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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Developing Physiologic Models for Emergency Medical Procedures Under Microgravity

Several technological enhancements have been made to METI's commercial Emergency Care Simulator (ECS) with regard to how microgravity affects human physiology. The ECS uses both a software-only lung simulation, and an integrated mannequin lung that uses a physical lung bag for creating chest excursions, and a digital simulation of lung mechanics and gas exchange. METI's patient simulators incorporate models of human physiology that simulate lung and chest wall mechanics, as well as pulmonary gas exchange. Microgravity affects how O2 and CO2 are exchanged in the lungs. Procedures were also developed to take into affect the Glasgow Coma Scale for determining levels of consciousness by varying the ECS eye-blinking function to partially indicate the level of consciousness of the patient. In addition, the ECS was modified to provide various levels of pulses from weak and thready to hyper-dynamic to assist in assessing patient conditions from the femoral, carotid, brachial, and pedal pulse locations.

Parker, Nigel↗
NASA NTRS2012

Developing Physiologic Models for Emergency Medical Procedures Under Microgravity

Several technological enhancements have been made to METI's commercial Emergency Care Simulator (ECS) with regard to how microgravity affects human physiology. The ECS uses both a software-only lung simulation, and an integrated mannequin lung that uses a physical lung bag for creating chest excursions, and a digital simulation of lung mechanics and gas exchange. METI s patient simulators incorporate models of human physiology that simulate lung and chest wall mechanics, as well as pulmonary gas exchange. Microgravity affects how O2 and CO2 are exchanged in the lungs. Procedures were also developed to take into affect the Glasgow Coma Scale for determining levels of consciousness by varying the ECS eye-blinking function to partially indicate the level of consciousness of the patient. In addition, the ECS was modified to provide various levels of pulses from weak and thready to hyper-dynamic to assist in assessing patient conditions from the femoral, carotid, brachial, and pedal pulse locations.

Parker, Nigel↗
DOE OSTI2015

SU-D-BRB-02: Patient-Specific Rectal Toxicity Predictor Based Plan Quality Control for Prostate Stereotactic Body Radiation Therapy (SBRT)

Purpose: To develop a patient-specific rectal toxicity predictor guided plan quality control tool for prostate SBRT plans. Methods: For prostate SBRT cases, four segments of rectal walls including peri-prostatic anterior rectal wall, peri-prostatic lateral rectal walls, peri-prostatic posterior rectal wall and rectum superior to prostate are identified as organs at risk and the circumference of rectal wall receiving more than 39 Gy (CRW39) and 24 Gy (CRW24) are rectal toxicity predictors. In this new geometry-dosimetry model, a patient geometry descriptor, differential circumference of rectal wall (dCRW) is used as model input geometry parameters and plan dosimetric endpoints CRW39 and CRW24 are output dosimetric parameters. Linear models are built to correlate dCRW to both CRW39 and CRW24 and established with both a linear regression method and a modified bagging ensemble machine learning method. 27 SBRT prostate cases are retrospectively studied from a dose-escalated clinical trial research. 20 prescribed 50 Gy SBRT cases are recruited to train the model and the other rescaled 7 cases are used to evaluated model feasibility and accuracy. Results: Each solved linear coefficient sequence related to CRW39 or CRW24 is a one-dimensional decreasing function of the distance from the PTV boundary, indicating that the different locations of each rectal circumference have different contributions to each particular dosimetric endpoint. The fitting errors for those trained 20 prostate SBRT cases are small with mean values of 2.39%, 2.45% relative to the endpoint values for SBRT rectal toxicity predictor CRW39 and CRW24 respectively. 1 out of 7 evaluation plans is identified as poor quality plan. After re-planning, the CRW39 and CRW24 can be reduced by 3.34% and 3%, without sacrificing PTV coverage. Conclusion: The proposed patient geometry-plan toxicity predictor model for SBRT plans can be successfully applied to plan quality control for prostate SBRT cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Modeling Uprighting of the Orion Crew Module using a Finite Element Method with Wave Coupling and Structural Deformability

The Crew Module Uprighting System (CMUS) for the Orion Crew Module (CM) includes five bags that inflate after splashdown to rotate and upright the CM as necessary. Wave conditions can be severe enough to damage the bags and tethers, which could make the recovery operation hazardous. The CMUS therefore has design limits that ultimately constrain where the CM can land based upon sea conditions. This manuscript describes a full fluid-structure interaction computational model being used for the CMUS. It is a finite element model that uses explicit time integration to directly simulate uprighting, a computational approach usually reserved for short duration, highly transient phenomena and novelly applied here. The goal of this current effort is to determine the feasibility of this approach for CMUS uprighting, and to estimate the loads the CMUS bags experience in different wave conditions. The results from this work will be used to supplement other experimental and computational data in support of NASA’s Artemis program.

