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At least 1,189 records · Page 66

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NASA NTRS1998

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric weight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level. ELAPS allows for the modeling of detailed geometry, metallic and composite materials, and non-structural mass coupled with analytical structural sizing to produce high-fidelity mass property analyses representing fully configured vehicles early in the design process. This capability is especially valuable for unusual configuration and advanced concept development where existing parametric weight equations are inapplicable and FEA is too time consuming for conceptual design. This paper contrasts the use of ELAPS relative to empirical weight equations and FEA. ELAPS modeling techniques are described and the ELAPS-based mass property analysis process is detailed. Examples of mass property stochastic calculations produced during a recent systems study are provided. This study involved the analysis of three remotely piloted aircraft required to carry scientific payloads to very high altitudes at subsonic speeds. Due to the extreme nature of this high-altitude flight regime, few existing vehicle designs are available for use in performance and weight prediction. ELAPS was employed within a concurrent engineering analysis process that simultaneously produces aerodynamic, structural, and static aeroelastic results for input to aircraft performance analyses. The ELAPS models produced for each concept were also used to provide stochastic analyses of wing structural mass properties. The results of this effort indicate that ELAPS is an efficient means to conduct multidisciplinary trade studies at the conceptual design level.

Sexstone, Matthew G.↗

NASA NTRSDate not supplied

Microscale Constitutive Model Sensitivity on Multiscale Modeling of Fiber Reinforced Composites

Fiber reinforced composites are desirable in applications where low weight and high strength are needed, but are susceptible to microscale variability during manufacturing, making failure predictions difficult. The impact microscale variability has on macroscale mechanical response is difficult to predict due to the computational efficiency needed to simulate many, large, high fidelity, microscale models. In this study, a multiscale approach was taken to model 3-point bend, 4-point bend, and tensile experiments of a unidirectional composite from only having microstructure scans of these samples and constituent properties from literature. These scans were sampled with different sized windows, and statistically equivalent microstructures were generated, then simulated for stiffness, strength, and fracture toughness using an efficient micromechanical. Mesoscale models were created where element sizes equaled microstructure size, and properties were assigned through sampling of microscale simulation results. First, this study showed the effect of using Weibull scaling on constituent matrix strength on macroscale response. Then, a comparison was made between different element sizes and experiments. Finally, model dimensions were fixed, and the effect of randomly distributed local properties alone was examined. Results showed that the scatter of strength and stiffness in the experiments could be predicted well using images of the microscale fiber morphologies and that using stochastic properties produced a 3% coefficient of variation of strength for all experiments.

statistical microstructure↗

DOE OSTI2023

A New Modeling Approach for a Priori Uncertainties of Laser Tracker Angle Measurements

Methods for modeling the uncertainty in laser tracker angle measurements vary within the metrology industry, leading to confusion and questionable stochastic modeling for survey network adjustments and error propagation analysis. Interpreting the published laser tracker manufacturer performance specifications to determine an a priori sigma value for weighting azimuth and zenith angle measurements can be confusing and has led to differing implementations. Furthermore, this paper proposes a unique way to model survey network a priori laser tracker angular uncertainties based on laser tracker manufacturers’ published maximum permissible error (MPE) values referenced to current standards for weighting survey network measurements. This paper’s proposed model takes into account the disparate effects that pointing errors, target centering errors, and leveling errors have on azimuth and zenith angular uncertainties for measurements with steep sightings and at near ranges.

42 ENGINEERING↗

DOE OSTI2019

Orbital selectivity versus Pomeranchuk instability in the iron–chalcogenide superconductors: A two-loop renormalization group study

We perform a two-loop renormalization group (RG) analysis of a 2D effective multiband model, which is relevant for describing the low-energy properties of some iron–chalcogenide superconducting materials. Crucial ingredients in this analysis are the calculation of higher-order contributions in the RG scheme that go beyond the widely-used parquet approximation and the consequent inclusion of nontrivial self-energy effects of the model that yield an anisotropic renormalization of the quasiparticle weight in the system. The motivation of our work is the experimental discovery by Sprau et al. (2017) that orbitally-selective renormalization of the quasiparticle weight in the Hund’s metal phase at moderate temperatures underpins the highly unusual gap in the superconducting phase of the FeSe compound at lower temperatures. One prediction we arrive here is that the underlying origin of nematicity in these systems may indeed come from orbital-selectivity, instead of a Pomeranchuk instability in the d{sub ±} channel. This orbital selectivity is driven by the presence of stripe-type antiferromagnetic fluctuations in the model. Therefore, we argue that the present RG results may provide a scenario from a weak-to-moderate coupling perspective, in which the role of orbital selectivity to describe the physical properties of some iron–chalcogenide superconductors is emphasized.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2012

Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors

Purpose: To develop a mathematical model utilizing more readily available measures than stimulation tests that identifies brain tumor survivors with high likelihood of abnormal growth hormone secretion after radiotherapy (RT), to avoid late recognition and a consequent delay in growth hormone replacement therapy. Methods and Materials: We analyzed 191 prospectively collected post-RT evaluations of peak growth hormone level (arginine tolerance/levodopa stimulation test), serum insulin-like growth factor 1 (IGF-1), IGF-binding protein 3, height, weight, growth velocity, and body mass index in 106 children and adolescents treated for ependymoma (n = 72), low-grade glioma (n = 28) or craniopharyngioma (n = 6), who had normal growth hormone levels before RT. Normal level in this study was defined as the peak growth hormone response to the stimulation test {>=}7 ng/mL. Results: Independent predictor variables identified by multivariate logistic regression with high statistical significance (p < 0.0001) included IGF-1 z score, weight z score, and hypothalamic dose. The developed predictive model demonstrated a strong discriminatory power with an area under the receiver operating characteristic curve of 0.883. At a potential cutoff point of probability of 0.3 the sensitivity was 80% and specificity 78%. Conclusions: Without unpleasant and expensive frequent stimulation tests, our model provides a quantitative approach to closely follow the growth hormone secretory capacity of brain tumor survivors. It allows identification of high-risk children for subsequent confirmatory tests and in-depth workup for diagnosis of growth hormone deficiency.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2001

A Global Optimization Methodology for Rocket Propulsion Applications

While the response surface method is an effective method in engineering optimization, its accuracy is often affected by the use of limited amount of data points for model construction. In this chapter, the issues related to the accuracy of the RS approximations and possible ways of improving the RS model using appropriate treatments, including the iteratively re-weighted least square (IRLS) technique and the radial-basis neural networks, are investigated. A main interest is to identify ways to offer added capabilities for the RS method to be able to at least selectively improve the accuracy in regions of importance. An example is to target the high efficiency region of a fluid machinery design space so that the predictive power of the RS can be maximized when it matters most. Analytical models based on polynomials, with controlled level of noise, are used to assess the performance of these techniques.

Source record↗

NASA NTRS2023

Mshpy23: A User-Friendly, Parameterized Model of Magnetosheath Conditions

Lunar Environment heliospheric X-ray Imager (LEXI) and Solar wind – Magnetosphere - Ionosphere Link Explorer (SMILE) will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under varioussolar wind conditions after their respective launches in 2024 and 2025. Magnetosheath conditions, namely, plasma density, velocity, and temperature, are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions. We developed a user-friendly model of magnetosheath that parameterizes number density, velocity, temperature, and magnetic field by utilizing the global Magnetohydrodynamics (MHD) model as well as the pre-existing gas-dynamic and analytic models. Using this parameterized magnetosheath model, scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models. First, we created an MHD based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities (1, 5, 10, 15, 20, 25, and 30cm−3) and 2 interplanetary magnetic field BZ components (± 4nT), and then parameterizing the results in new magnetosheath conditions. We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77, especially for plasma density and plasma velocity. Second, we compiled a suite of magnetosheath models incorporating previous magnetosheath models (gas-dynamic, analytic), and did two case studies to test the performance. The MHD based model was comparable to or better than the previous models while providing self consistency among the magnetosheath parameters. Third, we constructed a tool to calculate a soft X-ray image from any given vantage point, which can support the planning and data analysis of the aforementioned LEXI and SMILE missions. A release of the code has been uploaded to a Github repository.

Magnetosheath↗

NASA NTRS2014

Environmental and Socio-Demographic Determinants of Dengue Fever in Colombo City, Sri Lanka

Dengue fever has increased exponentially in Sri Lanka, from 24.4 cases per 100,000 in 2003 to 165.3 per 100,000 population in 2013. Although early warning systems using predictor models have been previously developed in other settings, it is important to develop such models in each local setting. Further, the ability of these models to be applicable at smaller geographic units will enhance current vector control and disease surveillance measures. The aim of this paper was to identify environmental and socio-economic status (SES) risk factors that may predict dengue fever at the Gram Niladhari Divisions (GND) level (smallest administrative unit) in Colombo city, Sri Lanka. These factors included landcover classes, amount of vegetation, population density, water access and neighborhood SES as determined by roof type. A geographically weighted regression (GWR) was used to develop the prediction model. A total 55 GND units covering an area of 37 sq km were investigated. We found that GND units with decreased vegetation, higher built-up area, higher population density and poor access to tap-water supply were associated with high risk of dengue; the pertinent GND units were concentrated in the center of the city. This is the first study in Sri Lanka to include both environmental and socio-demographic factors in prediction models for dengue fever. The methodology may be useful in enhancing ongoing dengue fever control measures in the country, and to be extended to other countries in the region that have an increasing incidence of dengue fever.

