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NASA NTRSDate not supplied

An Overview of NASA Langley Low-Speed CFD Contributions to the Space Launch System Program

In this review paper, low-speed computational work from NASA Langley in support of the Space Launch System (SLS) is discussed. This information includes both historic and present efforts with the Kestrel CFD solver. The low-speed aerodynamics of SLS is highly complex and analysis of the unsteady flowfield requires significant computational efforts. The SLS mission profile varies from the vehicle static on the launch pad through high-speed ascent, and this paper focuses on the prelaunch as well as liftoff and transition portions of the flight both in proximity to the launch tower and in isolation. High-alpha conditions, as large as 90~deg, result in a flowfield dominated by massive, large-scale flow separation and asymmetric vortices. High-fidelity solutions require an unsteady computational formulation to accurately capture the aerodynamics of the vehicle. A detailed discussion of the computational approach is presented, followed by key efforts to support the program.

Brent W Pomeroy↗
NASA NTRS2023

Exploration Medical Capability IMPACT Medical Database – Medical Item Database (MedID) Content Development Methods

The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project encompasses a suite of computational tools that are used to inform systematic trade study evaluations and research prioritizations regarding the optimization of spaceflight medical systems, including the provision of risk assessment metrics, for a given design reference mission (DRM). The IMPACT Medical Database (IMPACT-MD) is the component that virtually houses the clinical and engineering data for medical conditions, capabilities, and resources used to support these analyses. In addition, IMPACT-MD also provides data to the associated SysML model to support the Level 2, Level 3, and Level 4 requirements development needed for IMPACT. IMPACT-MD contains an internal Evidence Library (EL) sub-component that hosts the clinical evidence, treatment and outcome metric data, and incidence data for each medical condition identified for exploration-class missions. Furthermore, a separate Medical Item Database (MedID) sub-component hosts the engineering data and physical attributes (e.g., mass, volume, power, etc.) associated with each medical resource item identified to address the requisite medical conditions. The IMPACT project is conducted under the Exploration Medical Capability (ExMC) element of the Human Research Program (HRP) within NASA's Space Operations Mission Directorate (SOMD). The reader is referred to the documents in Appendix A for a more complete overview of the IMPACT tool suite and functionality as well as further detail on any associated aspects of the project. The primary purpose of this document is to describe the methods utilized in the collection and verification of engineering data content during the project development phase for the MedID sub-component of the IMPACT-MD project. Engineering content data (both existing and new/future) will be stored in IMPACT-MD for use by the IMPACT project. This document will detail the content development and data credibility for the engineering data housed within the MedID subcomponent of IMPACT-MD, which is used to inform the IMPACT 1.0 project deliverables.

Exploration Medical Capability↗
NASA NTRS2024

Low-Speed Space Launch System Computational Fluid Dynamics: A Comprehensive Overview

In this review paper, low-speed computational work from NASA Langley in support of the Space Launch System (SLS) is discussed. This information includes both historic and present efforts with the Kestrel CFD solver. The low-speed aerodynamics of SLS is highly complex and analysis of the unsteady flowfield requires significant computational efforts. The SLS mission profile varies from the vehicle static on the launch pad through high-speed ascent, and this paper focuses on the prelaunch as well as liftoff and transition portions of the flight both in proximity to the launch tower and in isolation. High-alpha conditions, as large as 90 deg, result in a flowfield dominated by massive, large-scale flow separation and asymmetric vortices. High-fidelity solutions require an unsteady computational formulation to accurately capture the aerodynamics of the vehicle. A detailed discussion of the computational approach is presented, followed by key efforts to support the program, both historic and present, including information which has been either previously published or that has never before published external to NASA.

Brent Pomeroy↗
NASA NTRS2023

A Pilot Project for Quantifying the Effect of Medical Provider Knowledge, Skills, and Abilities on Outcomes for Spaceflight Using a Probabilistic Risk Assessment Tool

In order to enable the future of long-duration deep space exploration we must confront the uncertainty in medical risk. Limitations of communication, resupply, and evacuation in deep space will require a high degree of crew autonomy and accurate risk assessment will be critical to ensure adequate crew training and medical system design. To address this, NASA’s Human Research Program Exploration Medical Capability Element has developed the Informing Mission Planning via Analysis of Complex Tradespaces Tool (IMPACT). IMPACT is a suite of tools that can provide evidence-based, data-driven trade space assessments between available medical resources in the mass- and volume-constrained environment of a deep space exploration vehicle. In the current model, medical conditions either can or cannot be treated based on the availability of medical system resources and equipment. However, medical outcomes often depend just as much on the knowledge, skills, and abilities (KSA) of the provider operating the system. This paper presents a method for modeling and quantifying the effect of medical officer KSA on medically relevant mission risk outcomes during spaceflight.

