We describe an algorithm that identifies a subnetwork of brain regions involved in producing a task-specific behavior, here visuomotor behavior, from an anatomically defined primate brain connectome. The algorithm first finds the brain regions connected to an output region (here, primary motor cortex, M1) by one connection. It then identifies all regions, termed “layer 2” regions, connected to these “layer 1” regions by one connection. This process continues until the layer containing the input region (here, primary visual cortex, V1) is reached. The algorithm then finds, subject to a user-set maximum step number, all paths linking the input and output regions. The brain regions in these paths constitute the initial subnetwork identification that performs the task. Regions known not to be task-involved (for example, regions in the ventral stream of visual information vs. the dorsal stream, which helps generate visuomotor behavior) are then removed. Structural subnetwork analysis showed that the intraparietal sulcus of the parietal cortex (PCIP) was most, and the secondary visual (V2) and superior parietal (PCS) cortices second-most, central to local network activity. Changing PCIP, V2 and PCS activity was thus most likely to alter activity of the entire subnetwork. Model sufficiency was tested by instantiating each brain region’s inherent activity with multiple versions of a simple two-dimensional (2D) model that can produce oscillatory activity and synaptically interconnecting the regions to produce a macroscopic visuomotor model. The model reproduced the experimental local field potential (LFP) activity of the brain regions identified as part of the visuomotor subnetwork.### Competing Interest StatementThe authors have declared no competing interest.

Abstract During long-duration spaceflight, astronauts will experience gravity-transitions (G-transitions) between Earth-gravity (1G) and hypogravity (gravity < 1G), as well as prolonged time in hypogravity environments (i.e., interplanar, the Moon and Mars). The neural substrate underlying coordinated, volitional visuomotor movements like reach-to-grasp is tuned to produce proper movement in 1G. During G-transitions and hypogravity, the brain’s visuomotor control network becomes inadvertently configured through its activity states and interconnections to produce erroneous movements. Thus, the need to identify the brain performance pathways underlying mission-critical behaviors like performing visuomotor tasks and understanding their neurophysiological responses during space travel is critical for astronauts’ safety and mission success. This work utilizes a whole-brain, primate connectome to identify a visuomotor subnetwork, using a novel modularity algorithm. The identified visuomotor subnetwork is analyzed using local graph theory measures, suggesting the parietal cortex intraparietal sulcus (PCIP), superior parietal cortex (PCS), and secondary visual cortex (V2) are structurally positioned to play an important role in network activity and function. 1G macroscopic neural network, neural ensemble models are developed and subjected to emulated hypogravity, analyzed using an unsupervised machine learning technique to cluster simulations into hypogravity-affected and -unaffected groups. Qualitative analyses of region-wise frequency distributions and means between groups suggest the cingulate cortex (CCP), PCS, ventrolateral pre-motor cortex (PMCVL), primary visual cortex (V1), and V2 are hypogravity-affected, producing non-normal neural activity in frequency space. Synthesizing the structural and modeling results, this work suggests PCS and V2 may be viable countermeasure targets, ensuring proper visuomotor performance during and after spaceflight.

Changes in urine chemistry potentially alter the risk of renal stone formation in astronauts. Quantifying spaceflight renal stone incidence risk compared to pre-flight levels remains a significant challenge for assessing the appropriate vehicle, mission, and countermeasure design. A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration is combined with a probabilistic analysis to predict the in- and post-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-h urine chemistries. Our simulations predict that in-flight fluid intake alone would need to increase from current prescriptions of 2.0–2.5 L/day to ~3.2 L/day to approach the CaOx IRR of the pre-flight population. Bone protective interventions would reduce CaOx risk to pre-flight levels if Ca excretion alone is reduced to <150 mg/day or if current levels are diminished to 190 mg/day in combination with increasing fluid intake to 2.5–2.7 L/day. This analysis provides a quantitative risk assessment that can influence the critical balance between engineering and astronaut health requirements.

