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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La 0.87 Sr 0.13 CrO 3 , Indium Tin Oxide (In 2 O 3 90 wt%, SnO 2 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH 4 in air, CH 4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

03 NATURAL GAS↗
DOE OSTI2023

A Probabilistic Approach To Selecting Build Configurations in Package Managers

In the past decade software has grown significantly in complexity and scale. Likewise the number of dependencies for most software has increased with typical software packages depending on tens to hundreds of other packages. Such large numbers of dependencies place a significant burden on package managers to correctly maintain dependency lists and constraints between them. Due to this package managers have incorporated sophisticated tooling such as SAT solvers into their dependency management mechanisms. Despite these tools package managers still rely on many human annotated constraints for dependency and version selection. These are error-prone and require a significant amount of labor to constantly update and test. In this paper we propose a methodology to make use of historical build results in selecting the version for package dependencies. Our method utilizes the flexibility of the Spack package manager’s heavily parameterized package configurations to incorporate a machine learning model trained to predict the probability of build outcomes. This work is able to build and install packages with a 13% higher success rate than the default version selection mechanism in Spack.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning

Resilient observer design for Cyber-Physical Systems (CPS) in the presence of adversarial false data injection attacks (FDIA) is an active area of research. The existing state-of-the-art algorithms tend to break down as more and more knowledge of the system is built into the attack model; also as the percentage of attacked nodes increases. From the view of optimization theory, the problem is often cast as a classical error correction problem for which a theoretical limit of has been established as the maximum percentage attacked nodes for which state recovery is guaranteed. Beyond this limit, the performance of -minimization based schemes, for instance, deteriorates rapidly. Similar performance degradation occurs for other types of resilient observers beyond certain percentages of attacked nodes. In order to increase the corresponding percentage of attacked nodes for which state recoveries can be guaranteed, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through a data-driven machine learning process. Existing results have shown a strong positive correlation between the maximum attacked percentages that can be tolerated and the accuracy of the data-driven model. Motivated by these results, this chapter examines the case for pruning algorithms designed to improve the Positive Prediction Value (PPV) of the resulting prior information, given stochastic uncertainty characteristics of the underlying machine learning model. Theoretical quantification of the achievable improvement is given. Simulation results show that the pruning algorithm significantly increases the maximum correctable percentage of attacked nodes, even for machine learning model whose prediction power is comparable to the random flip of a coin.

Resilient Observer, Cyber-physical Systems, Data-D↗
NASA NTRS2002

Design, Fabrication, Optical Testing, and Performance of Diamond Machined Aspheric Mirrors for Ground-Based Near-IR Astronomy

Challenges in fabrication and testing have historically limited the choice of surfaces available for the design of reflective optical instruments. Spherical and conic mirrors are common, but, for future science instruments, more degrees of freedom are necessary to meet challenging performance and packaging requirements. These instruments will be composed of unusual aspheres located far off-axis with large spherical departure, and some designs will require asymmetric surface profiles. In particular, single-surface astigmatism correction in spectrographs necessitates a toroidal surface, which lacks an axis of rotational symmetry. We describe the design, fabrication, optical testing, and performance of three rotationally symmetric, off-axis, aspheric mirrors and one toroidal, off-axis, biconic camera mirror on aluminum substrates for the Infrared Multi-Object Spectrograph (IRMOS) instrument. IRMOS is a facility instrument for the Kitt Peak National Observatory's Mayall Telescope (3.8 m) and an engineering prototype for a possible design of the Next Generation Space Telescope/Multi-Object Spectrograph. The symmetric mirrors range in aperture from 94x86 mm to 286x269 mm and in f-number from 0.9 to 2.4. They are various off-axis, convex and concave, prolate and oblate ellipsoids. The concave biconic mirror has a 94x76 mm aperture, Rx=377 mm, kx=0.0778, Ry=407 mm, and ky=0.1265 and is decentered. by -2 mm in x and 227 mm in y. The mirrors have an aspect ratio of approximately 4:1. The surface error fabrication tolerances are less than 63.3 nm RMS figure error and less than 10 nm RMS microroughness. The mirrors are attached to the instrument bench via a semi-kinematic, integral flexure mount. We describe mirror design, diamond machining, the results of figure testing using computer-generated holograms, and imaging and scattered light modeling and performance.

