Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MODEL CALIBRATION”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗

Atlantic hurricane surge response to geoengineering

Devastating Atlantic hurricanes are relatively rare events. However their intensity and frequency in a warming world may rapidly increase by a factor of 2-7 for each degree of increase in mean global temperature. Geoengineering by stratospheric sulphate aerosol injection cools the tropics relative to the polar regions, including the hurricane main development region in the Atlantic, suggesting that geoengineering may be an effective method of controlling hurricanes. We examine this hypothesis using 8 Earth System Model simulations of climate under the GeoMIP G3 and G4 schemes that use stratospheric aerosols to reduce the radiative forcing under the RCP4.5 scenario. Global mean temperature increases are greatly ameliorated by geoengineering, and tropical temperature increases are at most half of those in RCP4.5, but sulphate injection would have to double between 2020 and 2070 to balance RCP 4.5 to nearly 10 Tg SO2 yr-1, with consequent implications for damage to stratospheric ozone. We project changes in storm frequencies using a temperature-dependent Generalized Extreme Value statistical model calibrated by historical storm surges from 1923 and observed temperatures. The numbers of storm surge events as big as the one that caused the 2005 Katrina hurricane are reduced by about 50% compared with no geoengineering, but this is only marginally statistically significant. Furthermore, when sea level rise differences at 2070 between RCP4.5 and geoengineering are factored in to coastal flood risk, we find that expected flood levels are reduced by about 40 cm for 5 year events and perhaps halved for 50 year surges.

58 GEOSCIENCES↗

SECULAR STELLAR DYNAMICS NEAR A MASSIVE BLACK HOLE

The angular momentum evolution of stars close to massive black holes (MBHs) is driven by secular torques. In contrast to two-body relaxation, where interactions between stars are incoherent, the resulting resonant relaxation (RR) process is characterized by coherence times of hundreds of orbital periods. In this paper, we show that all the statistical properties of RR can be reproduced in an autoregressive moving average (ARMA) model. We use the ARMA model, calibrated with extensive N-body simulations, to analyze the long-term evolution of stellar systems around MBHs with Monte Carlo simulations. We show that for a single-mass system in steady state, a depression is carved out near an MBH as a result of tidal disruptions. Using Galactic center parameters, the extent of the depression is about 0.1 pc, of similar order to but less than the size of the observed 'hole' in the distribution of bright late-type stars. We also find that the velocity vectors of stars around an MBH are locally not isotropic. In a second application, we evolve the highly eccentric orbits that result from the tidal disruption of binary stars, which are considered to be plausible precursors of the 'S-stars' in the Galactic center. We find that RR predicts more highly eccentric (e > 0.9) S-star orbits than have been observed to date.

79 ASTRONOMY AND ASTROPHYSICS↗

Characterization of the benchmark binary NLTT 33370 {sup ,}

We confirm the binary nature of the nearby, very low mass (VLM) system NLTT 33370 with adaptive optics imaging and present resolved near-infrared photometry and integrated light optical and near-infrared spectroscopy to characterize the system. VLT-NaCo and LBTI-LMIRCam images show significant orbital motion between 2013 February and 2013 April. Optical spectra reveal weak, gravity-sensitive alkali lines and strong lithium 6708 Å absorption that indicate the system is younger than field age. VLT-SINFONI near-IR spectra also show weak, gravity-sensitive features and spectral morphology that is consistent with other young VLM dwarfs. We combine the constraints from all age diagnostics to estimate a system age of ∼30-200 Myr. The 1.2-4.7 μm spectral energy distribution of the components point toward T {sub eff} = 3200 ± 500 K and T {sub eff} = 3100 ± 500 K for NLTT 33370 A and B, respectively. The observed spectra, derived temperatures, and estimated age combine to constrain the component spectral types to the range M6-M8. Evolutionary models predict masses of 97{sub −48}{sup +41} M{sub Jup} and 91{sub −44}{sup +41} M{sub Jup} from the estimated luminosities of the components. KPNO-Phoenix spectra allow us to estimate the systemic radial velocity of the binary. The Galactic kinematics of NLTT 33370AB are broadly consistent with other young stars in the solar neighborhood. However, definitive membership in a young, kinematic group cannot be assigned at this time and further follow-up observations are necessary to fully constrain the system's kinematics. The proximity, age, and late-spectral type of this binary make it very novel and an ideal target for rapid, complete orbit determination. The system is one of only a few model calibration benchmarks at young ages and VLMs.

