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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 811 records · Page 45

Updated global analysis of neutrino oscillations in the presence of eV-scale sterile neutrinos

Here, we discuss the possibility to explain the anomalies in short-baseline neutrino oscillation experiments in terms of sterile neutrinos. We work in a 3 + 1 framework and pay special attention to recent new data from reactor experiments, IceCube and MINOS+. We find that results from the DANSS and NEOS reactor experiments support the sterile neutrino explanation of the reactor anomaly, based on an analysis that relies solely on the relative comparison of measured reactor spectra. Global data from the ν$_{e}$ disappearance channel favour sterile neutrino oscillations at the 3σ level with Δm$_{41}^{2}$ ≈1.3 eV$^{2}$ and |U$_{e4}$| ≈ 0.1, even without any assumptions on predicted reactor fluxes. In contrast, the anomalies in the ν$_{e}$ appearance channel (dominated by LSND) are in strong tension with improved bounds on ν$_{μ}$ disappearance, mostly driven by MINOS+ and IceCube. Under the sterile neutrino oscillation hypothesis, the p-value for those data sets being consistent is less than 2.6 × 10$^{-6}$. Therefore, an explanation of the LSND anomaly in terms of sterile neutrino oscillations in the 3 + 1 scenario is excluded at the 4.7σ level. This result is robust with respect to variations in the analysis and used data, in particular it depends neither on the theoretically predicted reactor neutrino fluxes, nor on constraints from any single experiment. Irrespective of the anomalies, we provide updated constraints on the allowed mixing strengths |U$_{α4}$| (α = e, μ, τ ) of active neutrinos with a fourth neutrino mass state in the eV range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dynamic responses of submerged components in advanced reactors: experimental and numerical studies

The seismic design of an advanced nuclear reactor must consider the interaction of vessel internal components with the surrounding coolant: fluid–structure interaction (FSI). Available analytical solutions for FSI of submerged components do not accommodate multiple-component, intense seismic inputs and complex reactor and internal geometries. Physical testing of reactor vessels and internals to inform seismic design is impractical and cost-prohibitive, leaving the use of verified and validated, robust numerical models as the only plausible option for analysis and design. Physical data that could be used for validating such numerical models for multi-component shaking are not available. This article describes an experimental program performed on a 6-degree-of-freedom earthquake simulator to generate data that could support validation of seismic FSI numerical models for submerged components in commercial finite element codes. A scaled model of a base-supported reactor vessel with simplified representations of submerged internals was tested to generate submerged-component response histories for a range of seismic inputs. The generated data were used to validate numerical models in the finite element code LS-DYNA. Numerical models were validated for calculation of hydrodynamic pressure responses on internals, in-water frequencies of internals, and acceleration responses of internals. The generated data and the analysis recommendations could aid engineering analysts designing submerged components and systems for seismic effects.

Engineering↗

Plant biomass fermentation by the extreme thermophile Caldicellulosiruptor bescii for co-production of green hydrogen and acetone: Technoeconomic analysis

A variety of chemical and biological processes have been proposed for conversion of sustainable low-cost feedstocks into industrial products. Here, a biorefinery concept is formulated, modeled, and analyzed in which a naturally (hemi)cellulolytic and extremely thermophilic bacterium, Caldicellulosiruptor bescii, is metabolically engineered to convert the carbohydrate content of lignocellulosic biomasses (i.e., soybean hulls, transgenic poplar) into green hydrogen and acetone. Experimental validation of C. bescii fermentative performance demonstrated 82% carbohydrate solubilization of soybean hulls and 55% for transgenic poplar. A detailed technical design, including equipment specifications, provides the basis for an economic analysis that establishes metabolic engineering targets. This robust industrial process leveraging metabolically engineered C. bescii yields 206 kg acetone and 25 kg H 2 per metric ton of soybean hull, or 174 kg acetone and 21 kg H 2 per metric ton transgenic poplar. Beyond this specific case, the model demonstrates industrial feasibility and economic advantages of thermophilic fermentation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

