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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of a recombinant anti-VEGFR2-EPCAM bispecific antibody to improve antiangiogenic efficiency

Highlights: • Targeted therapy can be a useful therapeutic method along with traditional methods for breast cancer treatment. • Decreasing Akt/Nf-Kβ signaling pathway is crucial target in order to decreasing angiogenesis in TNBC. • The recombinant antibody can have inhibitory effects on production IL6,8, MMP2, and MMP9. Tumor progression and metastasis, especially in invasive cancers (such as triple-negative breast cancer [TNBC]), depend on angiogenesis, in which vascular epithelial growth factor (VEGF)/vascular epithelial growth factor receptor [1] has a decisive role, followed by the metastatic spread of cancer cells. Although some studies have shown that anti-VEGFR2/VEGF monoclonal antibodies demonstrated favorable results in the clinic, this approach is not efficient, and further investigations are needed to improve the quality of cancer treatment. Besides, the increased expression of epithelial cell adhesion molecule (EpCAM) in various cancers, for instance, invasive breast cancer, contributes to angiogenesis, facilitating the migration of tumor cells to other parts of the body. Thus, the main goal of our study was to target either VEGFR2 or EpCAM as pivotal players in the progression of angiogenesis in breast cancer. Regarding cancer therapy, the production of bispecific antibodies is easier and more cost-effective compared to monoclonal antibodies, targeting more than one antigen or receptor; for this reason, we produced a recombinant antibody to target cells expressing EpCAM and VEGFR2 via a bispecific antibody to decrease the proliferation and metastasis of tumor cells. Following the cloning and expression of our desired anti-VEGFR2/EPCAM sequence in E. coli, the accuracy of the expression was confirmed by Western blot analysis, and its binding activities to VEGFR2 and EPCAM on MDA-MB-231 and MCF-7 cell lines were respectively indicated by flow cytometry. Then, its anti-proliferative potential was indicated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and apoptosis assay to evaluate inhibitory effects of the antibody on tumor cells. Subsequently, the data indicated that migration, invasion, and angiogenesis were inhibited in breast cancer cell lines via the bispecific antibody. Furthermore, cytokine analysis indicated that the bispecific antibody could moderate interleukin 8 (IL-8) and IL-6 as key mediators in angiogenesis progression in breast cancer. Thus, our bispecific antibody could be considered as a promising candidate tool to decrease angiogenesis in TNBC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Factors Governing Oxygen Vacancy Formation in Oxide Perovskites

The control of oxygen vacancy (V O ) formation is critical to advancing multiple metal-oxide-perovskite-based technologies. In this work, we report the construction of a compact linear model for the neutral V O formation energy in ABO 3 perovskites that reproduces, with reasonable fidelity, Hubbard-U-corrected density functional theory calculations based on the state-of-the-art, strongly constrained and appropriately normed exchange-correlation functional. We obtain a mean absolute error of 0.45 eV for perovskites stable at 298 K, an accuracy that holds across a large, electronically diverse set of ABO 3 perovskites. Our model considers perovskites containing alkaline-earth metals (Ca, Sr, and Ba) and lanthanides (La and Ce) on the A-site and 3d transition metals (Ti, V, Cr, Mn, Fe, Co, and Ni) on the B-site in six different crystal systems (cubic, tetragonal, orthorhombic, hexagonal, rhombohedral, and monoclinic) common to perovskites. Physically intuitive metrics easily extracted from existing experimental thermochemical data or via inexpensive quantum mechanical calculations, including crystal bond dissociation energies and (solid phase) reduction potentials, are key components of the model. Beyond validation of the model against known experimental trends in materials used in solid oxide fuel cells, the model yields new candidate perovskites not contained in our training data set, such as (Bi,Y)(Fe,Co)O 3 , which we predict may have favorable thermochemical water-splitting properties. The confluence of sufficient accuracy, efficiency, and interpretability afforded by our model not only facilitates high-throughput computational screening for any application that requires the precise control of V O concentrations but also provides a clear picture of the dominant physics governing V O formation in metal-oxide perovskites.

