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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Comparison of hot wire/laser velocimeter turbulence intensity measurements

The question of whether a random measure of particle velocities yields a good statistical estimate of the stationary condition of the turbulence flow field was investigated by comparing hot-wire and laser velocimeter turbulence intensity measurements. Great care was taken to insure that the instrument precision of both the laser velocimeter and hot wire was maximized. In this attempt to reduce the measurement uncertainties in the hot wire, direct digitization of the analog output signal was performed with point-by-point conversion to velocity through a spline fit calibration curve and the turbulence intensity function was calculated statistically. Frequent calibrations of the hot wire were performed using the laser velocimeter as the velocity standard to account for the presence of the small seed particles in the air flow and signal drift in the hot wire.

Meyers, J. F.↗
NASA NTRS1998

BIGEL analysis of gene expression in HL60 cells exposed to X rays or 60 Hz magnetic fields

We screened a panel of 1,920 randomly selected cDNAs to discover genes that are differentially expressed in HL60 cells exposed to 60 Hz magnetic fields (2 mT) or X rays (5 Gy) compared to unexposed cells. Identification of these clones was accomplished using our two-gel cDNA library screening method (BIGEL). Eighteen cDNAs differentially expressed in X-irradiated compared to control HL60 cells were recovered from a panel of 1,920 clones. Differential expression in experimental compared to control cells was confirmed independently by Northern blotting of paired total RNA samples hybridized to each of the 18 clone-specific cDNA probes. DNA sequencing revealed that 15 of the 18 cDNA clones produced matches with the database for genes related to cell growth, protein synthesis, energy metabolism, oxidative stress or apoptosis (including MYC, neuroleukin, copper zinc-dependent superoxide dismutase, TC4 RAS-like protein, peptide elongation factor 1alpha, BNIP3, GATA3, NF45, cytochrome c oxidase II and triosephosphate isomerase mRNAs). In contrast, BIGEL analysis of the same 1,920 cDNAs revealed no differences greater than 1.5-fold in expression levels in magnetic-field compared to sham-exposed cells. Magnetic-field-exposed and control samples were analyzed further for the presence of mRNA encoding X-ray-responsive genes by hybridization of the 18 specific cDNA probes to RNA from exposed and control HL60 cells. Our results suggest that differential gene expression is induced in approximately 1% of a random pool of cDNAs by ionizing radiation but not by 60 Hz magnetic fields under the present experimental conditions.

Non-NASA Center↗
DOE OSTI2025

Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. Here, this study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the TRACER and ESCAPE field campaigns, machine learning techniques, and parcel model simulations with a super-droplet microphysics scheme. A random forest algorithm identifies in-situ vertical velocity (w), temperature (T), and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μm (N c ,<50), 50 μm ≤ D max ≤ 3000 μm (N c,50–3000 ), and droplet effective diameter (D e ). Results show that LWC, N c,<50 , and N c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c,50–3000 increase, while N c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c,50–3000 , and smaller N c,<50 , while polluted environments produce greater N c,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N c,50–3000 . Polluted conditions result in a greater N c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

An unsupervised selection of polarimetric features useful for the segmentation and analysis of polarimetric synthetic aperture radar (SAR) data is presented. The technique is based on multidimensional clustering of the parameters composing the polarimetric covariance matrix of the data. Clustering is performed on the logarithm of these quantities. Once the polarimetric cluster centers have been determined, segmentation of the polarimetric data into regions is performed using a maximum likelihood polarimetric classifier. Segmentation maps are further improved using a Markov random field to describe the statistics of the regions and computing the maximum of the product of the local conditional densities. Examples with real polarimetric SAR imagery are given to illustrate the potential of this method.