COMPASO : A new halo finder for competitive assignment to spherical overdensities

We describe a new method (compaso) for identifying groups of particles in cosmological N-body simulations. compaso builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius around a smaller halo before competitively assigning halo membership to the particles. In this way, the compaso finder allows for more effective deblending of haloes in close proximity as well as the formation of new haloes on the outskirts of larger ones. This halo-finding algorithm is used in the abacussummit suite of N-body simulations, designed to meet the cosmological simulation requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. compaso is developed as a highly efficient on-the-fly group finder, which is crucial for enabling good load-balancing between the GPU and CPU and the creation of high-resolution merger trees. In this paper, we describe the halo-finding procedure and its particular implementation in abacus, accompanying it with a qualitative analysis of the finder. We test the robustness of the compaso catalogues before and after applying the cleaning method described in an accompanying paper and demonstrate its effectiveness by comparing it with other validation techniques. We then visualize the haloes and their density profiles, finding that they are well fit by the NFW formalism. Finally, we compare other properties such as radius–mass relationships and two-point correlation functions with that of another widely used halo finder, rockstar.

79 ASTRONOMY AND ASTROPHYSICS↗

Entry Dispersion Analysis for the Genesis Sample Return Capsule

Genesis will be the first mission to return samples from beyond the Earth-Moon system. The spacecraft will be inserted into a halo orbit about the L1 (Sun- Earth) libration point where it will remain for two years collecting solar wind particles. Upon Earth return, the sample return capsule, which is passively controlled, will descend under parachute to Utah. The present study describes the analysis of the entry, descent, and landing scenario of the returning sample cap- sule. The robustness of the entry sequence is assessed through a Monte Carlo dispersion analysis where the impact of off-nominal conditions is ascertained. The dispersion results indicate that the capsule attitude excursions near peak heating and drogue chute deployment are within Genesis mission limits. Additionally, the size of the resulting 3-sigma landing ellipse is 47.8 km in downrange by 15.2 km in crossrange, which is within the Utah Test and Training Range boundaries.

Desai, Prasun, N.↗

Towards Rocket Engine Components with Increased Strength and Robust Operating Characteristics

High-energy rotating machines, powering liquid propellant rocket engines, are subject to various sources of high and low cycle fatigue generated by unsteady flow phenomena. Given the tremendous need for reliability in a sustainable space exploration program, a fundamental change in the design methodology for engine components is required for both launch and space based systems. A design optimization system based on neural-networks has been applied and demonstrated in the redesign of the Space Shuttle Main Engine (SSME) Low Pressure Oxidizer Turbo Pump (LPOTP) turbine nozzle. One objective of the redesign effort was to increase airfoil thickness and thus increase its strength while at the same time detuning the vane natural frequency modes from the vortex shedding frequency. The second objective was to reduce the vortex shedding amplitude. The third objective was to maintain this low shedding amplitude even in the presence of large manufacturing tolerances. All of these objectives were achieved without generating any detrimental effects on the downstream flow through the turbine, and without introducing any penalty in performance. The airfoil redesign and preliminary assessment was performed in the Exploration Technology Directorate at NASA ARC. Boeing/Rocketdyne and NASA MSFC independently performed final CFD assessments of the design. Four different CFD codes were used in this process. They include WIL DCA T/CORSAIR (NASA), FLUENT (commercial), TIDAL (Boeing Rocketdyne) and, a new family (AardvarWPhantom) of CFD analysis codes developed at NASA MSFC employing LOX fluid properties and a Generalized Equation Set formulation. Extensive aerodynamic performance analysis and stress analysis carried out at Boeing Rocketdyne and NASA MSFC indicate that the redesign objectives have been fully met. The paper presents the results of the assessment analysis and discusses the future potential of robust optimal design for rocket engine components.