08 HYDROGEN↗
DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1978

The magnetic field of Mars - Mars 3 evidence reexamined

Dolginov et al. (1972, 1973) presented evidence for a Mars magnetic field based on Mars 2 and 3 measurements. This evidence is somewhat complex, based on a combination of arguments including the position of the bow shock, the identification of magnetopause crossings, and the simultaneous behavior of the plasma and the field. The evidence rests most heavily on one pass of Mars 3 on December 1, 1972 from which Dolginov et al. deduce the direction and orientation of the magnetic moment of Mars. It is the purpose of the present note that there is no reason to suppose that Mars 3 penetrated the Mars magnetosphere on this pass, that because of their locations all other putative magnetopause crossings are suspect and that the effective obstacle height is coincident with the height of the Mars ionosphere to within the accuracy of our knowledge of the proper scaling law for planetary bow shocks.

Russell, C. T.↗
NASA NTRS1991

Resolved rate and torque control schemes for large scale space based kinematically redundant manipulators

Resolved rate control of kinematically redundant ground based manipulators is a challenging problem. The structural, actuator, and control loop frequency characteristics of industrial grade robots generally allow operation with resolved rate control; a rate command is achievable with good accuracy. However, space based manipulators are different, typically have less structural stiffness, more motor and joint friction, and lower control loop cycle frequencies. These undesirable characteristics present a considerable Point of Resolution (POR) control problem for space based, kinematically redundant manipulators for the following reason: a kinematically redundant manipulator requires an arbitrary constraint to solve for the joint rate commands. A space manipulator will not respond to joint rate commands because of these characteristics. A space based manipulator simulation, including free end rigid body dynamics, motor dynamics, motor striction/friction, gearbox backlash, joint striction/friction, and Space Station Remote Manipulator System type configuration parameters, is used to evaluate the performance of a documented resolved rate control law. Alternate schemes which include torque control are also evaluated.

Robert W Bailey↗
NASA NTRS1994

An analysis of supersonic flows with low-Reynolds number compressible two-equation turbulence models using LU finite volume implicit numerical techniques

A generalized flow solver using an implicit Lower-upper (LU) diagonal decomposition based numerical technique has been coupled with three low-Reynolds number kappa-epsilon models for analysis of problems with engineering applications. The feasibility of using the LU technique to obtain efficient solutions to supersonic problems using the kappa-epsilon model has been demonstrated. The flow solver is then used to explore limitations and convergence characteristics of several popular two equation turbulence models. Several changes to the LU solver have been made to improve the efficiency of turbulent flow predictions. In general, the low-Reynolds number kappa-epsilon models are easier to implement than the models with wall-functions, but require much finer near-wall grid to accurately resolve the physics. The three kappa-epsilon models use different approaches to characterize the near wall regions of the flow. Therefore, the limitations imposed by the near wall characteristics have been carefully resolved. The convergence characteristics of a particular model using a given numerical technique are also an important, but most often overlooked, aspect of turbulence model predictions. It is found that some convergence characteristics could be sacrificed for more accurate near-wall prediction. However, even this gain in accuracy is not sufficient to model the effects of an external pressure gradient imposed by a shock-wave/ boundary-layer interaction. Additional work on turbulence models, especially for compressibility, is required since the solutions obtained with base line turbulence are in only reasonable agreement with the experimental data for the viscous interaction problems.

Lee, J.↗
DOE OSTI2021

A Machine Learns to Predict the Stability of Highly Diverse Multi-planetary Systems

Machine learning has proven to be an invaluable tool for characterizing the stability of planets in simplified planetary systems. In this work, we investigate the performance of a machine learning classifier on tightlypacked systems containing a rich diversity of planets, from Earths to Jupiters. Using information derived from short numerical simulations about a planet’s early orbital evolution and its relationship with the most massive planets in the system, we train a random forest classifier to predict instability with a > 88 percent accuracy. Our classifier relies on relative planet masses and the standard deviation of eccentricity for much of its predictive power. Most misclassified planets lie along a multi-dimensional boundary between stable and unstable planets, indicating that their early orbital evolution is ambiguous. The major reason for misclassification in this work is timescale: because our classifier uses information from only the first 137 years of simulation data, it is blind to late time interactions that cause or prevent instability. Machine learning methods like those utilized in this work provide powerful tools to complement numerical simulations across a wide range of planetary architectures.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Requirements for space shuttle scatter radar experiments

The feasibility of carrying out scatter radar experiments on the space shuttle was analyzed. Design criteria considered were the required average transmitter power, frequency resolution, spatial resolution, and statistical accuracy. Experiments analyzed were measurement of the naturally enhanced plasma line and the ion component of the incoherent scatter spectrum, and the plasma line artificially enhanced by an intense HF radio wave. The ion component measurement does not appear feasible, while the other two appear reasonable for short ranges only.

