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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparing serial X-ray crystallography and microcrystal electron diffraction (MicroED) as methods for routine structure determination from small macromolecular crystals

Innovative new crystallographic methods are facilitating structural studies from ever smaller crystals of biological macromolecules. In particular, serial X-ray crystallography and microcrystal electron diffraction (MicroED) have emerged as useful methods for obtaining structural information from crystals on the nanometre to micrometre scale. Despite the utility of these methods, their implementation can often be difficult, as they present many challenges that are not encountered in traditional macromolecular crystallography experiments. Here, XFEL serial crystallography experiments and MicroED experiments using batch-grown microcrystals of the enzyme cyclophilin A are described. The results provide a roadmap for researchers hoping to design macromolecular microcrystallography experiments, and they highlight the strengths and weaknesses of the two methods. Specifically, we focus on how the different physical conditions imposed by the sample-preparation and delivery methods required for each type of experiment affect the crystal structure of the enzyme.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nodal Admittance Matrix Based Area Partition Method for Small-Signal Stability Analysis of Large-Scale Power Electronics Based Power Systems

In power electronics-based power systems (PEPSs), small-signal stability is an important factor for system design and operation, where the impedance-based approach is often used. However, unlike small-scale PEPSs with simple and straightforward impedance models, it would take much more efforts to derive the large-scale PEPSs impedance model, which is very complicated and sometimes may get wrong results due to the elimination of right-half plane (RHP) poles during the impedance aggregation process. To simplify the derivation procedure and analyze the small-signal stability of large-scale PEPSs, this paper proposes a nodal admittance matrix (NAM) based area partition method. In this method, the large-scale PEPS is divided into several sub-areas, and the stability is analyzed within the sub-area first, and then the interconnection stability among these sub-areas is analyzed. The proposed method is scalable and can help to locate the weakest areas/converters that may cause instability in the whole system. In this paper, the concept of the proposed method and its application to an example system are introduced. Experimental results are also given to validate the effectiveness of the proposed method.

Qiao, Liang↗
DOE OSTI2026

Fast Active-Set Thresholding Method for Nonnegative Least Squares

Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.

Cobb, Benjamin [Georgia Institute of Technology]↗
DOE OSTI2023

Interpolation Methods to Enable Fast and Accurate EMT Simulation of PV Inverters

In this paper, interpolation methods to enable fast simulation of photovoltaic (PV) inverters are proposed. The interpolation methods are based on the event-driven interpolating method that can be introduced in the simulation model to run the model at a higher timestep while maintaining accuracy. The proposed methods are tested on a neutral point clamp (NPC) converter and a two-level converter (2LC) that are typically used in large-scale PV plants. The interpolation methods are integrated into the NPC and 2LC simulation models in PSCAD/Fortran and RSCAD C platforms. These models are tested at different simulation timesteps in off-line and real-time simulation platforms PSCAD and RSCAD respectively.

Marthi, Phani Ratna Vanamali↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Power Decoupling Method for Voltage Source Inverters Using Grid Voltage Modulated Direct Power Control in Unbalanced System

The grid voltage modulated direct power control (GVM-DPC) is a novel power regulation method for grid-connected voltage source inverters (VSI). However, interactions between real and reactive powers still exist in GVM-DPC under unbalanced grid voltage condition, which may deteriorate its transient performance. Here, this article proposes a power decoupling method for GVM-DPC-based VSI in unbalanced systems based on the dynamic feedforward power compensation strategy, which can reduce the coupling magnitudes of real and reactive powers in the transient stages. First, the power coupling mechanism of positive and negative sequence real and reactive powers of VSI under the unbalanced voltage condition is analyzed. Then, the power coupling magnitudes (PCM) are derived according to relationships among the positive sequence components of real power, reactive power and point of common coupling (PCC) voltages. Next, the PCM are compensated into the GVM-DPC for the better decoupling performance. Furthermore, a stability analysis of the proposed control system is studied based on the impedance model, and a comparison with the traditional power decoupling method based on virtual impedance is conducted to show the superiority of the proposed method. Finally, simulations, hardware-in-loop test and experiment are conducted to validate the effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Characterization and differentiation of aluminum powders used in improvised explosive devices. Part 2: Micromorphometric method refinement and preliminary statistical analysis