Marcu, Bogdan↗

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗

Bayesian Inference and the Effects of Varying Uncertainty Models in Charring Ablator Calibration and Uncertainty Quantification Problems

The Mars Science Laboratory (MSL) vehicle utilized a heat shield constructed from NASA’s Phenolic-Impregnated Carbon Ablator (PICA) material to protect the main structure from the high enthalpy environment encountered during hypersonic atmospheric entry. During the vehicle’s descent through Martian atmosphere, multiple thermocouples embedded within the heat shield captured in-depth material temperature data that allow for studies to be conducted on current material response reconstruction tools. In the present work, material temperature data obtained from thermocouples within the MISP-4 plug (MEDLI (Mars Science Laboratory Entry, Descent, and Landing Instrument) Integrated Sensor Plug) are utilized in the calibration of Theoretical Ablative Composite for Open Testing (TACOT) model parameters in conjunction with NASA’s Porous material Analysis Toolbox (PATO) through Bayesian inference where uncertainty due to parametric, modeling, and experimental sources is simultaneously quantified. Prior to the study, a sensitivity analysis is performed through computation of the robust Sobol indices in an effort to study the relationship between input space and model response and to reduce the dimensionality of the statistical inverse problem. The Bayesian inference methodology necessitates an a-priori choice to be made for the uncertainty model for which numerous possibilities are available. Across most works, however, only basic additive or multiplicative models are utilized with pre-defined magnitudes of uncertainty based on a-priori knowledge or to-be-calibrated multipliers of static covariance matrix structures. The present effort explores the effects of informed uncertainty models, ones with temporal dependence that are simultaneously calibrated through Bayesian inference, on calibrated results for parameters that make up the uncertain input space.

Sensitivity Analysis↗

F-15B Quiet Spike(TradeMark) Aeroservoelastic Flight-Test Data Analysis

System identification is utilized in the aerospace community for development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlin ear systems. In this report, we show the application of one such nonlinear system identification technique, structure detection, for the an alysis of Quiet Spike(TradeMark)(Gulfstream Aerospace Corporation, Savannah, Georgia) aeroservoelastic flight-test data. Structure detectio n is concerned with the selection of a subset of candidate terms that best describe the observed output. Structure computation as a tool fo r black-box modeling may be of critical importance for the development of robust, parsimonious models for the flight-test community. The ob jectives of this study are to demonstrate via analysis of Quiet Spike(TradeMark) aeroservoelastic flight-test data for several flight conditions that: linear models are inefficient for modelling aeroservoelast ic data, nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data an d the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗

Robust vortex lines, vortex rings, and hopfions in three-dimensional Bose-Einstein condensates

Performing a systematic Bogoliubov–de Gennes spectral analysis, we illustrate that stationary vortex lines, vortex rings, and more exotic states, such as hopfions, are robust in three-dimensional atomic Bose-Einstein condensates, for large parameter intervals. Importantly, we find that the hopfion can be stabilized in a simple parabolic trap, without the need for trap rotation or inhomogeneous interactions. We supplement our spectral analysis by studying the dynamics of such stationary states; we find them to be robust against significant perturbations of the initial state. In the unstable regimes, we not only identify the unstable mode, such as a quadrupolar or hexapolar mode, but we also observe the corresponding instability dynamics. Moreover, deep in the Thomas-Fermi regime, we investigate the particlelike behavior of vortex rings and hopfions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

An Antibody Cocktail-Based Immunoaffinity-LC-MS Method Enabled Ultra-Sensitive and Robust Quantification of Circulating Proinsulin Proteoforms and C-Peptide