John Puryear↗
DOE OSTI2018

Exploring natural aerosol formation from DMS oxidation and implications for aerosol forcing (Final Technical Report)

Because of their interactions with sunlight and with clouds, aerosol particles in the atmosphere can play an important role in the Earth’s climate. Understanding this role, and climate change in general, requires understanding the sources of these aerosol particles – not only anthropogenic sources (such as particles emitted from fossil fuel combustion) but also natural sources as well. One of the most important natural aerosol sources is the atmospheric oxidation of dimethyl sulfide (DMS). DMS, a gas emitted by oceanic phytoplankton, is the largest natural source of sulfur to the atmosphere. Once in the air it undergoes natural oxidation processes, converting the sulfur into sulfate, a major component of atmospheric aerosol. However, the chemical mechanism by which DMS oxidation forms sulfate particles is highly complex and uncertain. The goal of this project was therefore to better understand the chemistry of DMS oxidation, via laboratory and global-modeling studies. In the laboratory studies, DMS was oxidized in an environmental chamber (a large Teflon bag surrounded by UV lights to simulate sunlight), under a range of atmospheric conditions. A number of instruments were used to measure the reaction products (gases and particles) within the chamber. The total sulfur in the products equaled the sulfur in the reacted DMS, indicating that all major sulfur-containing species were measured. Yields of these products, and rates of important processes within the mechanism, were determined, providing improved parameters for use in chemical mechanisms. On the global modeling side, a detailed DMS oxidation scheme (that included chemistry occurring in both the gas phase and in cloud droplets) was incorporated into the Community Earth System Model (CESM), and the effects of this more complex mechanism on predicted sulfate levels and climate effects were explored. The expanded chemistry scheme was found to increase global sulfate levels under present-day conditions (~9%) and even more substantially (~30%) under preindustrial conditions. This additional predicted sulfate has implications for our understanding of the role of present-day aerosol pollution on climate, though because of in-cloud chemistry this effect is quite complex, highlighting the need for additional study.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Model Validation for Bigelow Expandable Activity Module (BEAM) with Stowage

The Bigelow Expandable Activity Module (BEAM) was a collaboration between NASA and Bigelow Aerospace to develop an expandable habitat technology that could be used for future space exploration missions such as the Artemis program. On April 26, 2016, the BEAM was launched and berthed to the International Space Station (ISS) on the aft port of Node 3. After the originally planned two-year mission life, BEAM was approved for use as a stowage module for a 109-cargotransfer bag equivalent (CTBE) to alleviate stowage limitations onboard the ISS. The present work builds on the previous 2017 model validation to flight data that demonstrated that BEAM could generally accommodate cargo stowage without adversely affecting BEAM thermal control, primarily for condensation management. Once the stowage was configured in early 2019, model validation efforts resumed. The thermal model revisions included the CTBE stowage and updated CFD-derived heat transfer coefficients for various intra-module ventilation (IMV) flow rates. The coefficients were provided by the Crew and Thermal Systems Division (EC) at NASA JSC and Jacobs Technology. The model is now revalidated and able to predict minimum temperatures with good agreement to flight temperatures (within 1 °C) for both historic and recurring minimum values. Additionally, the model is partially validated for reduced flow rates. With this model, the team was able to provide new flight rule recommendations for condensation and IMV operations management.

