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NASA NTRS2015

Characterizing Transiting Planets with JWST Spectra: Simulations and Retrievals

There are now well over a thousand confirmed exoplanets, ranging from hot to cold and large to small worlds. JWST spectra will provide much more detailed information on the molecular constituents, chemical compositions, and thermal properties of the atmospheres of transiting planets than is now known. We explore this by modeling clear, cloudy,and high mean molecular weight atmospheres of typical hot Jupiter, warm Neptune, warm sub-Neptune, and cool super-Earth planets and then simulating their JWST transmission and emission spectra. These simulations were performed for several JWST instrument modes over 1 - 11 microns and incorporate realistic signal and noise components. We then performed state-of the art retrievals to determine how well temperatures and abundances (CO, CO2, H2O, NH3) will be constrained and over what pressures for these different planet types. Using these results, we appraise what instrument modes will be most useful for determining what properties of the different planets, and we assess how well we can constrain their compositions, CO ratios, and temperature profiles.

James Webb Space Telescope↗

NASA NTRS2018

LADEE Simulation for Mission Operations

The Lunar Atmosphere Dust Environment Explorer (LADEE) model-based spacecraft simulator has been discussed previously at the Workshop on Spacecraft Flight Software including the verification and validation of the flight software, hardware integration of payloads, and multi-domain simulation. In addition to flight software development and testing the LADEE simulator was used by Mission Operations to develop and test spacecraft command scripts, train operators during mission simulations, verification of all tactical command sequence files uploaded to spacecraft during flight.This presentation will discuss the experience of the LADEE operations team using the spacecraft simulator including implementation, processes and lessons learned. We will also discuss a specific instance where the simulator was used in operations to debug and design a software fix for a spacecraft anomaly experienced with the star tracker.

Operations↗

NASA NTRS2019

Supply Chain Ecosystem for Urban Air Mobility

Future urban mobility promises to deliver transformative impact across the value chain. Consequently, suppliers will face disruption in the form of new technologies, stakeholders and market dynamics. How can supply networks be optimized to meet capabilities that are yet unknown? This session will explore what business models and supply chain strategies can best deliver value for urban air transport and will address how to scale these networks at the pace of this rapidly evolving ecosystem.

Urban Air Mobility↗

NASA NTRS2018

Predicting Crew Time Allocations for Lunar Orbital Missions Based on Historical ISS Operational Activities

As the National Aeronautics and Space Administration continues to define candidate architectures for the planned lunar “Gateway”, it will be necessary to have a detailed understanding of how the crew will inhabit, operate, and maintain the spacecraft. The nature of the Gateway vehicle systems configuration and operations will have a direct impact on the scope of work activities required of the crew. Crew work schedules are sensitive to variations in spacecraft architecture, visiting vehicle activities, and logistics operations – particularly within short duration missions as initially planned for the lunar Gateway. These system and operational configurations must be taken into account when planning for crew time availability to conduct science activities on Gateway missions. This paper presents a methodology that is used to predict crew time distributions for lunar Gateway missions, as applied in NASA’s Exploration Crew Time Model (ECTM). The process utilized for evaluating crew time distributions is based on the categorization of all crew activities into a standardized ontology. Historical ISS daily crew timeline data from July 20, 2011 (post STS retirement) to present day was captured via the Operational Planning Timeline Integration System (OPTimIS) database and characterized according to the standardized ontology. This process enabled correlation and statistical analysis of the ISS data according to common mission parameters such as crew size, ECLSS system design, vehicle traffic operations, and logistics delivery operations. The results of the statistical analysis are a set of crew time distributions for each activity category. These distributions are then utilized within the ECTM to examine crew time allocations based on mission parameter inputs, which serve to characterize the Gateway mission configurations. Results for predicted crew time allocations for representative short duration Gateway missions are presented. These results can be used to evaluate crew schedule availability for science and utilization activities. Variations in expected mission architectures and mission operations are accounted for to correct crew time predictions. The analysis is being leveraged to plan utilization capability objectives that are achievable on the Gateway missions, as well as inform the viability of various mission architecture options.