Abstract Aluminum (Al) powder is commonly encountered in improvised explosive devices (IEDs) as a metallic fuel due to its availability and low cost. Although available commercially in powder form, amateur bomb‐makers also produce their own Al powder via simple methods found online. In order to provide investigative leads and forensic intelligence, it is important to evaluate not only the composition of homemade devices, but also to distinguish between the various forms of Al powder they contain. To achieve this goal, a method using automated microscopy in combination with statistical techniques has been demonstrated to have the potential to provide source discrimination and investigative leads in source attribution of Al powders in IEDs. The present research refined this method and investigated 59 industrially and amateurly produced Al powder sources with seven subsamples per source using two traditional linear discriminant analyses (LDA), one with a standard data split for training and testing, and another using leave‐one‐out cross‐validation. Averaging the classification accuracies for the two LDA‐based analyses, LDA has the ability to correctly classify 59.26%, 83.35%, and 80.69% of the samples based on their powder source, type, and production method, respectively. This classification accuracy represents a 3407%, 317%, and 61.38% increase in accuracy from random class assignment, respectively. Further, in most instances of incorrect data attribution to a particular source, the subsample has been misidentified with another sample of the same powder type or production method.

Ommen, Danica M.↗
DOE OSTI2021

Synthesis method comparison of compositionally complex rare earth-based Ruddlesden–Popper n = 1 T'-type cuprates

The multicomponent approach has been successfully expanded to the Ruddlesden–Popper structure with the synthesis of two different high-entropy cuprate compositions: (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 and (La 0.2 Pr 0.2 Nd 0.2 Sm 0.2 Eu 0.2 ) 2 CuO 4 . Additionally, the effect of synthesis method is explored using both solid-state reaction and polymeric steric entrapment (PSE) methods. It is found that PSE leads to more randomly distributed cation species, providing an advantageous method of synthesis for the growing field of high entropy oxides. In situ high-temperature x-ray diffraction tracks the amorphous to crystalline phase transformation in (La 0.2 Nd 0.2 Gd 0.2 Tb 0.2 Dy 0.2 ) 2 CuO 4 powder, synthesized using the PSE method. Using the High-Temperature XRD data, a method for gaining information on the kinetic behavior is also applied. Magnetometry of both compositions indicates ferrimagnetic behavior at low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid method for computing the mechanical resonances of irregular objects

A solid object's geometry, density, and elastic moduli completely determine its spectrum of normal modes. Solving the inverse problem—determining a material's elastic moduli given a set of resonance frequencies and sample geometry—relies on the ability to compute resonance spectra accurately and efficiently. Established methods for calculating these spectra are either fast but limited to simple geometries, or are applicable to arbitrarily shaped samples at the cost of being prohibitively slow. Here, we describe a method to rapidly compute the normal modes of irregularly shaped objects using entirely open-source software. Our method's accuracy compares favorably with existing methods for simple geometries and shows a significant improvement in speed over existing methods for irregular geometries.

Acoustics↗
DOE OSTI2020

Efficient Low-Order Refined Preconditioners for High-Order Matrix-Free Continuous and Discontinuous Galerkin Methods

In this paper, we design preconditioners for the matrix-free solution of high-order continuous and discontinuous Galerkin discretizations of elliptic problems based on finite element method--spectral element method (FEM-SEM) equivalence and additive Schwarz methods. The high-order operators are applied without forming the system matrix, making use of sum factorization for efficient evaluation. The system is preconditioned using a spectrally equivalent low-order (p = 1) finite element operator discretization on a refined mesh. The low-order refined mesh is anisotropic and not shape regular in the polynomial degree of the high-order operator, requiring specialized solvers to treat the anisotropy. We make use of an element-structured, geometric multigrid V-cycle with ordered ILU(0) smoothing. The preconditioner is parallelized through an overlapping additive Schwarz method that is robust in h and p. The method is extended to interior penalty and Bassi and Rebay (BR2) discontinuous Galerkin discretizations, for which it is also robust in the size of the penalty parameter. Finally, numerical results are presented on a variety of examples, verifying the uniformity of the preconditioner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Analysis of the SiMPL Method for Density-Based Topology Optimization