Ohl, Raymond G.↗
DOE OSTI2020

Error field correction strategies in preparation to MAST-U operation

In magnetic fusion devices, unwanted non-axisymmetric magnetic field perturbations, known as error fields (EF), can have detrimental effects on plasma stability and confinement. Such EFs may originate from several sources, such as axi-symmetric coil misalignments, coil feeds, 3D structures in the wall surrounding the plasma, presence of ferromagnetic materials near the plasma surface, blanket materials in future devices. To minimize their impact on plasma performance and on the available operational space, it is important to identify the EF sources and develop EF control strategies. MAST Upgrade (MAST-U) is a spherical tokamak which will operate in the near future after a series of enhancements from the previous MAST experiment. To deliver a machine with EF amplitude low enough to allow a high quality experimental programme, systematic analysis of the intrinsic EF sources has been carried out for poloidal eld (P) and divertor (D) coils. To deliver a machine with EF amplitude low enough to allow a high quality experimental programme, systematic analysis of the intrinsic EF sources has been carried out for poloidal field (P) and divertor (D) coils, whose magnetic eld measurements were available when writing this paper. Such measurements reveal that P and D coils are 3D deformed and thus are responsible for intrinsic EFs, with mainly n=1 and n=2 toroidal mode numbers. In preparation to MAST-U operation, both passive and active EF control strategies have been adopted for n=1 and n=2 EFs compensation. Passive EF control consisted of installing finely each D and P coil within MAST-U device so as to minimize the intrinsic n=1 EF amplitude. The optimal coil alignment has been determined based on magnetic eld measurements and the corresponding 3D electro-magnetic modelling, and envisaged coil shift and tilt of 3.2 mm and 0.7 mrad, respectively, in the case of P coil named P4. Conversely, active control will be use during MAST-U operation to reduce the n=2 EF which is associated mainly with P4 and P5, as well. Since these coils have been re-used from the MAST device, studies attempting n=2 EF control, based on MAST plasmas, have been modelled utilizing the MARS-F code to interpret experimental results in MAST and to give hints for future n=2 EF control studies in MAST-U. Here, a model-based control set for n=2 EF control has been identified which would allow for the minimization of rotation braking, of the resonant magnetic field at the q=2 and of the plasma displacement in MAST-U, simultaneously.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

36 MATERIALS SCIENCE↗
NASA NTRS2021

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observation sand to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community.Evaluation of GEOS-CF against satellite, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions.The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

GEOS-CF↗
NASA NTRS1989

EAC: A program for the error analysis of STAGS results for plates

A computer code is now available for estimating the error in results from the STAGS finite element code for a shell unit consisting of a rectangular orthotropic plate. This memorandum contains basic information about the computer code EAC (Error Analysis and Correction) and describes the connection between the input data for the STAGS shell units and the input data necessary to run the error analysis code. The STAGS code returns a set of nodal displacements and a discrete set of stress resultants; the EAC code returns a continuous solution for displacements and stress resultants. The continuous solution is defined by a set of generalized coordinates computed in EAC. The theory and the assumptions that determine the continuous solution are also outlined in this memorandum. An example of application of the code is presented and instructions on its usage on the Cyber and the VAX machines have been provided.

Sistla, Rajaram↗
DOE OSTI2022

Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as $\sqrt{T/N}$. When only K$\ll$T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to $\sqrt{K/N}$. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Ensemble Data Mining Methods

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

Oza, Nikunj C.↗
NASA NTRS1992

SPPTOOLS: Programming tools for the IRAF SPP language

An IRAF package to assist in SPP code development and debugging is described. SPP is the machine-independent programming language used by virtually all IRAF tasks. Tools have been written to aide both novice and advanced SPP programmers with development and debugging by providing tasks to check the code for the number and type of arguments in all calls to IRAF VOS library procedures, list the calling sequences of IRAF tasks, create a database of identifiers for quick access, check for memory which is not freed, and a source code formatter. Debugging is simplified since the programmer is able to get a better understanding of the structure of his/her code, and IRAF library procedure calls (probably the most common source of errors) are automatically checked for correctness.

