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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Adaptive Modal Identification for Flutter Suppression Control

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

Aeroelasticity↗
NASA NTRSDate not supplied

Practical Aspects of Multiple-Input Design for Aircraft System Identification Flight Tests

Practical aspects of multiple-input design methods for aircraft system identification flight tests are discussed. The orthogonal optimized multisine multiple-input design technique is explained and demonstrated, along with useful variations. Practical implementation and application issues associated with applying orthogonal optimized multisine inputs in flight are explained, and solutions are demonstrated using flight data from full-scale and subscale aircraft, as well as simulated flight data from an F-16 nonlinear simulation. Topics include truncated and extended excitation inputs, variations for control effectors with limited movement capabilities, aircraft dynamic response to excitation inputs, pilot implementation, statistical uncertainty quantification, multiple-input efficiency and effectiveness, flight testing with active feedback control, frequency-response estimation, control actuator modeling, and control interaction effects. Examples demonstrate that the orthogonal optimized multisine input design method is a practical, versatile, and effective approach for designing multiple excitation inputs for aircraft system identification flight tests.

Multiple-Input Design↗
NASA NTRSDate not supplied

Advances in Aircraft System Identification at NASA Langley Research Center

Aircraft system identification involves the determination of dynamic models from measured flight data. Recently, the Journal of Aircraft published a special issue in which authors summarized advances in aircraft system identification made at their institutions over the last 20 years. This talk discusses some of those advances made at NASA Langley Research Center. Examples include X-planes, hypersonic and launch vehicles, aeroelastic aircraft and wind tunnel test articles, and subscale demonstrators.

Aircraft System Identification↗
DOE OSTI2023

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems: Preprint

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2023

Genetically Encoded Crosslinking Enables Identification of Multivalent Ubiquitin‐Deubiquitylating Enzyme Interactions

Abstract Ubiquitin (Ub) proteoforms control nearly every aspect of eukaryotic cell biology through their diversity. Inspired by the widely used Ub C‐terminal electrophiles (Ub−E), here we report the identification of multivalent binding of Ub with deubiquitylating enzymes (Dubs) using genetic code expansion (GCE) and crosslinking mass spectrometry. While the Ub−Es only gather structural information with the S1 Dub sites, we demonstrate that GCE of Ub with p‐benzoyl‐L‐phenylalanine enables identification of interaction modes beyond the S1 site with a panel of Dubs of both eukaryotic and prokaryotic origin. Collectively, this represents the next generation of Ub‐based affinity probes with a unique ability to unravel Ub interaction landscapes beyond what is afforded by cysteine‐based chemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Identification of hydrogen species in alunite-type minerals by multi-nuclear solid-state NMR spectroscopy

The various hydrogen species present in a series of synthetic hydroniumjarosite ((H3O)Fe3(SO4)2(OH)6), and ammonioalunite ((NH4)Al3(SO4)2(OH)6) as well synthetic potassium (Cr3+ and V3+) and hydronium (V3+, Cr3+, and Ga3+) analogues were identified and quantified by 1H and 2H MAS NMR spectroscopy. The results confirm the defect mechanism proposed for alunite (Nielsen et al., Am Miner 2007, 92, 587), and allow for identification and quantification of even a few percent structural defects. For the paramagnetic samples, the isotropic shift for G2-OH group (V3+, Cr3+, and Fe3+) span more than 1100 ppm, which is related to different d-electron configuration (d2, d3, and d5). Analysis of 1H and 27Al MAS NMR shows that the synthetic ammonioalunite contains small amounts (5-10%) of hydronium. Furthermore, the close structural relationship between of hydronium and gallium alunite is reflected by the 27Al and 71Ga quadrupole coupling parameters. Thus, the current work demonstrates the applicability of solid state NMR spectroscopy for identification and quantification of hydrogen species in both dia- and paramagnetic minerals.

jarosite, alunite, gallium alunite, solid state NM↗
DOE OSTI2017

Cryogenic Vibrational Spectroscopy Provides Unique Fingerprints for Glycan Identification

The structural characterization of glycans by mass spectrometry is particularly challenging. This is because of the high degree of isomerism in which glycans of the same mass can differ in their stereochemistry, attachment points, and degree of branching. Here we show that the addition of cryogenic vibrational spectroscopy to mass and mobility measurements allows one to uniquely identify and characterize these complex biopolymers. We investigate six disaccharide isomers that differ in their stereochemistry, attachment point of the glycosidic bond, and monosaccharide content, and demonstrate that we can identify each one unambiguously. Even disaccharides that differ by a single stereogenic center or in the monosaccharide sequence order show distinct vibrational fingerprints that would clearly allow their identification in a mixture, which is not possible by ion mobility spectrometry/mass spectrometry alone. Moreover, this technique can be applied to larger glycans, which we demonstrate by distinguishing isomeric branched and linear pentasaccharides. The creation of a database containing mass, collision cross section, and vibrational fingerprint measurements for glycan standards should allow unambiguous identification and characterization of these biopolymers in mixtures, providing an enabling technology for all fields of glycoscience. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Effect of Electrode Geometry on the Classification Performance of Rapid Evaporative Ionization Mass Spectrometric (REIMS) Bacterial Identification

