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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Probabilistic Models for Solar Particle Events

Probabilistic Models of Solar Particle Events (SPEs) are used in space mission design studies to provide a description of the worst-case radiation environment that the mission must be designed to tolerate.The models determine the worst-case environment using a description of the mission and a user-specified confidence level that the provided environment will not be exceeded. This poster will focus on completing the existing suite of models by developing models for peak flux and event-integrated fluence elemental spectra for the Z>2 elements. It will also discuss methods to take into account uncertainties in the data base and the uncertainties resulting from the limited number of solar particle events in the database. These new probabilistic models are based on an extensive survey of SPE measurements of peak and event-integrated elemental differential energy spectra. Attempts are made to fit the measured spectra with eight different published models. The model giving the best fit to each spectrum is chosen and used to represent that spectrum for any energy in the energy range covered by the measurements. The set of all such spectral representations for each element is then used to determine the worst case spectrum as a function of confidence level. The spectral representation that best fits these worst case spectra is found and its dependence on confidence level is parameterized. This procedure creates probabilistic models for the peak and event-integrated spectra.

Adams, James H., Jr.↗
NASA NTRS2010

Effects of Secondary Task Modality and Processing Code on Automation Trust and Utilization During Simulated Airline Luggage Screening

The purpose of this research was to examine the impact of environmental distractions on human trust and utilization of automation during the process of visual search. Participants performed a computer-simulated airline luggage screening task with the assistance of a 70% reliable automated decision aid (called DETECTOR) both with and without environmental distractions. The distraction was implemented as a secondary task in either a competing modality (visual) or non-competing modality (auditory). The secondary task processing code either competed with the luggage screening task (spatial code) or with the automation's textual directives (verbal code). We measured participants' system trust, perceived reliability of the system (when a target weapon was present and absent), compliance, reliance, and confidence when agreeing and disagreeing with the system under both distracted and undistracted conditions. Results revealed that system trust was lower in the visual-spatial and auditory-verbal conditions than in the visual-verbal and auditory-spatial conditions. Perceived reliability of the system (when the target was present) was significantly higher when the secondary task was visual rather than auditory. Compliance with the aid increased in all conditions except for the auditory-verbal condition, where it decreased. Similar to the pattern for trust, reliance on the automation was lower in the visual-spatial and auditory-verbal conditions than in the visual-verbal and auditory-spatial conditions. Confidence when agreeing with the system decreased with the addition of any kind of distraction; however, confidence when disagreeing increased with the addition of an auditory secondary task but decreased with the addition of a visual task. A model was developed to represent the research findings and demonstrate the relationship between secondary task modality, processing code, and automation use. Results suggest that the nature of environmental distractions influence interaction with automation via significant effects on trust and system utilization. These findings have implications for both automation design and operator training.

Phillips, Rachel↗
NASA NTRS2010

Epistemic Questions and Answers for Software System Safety

System safety is primarily concerned with epistemic questions, that is, questions concerning knowledge and the degree of confidence that can be placed in that knowledge. For systems with which human experience is long, such as roads, bridges, and mechanical devices, knowledge about what is required to make the systems safe is deep and detailed. High confidence can be placed in the validity of that knowledge. For other systems, however, with which human experience is comparatively short, such as those that rely in part or in whole on software, knowledge about what is required to ensure safety tends to be shallow and general. The confidence that can be placed in the validity of that knowledge is consequently low. In a previous paper, we enumerated a collection of foundational epistemic questions concerning software system safety. In this paper, we review and refine the questions, discuss some difficulties that attend to answering the questions today, and speculate on possible research to improve the situation.

Holloway, C. M.↗
NASA NTRS2011

Collaborative Supervised Learning for Sensor Networks

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Wagstaff, Kiri L.↗
NASA NTRS2011

An Empirical State Error Covariance Matrix for Batch State Estimation

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).

Frisbee, Joseph H., Jr.↗
NASA NTRS2006

Estimation of Stability and Control Derivatives of an F-15

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Smith, Mark↗
NASA NTRS2005

Program for Weibull Analysis of Fatigue Data

A Fortran computer program has been written for performing statistical analyses of fatigue-test data that are assumed to be adequately represented by a two-parameter Weibull distribution. This program calculates the following: (1) Maximum-likelihood estimates of the Weibull distribution; (2) Data for contour plots of relative likelihood for two parameters; (3) Data for contour plots of joint confidence regions; (4) Data for the profile likelihood of the Weibull-distribution parameters; (5) Data for the profile likelihood of any percentile of the distribution; and (6) Likelihood-based confidence intervals for parameters and/or percentiles of the distribution. The program can account for tests that are suspended without failure (the statistical term for such suspension of tests is "censoring"). The analytical approach followed in this program for the software is valid for type-I censoring, which is the removal of unfailed units at pre-specified times. Confidence regions and intervals are calculated by use of the likelihood-ratio method.