Harker, K. J.↗
NASA NTRS1979

Nationwide forestry applications program. Ten-Ecosystem Study (TES) site 7, Weld County, Colorado

The author has identified the following significant results. The best dates for automatic data processing analysis appeared to be in midsummer. The level 2 separation of grassland, water, and other resources was reasonably successful, but the level 3 separation of grassland into cultivated (growing crops) and weeds did not appear feasible. Low simulated inventory proportions of grassland indicated that the restricted inventory signature was not representative of all grassland classes and could not be extended with acceptable accuracy.

Weaver, J. E.↗
NASA NTRS1983

Methodology for interpretation of SST retrievals using the AVHRR split window algorithm

Intercomparisons of sea surface temperature (SST) products derived from the operational NOAA-7 AVHRR-II algorithm and in situ observations are made. The 1982 data sets consist of ship survey data during the winter from the Mid-Atlantic Bight (MAB), ship and buoy measurements during April and September in the Gulf of Mexico and shipboard observations during April off the N.W. Spanish coast. The analyses included single pixel comparisons and the warmest pixel technique for 2 x 2 pixel and 10 x 10 pixel areas. The reason for using multi-pixel areas was for avoiding cloud contaminated pixels in the vicinity of the field measurements. Care must be taken when applying the warmest pixel technique near oceanic fronts. The Gulf of Mexico results clearly indicate a persistent degradation in algorithm accuracy due to El Chichon aerosols. The MAB and Spanish data sets indicate that very accurate estimates can be achieved if care is taken to avoid clouds and oceanic fronts.

Barbieri, R. W.↗
NASA NTRS1987

Knowledge representation system for assembly using robots

Assembly robots combine the benefits of speed and accuracy with the capability of adaptation to changes in the work environment. However, an impediment to the use of robots is the complexity of the man-machine interface. This interface can be improved by providing a means of using a priori-knowledge and reasoning capabilities for controlling and monitoring the tasks performed by robots. Robots ought to be able to perform complex assembly tasks with the help of only supervisory guidance from human operators. For such supervisory quidance, it is important to express the commands in terms of the effects desired, rather than in terms of the motion the robot must undertake in order to achieve these effects. A suitable knowledge representation can facilitate the conversion of task level descriptions into explicit instructions to the robot. Such a system would use symbolic relationships describing the a priori information about the robot, its environment, and the tasks specified by the operator to generate the commands for the robot.

Jain, A.↗
NASA NTRS1993

Numerical analysis of base flowfield at high altitude for a four-engine clustered nozzle configuration

The objective of this study is to benchmark a four-engine clustered nozzle base flowfield with a computational fluid dynamics (CFD) model. The CFD model is a pressure based, viscous flow formulation. An adaptive upwind scheme is employed for the spatial discretization. The upwind scheme is based on second and fourth order central differencing with adaptive artificial dissipation. Qualitative base flow features such as the reverse jet, wall jet, recompression shock, and plume-plume impingement have been captured. The computed quantitative flow properties such as the radial base pressure distribution, model centerline Mach number and static pressure variation, and base pressure characteristic curve agreed reasonably well with those of the measurement. Parametric study on the effect of grid resolution, turbulence model, inlet boundary condition and difference scheme on convective terms has been performed. The results showed that grid resolution and turbulence model are two primary factors that influence the accuracy of the base flowfield prediction.

Wang, Ten-See↗
NASA NTRS1990

Error analysis for reducing noisy wide-gap concentric cylinder rheometric data for nonlinear fluids - Theory and applications

This work discusses the propagation of errors for the recovery of the shear rate from wide-gap concentric cylinder viscometric measurements of non-Newtonian fluids. A least-square regression of stress on angular velocity data to a system of arbitrary functions is used to propagate the errors for the series solution to the viscometric flow developed by Krieger and Elrod (1953) and Pawlowski (1953) ('power-law' approximation) and for the first term of the series developed by Krieger (1968). A numerical experiment shows that, for measurements affected by significant errors, the first term of the Krieger-Elrod-Pawlowski series ('infinite radius' approximation) and the power-law approximation may recover the shear rate with equal accuracy as the full Krieger-Elrod-Pawlowski solution. An experiment on a clay slurry indicates that the clay has a larger yield stress at rest than during shearing, and that, for the range of shear rates investigated, a four-parameter constitutive equation approximates reasonably well its rheology. The error analysis presented is useful for studying the rheology of fluids such as particle suspensions, slurries, foams, and magma.