Rignot, Eric↗
NASA NTRS1985

Aerodynamic properties of turbulent combustion fields

Flow fields involving turbulent flames in premixed gases under a variety of conditions are modeled by the use of a numerical technique based on the random vortex method to solve the Navier-Stokes equations and a flame propagation algorithm to trace the motion of the front and implement the Huygens principle, both due to Chorin. A successive over-relaxation hybrid method is applied to solve the Euler equation for flows in an arbitrarily shaped domain. The method of images, conformal transformation, and the integral-equation technique are also used to treat flows in special cases, according to their particular requirements. Salient features of turbulent flame propagation in premixed gases are interpreted by relating them to the aerodynamic properties of the flow field. Included among them is the well-known cellular structure of flames stabilized by bluff bodies, as well as the formation of the characteristic tulip shape of flames propagating in ducts. In its rudimentary form, the mechanism of propagation of a turbulent flame is shown to consist of: (1) rotary motion of eddies at the flame front, (2) self-advancement of the front at an appropriate normal burning speed, and (3) dynamic effects of expansion due to exothermicity of the combustion reaction. An idealized model is used to illustrate these fundamental mechanisms and to investigate basic aerodynamic features of flames in premixed gases. The case of a confined flame stabilized behind a rearward-facing step is given particular care and attention. Solutions are shown to be in satisfactory agreement with experimental results, especially with respect to global properties such as the average velocity profiles and reattachment length.

Hsiao, C. C.↗
DOE OSTI2020

Calculation of the detonation state of HN 3 with quantum accuracy

HN 3 is a unique liquid energetic material that exhibits ultrafast detonation chemistry and a transition to metallic states during detonation. We combine the Chebyshev interaction model for efficient simulation (ChIMES) many-body reactive force field and the extended-Lagrangian multiscale shock technique molecular dynamics method to calculate the detonation properties of HN 3 with the accuracy of Kohn–Sham density-functional theory. ChIMES is based on a Chebyshev polynomial expansion and can accurately reproduce density-functional theory molecular dynamics (DFT-MD) simulations for a wide range of unreactive and decomposition conditions of liquid HN 3 . We show that addition of random displacement configurations and the energies of gas-phase equilibrium products in the training set allows ChIMES to efficiently explore the complex potential energy surface. Schemes for selecting force field parameters and the inclusion of stress tensor and energy data in the training set are examined. Structural and dynamical properties and chemistry predictions for the resulting models are benchmarked against DFT-MD. We demonstrate that the inclusion of explicit four-body energy terms is necessary to capture the potential energy surface across a wide range of conditions. Our results generally retain the accuracy of DFT-MD while yielding a high degree of computational efficiency, allowing simulations to approach orders of magnitude larger time and spatial scales. The techniques and recipes for MD model creation we present allow for direct simulation of nanosecond shock compression experiments and calculation of the detonation properties of materials with the accuracy of Kohn–Sham density-functional theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
NASA NTRS1985

Turbulent disruptions from the Strauss equations

Preliminary results are reported from application of a three-dimensional spectral method model to the solution of the Strauss (1976) reduced MHD equations. The investigation was focused on describing MHD turbulence in a current-carrying bounded magnetofluid. A cylindrical geometry with a square cross-section was considered, with the walls being rigid perfect conductors with free-slip boundary conditions. A uniform magnetic field and the electric current density both point in the z-direction. Initial conditions are specified which feature small amounts of random noise expressed as Fourier modes. Linearized equations are defined for tracing the movement to equlibrium conditions or other temporal development. The model is further refined with nonlinear equations to examine the effects of the appearance of disruptions. Comparisons are drawn between solutions obtained with linear and nonlinear equations, with an eye to the associated physical realities.

Dahlburg, J. P.↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗
NASA NTRS2013

Investigation of a Cross-Correlation Based Optical Strain Measurement Technique for Detecting radial Growth on a Rotating Disk

The Aeronautical Sciences Project under NASA`s Fundamental Aeronautics Program is extremely interested in the development of novel measurement technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Center`s High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied high-contrast random speckle pattern and imaging the pattern under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.0-im in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will be undistorted; however, during rotation the cracked region will grow radially, thus causing the applied particle pattern to be .shifted`. The resulting particle displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential speckle patterns, for future use on the rotating disk, are developed and investigated in the controlled experiment. A range of known shifts are induced on the patterns; reference and data images are acquired before and after the induced shift, respectively, and the images are processed using the cross-correlation algorithms in order to determine the particle displacements. The effectiveness of each pattern at resolving the known shift is evaluated and discussed in order to choose the most suitable pattern to be implemented onto a rotating disk in the Rotordynamics Lab. Although testing on the rotating disk has not yet been performed, the driving principles behind the development of the present optical technique are based upon critical aspects of the future experiment, such as the amount of expected radial growth, disk analysis, and experimental design and are therefore addressed in the paper.

