Effect of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Spaceflight Contingency Scenario: “Co2 Walk Back”
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RFI and SCRIMP Model Development and Verification
Vacuum-Assisted Resin Transfer Molding (VARTM) processes are becoming promising technologies in the manufacturing of primary composite structures in the aircraft industry as well as infrastructure. A great deal of work still needs to be done on efforts to reduce the costly trial-and-error methods of VARTM processing that are currently in practice today. A computer simulation model of the VARTM process would provide a cost-effective tool in the manufacturing of composites utilizing this technique. Therefore, the objective of this research was to modify an existing three-dimensional, Resin Film Infusion (RFI)/Resin Transfer Molding (RTM) model to include VARTM simulation capabilities and to verify this model with the fabrication of aircraft structural composites. An additional objective was to use the VARTM model as a process analysis tool, where this tool would enable the user to configure the best process for manufacturing quality composites. Experimental verification of the model was performed by processing several flat composite panels. The parameters verified included flow front patterns and infiltration times. The flow front patterns were determined to be qualitatively accurate, while the simulated infiltration times over predicted experimental times by 8 to 10%. Capillary and gravitational forces were incorporated into the existing RFI/RTM model in order to simulate VARTM processing physics more accurately. The theoretical capillary pressure showed the capability to reduce the simulated infiltration times by as great as 6%. The gravity, on the other hand, was found to be negligible for all cases. Finally, the VARTM model was used as a process analysis tool. This enabled the user to determine such important process constraints as the location and type of injection ports and the permeability and location of the high-permeable media. A process for a three-stiffener composite panel was proposed. This configuration evolved from the variation of the process constraints in the modeling of several different composite panels. The configuration was proposed by considering such factors as: infiltration time, the number of vacuum ports, and possible areas of void entrapment.
Loos, Alfred C.↗
Bridging the gap between experiments and simulations using machine learning
The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Natural Air-Sea Flux of CO2 in Simulations of the NASA-GISS Climate Model: Sensitivity to the Physical Ocean Model Formulation
Results from twin control simulations of the preindustrial CO2 gas exchange (natural flux of CO2) between the ocean and the atmosphere are presented here using the NASA-GISS climate model, in which the same atmospheric component (modelE2) is coupled to two different ocean models, the Russell ocean model and HYCOM. Both incarnations of the GISS climate model are also coupled to the same ocean biogeochemistry module (NOBM) which estimates prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2. Model intercomparison is carried out at equilibrium conditions and model differences are contrasted with biases from present day climatologies. Although the models agree on the spatial patterns of the air-sea flux of CO2, they disagree on the strength of the North Atlantic and Southern Ocean sinks mainly because of kinematic (winds) and chemistry (pCO2) differences rather than thermodynamic (SST) ones. Biology/chemistry dissimilarities in the models stem from the different parameterizations of advective and diffusive processes, such as overturning, mixing and horizontal tracer advection and to a lesser degree from parameterizations of biogeochemical processes such as gravitational settling and sinking. The global meridional overturning circulation illustrates much of the different behavior of the biological pump in the two models, together with differences in mixed layer depth which are responsible for different SST, DIC and nutrient distributions in the two models and consequently different atmospheric feedbacks (in the wind, net heat and freshwater fluxes into the ocean).
Gas exchange↗
Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling
The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.
Digital twins↗
Development of a deep potential model for F and CF 2 etching of Si and SiO 2
An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.
Kounis-Melas, Andreas [Princeton Univ., NJ (United↗
Analysis of electron analog simulation of ion containment in magnetic fields
Physical model for plasma containment simulation in magnetic fields
Kaufman, H. R.↗
Improved turbulence models based on large eddy simulation of homogeneous, incompressible turbulent flows
The physical bases of large eddy simulation and subgrid modeling are studied. A subgrid scale similarity model is developed that can account for system rotation. Large eddy simulations of homogeneous shear flows with system rotation were carried out. Apparently contradictory experimental results were explained. The main effect of rotation is to increase the transverse length scales in the rotation direction, and thereby decrease the rates of dissipation. Experimental results are shown to be affected by conditions at the turbulence producing grid, which make the initial states a function of the rotation rate. A two equation model is proposed that accounts for effects of rotation and shows good agreement with experimental results. In addition, a Reynolds stress model is developed that represents the turbulence structure of homogeneous shear flows very well and can account also for the effects of system rotation.