Sydney Taylor↗
DOE OSTI2019

Relativistic strange stars in Tolman–Kuchowicz spacetime

Highlights: • We study relativistic strange star using Tolman–Kuchowicz metric. • Bag constant (55–75) MeV/fm{sup 3} remains within the predicted range for stable strange star. • Using observed values for mass and radius, we calculate different physical parameters. • This model satisfies energy conditions, TOV equations and all other stability criteria. • Solutions are completely free from any singularity and give stable stellar system. - Abstract: In this article we propose a relativistic model of a static spherically symmetric anisotropic strange star with the help of Tolman–Kuchowicz (TK) metric potentials (Tolman, 1939 and Kuchowicz, 1968). The form of the potentials are λ(r)=ln(1+ar{sup 2}+br{sup 4}) and ν(r)=Br{sup 2}+2lnC where a, b, B and C are constants which we have to evaluate using boundary conditions. We also consider the simplest form of the phenomenological MIT bag equation of state (EOS) to represent the strange quark matter (SQM) distribution inside the stellar system. Here, the radial pressure p{sub r} relates with the density profile ρ as follows, p{sub r}(r)=(1/3)[ρ(r)−4B{sub g}], where B{sub g} is the Bag constant. To check the physical acceptability and stability of the stellar system based on the obtained solutions, we have performed various physical tests. It is shown that the model satisfies all the stability criteria, including nonsingular nature of the density and pressure, implies stable nature. Here, the Bag constant for different strange star candidates are found to be (68–70) MeV/fm{sup 3} which satisfies all the acceptability criteria and remains in the experimental range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Compartment Fire Modeling of a Crew Cabin in 1-g and Lunar-g

A fire inside a spacecraft poses one of the greatest dangers to the crew and mission success. As missions become more complex and longer in duration, the risk of a fire becomes more probable and catastrophic. A greater understanding of the effects of a fire inside a crewed vehicle at potential exploration atmospheres is needed. These exploration atmospheres, such as those being proposed for upcoming Lunar missions, include higher oxygen concentrations and lower pressures, also known as Normoxic conditions. Full scale fire testing, such as those performed during previous space programs, is the most straightforward way to obtain this understanding. These tests are difficult to implement in 1-g and even more challenging to attempt in Lunar-g. Modeling can help inform experiments aimed at determining flammability properties of common materials at exploration atmospheres, as well as determine the effect a fire has inside a spacecraft. A model can also be easily simulated in Lunar-g to predict the effect of gravity on fire propagation. This work focuses on a model of a theoretical partial crew cabin, with potential fire sources being a laptop and a Nomex sleeping bag. A simulation of the crew cabin during a high Heat Release Rate (HRR) fire and low HRR fire at 1-g was compared to simulations at Lunar-g. It was found that in both the low and high HRR cases, the temperature in the cabin was higher in the Lunar-g case than in the 1-g case. This is likely due to the hot products moving out the door of the crew cabin faster in the 1-g case. Modeling case studies like these will help guide future full-scale flammability experiments on Earth and allow for the prediction of fire spread in a Lunar gravity environment in order to design a safer crew cabin.

Fire Safety↗
DOE OSTI2020

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗
NASA NTRS1993

Design of a recovery system for a reentry vehicle

Engineers are often required to design decelerator systems which are deployed in cross-wind orientations. If the system is not designed to minimize 'line sail', damage to the parachutes could result. A Reentry Vehicle Analysis Code (RVAC) and an accompanying graphics animation software program (DISPLAY) are presented in this paper. These computer codes allow the user to quickly apply the Purvis line sail modeling technique to any vehicle and then observe the relative motion of the vehicle, nose cap, suspension lines, pilot and drogue bags and canopies on a computer screen. Data files are created which allow plots of velocities, spacial positions, and dynamic pressures versus time to be generated. The code is an important tool for the design engineer because it integrates two degrees of freedom (DOF) line sail equations with a three DOF model of the reentry body and jettisoned nose cap to provide an animated output.

Von Eckroth, Wulf↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A DISTANT MIRROR: SOLAR OSCILLATIONS OBSERVED ON NEPTUNE BY THE KEPLER K 2 MISSION

Starting in 2014 December, Kepler K 2 observed Neptune continuously for 49 days at a 1 minute cadence. The goals consisted of studying its atmospheric dynamics, detecting its global acoustic oscillations, and those of the Sun, which we report on here. We present the first indirect detection of solar oscillations in intensity measurements. Beyond the remarkable technical performance, it indicates how Kepler would see a star like the Sun. The result from the global asteroseismic approach, which consists of measuring the oscillation frequency at maximum amplitude ν {sub max} and the mean frequency separation between mode overtones Δ ν , is surprising as the ν {sub max} measured from Neptune photometry is larger than the accepted value. Compared to the usual reference ν {sub max,⊙} = 3100 μ Hz, the asteroseismic scaling relations therefore make the solar mass and radius appear larger by 13.8 ± 5.8% and 4.3 ± 1.9%, respectively. The higher ν {sub max} is caused by a combination of the value of ν {sub max,⊙}, being larger at the time of observations than the usual reference from SOHO /VIRGO/SPM data (3160 ± 10 μ Hz), and the noise level of the K 2 time series, being 10 times larger than VIRGO’s. The peak-bagging method provides more consistent results: despite a low signal-to-noise ratio (S/N), we model 10 overtones for degrees ℓ = 0, 1, 2. We compare the K 2 data with simultaneous SOHO /VIRGO/SPM photometry and BiSON velocity measurements. The individual frequencies, widths, and amplitudes mostly match those from VIRGO and BiSON within 1 σ, except for the few peaks with the lowest S/N.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