79 ASTRONOMY AND ASTROPHYSICS↗

Behaviour of rippled shocks from ablatively-driven Richtmyer-Meshkov in metals accounting for strength

While numerous continuum material strength and phase transformation models have been proposed to capture their complex dependences on intensive properties and deformation history, few experimental methods are available to validate these models particularly in the large pressure and strain rate regime typical of strong shock and ramp dynamic loading. In the experiments and simulations we present, a rippled shock is created by laser-ablation of a periodic surface perturbation on a metal target. The strength of the shock can be tuned to access phase transitions in metals such as iron or simply to study high-pressure strength in isomorphic materials such as copper. Simulations, with models calibrated and validated to the experiments, show that the evolution of the amplitude of imprinted perturbations on the back surface by the rippled shock is strongly affected by strength and phase transformation kinetics. Increased strength has a smoothing effect on the perturbed shock front profile resulting in smaller perturbations on the free surface. Lastly, in iron, faster phase transformations kinetics had a similar effect as increased strength, leading to smoother pressure contours inside the samples and smaller amplitudes of free surface perturbations in our simulations.

36 MATERIALS SCIENCE↗

Extraction of ground-state nuclear deformations from ultrarelativistic heavy-ion collisions: Nuclear structure physics context

The collective-flow-assisted nuclear shape-imaging method in ultrarelativistic heavy-ion collisions (UHICs) has recently been used to characterize nuclear collective states. In this paper, we assess the foundations of the shape-imaging technique employed in these studies. We argue that some current UHIC nuclear imaging techniques neglect fundamental aspects of spontaneous symmetry breaking and symmetry restoration in colliding ions and incorrectly infer one-body multipole moments from studies of nucleonic correlations. Therefore, the impact of this approach on nuclear structure research has been overstated. Conversely, efforts to incorporate existing knowledge on nuclear shapes into analysis pipelines can be beneficial for benchmarking tools and calibrating models used to extract information from ultrarelativistic heavy-ion experiments.

Nuclear data analysis & compilation↗

Nuclear-matter saturation and symmetry energy within Δ -full chiral effective field theory

Nuclear saturation and the symmetry energy are key properties of low-energy nuclear physics that depend on fine details of the nuclear interaction. The equation of state around saturation is also an important anchor for extrapolations to higher densities and studies of neutron stars. Here we develop a unified statistical framework that uses realistic nuclear forces to link the theoretical modeling of finite nuclei and infinite nuclear matter. We construct fast and accurate emulators for nuclear-matter observables and employ an iterative history-matching approach to explore and reduce the enormous parameter domain of Δ -full chiral interactions. We perform rigorous uncertainty quantification and find that model calibration including O 16 observables gives saturation predictions that are more precise than those that only use few-body data. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Bayesian analysis of nucleon-nucleon scattering data in pionless effective field theory

We perform Bayesian model calibration of two-nucleon (NN) low-energy constants (LECs) appearing in an NN interaction based on pionless effective field theory (πEFT). The calibration is carried out for potentials constructed using naive dimensional analysis in NN relative momenta (p) up to next-to-leading order [NLO, O(p 2 )] and next-to-next-to-next-to-leading order [N3LO, O(p 4 )]. We consider two classes of pionless πEFT potential: one that acts in all partial waves and another that is dominated by s-wave physics. The two classes produce broadly similar results for calibrations to NN data up to E lab = 5 MeV. Our analysis accounts for the correlated uncertainties that arise from the truncation of the pionless πEFT. We simultaneously estimate both the πEFT LECs and the parameters that quantify the truncation error. This permits the first quantitative estimates of the pionless πEFT breakdown scale, Λ b : the 95% intervals are Λ b ∈[50.11,63.03] MeV at NLO and Λ b ∈[72.27,88.54] MeV at N3LO. Furthermore, invoking naive dimensional analysis for the NN potential, therefore, does not lead to consistent results across orders in pionless πEFT. This exemplifies the possible use of Bayesian tools to identify inconsistencies in a proposed EFT power counting.

Bayesian methods↗

Electromagnetic Tomography of Radial Flow in the Quark-Gluon Plasma

We present a novel multimessenger approach to extract the effective radial flow of the quark-gluon plasma (QGP) by jointly analyzing thermal photon and dilepton spectra in heavy-ion collisions. A key feature of this method is that it circumvents the need for a directly unmeasurable reference—the photon temperature in the absence of flow—by establishing, within a calibrated model framework, a stable, approximately linear correlation with the dilepton-inferred temperature. This construction defines an experimentally constructible quantity, 𝑣$^{eff}_{𝑟}$, which reflects early-time collectivity and exhibits a strong correlation with the spacetime-averaged radial velocity of the QGP. Together with previous results linking dilepton slopes to the initial QGP temperature, our work establishes a consistent framework for electromagnetic tomography of the QGP. Here, our framework quantifies the experimental precision target, thereby providing a concrete road map for future measurements at RHIC and the LHC and opening a new avenue to probe the early-time dynamics of hot QCD matter.

