Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “High dimensional data,”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Quantitative impact characterization of aeronautical CFRP materials with non-destructive testing methods

In recent years, an increasing number of safety-relevant structures are designed and manufactured from carbon fiber reinforced polymers (CFRP) in order to reduce weight of airplanes by taking the advantage of their specific strength into account. Non-destructive testing (NDT) methods for quantitative defect analysis of damages are liquid- or air-coupled ultrasonic testing (UT), phased array ultrasonic techniques, and active thermography (IR). The advantage of these testing methods is the applicability on large areas. However, their quantitative information is often limited on impact localization and size. In addition to these techniques, Airbus Group Innovations operates a micro x-ray computed tomography (μ-XCT) system, which was developed for CFRP characterization. It is an open system which allows different kinds of acquisition, reconstruction, and data evaluation. One main advantage of this μ-XCT system is its high resolution with 3-dimensional analysis and visualization opportunities, which enables to gain important quantitative information for composite part design and stress analysis. Within this study, different NDT methods will be compared at CFRP samples with specified artificial impact damages. The results can be used to select the most suitable NDT-method for specific application cases. Furthermore, novel evaluation and visualization methods for impact analyzes are developed and will be presented.

42 ENGINEERING↗
DOE OSTI2022

Orbital-induced crossover of the Fulde-Ferrell-Larkin-Ovchinnikov phase into Abrikosov-like states.

The Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state can emerge in superconductors for which the orbital critical field exceeds the Pauli limit. Here, we present angular-resolved specific-heat data of the quasi-two-dimensional organic superconductor ??-(ET)2Cu(NCS )2 , with a focus on high fields in the regime of the FFLO transition. For an increasing out-of-plane tilt of the applied magnetic field, which leads to an increase of orbital contributions, we found that the nature of the superconducting transition changes from second to first order and that a further transition appears within the high-field superconducting phase. However, the superconducting state above the Pauli limit is stable for field tilt of several degrees. Since any finite perpendicular component of the magnetic field necessarily leads to quantization of the orbital motion, the resulting vortex lattice states compete with the modulated order parameter of the FFLO state leading to complex high-field superconducting phases. By solving the linearized self-consistency equation within weak-coupling BCS theory, we show that our results are clear experimental evidence of an orbital-induced transformation of the FFLO order parameter into Abrikosov-like states of higher Landau levels.

Kotte, Tommy↗
DOE OSTI2025

Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics

Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 32 6 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent two-dimensional projections. Physics-guided superresolution diffusion transforms low-resolution images of the 6D density to high resolution 256 × 256 pixel images. Unsupervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multiparticle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without retraining. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A High-Order Discontinuous Galerkin Spectral Element Method for Compressible Reacting Flows

High-order methods have recently been shown to be an effective tool for high-fidelity flow computations like direct numerical simulations and large eddy simulations due to their strong balance between accuracy and computational cost. In this work, a high-order discontinuous Galerkin spectral element method (DGSEM) is developed to solve the chemically reactive Euler equations encountered in high-speed combustion. To handle the disparate length and time scales associated with these equations, we develop a novel method which combines the spectral accuracy of the SEM with the flexibility of DG approach. Thus, the framework is well suited to capture turbulence in smooth regions of the flow, while maintaining numerical stability in the presence of shocks. The numerical method is implemented within the spectral element solver Nek5000. Validation cases are conducted for both non-reactive and reactive discontinuous flows to demonstrate the solver capability. In particular, canonical one-dimensional and two-dimensional detonation simulations are performed and the high-order numerical results are validated against available literature data.

computational fluid dynamics (CFD)↗
NASA NTRS1979

Fuel spray data with LDV

Droplet size and two component velocities in the severe environment of an operating gas turbine combustor system can be measured simultaneously using the solar laser morphokinetomer (SLM) which incorporates the following capabilities: (1) measurement of a true two-dimensional velocity vector with a range of + or - (0.01-200 m/sec); (2) measurement of particle size (range 5 to 300 micron m) simultaneously with the measurement of velocity; (3) specification of probe volume position coordinates with a high degree of accuracy (+ or - 0.5 mm); (4) immediate on-line data checks; and (5) rapid computer storage of acquired data. The optical system of the SLM incorporates an ultrasonic beam splitter to allow the measurement of a two-dimensional velocity vector simultaneously with particle size. A microprocessor with a limited storage capability permits immediate analysis of test data in the test cell.

Rohy, D. A.↗
NASA NTRS1989

Quantitative characterization of a nonreacting, supersonic combustor flowfield using unified, laser-induced iodine fluorescence

A calibrated, nonintrusive optical technique, laser-induced iodine fluorescence (LIIF) was used to quantify the steady, compressible flowfield of a nonreacting, supersonic combustor. The combustor was configured with single and staged, transverse-air injection into a supersonic-air freestream behind a rearward-facing step. Pressure, temperature, two-velocity components, and injectant mole fraction were measured with high spatial resolution in the three-dimensional flowfields. These experimental results provide a benchmark set of data for validation of computational fluid dynamic (CFD) codes being developed to model supersonic combustor flowfields.