Accurately measuring circulating proinsulin proteoforms is crucial for clinical investigation of diabetes, but was previously not feasible owing to limited assay specificity/sensitivity. Here, in this study, we devised a highly sensitive LC-MS-based strategy to quantify intact proinsulin, des-31,32 and des-64,65 proinsulin, and C-peptide in circulation. The method involves: (i) quantitative, robust affinity capture using an optimized antibody cocktail, eliminating the severe quantitative bias across multiple proteoforms typically introduced when using a single antibody; (ii) Lys-C digestion producing unique signature peptides for each proteoform, and (iii) trapping-nano-LC coupled with FAIMS/dCV-MS for an ultrasensitive analysis. The selective trapping/delivery ensured sensitive/selective analysis of the targets while achieving excellent analytical robustness that is critical for clinical assays, and the FAIMS/dCV substantially reduces baseline noise/interferences, further enhancing S/N. The assay achieved exceptional sensitivity, with serum LOQs of 1.7, 2.3, and 3.6 pg/mL respectively for intact-proinsulin, des-31,32 and des-64,65, representing the first assay capable of sensitively quantifying these major circulating proinsulin proteoforms. We applied this assay to 78 subjects, including autoantibody positive (n = 20) and new-onset type 1 diabetes (T1D, n = 19) with respective age/sex/BMI-matched controls, enabling the first accurate profiling of proinsulin proteoforms in clinical groups. The assay results demonstrated a clear separation of control and new-onset T1D groups that a parallel total-proinsulin ELISA assay fails to capture. Furthermore, distinct expression patterns in relative abundance ratios among proteoforms were observed across clinical groups. This assay may provide valuable insights into the β-cell functions and the onset/progression of diabetes and other associated conditions. Moreover, the strategy is broadly applicable to targeted measurement of other biomarker proteoforms.

Shen, Qingqing [University at Buffalo, NY (United ↗

Opportunities for Breakthroughs in Large-Scale Computational Simulation and Design

Opportunities for breakthroughs in the large-scale computational simulation and design of aerospace vehicles are presented. Computational fluid dynamics tools to be used within multidisciplinary analysis and design methods are emphasized. The opportunities stem from speedups and robustness improvements in the underlying unit operations associated with simulation (geometry modeling, grid generation, physical modeling, analysis, etc.). Further, an improved programming environment can synergistically integrate these unit operations to leverage the gains. The speedups result from reducing the problem setup time through geometry modeling and grid generation operations, and reducing the solution time through the operation counts associated with solving the discretized equations to a sufficient accuracy. The opportunities are addressed only at a general level here, but an extensive list of references containing further details is included. The opportunities discussed are being addressed through the Fast Adaptive Aerospace Tools (FAAST) element of the Advanced Systems Concept to Test (ASCoT) and the third Generation Reusable Launch Vehicles (RLV) projects at NASA Langley Research Center. The overall goal is to enable greater inroads into the design process with large-scale simulations.

Alexandrov, Natalia↗

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis

Here, the accumulation of bacteria in surface attached biofilms, or biofouling, can be detrimental to human health, dental hygiene, and many industrial processes. A critical need in identifying and preventing the deleterious effects of biofilms is the ability to observe and quantify their development. Analytical methods capable of assessing early stage fouling are cumbersome or lab-confined, subjective, and qualitative. Herein, a novel photographic method is described that uses biomolecular staining and image analysis to enhance contrast of early stage biofouling. A robust algorithm was developed to objectively and quantitatively measure surface accumulation of Pseudomonas putida from photographs and results were compared to independent measurements of cell density. Results from image analysis quantified biofilm growth intensity accurately and with approximately the same precision of the more laborious cell counting method. This simple method for early stage biofilm detection enables quantifiable measurement of surface fouling and is flexible enough to be applied from the laboratory to the field. Broad spectrum staining highlights fouling biomass, photography quickly captures a large area of interest, and image analysis rapidly quantifies fouling in the image.