Krantz, Timothy L.↗
NASA NTRS2011

Reducing Our Ignorance: Finding Answers to Certain Epistemic Questions for Software Systems

In previous papers, we asserted that software system safety is primarily concerned with epistemic questions, that is, questions concerning knowledge and the degree of confidence that can be placed in that knowledge. We also enumerated a set of 21 foundational epistemic questions, discussed some of the difficulties that exist in answering these questions adequately today, and speculated briefly on possible research that may provide improved confidence in the sufficiency of answers in the future. This paper focuses on three of the foundational questions. For each of these questions, current answers are discussed and potential research is proposed to help increase the justifiable level of confidence.

Holloway, C. Michael↗
NASA NTRS2012

An Initial Examination for Verifying Separation Algorithms by Simulation

An open question in algorithms for aircraft is what can be validated by simulation where the simulation shows that the probability of undesirable events is below some given level at some confidence level. The problem is including enough realism to be convincing while retaining enough efficiency to run the large number of trials needed for high confidence. The paper first proposes a goal based on the number of flights per year in several regions. The paper examines the probabilistic interpretation of this goal and computes the number of trials needed to establish it at an equivalent confidence level. Since any simulation is likely to consider the algorithms for only one type of event and there are several types of events, the paper examines under what conditions this separate consideration is valid. This paper is an initial effort, and as such, it considers separation maneuvers, which are elementary but include numerous aspects of aircraft behavior. The scenario includes decisions under uncertainty since the position of each aircraft is only known to the other by broadcasting where GPS believes each aircraft to be (ADS-B). Each aircraft operates under feedback control with perturbations. It is shown that a scenario three or four orders of magnitude more complex is feasible. The question of what can be validated by simulation remains open, but there is reason to be optimistic.

White, Allan L.↗
NASA NTRS2014

Data Mining of Historical Human Data to Assess the Risk of Injury due to Dynamic Loads

The NASA Occupant Protection Group is charged with ensuring crewmembers are protected during all dynamic phases of spaceflight. Previous work with outside experts has led to the development of a definition of acceptable risk (DAR) for space capsule vehicles. The DAR defines allowable probability rates for various categories of injuries. An important question is how to validate these probabilities for a given vehicle. One approach is to impact test human volunteers under projected nominal landing loads. The main drawback is the large number of subject tests required to attain a reasonable level of confidence that the injury probability rates would meet those outlined in the DAR. An alternative is to mine existing databases containing human responses to impact. Testing an anthropomorphic test device (ATD) at the same human‐exposure levels could yield a range of ATD responses that would meet DAR. As one aspect of future vehicle validation, the ATD could be tested in the vehicle's seat and suit configuration at nominal landing loads and compared with the ATD responses supported by the human data set. This approach could reduce the number of human‐volunteer tests NASA would need to conduct to validate that a vehicle meets occupant protection standards. METHODS: The U.S. Air Force has recorded hundreds of human responses to frontal, lateral, and spinal impacts at many acceleration levels and pulse durations. All of this data are stored on the Collaborative Biomechanics Data Network (CBDN), which is maintained by the Wright Patterson Air Force Base (WPAFB). The test device for human occupant restraint (THOR) ATD was impact tested on WPAFB's horizontal impulse accelerator (HIA) matching human‐volunteer exposures on the HIA to 5 frontal and 3 spinal loading conditions. No human injuries occurred as a result of these impact conditions. Peak THOR response variables for neck axial tension and compression, and thoracic‐spine axial compression were collected. Maximal chest deflection was determined from motion capture video of the impact test. HIC‐ 15 and BRIC were calculated from head acceleration responses. Given the number of human subjects for each test condition a confidence interval of injury probability will be obtained. RESULTS: Results will be discussed in terms of injury‐risk probability estimates based on the human data set evaluated. Also, gaps in the data set will be identified. These gaps could be one of two types. One is areas where additional THOR testing would increase the comparable human data set, thereby improving confidence in the injury probability rate. The other is where additional human testing would assist in obtaining information on other acceleration levels or directions. DISCUSSION: The historical human data showed validity of the THOR ATD for supplemental testing. The historical human data are limited in scope, however. Further data are needed to characterize the effects of sex, age, anthropometry, and deconditioning due to spaceflight on risk of injury

Wells, Jesica↗
NASA NTRS2013

Mars Biosignature - Detection Capabilities: A Method for Objective Comparison of In Situ Measurements and Sample Return

A Mars sample-return mission has been proposed within NASA's Mars Exploration Program. Studying Martian samples in laboratories on Earth could address many important issues in planetary science, but arguably none is as scientifically compelling as the question of whether biosignatures indicative of past or present life exist on that planet. It is reasonable to ask before embarking on a sample-return mission whether equivalent investigation of Martian biosignatures could be conducted in situ. This study presents an approach to (1)identifying an optimal instrument suite for in situ detection of biosignatures on Mars,and (2)comparing the projected confidence level of in situ detection in a 2026 timeframe to that of Earth-based analysis. We identify a set of candidate instruments, the development of which is projected to be achievable by 2026 well within a $200 million cost cap. Assuming that any biosignatures near the surface of Mars are similar to those of terrestrial life, we find that this instrument suite, if successfully developed and deployed, would enable in situ biosignature detection at essentially the same level of confidence as that of Earth-based analysis of the same samples. At a cost cap of half that amount,the confidence level of in situbiosignature detection analysis could reach about 90% that of Earth-based investigations.