Fletcher, D. G.↗
NASA NTRS1990

Experimental investigation of a supersonic swept ramp injector using laser-induced iodine fluorescence

Planar measurements of injectant mole fraction and temperature have been conducted in a nonreacting supersonic combustor configured with underexpanded injection in the base of a swept ramp. The temperature measurements were conducted with a Mach 2 test section inlet in streamwise planes perpendicular to the test section wall on which the ramp was mounted. Injection concentration measurements, conducted in cross flow planes with both Mach 2 and Mach 2.9 free stream conditions, dramatically illustrate the domination of the mixing process by streamwise vorticity generated by the ramp. These measurements, conducted using a nonintrusive optical technique (laser-induced iodine fluorescence), provide an accurate and extensive experimental data base for the validation of computation fluid dynamic codes for the calculation of highly three-dimensional supersonic combustor flow fields.

Hartfield, Roy J.↗
NASA NTRS2000

Navier-Stokes Analysis of a High Wing Transport High-Lift Configuration with Externally Blown Flaps

Insights and lessons learned from the aerodynamic analysis of the High Wing Transport (HWT) high-lift configuration are presented. Three-dimensional Navier-Stokes CFD simulations using the OVERFLOW flow solver are compared with high Reynolds test data obtained in the NASA Ames 12 Foot Pressure Wind Tunnel (PWT) facility. Computational analysis of the baseline HWT high-lift configuration with and without Externally Blown Flap (EBF) jet effects is highlighted. Several additional aerodynamic investigations, such as nacelle strake effectiveness and wake vortex studies, are presented. Technical capabilities and shortcomings of the computational method are discussed and summarized.

Slotnick, Jeffrey P.↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2022

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference

Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in practice. Recently, there has been a significant interest in physics-based machine learning of dynamical (or Hamiltonian) systems and Hamiltonian neural networks (HNNs) is a noteworthy architecture. But these types of architectures have not been applied to solve Bayesian inference problems efficiently. We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients. We introduce latent variable outputs to HNNs (L-HNNs) for improved expressivity and reduced integration errors. We integrate L-HNNs in NUTS and further propose an online error monitoring scheme to prevent sampling degeneracy in regions where L-HNNs may have little training data. We demonstrate L-HNNs in NUTS with online error monitoring consider several complex high-dimensional posterior densities and compare its performance to NUTS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

WebGIobe - A cloud-based geospatial analysis framework for interacting with climate data

While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.

Sharma, Arun↗
DOE OSTI2025

ASCR Workshop Position Paper: Challenges and Opportunities in High Energy Physics

High energy particle physics and cosmology concern themselves with estimating fundamental parameters of nature, such as the masses and interactions of fundamental particles like the Higgs boson and the rate of expansion of the universe. In doing so, they analyze exabyte-scale datasets, some of the largest in all of science, and face many challenges in subsequent data analysis. These challenges are shared between the two disciplines, but we focus on particle physics to highlight one specific domain. In particle physics, the standard method for estimating parameters involves performing Monte Carlo (MC) integration as a function of both parameters of interest and nuisance parameters using an expensive simulator, counting the number of observed collision events (i.i.d. samples) from an experiment in the corresponding integration domains, and forming a Poisson likelihood function. This likelihood function is then used in a Frequentist manner to construct a maximum likelihood point estimate (MLE) and confidence set for the parameters. To sufficiently populate the high-dimensional integration domains, simulators consume billions of CPU-hours annually and produce hundreds of petabytes of intermediate output data. Several techniques have been developed to: optimize definitions of the integration domains so as to be maximally sensitive to a particular subset of parameters, efficiently estimate the integrals, and build robust surrogate models by interpolating between integral evaluations at different parameter points. One can view this whole endeavor as classical Simulation-Based Inference (SBI).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1992

Measurement and calibration of static distortion of position data from 3D trackers

Three-dimensional trackers are becoming increasingly important as user inputs in interactive computer systems. These trackers output the three-dimensional position, and often the orientation, of a sensor in space. The three-dimensional tracking is often, however, highly distorted and inaccurate. The purpose of this paper is to discuss methods for the measurement and characterization of the static distortion of the position data. When the distortion is constant, various methods can be used to calibrate the data from the tracker to increase accuracy. Several preliminary methods are discussed in this paper, including polynomial and weighted lookup methods. The measurement and calibration methods are applied to the Polhemus electromagnetic tracking system, but are applicable to tracking systems based on other technologies.

Bryson, Steve↗
NASA NTRS1991

Penn State axial flow turbine facility: Performance and nozzle flow field

The objective is to gain a thorough understanding of the flow field in a turbine stage including three-dimensional inviscid and viscid effects, unsteady flow field, rotor-stator interaction effects, unsteady blade pressures, shear stress, and velocity field in rotor passages. The performance of the turbine facility at the design condition is measured and compared with the design distribution. The data on the nozzle vane static pressure and wake characteristics are presented and interpreted. The wakes are found to be highly three-dimensional, with substantial radial inward velocity at most spanwise locations.

Lakshminarayana, B.↗
DOE OSTI2024

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\rm{std}(\sigma_{\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings.

Nevin, Rebecca↗
NASA NTRS1992

Identification of aerodynamic models for maneuvering aircraft

A Fourier analysis method was developed to analyze harmonic forced-oscillation data at high angles of attack as functions of the angle of attack and its time rate of change. The resulting aerodynamic responses at different frequencies are used to build up the aerodynamic models involving time integrals of the indicial type. An efficient numerical method was also developed to evaluate these time integrals for arbitrary motions based on a concept of equivalent harmonic motion. The method was verified by first using results from two-dimensional and three-dimensional linear theories. The developed models for C sub L, C sub D, and C sub M based on high-alpha data for a 70 deg delta wing in harmonic motions showed accurate results in reproducing hysteresis. The aerodynamic models are further verified by comparing with test data using ramp-type motions.

Lan, C. Edward↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