Tipre, Meghan↗

NASA NTRS2002

Control Oriented Modeling and Validation of Aeroservoelastic Systems

Lightweight aircraft design emphasizes the reduction of structural weight to maximize aircraft efficiency and agility at the cost of increasing the likelihood of structural dynamic instabilities. To ensure flight safety, extensive flight testing and active structural servo control strategies are required to explore and expand the boundary of the flight envelope. Aeroservoelastic (ASE) models can provide online flight monitoring of dynamic instabilities to reduce flight time testing and increase flight safety. The success of ASE models is determined by the ability to take into account varying flight conditions and the possibility to perform flight monitoring under the presence of active structural servo control strategies. In this continued study, these aspects are addressed by developing specific methodologies and algorithms for control relevant robust identification and model validation of aeroservoelastic structures. The closed-loop model robust identification and model validation are based on a fractional model approach where the model uncertainties are characterized in a closed-loop relevant way.

Crowder, Marianne↗

NASA NTRS1990

Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

Hipol, Philip J.↗

NASA NTRS2006

Sizing Structures and Predicting Weight of a Spacecraft

EZDESIT is a computer program for choosing the sizes of structural components and predicting the weight of a spacecraft, aircraft, or other vehicle. In designing a vehicle, EZDESIT is used in conjunction with a finite-element structural- analysis program: Each structural component is sized within EZDESIT to withstand the loads expected to be encountered during operation, then the weights of all the structural finite elements are added to obtain the structural weight of the vehicle. The sizing of the structural components elements also alters the stiffness properties of the finiteelement model. The finite-element analysis and structural component sizing are iterated until the weight of the vehicle converges to a prescribed iterative difference.

Cerro, Jeffrey↗

NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

DOE OSTI2019

Development of mathematical model and simulation tool for the high capacity production of carbon fiber

Weight reduction in the transportation is the most effective way for reducing fuel consumption. Carbon fiber composites have been increasingly used by automotive manufacturers in vehicle nonstructural and structural parts. Larger penetration of carbon fiber-based materials requires very large quantities of fiber at a low price, and of consistently high quality. The advancements in carbon fiber technology that are required to achieve this production scale-up and cost reduction, are very challenging. The most expensive production step is the conversion of textile-grade polyacrylonitrile (PAN) precursor to carbon fiber. The conversion takes place in three main stages: stabilization, low and high temperature carbonization. Most of the research to date has focused on the stabilization of the PAN precursors and oxidized PAN fibers. Comparingly little effort has been dedicated to the investigation of processing conditions during low temperature (LT) carbonization. During this stage of fiber conversion, the largest fiber mass loss occurs, and the fundamental carbon fiber structure and material properties are established. The understanding of the conversion processes in the LT furnace, and the effects of spatial and temporal variations of the LT process parameters on the carbon fiber properties are necessary to identify possible process improvements and cost savings. The objective of this research was to develop computational models for gas flow and energy transport in the gas and fiber phases of the LT furnace, and to couple them with the fiber conversion process models. The model is implemented in the three-dimensional framework that enables analysis of process condition variations in all directions. The formulation is based on modified transport equation to simulate steady state conditions and thus significantly reduce computational effort associated with transient methods. The model is implemented in open source computational fluid dynamics (CFD) code OpenFOAM. The model has been demonstrated on a prototype carbon fiber LT furnace and can be extended to other geometries and process conditions. Extensions to other physical effects, such as accounting evolution of multiple gas species, and fiber tensioning are straightforward. The model uses High Performance Computing (HPC) capabilities of OpenFOAM and can be implemented on multi-processor workstations or mid-size computer clusters common in the industrial setting.

36 MATERIALS SCIENCE↗

NASA NTRS2000

Cyclic Oxidation Testing and Modelling: A NASA Lewis Perspective

The Materials Division of the NASA Lewis Research Center has been heavily involved in the cyclic oxidation of high temperature materials for 30 years. Cyclic furnace and burner rig apparati have been developed, refined, and replicated to provide a large scale facility capable of evaluating many materials by a standard technique. Material behavior is characterized by weight change data obtained throughout the test, which has been modelled in a step-wise process of scale growth and spallation. This model and a coupled diffusion model have successfully described cyclic behavior for a number of systems and have provided insights regarding life prediction and variations in the spalling process. Performance ranking and mechanistic studies are discussed primarily for superalloys and coating alloys. Similar cyclic oxidation studies have been performed on steels, intermetallic compounds, thermal barrier coatings, ceramics, and ceramic composites. The most common oxidation test was performed in air at temperatures ranging from 800 deg. to 1600 C, for times up to 10000 h, and for cycle durations of 0.1 to 1000 h. Less controlled, but important, test parameters are the cooling temperature and humidity level. Heating and cooling rates are not likely to affect scale spallation. Broad experience has usually allowed for considerable focus and simplification of these test parameters, while still revealing the principal aspects of material behavior and performance. Extensive testing has been performed to statistically model the compositional effects of experimental alloys and to construct a comprehensive database of complex commercial alloys.