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent's susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4He in SD and 28Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut's impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for 4He, 16O, 28Si, 48Ti, or 56Fe up to 150 mGy, based on Attentional Set-shifting (ATSET) experimental tests. The ATSET assay consists of a series of cognitive performance tasks on irradiated male Wistar rats. The GCR ion doses represent the expected cumulative radiation astronauts may receive during a Mars mission on an individual ion basis. The primary objective is to synthesize and assess predictive models on a per-subject level through Machine Learning (ML) classifiers. The raw cognitive performance data from individual rodent subjects are used as features to train the models and to explore the capabilities of three different ML techniques for elucidating a range of correlations between received radiation on rodents and their performance outcomes. The analysis employs scores of selected input features and different normalization approaches which yield varying degrees of model performance. The current study shows that support vector machine, Gaussian naive Bayes, and random forest models are capable of predicting individual ion exposure using ATSET scores where corresponding Matthews correlation coefficients and F1 scores reflect model performance exceeding random chance. The study suggests a decremental effect on cognitive performance in rodents due to ≤150 mGy of single ion exposure, inasmuch as the models can discriminate between 0 mGy and any exposure level in the performance score feature space. A number of observations about the utility and limitations in specific normalization routines and evaluation scores are examined as well as best practices for ML with imbalanced datasets observed.

Experiments and modeling were performed to determine the surface kinetics of gaseous hydrogen chloride (HCl) with aluminum surfaces subjected to various treatments. HCl and other acid gases are a spacecraft fire safety concern because they are commonly found in products from electrical wire pyrolysis. Three types of aluminum surfaces were considered: surface with chromate conversion coating (iridite), anodized, and untreated. A test cell made of polytetrafluoroethylene was used to measure the difference in the HCl concentration from the inlet to the outlet. A simple one-step global surface reaction model that accounts for active surface sites was proposed. The kinetic constants were calibrated using the measured data. The calibrated model was validated against experiments with different flow rates and HCl inlet concentrations. The results showed that anodized aluminum had the most HCl uptake, followed by the iridite, and then the untreated aluminum. The amount of HCl uptake appeared to correlate well with the thickness of the oxide layer on aluminum. The relevance of these findings is discussed with respect to the design of large-scale fire safety experiments in space and various fire safety application scenarios.

Changes in urine chemistry, during and post flight, potentially increases the risk 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 exists and the question How much does this risk change with space flight? remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone occurrence.

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).

Upward flame spread tests were conducted on thin fuels in a sealed chamber capable of accommodating large-scale samples (1 m length). The primary objective of these tests was to measure flame spread and pressure rise in a large sealed chamber during and after flame spread and to characterize that data as a function of sample material, initial pressure, and sample size. The flame spread rate as a function of initial pressure has been measured for a given fuel and found to vary as ∼P2 in agreement with Grashof number scaling. The burning rate per unit area for a fixed pressure has been shown to be a constant independent of fuel area density or quantity of fuel burned. A steady upward flame spread was observed only at low pressure. The pressure rise in a sealed chamber has been shown to scale with the quantity of fuel burned, and the peak pressure has been shown to scale inversely with initial pressure, in agreement with the pressure dependence of the characteristic time associated with a simple analytical solution of an energy balance. The pressure rise per mass of fuel burned exhibits an exponential decay with burn-time, also in agreement with the analytical solution.

Human space missions generate waste materials. A 70-kg crewmember creates a waste stream of 1 kg per day, and a four-person crew on a deep space habitat for a 400+ day mission would create over 1600 kg of waste. Converted into methane, the carbon could be used as a fuel for propulsion or power. The NASA Advanced Exploration Systems (AES) Logistics Reduction and Repurposing (LRR) project is investing in space resource utilization with an emphasis on repurposing logistics materials for useful purposes and has selected steam reforming among many different competitive processes as the preferred method for repurposing organic waste into methane. Already demonstrated at the relevant processing rate of 5.4 kg of waste per day, high temperature oxygenated steam consumes waste and produces carbon dioxide, carbon monoxide, and hydrogen which can then be converted into methane catalytically. However, the steam reforming process has not been studied in microgravity. Data are critically needed to understand the mechanisms that allow use of steam reforming in a reduced gravity environment. This paper reviews the relevant literature, identifies gravity-dependent mechanisms within the steam gasification process, and describes an innovative experiment to acquire the crucial kinetic information in a small-scale reactor specifically designed to operate within the requirements of a reduced gravity aircraft flight. The experiment will determine if the steam reformer process is mass-transport limited, and if so, what level of forced convection will be needed to obtain performance comparable to that in 1-g.