A Distant Mirror: Solar Oscillations Observed on Neptune by the Kepler K2 Mission

Starting in 2014 December, Kepler (K2) observed Neptune continuously for 49 days at a 1-minute cadence. The goals consisted of studying its atmospheric dynamics, detecting its global acoustic oscillations, and those of the Sun, which we report on here. We present the first indirect detection of solar oscillations in intensity measurements. Beyond the remarkable technical performance, it indicates how Kepler would see a star like the Sun. The result from the global asteroseismic approach, which consists of measuring the oscillation frequency at maximum amplitude max velocity and the mean frequency separation between mode overtones delta velocity, is surprising as the max velocity measured from Neptune photometry is larger than the accepted value. Compared to the usual reference max velocity of the sun equal to 3100 microhertz, the asteroseismic scaling relations therefore make the solar mass and radius appear larger by 13.8 plus or minus 5.8 percent and 4.3 plus or minus 1.9 percent, respectively. The higher max velocity is caused by a combination of the value of max velocity of the sun, being larger at the time of observations than the usual reference from SOHO/VIRGO/SPM (Variability of solar IRradiance and Gravity Oscillations / on board SOHO (Solar and Heliospheric Observatory) / Sun PhotoMeters) data (3160 plus or minus 10 microhertz), and the noise level of the K2 time series, being 10 times larger than VIRGO's. The peak-bagging method provides more consistent results: despite a low signal-to-noise ratio (S/N), we model 10 overtones for degrees iota equal 0, 1, 2. We compare the K2 data with simultaneous SOHO/VIRGO/SPM photometry and Bison (Birmingham Solar-Oscillations Network) velocity measurements. The individual frequencies, widths, and amplitudes mostly match those from VIRGO and BiSON within 1 sigma, except for the few peaks with the lowest S/N.

planets and satellites: individual (Neptune) – s↗
NASA NTRS2024

Crew State and Risk Model Development to Predict Hydration Status During Extravehicular Activity Training Events

Introduction: Hydration is critical for optimal human health and performance and dehydration can lead to impaired cardiovascular function, thermal dysregulation, decreased blood plasma volume, and cognitive impacts, particularly during physical activity. Prolonged and repeated extravehicular activities (EVA) without sufficiently available drinking water may increase risk for dehydration, which could impair crew health and impact mission success. Understanding hydration needs and potential effects on health and performance are necessary to optimize crew well-being and enable successful EVA objectives. This study aims to develop a model of hydration status during EVA using water balance techniques. Methods: Water balance measures were collected on 15 healthy astronauts who performed ≈6-hour simulated microgravity extravehicular activity (EVA) training in the NASA Neutral Buoyancy Laboratory (NBL). Data collected included pre-and post-EVA nude body weight (BW), maximum absorption garment (MAG) weight, Disposable In-suit Drink Bag (DIDB) weight, urine specific gravity (USG), and extra pre-EVA intake (W). Variables were combined to create the water balance model as pre-EVA (Hn)= BWn+ MAGn+ DIDBn+ Wnand post EVA (Hn+1) = BWn+1+ MAGn+1+ DIDBn+1. Urine specific gravity values were used to refine water balance measures into hydration categories: Hydrated, Marginally Hydrated, and Dehydrated. Results: Pre-EVA modeling indicated53% of crew were hydrated, 20% were marginally hydrated, and 27% were dehydrated. Alternately, Hn+1 showed 13% of crew remained hydrated, 47% were marginally hydrated, and 40% were dehydrated at the end of the EVA. Furthermore, 75% of the crewmembers who started sufficiently hydrated finished the run marginally hydrated or dehydrated. According to USG indices presented by Casa and Lawrence, et al. (2000), only 25% of the crew started and remained hydrated throughout the EVA, and those who were dehydrated at the outset stayed dehydrated. Conclusion: Model outcomes assessing hydration status during 6-hour simulated microgravity EVAs demonstrate the necessity to further address hydration requirements for optimal human performance during spaceflight and EVA. This study enables additional baseline development of the Crew State and Risk Model Hydration, Nutrition, and Waste Management component that aims to provide individualized crew state and risk predictions during EVAs. Reference: Casa, D. J., Armstrong, L. E., et al. (2000). National Athletic Trainers’ Association Position Statement: Fluid Replacement for Athletes. Journal of Athletic Training, 35:212-224.