59 BASIC BIOLOGICAL SCIENCES↗

Analysis of airframe/engine interactions in integrated flight and propulsion control

An analysis framework for the assessment of dynamic cross-coupling between airframe and engine systems from the perspective of integrated flight/propulsion control is presented. This analysis involves to determining the significance of the interactions with respect to deterioration in stability robustness and performance, as well as critical frequency ranges where problems may occur due to these interactions. The analysis illustrated here investigates both the airframe's effects on the engine control loops and the engine's effects on the airframe control loops in two case studies. The second case study involves a multi-input/multi-output analysis of the airframe. Sensitivity studies are performed on critical interactions to examine the degradations in the system's stability robustness and performance. Magnitudes of the interactions required to cause instabilities, as well as the frequencies at which the instabilities occur are recorded. Finally, the analysis framework is expanded to include control laws which contain cross-feeds between the airframe and engine systems.

Schierman, John D.↗

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

New free-wake analysis of rotorcraft hover performance using influence coefficients

Free-wake analyses of helicopter rotor wakes in hover using time stepping have been shown to encounter instabilities which preclude convergence to valid free-vortex solutions for rotor-wake geometries. Previous work has demonstrated that these convergence difficulties can be overcome by implementing a new free-wake analysis method based on the use of influence coefficients. The present paper reviews this approach and documents its incorporation into a hover performance analysis called Evaluation of Hover Performance using Influence Coefficients (EHPIC). The technical principles underlying the EHPIC code are described with emphasis on steps taken to develop the single-filament wake models used in previous work into a multifilament wake valid for realistic hover performance predictions. The coupling of the wake model to a lifting surface loads analysis is described, and sample problems are solved that illustrate the robustness of the method. Performance calculations are also undertaken for hover to illustrate the utility of EHPIC in the analysis of rotorcraft performance.

Quackenbush, Todd R.↗

Performance Evaluation of an Intelligent Sensor Platform for Radiation Monitoring Applications

Accurate, rugged, and reliable radiation detection systems are important for area and environmental monitoring applications. The desire for spectroscopic capability has increased in monitoring aspects of the nuclear fuel cycle to provide fast characterization of the radiation profile of a situation, such as the planned or unplanned release of material. The reduction or elimination of having to conduct sampling for laboratory analysis can result in significant cost savings for an industry, government agency, or regulatory body. A new system, comprised of a NaI:Tl scintillator and a G-M tube, has been designed and tested, taking into account the following end-user requirements: ease-of-use, capability to network and supervise multiple units, compact form factor, low power consumption, versatility, and stability. The detector sizes were selected to accommodate a dose rate up to 1 Sv/hr. Several algorithms and analysis routines have been developed to incorporate these key needs without sacrificing on accuracy, dynamic range, nuclide identification, and sensitivity. This presentation will introduce the major hardware and software components of the platform, as well as the user interface and data analysis workflow. Key features of the hardware include an environmentally robust housing, low power signal processing electronics, patented LED-based gain stabilization, and an embedded processor for unattended instrument management and data analysis. New and improved algorithms for determining scintillator gamma dose rates, total integrated dose, and nuclide identification will be introduced. The two detector elements were modeled with MCNP and validated experimentally. The results of the radiological testing shall be presented including energy resolution, throughput, dose response, and minimum detectable activities. The dose response has been evaluated in simulations and with measurements to ensure accurate response with respect to energy spectrum of the dose field and direction of the incident radiation. Spectroscopy evaluations, such as nuclide detection probability and false alarm rates, were performed and comparisons to existing algorithms shall also be presented. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation

Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei Guo's 60th birthday with a review of the ODE Method and its recent evolution. The method has been regarded as a technique for algorithm analysis. It is argued that this viewpoint is backwards: The original stochastic approximation method was surely motivated by an ODE, and tools for analysis came much later (based on establishing robustness of Euler approximations). The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time, following by careful approximation to obtain a practical recursive algorithm. While these methods are usually presented in a stochastic setting, this is not a prerequisite. In fact, recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo. Subject to conditions, the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert. The conditions are not universal, but theory suggests alternatives to achieve acceleration. The theory is illustrated with applications to gradient-free optimization, and policy gradient algorithms for reinforcement learning.

learning and adaptive systems in artificial intell↗