capabilities↗
NASA NTRSDate not supplied

Improving the Fidelity of Capability & Resource Weighting in A Probalistic Risk Assessment Model for Spaceflight

INTRODUCTION NASA’s Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool uses Probabilistic Risk Assessment (PRA) to provide an evidence-based, data-driven estimate of how medical system capabilities affect mission outcomes. IMPACT maps condition incidence to available resources thereby facilitating the calculation of outcome metrics that allow the estimation of mission medical risk. Conditions can be nominally categorized as either treated or untreated depending on the availability of necessary diagnostic and therapeutic capabilities. This categorization enables IMPACT to estimate the effect of various medical system configurations on mission outcomes such as crew mortality, disability, crew member down-time, return to duty/recovery, and need for evacuation. IMPACT currently employs an equal weighting, “partial credit” approach to define treatment in which each of the capabilities associated with a given condition contributes an equal amount to management of the condition. This feature enables IMPACT to report values in between “fully untreated” and “fully treated” based on the proportion of capabilities available within the model. However, as is normal in medical/clinical practice, not all individual capabilities contribute equally to medical care. For example, the ability to provide intramuscular epinephrine during an anaphylactic episode contributes more likelihood of overall management success than does the administration of oral diphenhydramine. We hypothesize that weighting the relative contribution of each capability to each specific condition will improve outcome prediction and therefore will better provide mission planners with more nuanced and accurate options when designing space medical systems. METHODS Using a five-point Fibonacci scaling sequence (1, 2, 3, 5, 8) subject matter experts from NASA’s Exploration Medical Capabilities (ExMC) element assigned relative contribution weighting values to each identified capability within IMPACT. Since the relative importance of each capability varies depending on the specific condition, the resulting “partial” weighting was completed for more than 1,600 individual weighting assignments for 666 capabilities across 121 conditions. Each assignment required three-physician concurrence based on the overall importance of the capability to the diagnosis and management of the condition being considered and the difficulty with which it could be improvised by the crew. Once complete, 100,000 IMPACT simulations were run for a 6-month Lunar mission with a 30-day surface stay to evaluate the effect of this modification of the model. RESULTS Partial weighting significantly decreased predicted task time loss (TTL), evacuation, and loss of crew life without causing significant changes to the recommended medical system design. CONCLUSIONS The paucity of real-world referent data to support long-duration space missions of this type limits the ability to judge one predictive analytics method as superior to another. However, since the proposed method significantly reduces and optimizes outcome risks—without changing the medical system design—incorporating a partial weighting methodology is likely to provide a more accurate and operationally-relevant representation of medical risk without compromising IMPACTs ability to inform overarching medical system requirements.

Steller JG↗
NASA NTRSDate not supplied

Improving Quantification of Medical Evacuation Risk for Human Spaceflight in the IMPACT Model

Traditionally, mission planners have used a heuristic and qualitative approach to design medical systems based on prior experience however this approach may result in high variability and implicit bias to design and could put missions and crewmembers at risk. The Informing Mission Planning through Analysis of Complex Tradespaces (IMPACT) tool is a probabilistic risk assessment tool designed by NASA to model medical risk in long duration missions outside of low earth orbit (LEO). This tool can quantify risk metrics such as risk of crewmember death, loss of crew task time, and the need for medical evacuation to better inform and augment the more traditional approach to medical system design. The risk metric of Return to Definitive Care (RTDC) represents the likelihood of requiring medical evacuation and has been difficult to quantify in a reliable and standardized manner. Initial development of the RTDC metric was constrained due to the complexity of a medical evacuation decision and the lack of prior spaceflight data and this presentation will discuss the review and improvement process used to increase model accuracy and fidelity.