Stromgren, Chel↗

NASA NTRS2021

The Dynamics of a High Mach Number Quasi-Perpendicular Shock: MMS Observations

Shock parameters at Earth’s bow shock, in rare instances, can approach the Mach numbers predicted at astrophysical shocks and supernova remnants. We present our analysis of a high Alfv ́en Mach number (MA= 27) shock, by utilizing multipoint measurements from the Magnetospheric Multiscale (MMS) spacecraft during a crossing of Earth’s quasi-perpendicular bow shock. We find that the shock dynamics are mostly driven by reflected ions, perturbations that they generate, and nonlinear amplification of the perturbations. Our analyses indicate that reflected ions create modest magnetic enhancements upstream of the shock front which evolve in a nonlinear manner as they traverse the shock foot. They can transform into proto-shocks that propagate at small angles to the magnetic field and towards the bow shock. The nonstationary bow shock shows signatures of both reformation and surface ripples. Our observations indicate that although shock reformation occurs, the main shock layer never disappears. These observations are at high plasmaβ, a parameter regime which has not been well explored by numerical models.

H Madanian↗

NASA NTRS2020

Grain Formation around the AGB Star L2 Puppis Based On ALMA Observations

While models of grain formation in the outflows of carbon-rich stars have been relatively successful, models of outflows from oxygen-rich, asymptotic giant branch stars have been less fortunate. Under current modeling, it is difficult to produce sufficient amounts of silicate grains with high enough opacity to form a dust-driven wind from these stars. To investigate the cause of this difference, this work is a comparison between typical outflow model results and a model using input from Atacama Large Millimeter/submillimeter Array observations of L2Puppis. The temperatures from these observations are much lower than would typically be used in the standard outflow model. In addition, the observed gas densities are much higher than predicted from typical outflow models. Both of these differences make the formation of silicate grains much more favorable than predicted in current outflow models. We then explore the effects of other possible nonideal conditions including the efficiency of cluster growth prior to nucleation, the efficiency of grain growth following nucleation and the variation of grain coupling to stellar radiation during grain growth. Finally, we calculate the potential enhancement in grain production based on possible increased refractory abundances resulting from the vaporization of millimeter-scale and larger particles left over from the presence of a former planetary system.

Joseph A Nuth↗

NASA NTRSDate not supplied

Single-Scattering Properties of Melting Precipitation for GPM Passive Microwave and Radar Remote Sensing Applications

Over the past two decades, detailed computational simulations of the intricate three dimensional structures of ice-phase crystals and aggregates of those crystals have been developed. The microwave single-scattering properties of these simulated ice particles have been computed and used to improve quantitative estimates of snow rates and to better deRne the vertical structure of snow water contents in deep convective systems, as derived from satellite-borne passive microwave and/or radar remote sensing measurements. The same icephase particles have more recently been used as the starting point for simulations of melting precipitation using computational melting methods. In the current study, a heuristic melting method, as well as a physically-based melting procedure based on smoothed-particle hydrodynamics, are applied to ice particle models to describe the full evolution of the particles from dry snow to liquid drops. The discrete dipole approximation is utilized to calculate the single-scattering properties of the mixed-phase particles throughout the melting process. Then, the properties of the particles are “mapped” into simpliRed microphysical simulations of particle size spectra in the melting layers of stratiform, precipitating clouds. The bulk single-scattering properties of the melting layers and the sensitivity of their properties to modeling assumptions are explored, and the implications for combined radar-radiometer precipitation remote sensing from GPM are discussed.