Flammability experiments on silicone samples were conducted in anticipation of the Spacecraft Fire Experiment (Saffire). The sample geometry was chosen to match the NASA 6001 Test 1 specification, namely 5 cm wide by 30 cm tall. Four thicknesses of silicone (0.25, 0.36, 0.61 and 1.00 mm) were examined. Tests included traditional upward buoyant flame spread using Test 1 procedures, downward opposed-flow flame spread, horizontal and angled flame spread, and forced-flow upward and downward flame spread. In addition to these configurations, upward and downward tests were conducted in a chamber with varying oxygen concentrations. In the upward buoyant flame spread tests, the flame generally did not burn the entire sample. As thickness was increased, the flame spread distance decreased before flame extinguishment. For the thickest sample, ignition could not be achieved. In the downward tests, the two thinnest samples permitted the flame to burn the entire sample, but the spread rate was lower compared to the corresponding upward values. The other two thicknesses could not be ignited in the downward configuration. The increased flammability for downward spreading flames relative to upward ones is uncommon. The two thinnest samples also burned completely in the horizontal configuration, as well as at angles up to 75 degrees from the horizontal. Upward tests in air with an added forced flow were more flammable. The upward and downward flammability behavior was compared in atmospheres of varying oxygen concentration to determine a maximum oxygen concentration for each configuration. Complementary analyses using EDS, TGA, and SEM techniques suggest the importance of the silica layer deposited downstream onto the unburned sample surface.

The amount of waste generated on long-duration space missions away from Earth orbit creates the daunting challenge of how to manage the waste through reuse, rejection, or recycle. The option to merely dispose of the solid waste through an airlock to space was studied for both Earth-moon libration point missions and crewed Mars missions. Although the unique dynamic characteristics of an orbit around L2 might allow some discarded waste to intersect the lunar surface before re-impacting the spacecraft, the large amount of waste needed to be managed and potential hazards associated with volatiles recondensing on the spacecraft surfaces make this option problematic. A second option evaluated is to process the waste into useful gases to be either vented to space or used in various propulsion systems. These propellants could then be used to provide the yearly station-keeping needs at an L2 orbit, or if processed into oxygen and methane propellants, could be used to augment science exploration by enabling lunar mini landers to the far side of the moon.

As part of the risk mitigation effort supporting the development of a zero-gravity largescale fire demonstration experiment, normal-gravity flame-spread tests were conducted on thin fuels in a sealed chamber capable of accommodating large-scale samples. The primary objective of these tests was to measure pressure rise in a large sealed chamber during and after flame spread and to characterize that data as a function of sample material, burning direction (upward/downward), heat release rate, total heat release, and heat loss mechanisms. Ignition at the bottom of a sample resulted in a rapid acceleratory upward turbulent flame spread. As the amount of fuel burned increases, the pressure rise in the chamber increases since the volume of hot gas generated is larger. The upward flame spread slows down as initial pressure is reduced, causing a drop in peak pressure and a shift to the right in time for peak pressure. Ignition at the top of a sample resulted in a slow, steady flame spread with a very small flat flame across the top of each burning cheesecloth sample. For downward flame spread, the pressure rises to a maximum steady-state pressure during the burn. This indicates that the heat dissipation to the surroundings has matched the heat generated by the flame for this long, slow burn. Heat sinks were used for overpressure mitigation above the burning samples so that the flame plume would pass through the heat sink and deposit the heat there instead of heating the atmosphere. For upward and downward flame spread, the heat sink was very effective in reducing the pressure by a factor of at least four. The heat sink was determined to be an effective mitigation strategy for the flight experiment.

