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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 1,711 records · Page 95

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Kukreja, Sunil L.↗

Structural and Dynamic Analysis of Sulphur Dioxide Adsorption in a Series of Zirconium-Based Metal–Organic Frameworks

We report reversible high capacity adsorption of SO 2 in robust Zr-based metal–organic framework (MOF) materials. Zr-bptc (H 4 bptc=biphenyl-3,3',5,5'-tetracarboxylic acid) shows a high SO 2 uptake of 6.2 mmol g -1 at 0.1 bar and 298 K, reflecting excellent capture capability and removal of SO 2 at low concentration (2500 ppm). Dynamic breakthrough experiments confirm that the introduction of amine, atomically-dispersed Cu II or heteroatomic sulphur sites into the pores enhance the capture of SO 2 at low concentrations. The captured SO 2 can be converted quantitatively to a pharmaceutical intermediate, aryl N-aminosulfonamide, thus converting waste to chemical values. In situ X-ray diffraction, infrared micro-spectroscopy and inelastic neutron scattering enable the visualisation of the binding domains of adsorbed SO 2 molecules and host–guest binding dynamics in these materials at the atomic level. Refinement of the pore environment plays a critical role in designing efficient sorbent materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Regression Analysis with the Directed Infusion of Data

Integrating artificial intelligence and machine learning tools into industry necessitates large-scale collaborative efforts that ensure the robust and accurate execution of downstream analytics such as time series prediction, uncertainty quantification, grid optimization, and condition monitoring. However, concerns related to data privacy pervade the nuclear industry due to the proprietary nature of its data and the possibility of data leakage. Legacy techniques such as encryption often require the explicit transmission of data to trustworthy parties, thereby inviting data leakage concerns. The ideal collaboration scenario avoids the explicit dissemination of data/code while maintaining experimental fidelity, which is currently accomplished using various techniques such as trusted execution environments, homomorphic encryption, differential privacy, and multimatrix masking. These techniques, however, often necessitate a trade-off between trust, efficiency, and utility. This article extends a previously proposed technique called the directed infusion of data (DIOD) that ensures data privacy, allows for scalable obfuscation, and combats the risk of data leakage without compromising utility. The experiments discussed in this article examine a regression-type scenario using DIOD with the goal of preserving the inferential link between two variables. Using the point-kinetics equations, regression experiments compare the performance of a model trained using the original data to that of a model trained using the obfuscated data, which produced identical results. Our claim is further strengthened by an information theoretic proof and experiment, which showed that the inferential content between variables remains the same after obfuscation, thereby avoiding the required communication of the proprietary data.

47 - OTHER INSTRUMENTATION↗

Analysis of Covariance Intersection For Triangulation

In this document I will discuss different implementations of Covariance Intersection (CI) for object triangulation as well as the robustness of CI. For CI methods we will compare the performance of different methods and discuss edge cases which must be considered. For robustness we will focus on the impact of removing sensors on the final fused estimate. Here we look at factors which influence the final fused covariance matrix.

97 MATHEMATICS AND COMPUTING↗

Robust extraction of power corrections and nuclear dynamics from DIS at large $x$

We present recent updates from the CTEQ-JLab (CJ) global PDF analysis, focusing on the interplay and implementation systematics of the HT and offshell correction (CJ22ht). We also discuss preliminary results of the CJ25 global analysis, showing the impact of the full JLab 6 GeV datasets, that we recently collected in a comprehensive DIS database, and having a first look at early JLab 12 GeV measurements. We finally offer a few thoughts on how future data may help unraveling the nuclear and partonic dynamics in light nuclei.