Collective flow↗

Application of Kalman Filter to Improve Model Integrity for Securing Electricity Delivery

Power system model integrity is essential to many planning and operation tasks to ensure the safety and reliability of electricity delivery. Inaccurate system models would result in unreliable assessment of system security conditions and cause large-scale blackouts such as the 2003 Northeast Blackout. This dictates a strong need for model calibration and verification, which should be done periodically and preferably, in an automatic manner. Our previous work has demonstrated the feasibility of applying Extended Kalman Filter (EKF) to calibrate generator parameters using disturbance data recorded by phasor measurement units (PMU). This paper proposes to use Riccati equation to investigate EKF’s performance, especially regarding parameter identifiability. The covariance, which can be derived from the Riccati equation, offers insight into the uncertainties of parameters estimated by the EKF-based method. Simulation results show the effectiveness of the proposed approach.

calibration↗

Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression

Abstract The United States Food and Drug Administration (FDA) ruled that partially hydrogenated oils (PHO), the major dietary source of industrially produced trans fat (TF), were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, the objective of this study was to develop a rapid screening procedure using attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) spectroscopy in conjunction with partial least squares regression (PLSR) for the quantitative and accurate prediction of low concentrations of trans fatty acid (TFAs) (<1% of total fatty acids (FAs)). Broad‐based calibration models were developed for a combined set of samples consisting of edible oils and fast food lipid extracts. Predicted concentrations of TFAs in the two matrices showed good correlation with the primary reference data generated by gas chromatography (GC) ( R 2 > 0.99) and high accuracy as evidenced by low root‐mean‐square error of cross‐validation (RMSECV) values. The lowest TFA concentration, determined by GC to be 0.13% of total FAs, was accurately predicted by ATR‐FTIR/PLSR as 0.18% of total FAs. This simple, rapid ATR‐FTIR/PLSR methodology has the potential for use as a screening alternative to conventional gas chromatographic methods for predicting the TFA content of edible oils and food lipid extracts for regulatory purposes and quality control of raw material and processed food. Practical Applications F DA ruled that partially hydrogenated oils were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, we have proposed a rapid screening procedure, based on infrared spectroscopy and chemometrics, to rapidly and accurately predict low concentrations of trans fatty acids (<1% of total fatty acids) in edible oils and food lipid extracts.

Karunathilaka, Sanjeewa R.↗

Fractured rock stress‐permeability relationships from in situ data and effects of temperature and chemical‐mechanical couplings

Abstract The purpose of this paper is to (i) review field data on stress‐induced permeability changes in fractured rock; (ii) describe estimation of fractured rock stress‐permeability relationships through model calibration against such field data; and (iii) discuss observations of temperature and chemically mediated fracture closure and its effect on fractured rock permeability. The field data that are reviewed include in situ block experiments, excavation‐induced changes in permeability around tunnels, borehole injection experiments, depth (and stress) dependent permeability, and permeability changes associated with a large‐scale rock‐mass heating experiment. Data show how the stress‐permeability relationship of fractured rock very much depends on local in situ conditions, such as fracture shear offset and fracture infilling by mineral precipitation. Field and laboratory experiments involving temperature have shown significant temperature‐driven fracture closure even under constant stress. Such temperature‐driven fracture closure has been described as thermal overclosure and relates to better fitting of opposing fracture surfaces at high temperatures, or is attributed to chemically mediated fracture closure related to pressure solution (and compaction) of stressed fracture surface asperities. Back‐calculated stress‐permeability relationships from field data may implicitly account for such effects, but the relative contribution of purely thermal‐mechanical and chemically mediated changes is difficult to isolate. Therefore, it is concluded that further laboratory and in situ experiments are needed to increase the knowledge of the true mechanisms behind thermally driven fracture closure, and to further assess the importance of chemical‐mechanical coupling for the long‐term evolution of fractured rock permeability.

58 GEOSCIENCES↗

Land change, fire, and climate weaken carbon sink in the conterminous United States

The land carbon sink of the conterminous United States was evaluated using a bottom-up modeling framework and 30-meter land change data from 1985 to 2020. This cross-scale, cross-landscape, and cross-system approach tracked fractional land cover changes and applied regional model calibration. Results show average terrestrial and aquatic carbon sinks of +110 ± 37 and +19 ± 0.5 teragrams of carbon per year, respectively. The terrestrial carbon sink, showing no clear trend, peaked in the 1990s, with more years as a carbon source since 2000, contradicting recent national and global studies. Land change had the largest impact (−70 ± 5.5 teragrams of carbon per year), exceeding impacts of climate (−33 ± 48 teragrams of carbon per year), wildfire (−7.7 ± 2.4 teragrams of carbon per year), and erosion transport (−1.9 ± 0.13 teragrams of carbon per year). The positive CO 2 fertilization effect (+69 ± 12 teragrams of carbon per year) was insufficient to maintain the carbon sink strength. Our framework reveals key paths of carbon loss, with implications for carbon budget and energy policies in the United States and beyond.