The recently developed automated, high-throughput monopolar REIMS platform is suited for the identification of clinically important microorganisms. Although already comparable to the previously reported bipolar forceps method, optimization of the geometry of monopolar electrodes, at the heart of the system, holds the most scope for further improvements to be made. For this, sharp tip and round shaped electrodes were optimized to maximize species-level classification accuracy. Following optimization of the distance between the sample contact point and tube inlet with the sharp tip electrodes, the overall cross-validation accuracy improved from 77% to 93% in negative and from 33% to 63% in positive ion detection modes, compared with the original 4 mm distance electrode. As an alternative geometry, round tube shaped electrodes were developed. Geometry optimization of these included hole size, number, and position, which were also required to prevent plate pick-up due to vacuum formation. Additional features, namely a metal “X”-shaped insert and a pin in the middle were included to increase the contact surface with a microbial biomass to maximize aerosol production. Following optimization, cross-validation scores showed improvement in classification accuracy from 77% to 93% in negative and from 33% to 91% in positive ion detection modes. Supervised models were also built, and after the leave 20% out cross-validation, the overall classification accuracy was 98.5% in negative and 99% in positive ion detection modes. This suggests that the new generation of monopolar REIMS electrodes could provide substantially improved species level identification accuracies in both polarity detection modes. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Isolated oxygen vacancies in strontium titanate shine red: Optical identification of Ti 3+ polarons

Oxide perovskites exhibit fascinating physical properties that identify them as key materials for the next generation of oxide-based functional electronic devices, as well as for catalysis and photochemistry applications. In strontium titanate, substantial efforts have been devoted to elucidate the role of oxygen vacancies and localized electronic states, such as polarons, on those properties. In this paper, a new model is presented that assigns a definitive red luminescence signature at 2.0 eV to Ti 3+ polarons trapped at isolated oxygen vacancies. This emission provides an unequivocal identification for the oxygen vacancies, which allows monitoring their creation and annealing by a variety of physio-chemical treatments. Ionoluminescence with energetic (MeV) ion beams enables such identification by combining the sensitivity and resolution of spectroscopic techniques with their in situ character, as well as controlled incorporation of point defects, such as oxygen vacancies. Alternative models assigning the blue luminescence emission at 2.8 eV to oxygen vacancies are not supported by the experimental results. Finally and therefore, oxygen vacancies shine red and not blue, as previously proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Direct and rapid identification of T315I-Mutated BCR-ABL expressing leukemic cells using infrared microspectroscopy

Highlights: • BCR-ABL T315I mutation can be detected in single leukemic cells using infrared microspectroscopy. • The specificity of this spectral signature is demonstrated using Dox-inducible T315I-mutated BCR-ABL expressing cell lines. • Leukemic cells expressing T315I-mutated BCR-ABL exhibit a distinct transcriptome signature. • This technology could be applied to rapid detection of leukemic cells expressing T315I and to drug screening strategies. Despite the major success obtained by the use of tyrosine kinase inhibitors (TKI) in chronic myeloid leukemia (CML), resistances to therapies occur due to mutations in the ABL-kinase domain of the BCR-ABL oncogene. Amongst these mutations, the “gatekeeper” T315I is a major concern as it renders leukemic cells resistant to all licenced TKI except Ponatinib. We report here that Fourier transform infrared (FTIR) microspectroscopy is a powerful methodology allowing rapid and direct identification of a spectral signature in single cells expressing T315I-mutated BCR-ABL. The specificity of this spectral signature is confirmed using a Dox-inducible T315I-mutated BCR-ABL-expressing human UT-7 cells as well as in murine embryonic stem cells. Transcriptome analysis of UT-7 cells expressing BCR-ABL as compared to BCR-ABL T315I clearly identified a molecular signature which could be at the origin of the generation of metabolic changes giving rise to the spectral signature. Thus, these results suggest that this new methodology can be applied to the identification of leukemic cells harbouring the T315I mutation at the single cell level and could represent a novel early detection tool of mutant clones. It could also be applied to drug screening strategies to target T315I-mutated leukemic cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

LaSDI: Parametric Latent Space Dynamics Identification

Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. Herein we introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving 0 (100)x speed-up and 0 (1)% relative error with respect to the corresponding full order models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Interlaboratory Study: Testing Reproducibility of Solid Biofuels Component Identification Using Reflected Light Microscopy

Considering global market trends and concerns about climate change and sustainability, increased biomass use for energy is expected to continue. As more diverse materials are being utilized to manufacture solid biomass fuels, it is critical to implement quality assessment methods to analyze these fuels thoroughly. One such method is reflected light microscopy (RLM), which has the potential to complement and enhance current standard testing, leading to improving fuel quality assessment and, ultimately, preventing avoidable air pollution. An interlaboratory study (ILS) was conducted to test the reproducibility of biomass fuels component identification using a reflected light microscopy technique. The exercise was conducted on thirty photomicrographs showing biomass and various undesired components (like plastics or mineral matter), which were purposely added (by the ILS organizers) to contaminate wood pellets and charcoal-based grilling fuels. Forty-six participants had various levels of difficulty identifying the marked components, and as a result, the percentage of correct answers ranged from 52.2 to 94.4%. Among the most difficult components to distinguish were petroleum products and inorganic matter. Various reasons led to the misidentification, including insufficient morphological descriptions of the components provided to participants, ambiguities of the nomenclature, limitations of the analytical and exercise method, and insufficient experience of the participants. Overall, the results indicate that RLM has the potential to enhance the quality assessment of biomass fuels. However, they also demonstrate that the petrographic classification used in this exercise requires further refinement before it can be standardized. While a new simplified classification of solid biomass fuels components was created as an outcome of this study, future research is necessary to refine the nomenclature, develop a microscopic morphological description of the components, and verify the accuracy of component identification with a follow-up ILS.

biomass↗
DOE OSTI2024

Parameter identification methods for low-order gray box building energy models: A critical review

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