Accardi, Alberto [Thomas Jefferson National Accele↗

Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

14 SOLAR ENERGY↗

Catalysis Science Initiative: Catalyst Design by Discovery Informatics

Catalysts selectively enhance the rates of chemical reactions toward desired products. Such reactions provide great benefit to society in major commercial sectors such as energy production, protecting the environment, and polymer products and thereby contribute heavily to the country’s gross national product. Our premise is that the level of fundamental understanding of catalytic events at the atomic and molecular scale has reached the point that more predictive methods can be developed to shorten the cycle time to new processes. The field of catalysis can be divided into two regimes: heterogeneous and homogeneous. For the heterogeneous catalysis regime, we have used the water-gas shift (WGS) reaction (CO + H2O + CO2 + H2O) over supported metals as a test bed. Detailed analysis and strong coupling of theory with experiment have led to the following conclusions: • The sequence of elementary steps goes through a COOH intermediate • The CO binding energy is a strong function of coverage of CO adsorbed on the surface in many systems • In the case of Au catalysts, the CO adsorption is generally too weak on surface with close atomic packing, but the enhanced binding at corner atoms (which are missing bonding partners) of cubo-octahedral nanoparticles increases the energy to a near optimal value and produces very active catalysts. • Reaction on the metal alone cannot account for the experimental results. The reaction is dual functional with water activation occurring at the metal-support interface. It is clear from our work that the theory component is essential, not only for prediction of new systems, but also for reconciling data and testing hypotheses regarding potential descriptors. Particularly important is the finding that the interface between nano-sized metal particles and the oxides that are used to support them represent a new state of matter in the sense that the interfacial bonding perturbs the chemical state of both metals atoms and the support atoms in the interfacial region. Some of the first theoretical descriptions of this important chemistry and potential new source of control of catalyst properties are be in preparation for submission. On the homogeneous catalysis side, we have used single site olefin polymerization as the testbed. This system is important because changes in a single ligand bonded to the catalytically active metal site can alter the rates of individual steps in the polymerization sequence and thereby change the properties of the resulting polymer, potentially improving its value in a hundred million pound per year industry. We have made a major advance in understanding such systems by developing a population balance kinetic model that allows us to predict the molecular weight distribution (MWD) of the product. That, in turn, allows use of MWD data to fit kinetic parameters. By combining monomer loss data, MWD, measurement of the number of working active sites, and polymer end group analysis, we have a rich data set that is highly discriminating of kinetic mechanism. Thus, we have a robust tool for producing high quality, detailed kinetic parameters, which we have used to refine mechanisms presented in the literature and discover relationships between steric and electronic properties of group IV catalysts and individual rate constants in a number of systems. Our recent work on six-coordinate Zr, Ti, and Hf amine bis(phenolate) systems, we have shown that: • The sterics (bulkiness) of the ligands specifically affect the chain termination reaction • The electron density on the metal controls misinsertion (flipped orientation) of the olefin into the growing polymer • Steric effects related to the size of the ortho ligand on the catalyst have been shown to strongly affect its the degree of dormancy, i.e. tendency to stop reacting • Changes in the size of the amine pendent group on the catalyst can have such a strong effect on chain termination as to change the catalyst from one that produces only oligomers to one that incorporates oligomers in polymer chains to introduce chain branching. These effects control the length of molecular chains, the number of errors in the regularity of the chains, and the variation of chain lengths in the final product distribution. These characteristics, in turn, determine the properties of the resulting polymer material. Predictive modeling of polymerization process cannot be done without the quantitative rate constant determination that our unique and comprehensive approach has produced. This tool, combined with insights on how catalyst chemistry affects the rate constants, is an important step toward establishing the quantitative relationships between catalyst chemistry and polymer properties that will allow more efficient searches for new catalysts and speed the discovery process. Thus, in both the heterogeneous and homogeneous catalysis areas, this work has brought important new understanding to the field. The technical effectiveness of our approach is demonstrated by its success. For heterogeneous catalysis, experimental work alone showed the important of the metal-support interface, but the integration with theory has allowed deeper understanding of the fundamental source of the effects and shown the path for how to use this understanding for discovery. In the case of homogeneous catalysis, methodology for determination of which reactions are needed to account for the data and the extraction of quantitative values for rate constants for those reactions allows accurate modeling of reaction behavior. This allowed correlation of trends in catalyst properties with alterations of specific rates, thus contributing to a data base that will guide catalyst discovery. While not yet realized, the economic benefits of this approach will come in significantly shortened cycle times for discovery and ability to fill product demand at lower price.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Analysis of a Landing System for Planetary Payloads Utilizing Passive Energy Absorbing Composite Structure