National Environmental Policy Act (NEPA)↗
NASA NTRS2015

NASA DOEPOD NDE Capabilities Data Book

This data book contains the Directed Design of Experiments for Validating Probability of Detection (POD) Capability of NDE Systems (DOEPOD) analyses of the nondestructive inspection data presented in the NTIAC, Nondestructive Evaluation (NDE) Capabilities Data Book. DOEPOD is designed as a decision support system to validate inspection system, personnel, and protocol demonstrating 0.90 POD with 95% confidence at critical flaw sizes, a90/95. Although 0.90 POD with 95% confidence at critical flaw sizes is often stated as an inspection requirement in inspection documents, including NASA Standards, NASA critical aerospace applications have historically only accepted 0.978 POD or better with a 95% one-sided lower confidence bound exceeding 0.90 at critical flaw sizes, a90/95.

Generazio, Edward R.↗
NASA NTRS2014

Goal Based Testing: A Risk Informed Process

A process for life demonstration testing is developed, which can reduce the number of resources required by conventional sampling theory while still maintaining the same degree of rigor and confidence level. This process incorporates state-of-the-art probabilistic thinking and is consistent with existing NASA guidance documentation. This view of life testing changes the paradigm of testing a system for many hours to show confidence that a system will last for the required number of years to one that focuses efforts and resources on exploring how the system can fail at end-of-life and building confidence that the failure mechanisms are understood and well mitigated.

Everline, Chester↗
NASA NTRS2018

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple “phase learning†from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

surface temperatures↗
NASA NTRS2020

A NICER View of Spectral and Profile Evolution for Three X-ray-Emitting Millisecond Pulsars

We present two years of Neutron star Interior Composition Explorer (NICER) X-ray observations of three energetic rotation-powered millisecond pulsars (MSPs): PSRs B1937+21, B1821−24, and J0218+4232. We fit Gaussians and Lorentzians to the pulse profiles for different energy sub-bands of the soft X-ray regime to measure the energy dependence of pulse separation and width. We find that the separation between pulse components of PSR J0218+4232 decreases with increasing energy at >3σ confidence. The 95% upper limit on pulse separation evolution for PSRs B1937+21 and B1821−24 is less than 2 milliperiods per keV. Our phase-resolved spectral results provide updated constraints on the non-thermal X-ray emission of these three pulsars. The photon indices of the modeled X-ray emission spectra for each pulse component of PSR B1937+21 are inconsistent with each other at the 90% confidence level, suggesting different emission origins for each pulse. We find that the PSR B1821−24 and PSR J0218+4232 emission spectra are invariant with phase at the 90% confidence level. We describe the implications of our profile and spectral results in the context of equatorial current sheet emission models for these three MSPs with non-thermal, magnetospheric X-ray emission.

Dominick M Rowan↗
NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
NASA NTRS2020

Demonstrating Assurance of Model-Based Fault Diagnosis Systems on an Operational Mission