Prashant Parmar↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗
NASA NTRSDate not supplied

Baseline Medical System Translation for the Impact Medical Database

NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model (IMM). This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). Whereas IMM focuses on the resources and risks associated with International Space Station (ISS) and Low Earth Orbit (LEO) missions, IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. One of the services offered by IMM is a series of generic or baseline medical systems associated with typical mission types or DRMs (Design Reference Missions), such that requestors may prioritize questions pertaining to the mission itself over the medical supplies indicated by the model outputs for that DRM. In a mission focused request, the appropriate baseline medical system is used in place of a prepared or optimized medical kit. In preparation, an effort was undertaken to create baseline systems of medical resources within IMPACT suitable for typical DRMs. Using an existing baseline medical system within IMM’s Integrated Medical Evidence Database (iMED) as a starting point, the resources found within the medical kit were compared to and substituted for equivalent resources available within the IMPACT MD (Medical Database). In consultation with clinicians with knowledge of IMPACT’s MD, each resource was matched as closely as possible to a similarly purposed resource in IMPACT, seeking to preserve the treatment capabilities and procedures offered by the IMM medical system while reconciling the differences in modeled resources and medical conditions between the models. To demonstrate the efficacy of this work, a prototype ISS medical system in IMPACT was translated from the baseline used in the IMM. The appropriate medical system was then run through its associated medical model to compare and validate the resultant risks and risk mitigation provided by each baseline ISS medical kit.

S Schwartz↗
NASA NTRSDate not supplied

An Approach to Solving Enclosure Radiation Problems in A Multi-Physics Context

Thermal protection system analysis of complex features or damage sites can sometimes require modeling of high temperature enclosures. Implementing efficient and accurate view-factor algorithms required to model such problems is complex. The current work leverages the Non-equilibrium Radiation (NERO) software, which solves the radiation transport equation in a finite-volume scheme, to alleviating challenges often faced with view-factor calculations. By assuming heat transfer occurs only between grey bodies and that the medium is non-participating, computational cost of the method is significantly reduced. The enclosure physics are modeled through emitting and reflecting boundary conditions in NERO. The emitted radiative flux is dependent on the wall temperature which is a solution to the material response, obtained from Icarus, in this context. The Ares framework manages the time-advancement and exchange of the necessary data between the solvers. The surface energy balance is modified to account for the enclosure terms within the material response boundary condition. The methodology was verified against analytical solutions including radiating parallel plates, a hollow cylinder (shown in Fig. 1), and a hemisphere. Application of the methodology to inform the design of components of the Dragonfly system will be shown.

Ablation↗
NASA NTRS1998

Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures

Dynamic analysis techniques may uncover abnormalities in heart rate (HR) behavior that are not easily detectable with conventional statistical measures. However, the applicability of these new methods for detecting possible abnormalities in HR behavior in various cardiovascular disorders is not well established. Conventional measures of HR variability were compared with short-term (< or = 11 beats, alpha1) and long-term (> 11 beats, alpha2) fractal correlation properties and with approximate entropy of RR interval data in 38 patients with stable angina pectoris without previous myocardial infarction or cardiac medication at the time of the study and 38 age-matched healthy controls. The short- and long-term fractal scaling exponents (alpha1, alpha2) were significantly higher in the coronary patients than in the healthy controls (1.34 +/- 0.15 vs 1.11 +/- 0.12 [p <0.001] and 1.10 +/- 0.08 vs 1.04 +/- 0.06 [p <0.01], respectively), and they also had lower approximate entropy (p <0.05), standard deviation of all RR intervals (p <0.01), and high-frequency spectral component of HR variability (p <0.05). The short-term fractal scaling exponent performed better than other heart rate variability parameters in differentiating patients with coronary artery disease from healthy subjects, but it was not related to the clinical or angiographic severity of coronary artery disease or any single nonspectral or spectral measure of HR variability in this retrospective study. Patients with stable angina pectoris have altered fractal properties and reduced complexity in their RR interval dynamics relative to age-matched healthy subjects. Dynamic analysis may complement traditional analyses in detecting altered HR behavior in patients with stable angina pectoris.

NASA Discipline Cardiopulmonary↗
NASA NTRS2021

Simons Observatory HoloSim-ML: Machine Learning Applied to the Efficient Analysis of Radio Holography Measurements of Complex Optical Systems

Near-field radio holography is a common method for measuring and aligning mirror surfaces for millimeter andsub-millimeter telescopes. In instruments with more than a single mirror, degeneracies arise in the holographymeasurement, requiring multiple measurements and new fitting methods. We present HoloSim-ML, a Pythoncode for beam simulation and analysis of radio holography data from complex optical systems. This code usesmachine learning to efficiently determine the position of hundreds of mirror adjusters on multiple mirrors with fewmicrometer accuracy. We apply this approach to the example of the Simons Observatory 6 m telescope.