Delivery of a payload from space to a planetary surface currently requires the development of an application specific landing system to protect the payload from forces imparted during impact with the planet surface. Often, active energy attenuating systems such as retro-rockets, deployable parachutes, and airbags are utilized within these landing systems to reduce landing impact energy. Unfortunately, these active systems come at a cost; active energy attenuating systems are susceptible to system faults which may limit or completely negate their energy attenuating capability. Additionally, components needing to be stowed such as fuel, parachutes, and airbags increase design complexity, cost, and weight. To overcome these limitations, this study examines the potential of passive energy attenuation through energy absorbing structural design and composite materials to mitigate landing loads for small payload planetary delivery. Researchers at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) have conducted extensive research into developing energy absorbing structures and components for the attenuation of impact energy under various loading conditions including aircraft crash and spacecraft impact. The current study leverages this research to design a lightweight planetary delivery system which utilizes unique outer mold line (OML) geometry and passive energy absorbing structural design to limit landing loads across potential planetary surface environments. The OML geometry is designed to control impact orientation and provide self-righting capabilities for slopped impact surfaces. The internal structure is composed of composite material structures arranged to provide energy absorption which is robust to impact angle and impact velocity. The developed planetary delivery design concept will be evaluated using finite element (FE) model analysis. Simulations of landing impacts with representative soil surface environments will be used to characterize the energy absorbing capabilities of the landing system. Sensitivity of predicted impact force to landing environment, impact angle, and impact velocity will be assessed to identify capabilities and limitations of the initial structural design. Results will be used to determine the feasibility of a lightweight composite structure to passively absorb landing energy for robust planetary payload delivery.

Crashworthiness↗

Analysis of a Dynamic Flexed Flat Cable Harness

A dynamic flex test was performed on a spacecraft instrument harness composed of multiple individual flex cables, with each flex cable containing multiple copper traces. The purpose of the test was to demonstrate the capability of the harness to survive the number of expected flex cycles during the planned mission with appropriate margin. However, during testing, increased trace resistances and open circuits were observed beginning at approximately 10% of the total number of planned flex cycles. This paper discusses the various proximate causes that contributed to increased and open resistance in the flex cables and the subsequent redesign, manufacturing, reliability, and quality-related changes that were instituted. An analysis process, including failure analysis, non-destructive evaluation, digital imaging correlation, and parametric modeling, will also be discussed. The paper will cover the development of a robust dynamic flex harness design and include recommendations to extend flex harness life; make changes to the flex harness life test; and improve flex cable manufacturing, quality, and reliability.

Flexible PCB↗

Analysis of a Dynamic Flexed Flat Cable Harness

A dynamic flex test was performed on a spacecraft instrument harness composed of multiple individual flex cables, with each flex cable containing multiple copper traces. The purpose of the test was to demonstrate the capability of the harness to survive the number of expected flex cycles during the planned mission with appropriate margin. However, during testing, increased trace resistances and open circuits were observed beginning at approximately 10% of the total number of planned flex cycles. This paper discusses the various proximate causes that contributed to increased and open resistance in the flex cables and the subsequent redesign, manufacturing, reliability, and quality-related changes that were instituted. An analysis process, including failure analysis, non-destructive evaluation, digital imaging correlation, and parametric modeling, will also be discussed. The paper will cover the development of a robust dynamic flex harness design and include recommendations to extend flex harness life; make changes to the flex harness life test; and improve flex cable manufacturing, quality, and reliability.

Flexible PCB↗

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.