Human space missions generate trash with a substantial amount of plastic (20% or greater by mass). The trash also contains water trapped in food residue and paper products and other trash items. The Heat Melt Compactor (HMC) under development by NASA Ames Research Center (ARC) compresses the waste, dries it to recover water and melts the plastic to encapsulate the compressed trash. The resulting waste disk or puck represents an approximately ten-fold reduction in the volume of the initial trash loaded into the HMC. In the current design concept being pursued, the trash is compressed by a piston after it is loaded into the trash chamber. The piston face, the side walls of the waste processing chamber and the end surface in contact with the waste can be heated to evaporate the water and to melt the plastic. Water is recovered by the HMC in two phases. The first is a pre-process compaction without heat or with the heaters initially turned on but before the waste heats up. Tests have shown that during this step some liquid water may be expelled from the chamber. This water is believed to be free water (i.e., not bound with or absorbed in other waste constituents) that is present in the trash. This phase is herein termed Phase A of the water recovery process. During HMC operations, it is desired that liquid water recovery in Phase A be eliminated or minimized so that water-vapor processing equipment (e.g., condensers) downstream of the HMC are not fouled by liquid water and its constituents (i.e., suspended or dissolved matter) exiting the HMC. The primary water recovery process takes place next where the trash is further compacted while the heated surfaces reach their set temperatures for this step. This step will be referred to herein as Phase B of the water recovery process. During this step the waste chamber may be exposed to different selected pressures such as ambient, low pressure (e.g., 0.2 atm), or vacuum. The objective for this step is to remove both bound and any remaining free water in the trash by evaporation. The temperature settings of the heated surfaces are usually kept above the saturation temperature of water but below the melting temperature of the plastic in the waste during this step to avoid any encapsulation of wet trash which would reduce the amount of recovered water by blocking the vapor escape. In this paper, we analyze the water recovery rate during Phase B where the trash is heated and water leaves the waste chamber as vapor, for operation of the HMC in reduced gravity. We pursue a quasi-one-dimensional model with and without sidewall heating to determine the water recovery rate and the trash drying time. The influences of the trash thermal properties, the amount of water loading, and the distribution of the water in the trash on the water recovery rates are determined.

Distribution of water concentration on the Moon is currently an area of active research. Recent studies suggest the presence of ice particles, and perhaps even ice blocks and ice-cemented regolith on the Moon. Thermal extraction of the in-situ water is an attractive means of sa tisfying water requirements for a lunar mission. In this paper, a model is presented to analyze the processes occurring during the heat-up of icy regolith and extraction of the evolved water vapor. The wet regolith is assumed to be present in an initially evacuated and sealed cell which is subsequently heated. The first step of the analysis invol ves calculating the gradual increase of vapor pressure in the closed cell as the temperature is raised. Then, in the second step, the cell is evacuated to low pressure (e.g., vacuum), allowing the water vapor to leave the cell and be captured. The parameters affecting water vap or pressure build-up and evacuation for the purpose of extracting water from lunar regolith are discussed in the paper. Some comparisons wi th available experimental measurements are also made.

The combustion of flammable material in a sealed chamber invariably leads to an initial pressure rise in the volume. The pressure rise is due to the increase in the total number of gaseous moles (condensed fuel plus chamber oxygen combining to form gaseous carbon dioxide and water vapor) and, most importantly, the temperature rise of the gas in the chamber. Though the rise in temperature and pressure would reduce with time after flame extinguishment due to the absorption of heat by the walls and contents of the sealed spacecraft, the initial pressure rise from a fire, if large enough, could lead to a vehicle overpressure and the release of gas through the pressure relief valve. This paper presents a simple lumped-parameter model of the pressure rise in a sealed chamber resulting from the heat release during combustion. The transient model considers the increase in gaseous moles due to combustion, and heat transfer to the chamber walls by convection and radiation and to the fuel-sample holder by conduction, as a function of the burning rate of the material. The results of the model are compared to the pressure rise in an experimental chamber during flame spread tests as well as to the pressure fall-off after flame extinguishment. The experiments involve flame spread over thin solid fuel samples. Estimates of the heat release rate profiles for input to the model come from the assumed stoichiometric burning of the fuel along with the observed flame spread behavior. The sensitivity of the model to predict maximum chamber pressure is determined with respect to the uncertainties in input parameters. Model predictions are also presented for the pressure profile anticipated in the Fire Safety-1 experiment, a material flammability and fire safety experiment proposed for the European Space Agency (ESA) Automated Transfer Vehicle (ATV). Computations are done for a range of scenarios including various initial pressures and sample sizes. Based on these results, various mitigation approaches are suggested to prevent vehicle over-pressurization and help guide the definition of the space experiment. Nomenclature Af = area of the flame over the fuel-sample surface, m 2 Aw = area of the total available surfaces heat is convected to, m 2