Grace E Chesmore↗
NASA NTRS1972

Post impact behavior of mobile reactor core containment systems

The reactor core containment vessel temperatures after impact, and the design variables that affect the post impact survival of the system are analyzed. The heat transfer analysis includes conduction, radiation, and convection in addition to the core material heats of fusion and vaporization under partially burial conditions. Also, included is the fact that fission products vaporize and transport radially outward and condense outward and condense on cooler surfaces, resulting in a moving heat source. A computer program entitled Executive Subroutines for Afterheat Temperature Analysis (ESATA) was written to consider this complex heat transfer analysis. Seven cases were calculated of a reactor power system capable of delivering up to 300 MW of thermal power to a nuclear airplane.

Puthoff, R. L.↗
NASA NTRS1999

[Cyto-embryologic investigation of super dwarf wheat grown on board of the Mir orbital complex]

The cytoembryologic analysis of wet and dry samples of super dwarf wheat cultivated in greenhouse SVET aboard the MIR station over the whole cycle of vegetation was made with the use of light microscopy. Characteristic features of wheat development in space flight are plentiful early tillering, and formation and rapid growth of side shoots. Elementary spikelets in the composite head were more numerous but the top spikelets were rudimentary and, therefore, the ripe head contained less of these spikelets as compared with the ground controls (9-13 and 14, respectively). The number of florets in a spike was also higher reaching 14-16 vs. 7-8 in the control. Typically, no more than 4 to 5 florets vs. 3 in the control were fully differentiated while the others died off earlier in development. The fact that there were no caryopses found in the flight crop is explained by absolute male sterility appearing at different stages of staminal development: before archesporium formation, on the stage of differentiated archesporium, during meiosis, on the stage of microspores or uninucleate "pollen". The female generative system developed mainly without abnormalities. An assumption was made that elevated ethylene concentrations in the MIR atmosphere at the time of the wheat experiment were the cause for abnormal development of the male generative system which led to barrenness of the super dwarf wheat crop.

manned↗
NASA NTRS2016

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

microwave↗
NASA NTRS2019

Modal Analysis of a Two-Parachute System

The Orion capsule is designed to land under a nominal configuration of three main parachutes; however, the system is required to be fault tolerant and land successfully if one of the main parachutes fails to open. The Capsule Parachute Assembly System (CPAS) Team performed a series of drop tests in order to characterize the performance of the system with two main parachutes. During the series of drop tests, several distinct dynamical modes were observed. The most consequential of these is the pendulum mode. Three other modes are benign: flyout (scissors), maypole, and breathing. The actual multi-body system is nonlinear, flexible, and possesses significant cross-coupling. Rather than perform analysis of this highly complex system directly, we conduct analysis of each dynamical mode observed during flight, based on first principles. This approach is analogous to traditional aircraft flight dynamics analysis in which the full nonlinear behavior of the airframe is decomposed into longitudinal dynamics (phugoid and short-period modes) and lateral dynamics (spiral, roll-subsidence, and dutch-roll modes). This analysis is intended to supplement multi-body nonlinear simulations in order to provide further insight into the system.

Pei, Jing↗
NASA NTRS1999

Analysis of the ST-T complex of the electrocardiogram using the Karhunen--Loeve transform: adaptive monitoring and alternans detection

The Karhunen-Loeve transform (KLT) is applied to study the ventricular repolarisation period as reflected in the ST-T complex of the surface ECG. The KLT coefficients provide a sensitive means of quantitating ST-T shapes. A training set of ST-T complexes is used to derive a set of KLT basis vectors that permits representation of 90% of the signal energy using four KLT coefficients. As a truncated KLT expansion tends to favor representation of the signal over any additive noise, a time series of KLT coefficients obtained from successive ST-T complexes is better suited for representation of both medium-term variations (such as ischemic changes) and short-term variations (such as ST-T alternans) than discrete parameters such as the ST level or other local indices. For analysis of ischemic changes, an adaptive filter is described that can be used to estimate the KLT coefficient, yielding an increase in the signal-to-noise ratio of 10 dB (u = 0.1), with a convergence time of about three beats. A beat spectrum of the unfiltered KLT coefficient series is used for detection of ST-T alterans. These methods are illustrated with examples from the European ST-T Database. About 20% of records revealed quasi-periodic salvos of ischemic ST-T change episodes and another 20% exhibit repetitive, but not clearly periodic patterns of ST-T change episodes. About 5% of ischemic episodes were associated with ST-T alterans.

NASA Discipline Cardiopulmonary↗