need for care in handling thermal flow meters after they have been calibrated. Small changes in configuration, mounting, or internal structure could have dramatic and unexpected effects on their operation and accuracy. Based on the experience gained during this experiment and the subsequent results, it is suggested that when operating thermal flow meters in low-gravity environments, the greatest accuracy can be obtained by the in-situ calibration of the flow meter.