William S Olson↗

NASA NTRS2019

Integration of Airborne and Ground Observations of Nitryl Chloride in the Seoul Metropolitan Area and the Implications on Regional Oxidation Capacity During KORUS-AQ 2016

Nitryl chloride (ClNO2) is a radical reservoir species that releases chlorine radicals upon photolysis. An integrated analysis of the impact of ClNO2on regional photochemistry in the Seoul metropolitan area (SMA) during the Korea–United States Air Quality Study (KORUS-AQ) 2016 field campaign is presented. Comprehensive multiplatform observations were conducted aboard the NASA DC-8 and at two ground sites (Olympic Park, OP; Taehwa Research Forest, TRF), representing an urbanized area and a forested suburban region, respectively. Positive correlations between daytime Cl2 and ClNO2 were observed at both sites, the slope of which was dependent on O3 levels. The possible mechanisms are explored through box model simulations constrained with observations. The overall diurnal variations in ClNO2 at both sites appeared similar but the night-time variations were systematically different. For about half of the observation days at the OP site the level of ClNO2 increased at sunset but rapidly decreased at around midnight. On the other hand, high levels were observed throughout the night at the TRF site. Significant levels of ClNO2 were observed at both sites for 4–5 h after sunrise. Airborne observations, box model calculations, and back-trajectory analysis consistently show that these high levels of ClNO2 in the morning are likely from vertical or horizontal transport of air masses from the west. Box model results show that chlorine-radical-initiated chemistry can impact the regional photochemistry by elevating net chemical production rates of ozone by ∼25 % in the morning.

Nitryl chloride (ClNO2)↗

NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repairs Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, it will be necessary to understand how crew repair and maintenance timelines are impacted by mission operations and technology changes. Past work has been done using historical ISS data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available data sets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat, and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA International Space Station (ISS) maintenance and crew time data are logged into two central databases, the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS) respectively. Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. MDC provides a detailed catalog of failure events and an overview of the failure’s required maintenance and OPTimIS provides a description of crew activities and crew time durations dedicated to maintenance. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair times for each failure event. Data is also classified by the outcome of each repair event, whether the failed component was replaced or whether it was repaired in place. The entire maintenance activity dataset is then categorized based on the class of failed component to allow for a statistically significant sample size for each class and to provide accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time To Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗

NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗

NASA NTRS2022

Boulder County Disasters: Mapping Forest Carbon Stocks to Understand Carbon Implications of Treatment and Wildfire

In recent years, record-breaking wildfire activities in the western US illustrate the need for fire mitigation efforts, such as forest fuels reduction treatments. Forests serve as crucial carbon sinks that combat the increasing effects of climate change while fuels reduction treatments may remove carbon from forested systems. As a result, forest managers need to find a balance between fire mitigation and carbon preservation. This project partnered with Boulder County Parks and Open Space (BCPOS) and the University of Colorado, Denver to investigate the 2020 Cal-Wood fire in Boulder County, Colorado. Using remote sensing data from Landsat 8 OLI, SRTM, Sentinel-2 MSI, and LiDAR, we mapped post-fire forest carbon pools and compared these values with values derived from measurements from plots on the ground. Results indicate that the correlations are R2=0.76 for aboveground live carbon, R2=0.44 for standing carbon, and R2=0.43 for aboveground dead carbon (R2=0.43), and R2=0.35 for total carbon (R2=0.35). Additionally, 38.5% of total carbon was stored in dead carbon and 14.4% was stored in live carbon. We next compared the post-fire pools between treated and untreated areas. Our analysis suggests fuels reduction treatments did not reduce carbon loss in the presence of wildfire enough to clearly distinguish the post-fire carbon in the treated and untreated areas. However, our final carbon maps still provide BCPOS and researchers with an opportunity to explore carbon estimation models based on remotely sensed data as well as a framework to evaluate fuels reduction treatment effectiveness and impact on forest carbon stocks for future wildfire events.