Fitzpatrick, M.↗
DOE OSTI2022

Measuring He i Stark Line Shapes in the Laboratory to Examine Differences in Photometric and Spectroscopic DB White Dwarf Masses

Abstract Accurate helium White Dwarf (DB) masses are critical for understanding the star’s evolution. DB masses derived from the spectroscopic and photometric methods are inconsistent. Photometric masses agree better with currently accepted DB evolutionary theories and are mostly consistent across a large range of surface temperatures. Spectroscopic masses rely on untested He i Stark line-shape and Van der Waals broadening predictions, show unexpected surface temperature trends, and are thus viewed as less reliable. To test this conclusion, we present in this paper detailed He i Stark line-shape measurements at conditions relevant to DB atmospheres ( T electron ≈12,000–17,000 K, n electron ≈ 10 17 cm −3 ). We use X-rays from Sandia National Laboratories’ Z -machine to create a uniform ≈120 mm long hydrogen–helium mixture plasma. Van der Waals broadening is negligible at our experimental conditions, allowing us to measure He i Stark profiles only. H β , which has been well-studied in our platform and elsewhere, serves as the n e diagnostic. We find that He i Stark broadening models used in DB analyses are accurate within errors at tested conditions. It therefore seems unlikely that line-shape models are solely responsible for the observed spectroscopic mass trends. Our results should motivate the WD community to further scrutinize the validity of other spectroscopic and photometric input parameters, like atmospheric structure assumptions and convection corrections. These parameters can significantly change the derived DB mass. Identifying potential weaknesses in any input parameters could further our understanding of DBs, help elucidate their evolutionary origins, and strengthen confidence in both spectroscopic and photometric masses.

Schaeuble, M. -A. (ORCID:0000000188695203)↗
DOE OSTI2023

Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation

Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Accelerator Physics at NSLS-II: Research Accomplishments in 2023

NSLS-II accelerator physicists provided operation support for user operations and machine start-ups, carried out routine lattice characterization, and produced regular reports on beam dynamics. The commissioning of a superconducting wiggler for the HEX beamline is complete, and the wiggler effects on beam dynamics have been studied. A Facility Improvement Project for absolute lattice characterization has been approved and funded. We implemented and tested a new orbit-based technique of lattice characterization and correction. We developed a theory to calculate the beam-induced power in ceramic vacuum chambers and a new convergence map technique for nonlinear lattice characterization. We continued working on the NSLS-II upgrade including lattice development, commissioning a low-energy complex bend prototype, analyzing beam sensitivity to various errors, implementing higher-harmonic RF cavities, studying collective effects, and assessing FEL options. We also supported lattice optimization and beam diagnostics development for the EIC project. Main research accomplishments achieved in 2023, are summarized in this report.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

hurricane↗
DOE OSTI2023

Invertible neural networks for real-time control of extrusion additive manufacturing

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optical and X-ray GRB Fundamental Planes as cosmological distance indicators

ABSTRACT Gamma-ray bursts (GRBs), can be employed as standardized candles, extending the distance ladder beyond Type Ia supernovae (SNe Ia, z = 2.26). We standardize GRBs using the three-dimensional (3D) Fundamental Plane relation (the Dainotti relation) among the rest-frame end time of the X-ray plateau emission, its corresponding luminosity, and the peak prompt luminosity. Combining SNe Ia and GRBs, we constrain ΩM = 0.299 ± 0.009 assuming a flat Λ cold dark matter (ΛCDM) cosmology with and without correcting GRBs for selection biases and redshift evolution. Using a 3D optical Dainotti correlation, we find this sample is as efficacious in the determination of ΩM as the X-ray sample. We trimmed our GRB samples to achieve tighter planes to simulate additional GRBs. We determined how many GRBs are needed as stand-alone probes to achieve a comparable precision on ΩM to the one obtained by SNe Ia only. We reach the same error measurements derived using SNe Ia in 2011 and 2014 with 142 and 284 simulated optical GRBs, respectively, considering the error bars on the variables halved. These error limits will be reached in 2038 and in 2047, respectively. Using a doubled sample (obtained by future machine learning approaches allowing a light-curve reconstruction and the estimates of GRB redshifts when z is unknown) compared to the current sample, with error bars halved we will reach the same precision as SNe Ia in 2011 and 2014, now and in 2026, respectively. If we consider the current SNe precision, this will be reached with 390 optical GRBs by 2054.

Dainotti, M. G.↗
DOE OSTI2025

Systematic softening in universal machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.

36 MATERIALS SCIENCE↗