Bardino, J.↗
Immersive Technologies for Human-in-the-Loop Lunar Surface Simulations
NASA, the National Aeronautics and Space Administration, continually seeks innovative solutions to enhance its operations, particularly in the realms of testing, evaluation, and training for future missions. Immersive technologies, such as virtual, augmented, and mixed reality have proven to be powerful tools for realistic, interactive, and engaging environments. This paper explores how the Simulation and Graphics Branch at NASA’s Johnson Space Center (JSC) leverages immersive technology, modern commercial rendering engines, and physics-based systems simulations to develop human-in-the-loop systems for humanity’s return to the Moon through the Artemis program. When NASA returns to the Moon, astronauts will travel to the Moon’s South Pole where lighting conditions will cause a more complex operational environment. Human-in-the-loop testing plays a crucial role in NASA's mission planning, spacecraft and space systems development, and evaluation of operational scenarios. The development of immersive environments such as a lunar rover mockup at a video wall enables engineers and astronauts to simulate and experience mission scenarios, integrated spacecraft systems, and operational procedures in a relevant environment before deployment. By integrating realistic virtual environments, immersive technology allows for the visualization and interaction with virtual spacecraft models, mission landscapes, and complex operational tasks. This approach helps identify potential design flaws, operational challenges, and safety considerations. It also provides valuable insights for risk reduction and helps improve mission efficiency and effectiveness. With advanced motion tracking systems and custom virtual environments data can be gathered and evaluated to help NASA refine training protocols, develop specialized training procedures and optimize human-robotic interactions for future space missions. Furthermore, immersive technology offers opportunities for future training initiatives at NASA. The Virtual Reality Laboratory at JSC has pioneered training with Virtual Reality (VR) since the Hubble Space Telescope repair missions in the early 1990’s. Extended Reality (XR) simulations enable astronauts to rehearse complex spacewalks, spacecraft maneuvers, and extravehicular activities in a safe and controlled environment. By replicating the physical and cognitive challenges of space missions, immersive training experiences enhance astronauts' situational awareness, decision-making abilities, and adaptability to unexpected scenarios. Additionally, immersive technology facilitates collaborative training, allowing geographically dispersed crew and mission control personnel to engage in synchronized simulations, fostering teamwork and effective communication. The adoption of immersive technology in NASA's testing, evaluation, and future training programs has yielded significant benefits. By incorporating human-in-the-loop testing for studies involving Extra Vehicular Activities (EVA), surface mobility and landing systems, NASA can identify and mitigate risks, optimize operational procedures, and enhance mission success. Ultimately, immersive training experiences can empower astronauts to better navigate the complexities of space missions, ensuring their safety, productivity, and success in the dynamic and challenging environments they will experience at the Lunar South Pole.
Simulation Modeling Virtual Reality Immersive Tech↗
Immersive Technologies for Human-in-the-Loop Lunar Surface Simulations
NASA, the National Aeronautics and Space Administration, continually seeks innovative solutions to enhance its operations, particularly in the realms of testing, evaluation, and training for future missions. Immersive technologies, such as virtual, augmented, and mixed reality have proven to be powerful tools for immersing users in realistic, interactive, and engaging environments. This paper explores how the Simulation and Graphics Branch at NASA’s Johnson Space Center (JSC) leverages immersive technology, modern commercial rendering engines, and physics-based systems simulations to develop human-in-the-loop systems for humanity’s return to the Moon through the Artemis program. When NASA returns to the Moon, astronauts will travel to the Moon’s South Pole where lighting conditions will cause a more complex operational environment. Human-in-the-loop simulations play a crucial role in NASA’s mission planning, spacecraft and space systems development, and evaluation of operational scenarios. The development of immersive environments such as a lunar rover mockup at a video wall enables engineers and astronauts to simulate and experience mission scenarios, integrated spacecraft systems, and operational procedures in a relevant environment before deployment. By integrating realistic virtual environments, immersive technology allows for the visualization and interaction with virtual spacecraft models, mission landscapes, and complex operational tasks. This approach helps identify potential design flaws, operational challenges, and safety considerations. It also provides valuable insights for risk reduction and helps improve mission efficiency and effectiveness. With advanced motion tracking systems and custom virtual environments, data can be gathered and evaluated to help NASA refine training protocols, develop specialized training procedures, and optimize human-robotic interactions for future space missions. Furthermore, immersive technology offers opportunities for future training initiatives at NASA. The Virtual Reality Laboratory at JSC has pioneered training with Virtual Reality (VR) since the Hubble Space Telescope repair missions in the early 1990’s. Extended Reality (XR) simulations enable astronauts to rehearse complex spacewalks, spacecraft maneuvers, and extravehicular activities in a safe and controlled environment. By replicating the physical and cognitive challenges of space missions, immersive training experiences enhance astronauts’ situational awareness, decision-making abilities, and adaptability to unexpected scenarios. Additionally, immersive technology facilitates collaborative training, allowing geographically dispersed crew and mission control personnel to engage in synchronized simulations, fostering teamwork and effective communication. The adoption of immersive technology in NASA’s testing, evaluation, and future training programs has yielded significant benefits. By incorporating human-in-the-loop simulations for studies involving Extra Vehicular Activities (EVA), surface mobility and landing systems, NASA can identify and mitigate risks, optimize operational procedures, and enhance mission success. Ultimately, immersive simulation experiences can empower astronauts to better navigate the complexities of space missions, ensuring their safety, productivity, and success in the dynamic and challenging environments they will experience at the Lunar South Pole.