Sarah Hettema↗

NASA NTRS2023

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

machine learning↗

NASA NTRSDate not supplied

Mapping Forest Carbon Stocks to Understand Carbon Implications of Treatment on Wildfire for the Calwood Fire, Boulder County Colorado

Recent, record-breaking wildfire activity in the western U.S. illustrates the need for fire mitigation, such as forest fuels reduction treatments. Forests serve as crucial carbon sinks that combat the increasing effects of climate change, but fuels reduction treatments may remove carbon from forested systems. As a result, forest managers need to find a balance between fire mitigation and carbon preservation. This project partnered with Boulder County Parks and Open Space (BCPOS) and the University of Colorado, Denver to investigate the 2020 Cal-Wood fire in Boulder County, Colorado. Using remote sensing data from Landsat 8 Operational Land Imager, Shuttle Radar Topography Mission, Sentinel-2 MultiSpectral Imagery, and LiDAR, we mapped post-fire forest carbon pools and compared these values with values derived from field measurements. The analysis suggested that fuels reduction treatments did not reduce carbon loss in the presence of wildfire enough to clearly distinguish post-fire carbon between treated and untreated areas. However, the final carbon maps provide BCPOS and researchers with an opportunity to explore carbon estimation models based on remote sensing data as well as a framework to evaluate fuels reduction treatment effectiveness and impact on forest carbon stocks for future wildfire events.

Sarah Hettema↗

NASA NTRS2024

Analysis of Ryugu Fluid Inclusions: An Update

The most direct and convincing evidence for the presence of water and organic molecules on protoplanetary bodies is provided by fluid inclusions trapped in secondary minerals. Our previous work has demonstrated that early solar system fluids have survived as fluid inclusions in a Ryugu pyrrhotite crystal [2]. We hypothesize that the bulk molecular and isotopic composition of individual Ryugu fluid inclusions can be measured to provide ground truth for exploring and thermochemical modeling of the compositional and isotopic evolution of fluids in protoplanetary bodies including asteroids, comets and icy moons. This presentation is an update on our efforts to make measurements of elemental, molecular and, in particular, isotopic compositions of individual aqueous fluid inclusions in Ryugu samples. The measurements we plan will permit us to (1) understand elemental composition of the aqueous fluids present on Ryugu’s progenitor body, (2) track changes in the chemical and stable isotopic composition of altering fluids in small bodies over time, (3) verify the presence of organics in Ryugu fluids, (4) verify the reported significant CO 2 in Ryugu fluids, (5) measure O and H isotopes in preserved Ryugu fluids, (6) provide constraints on temperature range calculations for the mineralizing reactions being performed by other groups using mineral pairs, (7) measure sulfur/chloride and chloride/phosphorus ratios of Ryugu fluids. We note here that recent results from Enceladus reveal the importance of phosphorus in small body aqueous brines, indicating that our proposed research will benefit studies of all current ocean worlds.

Andrei Dolocan↗

DOE OSTI2020

Community-Scale Energy Efficiency Assessment for Zero Net Energy Using the URBANopt Simulation Platform: Preprint

This project explored an innovative modeling approach for assessing the energy efficiency potential of the existing low-income Oceano neighborhood, in unincorporated San Luis Obispo County, and its effects on renewable generation requirements for achieving zero net energy (ZNE). One particular challenge faced was ascertaining the technical energy savings potential across a large neighborhood in a timely manner. The project overcame this challenge through the use of the URBANopt simulation platform, developed at the National Renewable Energy Laboratory. URBANopt manages the automated model creation, simulation, and result aggregation for different design scenarios of existing or candidate buildings in a large neighborhood. Customized energy efficiency packages considered for the residential and commercial sector in the neighborhood included lighting, HVAC, plug load, water heating, envelope, and energy education measures. As a result of the analysis for the Oceano community, energy efficiency measures that are available through incentive programs may provide a 7.4% reduction in the required distributed energy resource (DER) size, corresponding to a 25,000 ft2 decrease in PV array area, to achieve ZNE. Through this work, a computational tool was developed and is being leveraged by the County of San Luis Obispo (CoSLO). The tool has enabled the CoSLO staff to perform similar analysis of the technical potential for energy efficiency, and an understanding of the DER asset requirements and potential savings in other communities within the county.