Developers of robotic scientific and commercial spacecraft are trending towards use of onboard autonomous capabilities for responding quickly to dynamic environments and rapidly changing situations. These capabilities need to know the state of the spacecraft’s health. Model-based fault diagnosis (MBFD) is an approach to estimating health by continuously verifying accurate behavior and diagnosing off-nominal behavior. Proper functioning of MBFD depends on 1) the quality of the diagnostic system model that is analyzed and compared to commands and onboard measurements to estimate a system’s health state, and 2) the correct functionality of the diagnosis engine interrogating the model and comparing its analyses to observed system behavior. Our goal is to develop Verification and Validation (V&V) techniques for MBFD to provide future missions sufficient confidence in its functionality and performance to deploy it on the systems they develop. Our work has been focused on infusing the techniques we developed earlier to an operational mission. First, we are constructing diagnostic models of a spacecraft attitude control system and updating our diagnostic engine so they can be demonstrated aboard the Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) mission, an operational spacecraft for which experiments in autonomy are being planned and executed, using the V&V techniques we have previously developed to assure they are both correct and complete. Since it is nearing the end of its life, ASTERIA provides a unique opportunity to demonstrate MBFD since the monitored components are expected to fail. Our demonstration will give system developers additional confidence to make timely, informed MBFD deployment decisions. Second, we will be completing performance assessments of the diagnostic engine/diagnostic model ensemble both on the flight system and ground-based testbeds to gain confidence in MBFD’s ability to run successfully in a spacecraft’s resource-constrained environment without adversely affecting other on-board activities. Finally, we are capturing our experience in preparing this demonstration in a set of checklists and guidance documents. Current practice includes high-level institutional guidance documents and standards, but at a high level of abstraction that does not necessarily address specific MBFD concerns. The purpose of the new checklists is to provide future mission developers clear, unambiguous, procedure-oriented guidance on assuring MBFD. This paper describes our work in these areas. For the first area, we describe the diagnostic models and updated diagnostic engine that will be used for the on-board demonstration. We describe how the V&V techniques we developed earlier are used to assure model and engine correctness and completeness. For the second area, we identify the performance measurement and assessment techniques used to characterize the diagnostic engine and diagnostic models, and discuss the effect of measured performance on overall mission operation. Finally, we present the checklist and guidance documents and describe how they meet the goals of providing system developers with clear, unambiguous, procedure-oriented guidance on MBFD assurance. We show how the techniques we have developed map into those artifacts.

Nikora, Allen↗
NASA NTRSDate not supplied

Multiphase Simulations of the SLS Launch Environment

NASA’s Space Launch System (SLS), which will send astronauts back to the Moon in the next few years, is powered by four RS-25 engines and two RSRMV solid rocket boosters (SRBs). During launch the SLS propulsion system generates intense acoustics and other powerful waves, such as ignition overpressure (IOP) which, if unmitigated, have the potential to damage the vehicle and possibly cause loss of mission or crew. To protect the vehicle from these powerful waves, the SLS launch pad design includes an ignition overpressure/sound suppression (IOP/SS) system which sprays 270,000 gallons per minute of water very close to the SRB and RS-25 nozzles. The SRB and RS-25 engine plumes, and the proximity of the IOP/SS water, create a complex multiphase (gas and liquid) environment during the SLS ignition sequence. The interplay among these systems creates challenges related to water spray into/onto engine nozzles, potential debris transport, and additional transient loads due to strong plume-water interactions - all of which the SLS vehicle must be able to withstand. Prior to the Artemis I launch, the SLS multiphase liftoff environment was largely unknown due to differences from the Space Shuttle and other programs. Some data was available from tests of individual systems, but no integrated testing or analysis was available. Even post-launch analysis of Artemis I cannot provide a full understanding of the complex physics involved due to limited (or obstructed) camera views and instrumentation. Computational fluid dynamics (CFD) is being used to investigate the details of the multiphase environment which could not be measured, help comprehend the data gathered from the launch, and ultimately identify phenomena that are a concern for future flights. Project Details Engineers at NASA’s Marshall Space Flight Center (MSFC) have executed simulations using the Loci/STREAM-Volume of Fluid (VoF) multiphase CFD solver to understand this environment. Initial efforts successfully validated the CFD solver on various tests, giving confidence to simulate the SLS multiphase liftoff environment prior to the Artemis I launch. The CFD simulation of the SLS ignition sequence was conducted in three phases. First the IOP/SS water system was simulated for approximately 6 seconds to reach a quasi-steady state. Next, the RS-25 engine plumes were activated and held at full power for 1 second. Lastly, the SRB booster was activated and the simulation was carried out until just prior to vehicle motion. This simulation process mimics the conditions that exist at launch. Results and Impact The SLS ignition sequence simulation results provide deep understanding of the underlying physics occuring during launch. Observations from the simulation include reduction of water splashing into/onto the engine nozzles, change in angling of the dense water sheets, and the origin of the powerful ignition overpressure (IOP) wave. These observations directly inform the SLS program on subjects including plume-water induced side loads, debris transport, and the acoustic launch environment. Additionally, with post launch comparison of CFD observations to flight data, these tools can be applied to launch vehicles and environments other than SLS with confidence. Why HPC Matters The SLS ignition sequence CFD simulations are conducted on meshes up to hundreds of millions of cells on thousands of processors for weeks at a time. These simulations generate terabytes of data that must also be stored and archived for future use on HPC systems. Simply put, the CFD simulations would not be possible without NASA HPC resources. What’s Next Comparisons between the Artemis I flight data and the CFD simulations will be continued to both improve confidence in the CFD results and provide deeper understanding into the SLS multiphase launch environment. This will be used to provide insight for decision making for the first manned SLS flight, Artemis II. Future simulations will target new configurations of the SLS IOP/SS water required to support the more powerful variants of the SLS vehicle, such as Block 1B. Additionally, this capability provides NASA the ability to investigate launch environments for vehicles other than SLS to support other missions.

Travis Rivord↗