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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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Designed-in looseness at right locations helps to distribute torques more evenly. Gear-drive mechanism helps to apportion torques nearly equally along two parallel drive paths from input bevel gear to output bull gear. Mechanism of this type used as part of redundant drive train between engine and rotor of helicopter. Weighs less than comparably rated prior torque-splitting mechanisms.
Acoustic emission (AE) signal analysis has been used to measure the effects of impact damage on burst pressure in 5.75 inch diameter, inert propellant filled, filament wound pressure vessels. The AE data were collected from fifteen graphite/epoxy pressure vessels featuring five damage states and three resin systems. A burst pressure prediction model was developed by correlating the AE amplitude (frequency) distribution, generated during the first pressure ramp to 800 psig (approximately 25% of the average expected burst pressure for an undamaged vessel) to known burst pressures using a four layered back propagation neural network. The neural network, trained on three vessels from each resin system, was able to predict burst pressures with a worst case error of 5.7% for the entire fifteen bottle set.
Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.
Scientists and engineers constantly face new challenges, despite myriad advances in computing. More sets of data are collected today from earth and sky than there is time or resources available to carefully analyze them. Some problems either don't have fast algorithms to solve them or have solutions that must be found among millions of options, a situation akin to finding a needle in a haystack. But all hope is not lost: advances in technology and the Internet have empowered the general public to participate in the scientific process via individual computational resources and brain cognition, which isn't matched by any machine. Citizen scientists are volunteers who perform scientific work by making observations, collecting and disseminating data, making measurements, and analyzing or interpreting data without necessarily having any scientific training. In so doing, individuals from all over the world can contribute to science in ways that wouldn't have been otherwise possible.
We describe a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) measurements for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Initial calculations of seasonal clear-sky aerosol radiative forcing based on our multi-sensor aerosol retrievals compare well with over-ocean and top of the atmosphere IPCC-2007 model-based results, and with more recent assessments in the "Climate Change Science Program Report: Atmospheric Aerosol Properties and Climate Impacts" (2009). We discuss some of the challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed. We also discuss a methodology for using the multi-sensor aerosol retrievals for aerosol type classification based on advanced clustering techniques. The combination of research results permits conclusions regarding the attribution of aerosol radiative forcing to aerosol type.
X-ray self-heating is a common by-product of X-ray Free Electron Laser (XFEL) techniques that can affect targets, optics, and other irradiated materials. Diagnosis of heating and induced changes in samples may be performed using the x-ray beam itself as a probe. However, the relationship between conditions created by and inferred from x-ray irradiation is unclear and may be highly dependent on the material system under consideration. Here, we report on a simple case study of a titanium foil irradiated, heated, and probed by a MHz XFEL pulse train at 18.1 keV delivered by the European XFEL using measured x-ray diffraction to determine temperature and finite element analysis to interpret the experimental data. We find a complex relationship between apparent temperatures and sample temperature distributions that must be accounted for to adequately interpret the data, including beam averaging effects, multivalued temperatures due to sample phase transitions, and jumps and gaps in the observable temperature near phase transformations. The results have implications for studies employing x-ray probing of systems with large temperature gradients, particularly where these gradients are produced by the beam itself. Finally, this study shows the potential complexity of studying nonlinear sample behavior, such as phase transformations, where biasing effects of temperature gradients can become paramount, precluding clear observation of true transformation conditions.
Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width—which govern the evolution of observables during training—saturate at a depth of ~20, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep non-linear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.
The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.
Over the past year, I have worked for my mentor, Beth Cooper, on a large variety of projects. Beth is the Manager of the Acoustical Testing Laboratory, which tests the acoustical emissions of payloads destined for the International Space Station. She is also responsible for educating, and developing new methods of educating, people of all occupational and educational backgrounds in hearing conservation. Beth spends much of her time developing new materials and strategies with which to train people and teach other people to train people in hearing conservation and noise emissions control. I have been helping Beth develop and market these materials by way of graphic design and scientific illustration. Last summer, I spent much of my time creating educational illustrations that visually explained particular concepts in Beth's presentations. Sometimes these illustrations were small "comics" while, at other times, they were an instructional series of illustrations. Since then, Beth and her lab have been developing and updating some materials which will be distributed free to hearing conservation and noise control professionals and others in related fields. I have helped with these projects by designing their packaging. In each instance, it was my responsibility to develop an aesthetically appealing package that would also, through its imagery, describe or summarize the contents of the product. I did this for 3 CD's (Auditory Demonstrations 11, MACSUG, and JeopEARdy) and saw them through their actual production and distribution. In addition to working with Beth, I work with the Imaging Technology Center on various imaging projects. Some of my activities include photo retouching and manipulation for videos and print. This summer, I also had the opportunity to develop a screen saver that would show of some of the photography contained on the soon-to-be-released "Highlights of the GRC Image Archives, vol. 2". I was also able to utilize my medical training to help several of ITC s videographers identify the best histological examples of cancerous cells for incorporation in one of their videos. Over the last part of this summer and then throughout the school year, I will be working with Beth to develop a "pre-packaged" lecture series about the physics of acoustics in the context of hearing conservation. These lectures will be used to teach people of all backgrounds the fundamental concepts involved in acoustical physics so they might be better aware of their own and others auditory health in and out of the work place, and, in the case of payload developers, to design and build more quiet science experiments for the ISS. Even though it may not seem as such, this project is precisely what I am learning to do as a student of the Cleveland Institute of Art's Medical Illustration Department. From my perspective, this project is about taking technical information and translating it into terms that anyone, regardless of background, can understand.
NREL will assist the Colorado School of Mines (Mines) in supporting a series of one-week training workshops. This proposed training program will be two, week-long summer school sessions in each of the next two years to help give early career economists and social scientists a solid introduction and grounding on the technical components of the electrical distribution system.
This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.
Abstract The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.
Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.
We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.
Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.
Multidimensional effects degrade the neutron yield and the compressed areal density of laser-direct-drive inertial confinement fusion implosions of layered deuterium–tritium cryogenic targets on the OMEGA Laser System with respect to 1D radiation-hydrodynamic simulation predictions. A comprehensive physics-informed 3D reconstruction effort is under way to infer hot-spot and shell conditions at stagnation from four x-ray and seven neutron detectors distributed around the OMEGA target chamber. Neutron diagnostics, providing measurements of the neutron yield, hot-spot flow velocity, and apparent ion-temperature distribution, are used to infer the mode-1 perturbation at stagnation. The x-ray imagers record the shape of the hot-spot plasma to diagnose mode-1 and mode-2 perturbations. A deep-learning convolutional neural network trained on an extensive set of 3D radiation-hydrodynamic simulations is used to interpret the x-ray and nuclear measurements to infer the 3D profiles of the hot-spot plasma conditions and the amount of laser energy coupled to the hot-spot plasma. A 3D simulation database shows that larger mode-1 asymmetries are correlated with higher hot-spot flow velocities and reduced laser-energy coupling and neutron yield. Three-dimensional hot-spot reconstructions from x-ray measurements indicate that higher amounts of residual kinetic energy are correlated with higher measured hot-spot flow velocities, consistent with 3D simulations.
Abstract Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection (AD) for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for AD. We demonstrate the benefit of the proposed robust multi-background AD algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.