building energy modeling↗

DOE OSTI2024

Low-Energy Neutrino-Nucleus Scattering and New Physics

The interactions of low-energy neutrinos with nuclei provide a unique window to explore various Standard Model (SM) and Beyond the Standard Model (BSM) processes. In particular, the recent observation of coherent elastic neutrino-nucleus scattering (CEvNS), predicted over five decades ago, has generated significant interest across disciplines. With its high cross section and suitability for compact detectors, particularly with stopped pion neutrinos, CEvNS offers a powerful probe for light, weakly coupled new physics. Ongoing global experimental efforts now aim to leverage CEvNS to test SM predictions and search for BSM signals, where deviations in event rates or spectra could reveal new physics. We present here an estimate of the number of recoil events obtained from CEvNS using the current and upcoming liquid argon based experiments. Furthermore, the event rate due to the inclusion of neutrino magnetic moment is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Measurement of J/psi Production near Threshold in J/psi -> µ+µ-

This dissertation presents a detailed analysis of J/psi photoproduction near the kinematic threshold in the J/psi -> µ+µ- decay channel, based on data collected by the GlueX experiment at Jefferson Lab. The study aims to probe the structure of the proton and the underlying mechanisms of heavy vector quarkonium photoproduction, contributing to a deeper understanding of Quantum Chromodynamics (QCD) in the non-perturbative regime. The measurement focuses on the total and differential cross-sections of J/psi photoproduction and explores various theoretical models, including two-gluon and three-gluon exchange, open-charm contributions, and potential exotic states like pentaquarks. The experimental setup, featuring a high-precision tagged photon beam and advanced particle identification techniques, allowed for the separation of J/psi events from background processes. The analysis of the J/psi -> µ+µ- channel yields cross-sections that complement previous measurements in the J/psi -> e+e- decay mode, providing new insights into gluon dynamics at low momentum transfer. This work also examines the systematic uncertainties and provides a comprehensive comparison of results with theoretical predictions, highlighting the role of gluon exchange mechanisms in the photoproduction process. The results presented in this dissertation help refine our understanding of proton structure and QCD dynamics, offering a robust dataset for future theoretical and experimental studies in hadronic physics.

Ebersole, Donavan [Florida State Univ., Tallahasse↗

DOE OSTI2026

Cosmological constraints from the DESI DR1 joint power spectrum and bispectrum analysis

We derive cosmological parameter constraints from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) galaxy clustering data, based on a joint full-shape analysis of the power spectrum multipoles and the bispectrum monopole using the ShapeFit framework. This is the follow-up of our previous work, in which we obtained for the first time constraints on the ShapeFit parameters using the bispectrum of DESI DR1. Here we present the first ShapeFit cosmological inference results using the bispectrum of DESI DR1. We recover values for the matter density parameter and Hubble constant of respectively $Ω_m=0.310\pm0.012$ and $H_0=[68.92\pm0.97]\,\mathrm{km\, s^{-1} Mpc^{-1}}$, consistent with previous results from the full DESI DR1 dataset that did not use the bispectrum signal. The inclusion of the bispectrum significantly tightens the constraints on the amplitude of fluctuations, reducing the error-bars in $\ln(A_s\times10^{10})$ by approximately 20%, compared to using the power spectrum alone. We also explore extended cosmological models by performing fits for the evolving dark energy equation of state $w_0w_a$, and the sum of neutrino masses $\sum m_ν$. In these cases, we obtain constraints slightly larger than the ones from previous works from the DESI collaboration, due to not combining the full-shape results with other probes in all tracers. We find no strong evidence of deviations from standard $Λ$CDM, with the dark energy equation-of-state remaining within 2$σ$ from a cosmological constant $Λ$, and the neutrino mass being consistent with the normal hierarchy, $\sum m_ν<0.1\,[eV]$ at 95% confidence limit. These constraints are broadly consistent with other DESI DR1 analyses, thus validating the robustness of the ShapeFit compression approach and the inclusion of the bispectrum for cosmological inference.

Novell-Masot, S. [ICC, Barcelona U.; Geneva U., De↗