L Cooper↗
DOE OSTI2020

Numerical Model Development of a Variable-Geometry Attenuator Wave Energy Converter: Preprint

Because the wave energy industry is still in its infancy, an optimal design for wave energy converters (WECs) has yet to be established; more work is needed to explore various cost reduction pathways. The primary cost-reduction pathway considered for this work is the optimization of the geometric profile on an attenuator WEC to maximize power production while, at the same time, minimizing capital expenditures through the use of variable-geometry modules. In this investigation, the variable geometry modules consist of inflatable bags placed on either side of a base central steel cylinder that would be inflated in low moderate sea states to maximize power capture and then deflated in moderate-extreme sea states to minimize wave loading. The numerical model and simulation of the attenuator WEC were developed and completed using WEC-Sim, which is an open-source code that is appropriate for use in evaluating the dynamic response of the different WEC models in operational seas. The power production estimates were obtained from the Wave Energy Prize (WEP) sea states, which are representative of U. S. deployment sites, to calculate the average climate capture width that is used in the WEP ACE calculation. Preliminary capital expenditure costs were obtained assuming the base central steel cylinder mass was equal to the fluid displaced mass, minus the mass of the variable-geometry bags. The additional weight required to offset the additional buoyancy from the variable-geometry bags was assumed to come from the addition of seawater ballast. The variable-geometry attenuator model was found to have a similar power capture efficiency as a fixed-body model, but is expected to have a lower characteristic capital expenditure given its more streamlined profile, which demonstrates that variable-geometry modules may provide a realistic cost-reduction pathway to help design a more cost-competitive WEC.

50 EE - Wind and Water Power Program - Water (EE-4↗
NASA NTRS1960

Landing Characteristics of a Reentry Capsule with a Torus-Shaped Air Bag for Load Alleviation

An experimental investigation has been made to determine the landing characteristics of a conical-shaped reentry capsule by using torus-shaped air bags for impact-load alleviation. An impact bag was attached below the large end of the capsule to absorb initial impact loads and a second bag was attached around the canister to absorb loads resulting from impact on the canister when the capsule overturned. A 1/6-scale dynamic model of the configuration was tested for nominal flight paths of 60 deg. and 90 deg. (vertical), a range of contact attitudes from -25 deg. to 30 deg., and a vertical contact velocity of 12.25 feet per second. Accelerations were measured along the X-axis (roll) and Z-axis (yaw) by accelerometers rigidly installed at the center of gravity of the model. Actual flight path, contact attitudes, and motions were determined from high-speed motion pictures. Landings were made on concrete and on water. The peak accelerations along the X-axis for landings on concrete were in the order of 3Og for a 0 deg. contact attitude. A horizontal velocity of 7 feet per second, corresponding to a flight path of 60 deg., had very little effect upon the peak accelerations obtained for landings on concrete. For contact attitudes of -25 deg. and 30 deg. the peak accelerations along the Z-axis were about +/- l5g, respectively. The peak accelerations measured for the water landings were about one-third lower than the peak accelerations measured for the landings on concrete. Assuming a rigid body, computations were made by using Newton's second law of motion and the force-stroke characteristics of the air bag to determine accelerations for a flight path of 90 deg. (vertical) and a contact attitude of 0 deg. The computed and experimental peak accelerations and strokes at peak acceleration were in good agreement for the model. The special scaling appears to be applicable for predicting full-scale time and stroke at peak acceleration for a landing on concrete from a 90 deg. flight path at a 0 deg. It appears that the full-scale approximately the same as those obtained from the model for the range of attitudes and flight paths investigated.

McGehee, John R.↗