Nakayasu, Ernesto S.↗

THE RADIAL VELOCITY DETECTION OF EARTH-MASS PLANETS IN THE PRESENCE OF ACTIVITY NOISE: THE CASE OF {alpha} CENTAURI Bb

We present an analysis of the publicly available HARPS radial velocity (RV) measurements for {alpha} Cen B, a star hosting an Earth-mass planet candidate in a 3.24 day orbit. The goal is to devise robust ways of extracting low-amplitude RV signals of low-mass planets in the presence of activity noise. Two approaches were used to remove the stellar activity signal which dominates the RV variations: (1) Fourier component analysis (pre-whitening), and (2) local trend filtering (LTF) of the activity using short time windows of the data. The Fourier procedure results in a signal at P = 3.236 days and K = 0.42 m s{sup -1}, which is consistent with the presence of an Earth-mass planet, but the false alarm probability for this signal is rather high at a few percent. The LTF results in no significant detection of the planet signal, although it is possible to detect a marginal planet signal with this method using a different choice of time windows and fitting functions. However, even in this case the significance of the 3.24 day signal depends on the details of how a time window containing only 10% of the data is filtered. Both methods should have detected the presence of {alpha} Cen Bb at a higher significance than is actually seen. We also investigated the influence of random noise with a standard deviation comparable to the HARPS data and sampled in the same way. The distribution of the noise peaks in the period range 2.8-3.3 days has a maximum of Almost-Equal-To 3.2 days and amplitudes approximately one-half of the K-amplitude for the planet. The presence of the activity signal may boost the velocity amplitude of these signals to values comparable to the planet. It may be premature to attribute the 3.24 day RV variations to an Earth-mass planet. A better understanding of the noise characteristics in the RV data as well as more measurements with better sampling will be needed to confirm this exoplanet.

79 ASTRONOMY AND ASTROPHYSICS↗

X-ray free electron laser tuning for variable-gap undulators

A tuning method for hard x-ray free electron lasers is presented which combines electron-beam-based alignment (EBBA) with undulator radiation spectrum analysis. This method is conducive to achieving a dispersion-free orbit, finding the undulator field center, and determining the accurate value of the gap distance for the operating undulator K for each undulator segment, and it proves to be highly reliable and robust for free electron laser lasing with variable-gap undulators. The effect of the natural focusing of the undulator on the EBBA is confirmed with measurements and a theoretical analysis. The tilt of an undulator segment subject to ground movement introduces an undulator field change which the electron beam interacts with and can be corrected by K tuning to find a precise compensating gap distance. Lattice matching with a wire-scanner-based Twiss parameter measurement along the undulator line enables the EBBA reference orbit to coincide with the e-beam distribution.

47 OTHER INSTRUMENTATION↗

Perspectives and Challenges in Bolide Infrasound Processing and Interpretation: A Focused Review with Case Studies

Infrasound sensing plays a critical role in the detection and analysis of bolides, offering passive, cost-effective global monitoring capabilities. Key objectives include determining the timing, location, and yield of these events. Achieving these goals requires a robust approach to detect, analyze, and interpret rapidly moving elevated sources such as bolides (also re-entry). In light of advancements in infrasonic methodologies, there is a need for a comprehensive overview of the characteristics that distinguish bolides from other infrasound sources and methodologies for bolide infrasound analysis. This paper provides a focused review of key considerations and presents a unified framework to enhance infrasound processing approaches specifically tailored for bolides. Three representative case studies are presented to demonstrate the practical application of infrasound processing methodologies and deriving source parameters while exploring challenges associated with bolide-generated infrasound. These case studies underscore the effectiveness of infrasound in determining source parameters and highlight interpretative challenges, such as variations in signal period measurements across different studies. Future research should place emphasis on improving geolocation and yield accuracy. This can be achieved through rigorous and systematic analyses of large, statistically significant samples of such events, aiming to resolve interpretative inconsistencies and explore the causes for variability in signal periods and back azimuths. The topic described here is also relevant to space exploration involving planetary bodies with atmospheres, such as Venus, Mars, and Titan.

79 ASTRONOMY AND ASTROPHYSICS↗

Analysis of signals under compositional noise with applications to SONAR data

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between the aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.

47 OTHER INSTRUMENTATION↗

Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

59 BASIC BIOLOGICAL SCIENCES↗

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

59 BASIC BIOLOGICAL SCIENCES↗

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