HITL↗
TESSIM: A Simulator for the Athena-X-IFU
We present the design of tessim, a simulator for the physics of transition edge sensors developed in the framework of the Athena end to end simulation effort. Designed to represent the general behavior of transition edge sensors and to provide input for engineering and science studies for Athena, tessim implements a numerical solution of the linearized equations describing these devices. The simulation includes a model for the relevant noise sources and several implementations of possible trigger algorithms. Input and output of the software are standard FITS-les which can be visualized and processed using standard X-ray astronomical tool packages. Tessim is freely available as part of the SIXTE package (http:www.sternwarte.uni-erlangen.deresearchsixte).
simulation↗
Informing the Space Launch System Booster Separation Initial CFD Run Matrix with Observed Parametric Sensitivity
It currently requires significant computational cost to simulate the flow physics of the booster separation event on the Space Launch System. This comes from the large parametric space in which the event occurs, as pre-separation flight conditions and separated booster core-relative trajectories can vary. Functionally removing the risk of core-booster collision mandates careful assessment of the fluid dynamics in terms of several trajectory parameters. However, simulating the entire trajectory envelope is computationally intractable given the high parametric dimension. In order to reduce the uncertainty in the resulting low-parametric-resolution aerodynamic booster separation database, a data-driven approach was developed to select which breakpoints should be studied by simulations and experiments and which should be relegated to a regression-based interpolation procedure. This technique works by simulating cases where the flow physics are most sensitive to changes in the parameters and leaving the less parametrically sensitive regions for interpolation. The result is a booster separation run matrix whose computational cost is comparable to that of previous database generations but has lower interpolation errors.
SLS↗
Informing the Space Launch System Booster Separation Initial CFD Run Matrix with Observed Parametric Sensitivity
It currently requires significant computational cost to simulate the flow physics of the booster separation event on the Space Launch System. This comes from the large parametric space in which the event occurs, as pre-separation flight conditions and separated booster core-relative trajectories can vary. Functionally removing the risk of core-booster collision mandates careful assessment of the fluid dynamics in terms of several trajectory parameters. However, simulating the entire trajectory envelope is computationally intractable given the high parametric dimension. In order to reduce the uncertainty in the resulting low-parametric-resolution aerodynamic booster separation database, a data-driven approach was developed to select which breakpoints should be studied by simulations and experiments and which should be relegated to a regression-based interpolation procedure. This technique works by simulating cases where the flow physics are most sensitive to changes in the parameters and leaving the less parametrically sensitive regions for interpolation. The result is a booster separation run matrix whose computational cost is comparable to that of previous database generations but has lower interpolation errors.
SLS↗
Theoretical Technology Research for the International Solar Terrestrial Physics (ISTP) Program
During the last four years the UCLA (University of California, Los Angeles) IGPP (Institute of Geophysics and Planetary Physics) Space Plasma Simulation Group has continued its theoretical effort to develop a Mission Oriented Theory (MOT) for the International Solar Terrestrial Physics (ISTP) program. This effort has been based on a combination of approaches: analytical theory, large-scale kinetic (LSK) calculations, global magnetohydrodynamic (MHD) simulations and self-consistent plasma kinetic (SCK) simulations. These models have been used to formulate a global interpretation of local measurements made by the ISTP spacecraft. The regions of applications of the MOT cover most of the magnetosphere: solar wind, low- and high- latitude magnetospheric boundary, near-Earth and distant magnetotail, and auroral region. Most recent investigations include: plasma processes in the electron foreshock, response of the magnetospheric cusp, particle entry in the magnetosphere, sources of observed distribution functions in the magnetotail, transport of oxygen ions, self-consistent evolution of the magnetotail, substorm studies, effects of explosive reconnection, and auroral acceleration simulations. A complete list of the activities completed under the grant follow.