Clem, Michelle M.↗
DOE OSTI2015

The atomistic origin of the extraordinary oxygen reduction activity of Pt 3 Ni 7 fuel cell catalysts

Recently Debe et al. reported that Pt 3 Ni 7 leads to extraordinary Oxygen Reduction Reaction (ORR) activity. However, several reports show that hardly any Ni remains in the layers of the catalysts close to the surface (“Pt-skin effect”). This paradox that Ni is essential to the high catalytic activity with the peak ORR activity at Pt 3 Ni 7 while little or no Ni remains close to the surface is explained here using large-scale first-principles-based simulations. We make the radical assumption that processing Pt–Ni catalysts under ORR conditions would leach out all Ni accessible to the solvent. To simulate this process we use the ReaxFF reactive force field, starting with random alloy particles ranging from 50% Ni to 90% Ni and containing up to ~300 000 atoms, deleting the Ni atoms, and equilibrating the resulting structures. We find that the Pt 3 Ni 7 case and a final particle radius around 7.5 nm lead to internal voids in communication with the exterior, doubling the external surface footprint, in fair agreement with experiment. Then we examine the surface character of these nanoporous systems and find that a prominent feature in the surface of the de-alloyed particles is a rhombic structure involving 4 surface atoms which is crystalline-like but under-coordinated. Using density-functional theory, we calculate the energy barriers of ORR steps on Pt nanoporous catalysts, focusing on the O ad -hydration reaction (O ad + H 2 O ad → OH ad + OH ad ) but including the barriers of O 2 dissociation (O 2ad → O ad + O ad ) and water formation (OH ad + H ad → H 2 O ad ). We find that the reaction barrier for the O ad -hydration rate-determining-step is reduced significantly on the de-alloyed surface sites compared to Pt(111). Moreover we find that these active sites are prevalent on the surface of particles de-alloyed from a Pt–Ni 30 : 70 initial composition. These simulations explain the peak in surface reactivity at Pt 3 Ni 7 , and provide a rational guide to use for further optimization of improved catalytic and nanoporous materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

A review of crack propagation under unsteady loading

The theories and research current available on crack propagation under unsteady loadings, especially those of acoustic origin, are reviewed. Since the original theories on fatigue failure did not account for random loading conditions, modified theories which provide statistical methods for evaluating the random loading have emerged. The impact of acoustic fatigue in the aerospace industry, basic principles such as fatigue crack initiation and propagation and load interactions, and testing procedures are discussed. Attention is also given to metal and metal alloy structures, fiber-reinforced composites and nonmetallic structures, short crack growth, and the effects of temperature, moisture, and corrosion on structures. Suggestions for future research in this field are presented, namely, studies on the effect of 'snap-through' response and associated crack growth patterns, studies in microcrack and 'small crack'; propagation under unsteady loading conditions, and the development of an accurate analytical model to predict acceleration and retardation effects in fatigue crack growth under random loading conditions.

Bryan, H. H.↗
DOE OSTI2016

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗
NASA NTRS2013

Progress of a Cross-correlation Based Optical Strain Measurement Technique for Detecting Radial Growth on a Rotating Disk

The Aeronautical Sciences Project under NASAs Fundamental Aeronautics Program is extremely interested in the development of fault detection technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Centers High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied background consisting of a high-contrast random speckle pattern and imaging the background under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.8-m in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will appear shifted. The resulting background displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential backgrounds, for future use on the rotating disk, are developed and investigated in the controlled experiment. A range of known shifts are induced on the backgrounds; reference and data images are acquired before and after the induced shift, respectively, and the images are processed using the cross- correlation algorithms in order to determine the background displacements. The effectiveness of each background at resolving the known shift is evaluated and discussed in order to choose to the most suitable background to be implemented onto a rotating disk in the Rotordynamics Lab. Although testing on the rotating disk has not yet been performed, the driving principles behind the development of the present optical technique are based upon critical aspects of the future experiment, such as the amount of expected radial growth, disk analysis, and experimental design and are therefore addressed in the paper.

Clem, Michelle M.↗