The carbothermal processing of lunar regolith has been proposed as a means to produce carbon monoxide and ultimately oxygen to support human exploration of the moon. In this process, gaseous methane is pyrolyzed as it flows over the hot surface of a molten zone of lunar regolith and is converted to carbon and hydrogen. Carbon gets deposited on the surface of the melt, and mixes and reacts with the metal oxides in it to produce carbon monoxide that bubbles out of the melt. Carbon monoxide is further processed in other reactors downstream to ultimately produce oxygen. The amount of oxygen produced crucially depends on the amount of regolith that is molten. In this paper we develop a model of the heat transfer in carbothermal processing. Regolith in a suitable container is heated by a heat flux at its surface such as by continuously shining a beam of solar energy or a laser on it. The regolith on the surface absorbs the energy and its temperature rises until it attains the melting point. The energy from the heat flux is then used for the latent heat necessary to change phase from solid to liquid, after which the temperature continues to rise. Thus a small melt pool appears under the heated zone shortly after the heat flux is turned on. As time progresses, the pool absorbs more heat and supplies the energy required to melt more of the regolith, and the size of the molten zone increases. Ultimately, a steady-state is achieved when the heat flux absorbed by the melt is balanced by radiative losses from the surface. In this paper, we model the melting and the growth of the melt zone with time in a bed of regolith when a portion of its surface is subjected to a constant heat flux. The heat flux is assumed to impinge on a circular area. Our model is based on an axisymmetric three-dimensional variation of the temperature field in the domain. Heat transfer occurs only by conduction, and effects of convective heat transport are assumed negligible. Radiative heat loss from the surface of the melt and the regolith to the surroundings is permitted. We perform numerical computations to determine the shape and the mass of the melt at steady state and its time evolution. We first neglect the volume change upon melting, and subsequently perform calculations including it. Predictions from our model are compared to test data to determine the effective thermal conductivities of the regolith and the melt that are compatible with the data

In this paper, we study the effect of boundary conditions on the behavior of an array of uniformly-spaced fluidic diverters with an ultimate goal to passively control their output phase. This understanding will aid in the development of advanced designs of actuators for flow control applications in turbomachinery. Computations show that a potential design is capable of generating synchronous outputs for various inlet boundary conditions if the flow inside the array is initiated from quiescence. However, when the array operation is originally asynchronous, several approaches investigated numerically demonstrate that re-synchronization of the actuators in the array is not practical since it is very sensitive to asymmetric perturbations and imperfections. Experimental verification of the insights obtained from the present study is currently being pursued.

Long duration missions on the moon's equator must survive lunar nights. With 350 hours of cryogenic temperatures, lunar nights present a challenge to robotic survival. Insulation is imperfect, so it is not possible to passively contain enough heat to stay warm through the night. Components that enable mobility, environmental sensing and solar power generation must be exposed, and they leak heat. Small, lightweight rovers cannot store enough energy to warm components throughout the night without some external source of heat or power. Thermal wadis, however, can act as external heat sources to keep robots warm through the lunar night. Electrical power can also be provided to rovers during the night from batteries stored in the ground beside wadis. Buried batteries can be warmed by the wadi's heat. Results from analysis of the interaction between a rover and a wadi are presented. A detailed three-dimensional thermal model and an easily configurable two- dimensional thermal model are used for analysis.