Borgia, Andrea↗
NASA NTRS2010

Validating Human Performance Models of the Future Orion Crew Exploration Vehicle

NASA's Orion Crew Exploration Vehicle (CEV) will provide transportation for crew and cargo to and from destinations in support of the Constellation Architecture Design Reference Missions. Discrete Event Simulation (DES) is one of the design methods NASA employs for crew performance of the CEV. During the early development of the CEV, NASA and its prime Orion contractor Lockheed Martin (LM) strived to seek an effective low-cost method for developing and validating human performance DES models. This paper focuses on the method developed while creating a DES model for the CEV Rendezvous, Proximity Operations, and Docking (RPOD) task to the International Space Station. Our approach to validation was to attack the problem from several fronts. First, we began the development of the model early in the CEV design stage. Second, we adhered strictly to M&S development standards. Third, we involved the stakeholders, NASA astronauts, subject matter experts, and NASA's modeling and simulation development community throughout. Fourth, we applied standard and easy-to-conduct methods to ensure the model's accuracy. Lastly, we reviewed the data from an earlier human-in-the-loop RPOD simulation that had different objectives, which provided us an additional means to estimate the model's confidence level. The results revealed that a majority of the DES model was a reasonable representation of the current CEV design.

Wong, Douglas T.↗
DOE OSTI2024

A RELAP5-3D Model of the Lobo Lead Loop

The goal of our research is to build upon the capability of RELAP5-3D to model molten lead systems. Molten lead has several potential uses in future advanced reactors like the lead fast reactor or fusion reactors that utilize dual-coolant lead lithium blankets. This potential for use in future generations of reactors highlights the necessity to develop molten lead models to ensure that they can accurately predict the thermohydraulic behavior. We have developed a RELAP5-3D model of the Lobo Lead Loop facility, located at the University of New Mexico, to verify the accuracy of RELAP5-3D via comparison to existing computational fluid dynamics results and analytical calculations. It was found that RELAP5-3D accurately calculated radiative heat transfer (within < 1%) when compared to theoretical calculations. In addition, pressure drop calculations done in RELAP5-3D demonstrated reasonable agreement within 20 kPa, mostly within ~7-15%, when compared to the computational fluid dynamics model of the facility developed by the University of New Mexico, and captured the dependence of pressure drop on flow velocity accurately. Finally, a hypothetical loss of flow transient was imposed on the RELAP5-3D model to determine the feasibility of performing a similar experiment with the Lobo Lead Loop. It was found that such an experiment could be possible as the RELAP5-3D model indicated that the temperatures of the fluid would not exceed limiting temperatures of the structure (1658 K) nor the maximum temperature of the electromagnetic pump inlet (823 K). Although there is not experimental data to begin validation, the model will be readily available for future validation studies when the experimental data is generated, especially as the model continues to evolve over time. Furthermore, the results so far demonstrate a promising first step in the verification/validation of the RELAP5-3D model of the Lobo Lead Loop.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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↗
NASA NTRS1992

Eno-Osher schemes for Euler equations

The combination of the Osher approximate Riemann solver for the Euler equations and various ENO schemes is discussed for one-dimensional flow. The three basic approaches, viz. the ENO scheme using primitive variable reconstruction, either with Cauchy-Kowalewski procedure for time integration or the TVD Runge-Kutta scheme, and the flux-ENO method are tested on different shock tube cases. The shock tube cases were chosen to present a serious challenge to the ENO schemes in order to test their ability to capture flow discontinuities, such as shocks. Also the effect of the ordering of the eigen values, viz. natural or reversed ordering, in the Osher scheme is investigated. The ENO schemes are tested up to fifth order accuracy in space and time. The ENO-Osher scheme using the Cauchy-Kowalewski procedure for time integration is found to be the most accurate and robust compared with the other methods and is also computationally efficient. The tests showed that the ENO schemes perform reasonably well, but have problems in cases where two discontinuities are close together. In that case there are not enough points in the smooth part of the flow to create a non-oscillatory interpolation.

Vandervegt, Jacobus J.↗