We present a rigorous convergence analysis of a new method for density-based topology optimization that provides pointwise bound-preserving design updates and faster convergence than other popular first-order topology optimization methods. Due to its strong bound preservation, the method is exceptionally robust, as demonstrated in numerous examples here and in the companion article [D. Kim et al., Struct. Multidiscip. Optim., 68 (2025), 74]. Furthermore, it is easy to implement with clear structure and analytical expressions for the updates. Our analysis covers two versions of the method, characterized by the employed line search strategies. We consider a modified Armijo backtracking line search and a Bregman backtracking line search. For both line search algorithms, our algorithm delivers a strict monotone decrease in the objective function and further intuitive convergence properties, e.g., strong and pointwise convergence of the density variables on the active sets, norm convergence to zero of the increments, convergence of the Lagrange multipliers, and more. In addition, the numerical experiments demonstrate apparent mesh-independent convergence of the algorithm. Here, we refer to the new algorithm as the SiMPL method (pronounced “simple”), which stands for Sigmoidal Mirror descent with a Projected Latent variable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of Trigger Method on Fire Propagation during the Thermal Runaway Process in Li-ion Batteries

Lithium-ion batteries are prone to fire hazards due to the possibility of thermal runaway propagation. During battery product development and subsequent safety tests for design validation and safety certification, the thermal runaway onset is triggered by various test methods such as nail penetration, thermal ramp, or external short circuit. This failure initiation method affects the amount of heat contributions and the composition of gas generations. This study compares two such trigger methods, external heating and using a thermally-activated internal short circuit device (ISCD). The effects of the trigger method on total heat generation are experimentally investigated within 18650 cylindrical cells at single cell level as well as at multiple cell configuration level. The severity of failure was observed to be worse for cells with ISCDs at single cell level, whereas quite the opposite results were observed at multiple cell configuration level. A preliminary numerical analysis was performed to better understand the battery safety performance with respect to thermal runaway trigger methods and heat transfer conditions.

25 ENERGY STORAGE↗
DOE OSTI2022

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase-to-phase-ground and phase-to-ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase-to-ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and nonfaulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

42 ENGINEERING↗
DOE OSTI2020

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s -1 , respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m -2 with the benchmark microwave method.

Engineering↗
DOE OSTI2022

Adhesively-bonded Metal-CFRTP Bi-materials: Enhanced Crack Growth Resistance via Plasma and Quantified Fracture via Size Effect Method

This work investigated air plasma effect on the Mode I crack growth resistance of adhesively-bonded metal-CFRTP dissimilar joints by using aluminum alloy (AA6061) and short-carbon-fiber-reinforced polyamide 66 (CFRPA66) as an example, and the Double Cantilever Beam (DCB) fracture testing as an evaluation method. The results show that air plasma treatment can significantly improve the fracture resistance of adhesively-bonded AA6061-CFRPA66 dissimilar joints with about 140% enhanced Mode I fracture energy in maximum compared to non-treated ones. The quantified fracture energies from size effect method for both non-treated and plasma-treated cases are geometry-independent, whereas this is not true for modified beam theory causing geometry-dependent results. The foregoing improvement was confirmed from the failure surface morphology of plasma-treated specimens, showing fibers peeling off from CFRPA66 surface due to plasma-enhanced bonding between CFRPA66 and adhesive by the formation of covalent bonds at their interface. This study demonstrated air plasma as an efficient surface modification method to enhance the fracture resistance of adhesively-bonded metal-CFRTP dissimilar joints, and size effect method as a characterization method to properly quantify their fracture properties. These aspects are valuable in the area of multi-materials joining.

Plasma, Adhesive Bonding, Slow Crack Growth, fract↗