SEARCH · Engineering Papers

Results for “Data fitting”

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.

SourceResearch connectionFrom yearThrough year

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

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Error field measurements with rotating RMP fields for DIII-D H-mode

3D magnetic sensors are employed to identify the amplitude and toroidal phase of error fields (EF) by analyzing the torque balance for magnetic islands entrained by rotating resonant magnetic perturbations (RMPs) in DIII-D H-mode plasmas. This technique of torque balance allows for efficient error field identification, offering a valuable tool for scenario-specific and optimized error field compensation (EFC) and requires only magnetic diagnostics. The torque balance used in this work includes the contribution from electromagnetic torque due to error fields, wall response, RMP fields, and viscous. Results show that viscous torque plays a crucial role, particularly during locked modes and H-mode plasmas, ensuring accurate data fits with lower residuals. The torque balance technique reveals that the L- and H-mode plasmas have distinct EF configurations, and consistent with a model-based EF analysis including MHD response in IPEC and the SURFMN EF simulation. This technique shows great robustness in measuring the intrinsic EF amplitude regardless of its amplitude or toroidal phase. Repeated discharges with EFC disparities exhibit consistent results of intrinsic error field within a reasonable range near the “standard” error field compensation. Additionally, the use of a rotating n = 1 resonant magnetic perturbation offers the advantage of reducing disruption risks by entraining saturated magnetic islands. These findings are instrumental for optimizing EF correction in fusion devices, thereby enhancing tearing mode suppression and overall plasma stability.

3D magnetic sensors↗

DOE OSTI2025

Numerical calculation of Coulomb corrections in forward elastic 𝑝↑⁢𝑝 and 𝑝↑⁢𝐴 scattering

The analysis of RHIC hydrogen gas jet target polarimeter measurements of transverse analyzing powers 𝐴 N ⁡(𝑡) in proton-nucleus scattering requires accurate Coulomb corrections to both spin-flip and nonflip amplitudes. These corrections must cover a wide range of nuclear charges 𝑍 and form factor slopes, with flexibility to vary form factors during data fitting. To avoid technically challenging calculations involving a small but finite fictitious photon mass, the Coulomb correction to the nonflip electromagnetic amplitude with an exponential form factor was related to the corresponding correction for the spin-flip amplitude. This approach allows soft photon contributions to all amplitudes, including those with nonexponential form factors, to be calculated in the massless photon limit using only analytical expressions and numerically stable integrals with nonsingular integrands and finite integration limits. In addition, an absorptive correction to the spin-flip electromagnetic amplitude, which plays a critical role in spin effects in forward polarized proton-nucleus scattering, was accurately evaluated.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Effect of Strain on the Resistivity and Thermal Conductivity of High Purity Niobium

High purity niobium (Nb) is a technologically important material for large-scale accelerator and nano-scale quantum computing applications in microwave frequency range. The high thermal conductivity and low resistivity of Nb are critical to the high performance at temperature range of 0.01–4.0 K. The presence of interstitials such as O, N, H, and C act as scattering centers and alter the mean free path, reducing resistivity and thermal conductivity, and contributing significantly to Nb's thermal performance for temperatures of 2.0 K and above. The residual resistivity ratio (RRR), defined as the ratio of the normal state resistivity at 300 K to that at 4.2 K (Nb, T c = 9.2 K), is an accepted direct estimate of the impurity content of fully recrystallized Nb. Complete re-crystallization of Nb is challenging unless very high temperatures are employed, which is often impractical, hence, in practice, dislocation and dislocation structures impact the thermal performance of Nb due to strong phonon scattering contributions. Furthermore, this paper reports on the degradation of thermal conductivity and RRR of high purity Nb large grain, single crystal with fixed impurity, varying strain, and dislocation content levels. Experimental thermal conductivity data fits the Boltzmann transport equation incorporating dislocation density.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Measurement of Dielectric Loss in Silicon Nitride at Centimeter and Millimeter Wavelengths