Smialek, J. L.↗

DOE OSTI2019

Analytical Model of High-Velocity Impact of a Deformable Projectile Against Textile-Based Composites

Textile-based composites are used in manufacturing of ballistic shields due to their favorable combination of mechanical properties which enables weight saving and efficiency while retaining or even improving ballistic performance. Predictive models are an important tool in reducing experimental tests which are still an important step in ballistic shield design. Among those, analytical models provide less accurate results but require far less computational cost than numerical models and can be used for preliminary analyses. Many authors in the literature have developed analytical models for high-velocity impact on textile-based composites based on wave propagation theory and an energy equilibrium approach. The main limitation of these models is the assumption of rigid projectile which leads to the underestimation of the ballistic limit for soft core projectiles, which are a very common type of threat for this kind of protection. For this reason, an enhanced version of these models is reported in this paper, which releases the assumption of rigid projectile and adds erosion and deformation of the projectile to the formulation. Ballistic tests were performed on panels manufactured from layers of Kevlar 29 plain weave fabric embedded in an epoxy matrix. An insight into the different kinds of energy absorption considered by the model shows that projectile deformation plays an important role in the dissipation of the kinetic energy of the projectile in case of intermediate impact velocity. For this reason, the present model delivers more accurate results than the models present in the literature. In particular, the estimation of the ballistic limit is drastically improved which is a key step in ballistic shields optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

THE CIRCUMSTELLAR DISK OF THE Be STAR o AQUARII AS CONSTRAINED BY SIMULTANEOUS SPECTROSCOPY AND OPTICAL INTERFEROMETRY

Omicron Aquarii is a late-type, Be shell star with a stable and nearly symmetric Hα emission line. We combine Hα interferometric observations obtained with the Navy Precision Optical Interferometer covering 2007 through 2014 with Hα spectroscopic observations over the same period and a 2008 observation of the system's near-infrared spectral energy distribution to constrain the properties of o Aqr's circumstellar disk. All observations are consistent with a circumstellar disk seen at an inclination of 75° ± 3° with a position angle on the sky of 110° ± 8° measured East from North. From the best-fit disk density model, we find that 90% of the Hα emission arises from within 9.5 stellar radii, and the mass associated with this Hα disk is ∼1.8 × 10{sup −10} of the stellar mass, and that the associated angular momentum, assuming Keplerian rotation for the disk, is ∼1.6 × 10{sup −8} of the total stellar angular momentum. The occurrence of a central quasi-emission feature in Mg ii λ4481 is also predicted by this best-fit disk model and the computed profile compares successfully with observations from 1999. To obtain consistency between the Hα line profile modeling and the other constraints, it was necessary in the profile fitting to weight the line core (emission peaks and central depression) more heavily than the line wings, which were not well reproduced by our models. This may reflect the limitation of assuming a single power law for the disk's variation in equatorial density. The best-fit disk density model for o Aqr predicts that Hα is near its maximum strength as a function of disk density, and hence the Hα equivalent width and line profile change only weakly in response to large (factor of ∼5) changes in the disk density. This may in part explain the remarkable observed stability of o Aqr's Hα emission line profile.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

ROP model for PDC bits in Geothermal drilling

Geothermal energy is a renewable source of energy, where heat extraction is preferentially balanced with the reservoir's natural heat recharge rate. The objective of this paper is to present and validate a novel rate of penetration (ROP) model for drilling hard and abrasive formations including granite formations for polycrystalline diamond compact (PDC) bits. The ROP model was developed based on a derived relationship of a threshold weight on cutter (WOC) and its corresponding depth of cut (DOC) for a single cutter. Laboratory data was used to scale the derived single cutter relationship to a full-hole ROP model for PDC bits. The ROP model includes a non-linear correlation for Phase I (inefficient drilling due to low WOB values) and a linear Phase II (efficient drilling) ROP response to WOB. The ROP model was verified using measured drilling parameter data from Utah FORGE well# 58-32 and data from Chocolate Mountains well # 17-8 in Southern California. When compared to oil and gas well drilling, geothermal drilling in granitic formations can be more difficult and complicated due to rock hardness and high temperatures. PDC bits can increase ROP and optimize drilling for these types of hard formations. This paper provides novel insight into the ROP response of PDC bits to drilling operational parameters.

15 GEOTHERMAL ENERGY↗