Liu, Jinxun [US Geological Survey, Reston, VA (Uni↗

Mads.jl

Mads.jl (Model analysis and decision support in Julia) is a code that streamlines the process of using data and models for analysis and decision support. It is based on another open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11- 035). Mads.jl can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. It enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. The code also can use a series of alternative adaptive computational techniques for Bayesian sampling, Monte Carlo, and Bayesian Information-Gap Decision Theory. The code is implemented in the Julia programming language, and has high-performance (parallel) and memory management capabilities. The code uses a series of third party modules developed by others. The code development will also include contributions to the existing third party modules written in Julia; this contributions will be important for the efficient implementation of the algorithm used by Mads.jl. The code also uses a series of LANL developed modules that are developed by Dan O'Malley; these modules will be also a part of the Mads.jl release. Mads.jl will be released under GPL V3 license. The code will be distributed as a Git repo at gitlab.com and github.com. Mads.jl manual and documentation will be posted at madsjulia.lanl.gov.

Vesselinov, Velimir↗

MADSpython 1.x

MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.

O'Malley, Daniel↗

Vibrational Spectroscopy for the Determination of Ionizable Group Content in Ionomer Materials

An approach based on vibrational spectral measurements is described for determining the ionizable group content of ion conducting polymer membrane materials. Aimed at supporting the assessment of membrane stability and wear characteristics, performance is evaluated for attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy, confocal Raman microscopy, and ATR FT-IR microscopy using perfluorinated ionomer membrane standards. One set of ionomer standards contained a sulfonic acid ionizable group and the other a sulfonyl imide group. The average number of backbone tetrafluoroethylene (TFE) units separating the ionizable-group containing side chains was in the range of 7.2–2.1 (sulfonic acid set) and 10.5–4.6 (sulfonyl imide set). A poly(tetrafluoroethylene) (PTFE) sample was included as a blank, representing the limit of zero ionizable group (and maximum TFE) content. Calibration relationships were derived from area-normalized vibrational spectra. For all three methods, calibration models applied to independent spectral measurements of samples predicted the ratio of backbone TFE groups to ionizable groups in the repeat unit ( m) with a standard error of ≤ ±0.3. The confocal Raman and ATR FT-IR microscopy techniques achieved ideal blank responses and the lowest prediction errors, down to m ± 0.1 at the 90% confidence level. With its relative simplicity, low sample size requirements, and potential for quantitative micron-scale spatial mapping of the ionizable group content within a membrane, the approach has application to advancing materials development, including exploratory synthesis, durability and wear assessment, and in situ studies of membrane process.

Korzeniewski, Carol↗

pyFLANK, a graph neural network based null distribution inference model for F ST outlier detection

Detecting genomic regions under selection is essential for understanding how populations adapt to different environments, yet it remains challenging due to the confounding effects of demographic history and linkage disequilibrium (LD). Fixation index (F ST ) is a widely used statistic to identify genomic regions under adaptation. However, identifying genes under selection by defining F ST outliers often remains challenging, owing to confounding effects of underlying demographic history. Traditional methods assume independence among loci and rely on simple demographic models, while newer models perform much better but are computationally expensive and not easily scalable. Here, we present pyFLANK, an open-source and automated Python implementation which detects F ST outliers using a null distribution inferred from quasi-independent loci. Our tool integrates three approaches to identify loci obeying a null distribution: graph neural network (GNN) inference, linkage disequilibrium (LD)-based inference, and user-defined input. Because pyFLANK uses GNN-based inference of quasi-independent loci, it yields a more accurate null model with less need for user parameter input. In simulation experiments, pyFLANK achieved lower false positive rates than current methods while maintaining comparable detection power, indicating that its refined null model better distinguishes true adaptive loci from background variation. The GNN-based model, in particular, detected additional loci associated with phenotypic variance that were not identified by existing methods. Assessments of simulation and real data from different species demonstrate that pyFLANK achieves lower false positive rates compared with other commonly used F ST outlier detectors, while maintaining comparable detection power and excellent computational performance, providing a robust and user-friendly tool for identifying loci under divergent selection. It extends existing F ST outlier frameworks by incorporating explicit LD-aware strategies for null model calibration. The method is intended as a practical and scalable complement to existing genome scan approaches.

FST↗

Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis . These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation. In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix. We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.

09 BIOMASS FUELS↗