Ashour-Abdalla, Maha↗
Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay
In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.
54 ENVIRONMENTAL SCIENCES↗
Observing Planets and Small Bodies in Sputtered High Energy Atom (SHEA) Fluxes
The evolution of the surfaces of bodies unprotected by either strong magnetic fields or thick atmospheres in the Solar System is caused by various processes, induced by photons, energetic ions and micrometeoroids. Among these processes, the continuous bombardment of the solar wind or energetic magnetospheric ions onto the bodies may significantly affect their surfaces, with implications for their evolution. Ion precipitation produces neutral atom releases into the exosphere through ion sputtering, with velocity distribution extending well above the particle escape limits. We refer to this component of the surface ejecta as sputtered high-energy atoms (SHEA). The use of ion sputtering emission for studying the interaction of exposed bodies (EB) with ion environments is described here. Remote sensing in SHEA in the vicinity of EB can provide mapping of the bodies exposed to ion sputtering action with temporal and mass resolution. This paper speculates on the possibility of performing remote sensing of exposed bodies using SHEA The evolution of the surfaces of bodies unprotected by either strong magnetic fields or thick atmospheres in the Solar System is caused by various processes, induced by photons, energetic ions and micrometeoroids. Among these processes, the continuous bombardment of the solar wind or energetic magnetospheric ions onto the bodies may significantly affect their surfaces, with implications for their evolution. Ion precipitation produces neutral atom releases into the exosphere through ion sputtering, with velocity distribution extending well above the particle escape limits. We refer to this component of the surface ejecta as sputtered high-energy atoms (SHEA). The use of ion sputtering emission for studying the interaction of exposed bodies (EB) with ion environments is described here. Remote sensing in SHEA in the vicinity of EB can provide mapping of the bodies exposed to ion sputtering action with temporal and mass resolution. This paper speculates on the possibility of performing remote sensing of exposed bodies using SHEA and suggests the need for quantitative results from laboratory simulations and molecular physic modeling in order to understand SHEA data from planetary missions. In the Appendix, referenced computer simulations using existing sputtering data are reviewed.
Milillo, A.↗
Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance
The maturation of computational materials approaches for fatigue performance prediction in a qualification and certification process is stifled by the ability to validate complex, microstructure-based simulations. Such a validation strategy bears immediate challenges including generating accurate virtual microstructures, efficiently solving physics-based mechanical simulations over relevant spatial and temporal scales, and acquiring high-fidelity calibration and validation data at the appropriate length scale. This presentation will overview these common challenges and present a case study to demonstrate a computational materials workflow for additively manufactured metallic materials. In this study, process-specific defects are characterized using segmented X-Ray micro-computed tomography measurements and overlaid on virtual microstructures. Accelerated crystal plasticity-based fatigue simulations are performed to demonstrate cyclic evolution and localization of mechanical fields in the vicinity of defects in response to their precise spatial configuration. An example of how this computational materials workflow may support next-generation qualification is discussed.
computational materials↗
A Simulation Based Investigation of High Latency Space Systems Operations
This study was the first in a series of planned tests to use physics-based subsystem simulations to investigate the interactions between a spacecraft's crew and a ground-based mission control center for vehicle subsystem operations across long communication delays. The simulation models the life support system of a deep space habitat. It contains models of an environmental control and life support system, an electrical power system, an active thermal control systems, and crew metabolic functions. The simulation has three interfaces: 1) a real-time crew interface that can be use to monitor and control the subsystems; 2) a mission control center interface with data transport delays up to 15 minute each way; and 3) a real-time simulation test conductor interface used to insert subsystem malfunctions and observe the interactions between the crew, ground, and simulated vehicle. The study was conducted at the 21st NASA Extreme Environment Mission Operations (NEEMO) mission. The NEEMO crew and ground support team performed a number of relevant deep space mission scenarios that included both nominal activities and activities with system malfunctions. While this initial test sequence was focused on test infrastructure and procedures development, the data collected in the study already indicate that long communication delays have notable impacts on the operation of deep space systems. For future human missions beyond cis-lunar, NASA will need to design systems and support tools to meet these challenges. These will be used to train the crew to handle critical malfunctions on their own, to predict malfunctions and assist with vehicle operations. Subsequent more detailed and involved studies will be conducted to continue advancing NASA's understanding of space systems operations across long communications delays.
Li, Zu Qun↗