Titanium-Water Thermosyphon Gamma Radiation Exposure and Results

Titanium-water thermosyphons are being considered for use in heat rejection systems for fission power systems. Their proximity to the nuclear reactor will result in some gamma irradiation. Noncondensable gas formation from radiation-induced breakdown of water over time may render portions of the thermosyphon condenser inoperable. A series of developmental thermosyphons were operated at nominal operating temperature under accelerated gamma irradiation, with exposures on the same order of magnitude as that expected in 8 years of heat rejection system operation. Temperature data were obtained during exposure at three locations on each thermosyphon: evaporator, condenser, and condenser end cap. Some noncondensable gas was evident; however, thermosyphon performance was not affected because the noncondensable gas was compressed into the fill tube region at the top of the thermosyphon, away from the heat rejecting fin. The trend appeared to be an increasing amount of noncondensable gas formation with increasing gamma irradiation dose. Hydrogen is thought to be the most likely candidate for the noncondensable gas and hydrogen is known to diffuse through grain boundaries. Post-exposure evaluation of one thermosyphon in a vacuum chamber and at temperature revealed that the noncondensable gas diffused out of the thermosyphon over a relatively short period of time. Further research shows a number of experimental and theoretical examples of radiolysis occurring through gamma radiation alone in pure water

Titanium-Water Thermosyphon Gamma Radiation Effects and Results

Titanium-water thermosyphons are being considered for use in heat rejection systems for fission power systems. Their proximity to the nuclear reactor will result in some exposure to gamma irradiation. Non-condensable gas formation from radiation may breakdown water over time and render a portion of the thermosyphon condenser inoperable. A series of developmental thermosyphons were operated at nominal operating temperature with accelerated gamma irradiation exposures on the same order of magnitude that is expected in eight years of heat rejection system operation. Temperature data were obtained during exposure at three locations on each thermosyphon; evaporator, condenser, and condenser end cap. Some non-condensable gas was evident, however thermosyphon performance was not affected because the non-condensable gas was compressed into the fill tube region at the top of the thermosyphon, away from the heat rejecting fin. The trend appeared to be an increasing amount of non-condensable gas formation with increasing gamma irradiation dose. Hydrogen is thought to be the most likely candidate for the non-condensable gas and hydrogen is known to diffuse through grain boundaries. Post-exposure evaluation of selected thermosyphons at temperature and in a vacuum chamber revealed that the non-condensable gas likely diffused out of the thermosyphons over a relatively short period of time. Further research shows a number of experimental and theoretical examples of radiolysis occurring through gamma radiation alone in pure water

Can Simulation Credibility Be Improved Using Sensitivity Analysis to Understand Input Data Effects on Model Outcome?

Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations

The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints

The Integrated Medical Model: A Probabilistic Simulation Model for Predicting In-Flight Medical Risks

The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting mass and volume constraints

Validation of the NASA Integrated Medical Model: a Space Flight Medical Risk Prediction Tool

The Human Research Program funded the development of the Integrated Medical Model (IMM) to quantify the medical component of overall mission risk. The IMM uses Monte Carlo simulation methodology, incorporating space flight and ground medical data, to estimate the probability of mission medical outcomes and resource utilization. To determine the credibility of IMM output, the IMM project team completed two validation studies that compared IMM predicted output to observed medical events from a selection of Shuttle Transportation System (STS) and International Space Station (ISS) missions. The validation study results showed that the IMM underpredicted the occurrence of ~10% of the modeled medical conditions for the STS missions and overpredicted ~20% of the modeled medical conditions for the ISS missions. These findings imply that the strength of IMM predictions to inform decisions depends on simulated mission specifications including length. This discrepancy could result from medical recording differences between ISS and STS that possibly influence observed incidence rates, IMM combining all "mission type" data as constant occurrence rate or fixed proportion across both mission types, misspecification of symptoms to conditions, and gaps in the literature informing the model. Some of these issues will be alleviated by updating the IMM source data through incorporation of the observed validation data

Community Violence and Youth: Affect, Behavior, Substance Use, and Academics

Community violence is recognized as a major public health problem (WHO, World Report on Violence and Health,2002) that Americans increasingly understand has adverse implications beyond inner-cities. However, the majority of research on chronic community violence exposure focuses on ethnic minority, impoverished, and/or crime-ridden communities while treatment and prevention focuses on the perpetrators of the violence, not on the youth who are its direct or indirect victims. School-based treatment and preventive interventions are needed for children at elevated risk for exposure to community violence. In preparation, a longitudinal, community epidemiological study, The Multiple Opportunities to Reach Excellence (MORE) Project, is being fielded to address some of the methodological weaknesses presented in previous studies. This study was designed to better understand the impact of children’s chronic exposure to community violence on their emotional, behavioral, substance use, and academic functioning with an overarching goal to identify malleable risk and protective factors which can be targeted in preventive and intervention programs. This paper describes the MORE Project, its conceptual underpinnings, goals, and methodology, as well as implications for treatment and preventive interventions and future research