This work presents a suite of measurement techniques for characterizing the dielectric loss tangent across a wide frequency range from similar to 1 GHz to 150 GHz using the same test chip. In the first method, we fit data from a microwave resonator at different temperatures to a model that captures the two-level system (TLS) response to extract and characterize both the real and imaginary components of the dielectric loss. The inverse of the internal quality factor is a second measure of the overall loss of the resonator, where TLS loss through the dielectric material is typically the dominant source. The third technique is a differential optical measurement at 150 GHz. The same antenna feeds two microstrip lines with different lengths that terminate in two microwave kinetic inductance detectors (MKIDs). The difference in the detector response is used to estimate the loss per unit length of the microstrip line. Our results suggest a larger loss for SiNx at 150 GHz of tan delta similar to 4 x 10(-3) compared to 2.0 x 10(-3) and greater than or similar to 1 x 10(-3) measured at similar to 1 GHz using the other two methods. These measurement techniques can be applied to other dielectrics by adjusting the microstrip lengths to provide enough optical efficiency contrast and other mm/sub-mm frequency ranges by tuning the antenna and feedhorn accordingly.

Dielectrics loss↗

DOE OSTI2022

PVcircuit [SWR-22-26]

The software contains objects that are building blocks for PV modeling and interactive data fitting based on: Optoelectronic models for tandem/multijunction solar cells including resistive and luminescent coupling; simulation of modules composed of 2T, 3T, and 4T tandem solar cells; and energy yield analysis of PV systems composed of tandem solar cells.

Geisz, John↗

DOE OSTI2023

LLNL Automized Surface Titration Model

The LLNL Automized Surface Titration Model (L-ASTM) is a community data-driven surface complexation modeling workflow for simulating potentiometric titration of mineral surfaces. The model accepts raw experimental potentiometric titration data formatted in a findable, accessible, interoperable, and reusable (FAIR) structure. The workflow was coded in Python and coupled to PHREEQC for surface complexation modeling and PEST for data fitting and parameter estimation.

Solchan, Han↗

DOE OSTI2022

PVcircuit v0.0.6 [SWR-22-26]

Geisz, John↗

DOE OSTI2023

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts

Abstract Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides ) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Heat Loss Characteristics and Energy Use of Piperazine with the Advanced Stripper (PZAS) at the UT-SRP Pilot Plant

Heat duty and heat loss were measured at the pilot plant at UT Austin. Heat loss was measured with energy balances using water. Heat loss was studied using surface temperature measurements over 68 different locations at the pilot plant. Surface temperature measurements indicated that bare metal surfaces were the primary source of heat loss from the pilot plant. Heat loss from bare metal surfaces was at least 50% controlled by natural convection and in many cases was as high as 75% natural convection controlled. Measured heat loss at the pilot plant was 20100 BTU/hr and overall heat loss did not show any dependence on the heat rate of the plant. Compared to PZAS™ at the National Carbon Capture Center, heat loss relative to heat rate was higher at 38%. The relative heat loss at the pilot plants was found to decrease by 20% per MW of added capacity. Measured net heat duty was found to be dependent on measured lean loading and cold rich bypass flow rate. Measured net heat duty was between 2.2 and 2.4 GJ/tonne at an optimum lean loading of 0.2–0.21 mol/mol. Data reconciliation by Aspen Plus® Data Fit™ underpredicted CO2 flow rate by 20% due to an overprediction of lean loading by 19%, indicating a necessary change in thermodynamic parameters in the model. This resulted in an over prediction of heat duty by 33% on average.

Amine scrubbing, stripper, energy requirement, hea↗

DOE OSTI2022

Multiphase equation of state for Ta 2 O 5

A new equation of state for Ta 2 O 5 is presented. The EOS is constructed using the OpenSesame software and is referred to as SESAME 3530. The EOS uses a combination of density functional theory (DFT) calculations and experimental data. DFT calculations include cold curves and phonons of the solid phases, as well as DFT-based molecular dynamics simulations of the liquid phase. Experimental data includes isobaric, diamond anvil cell, and porous shock Hugoniot data. To fit the data, we create a multiphase EOS consisting of two solid phases and the liquid. Overall agreement with experimental data is shown, and we provide some suggestions for future experiments that could improve our knowledge of the phase diagram.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Enabling in-situ LIBS measurements of liquids and slurries

The aim of this work was to explore the use of, and further develop, a in-situ and near-real time LIBS (Laser Induced Breakdown Spectroscopy) instrument to measure liquids relevant to the Hanford site. Specifically, we investigated the feasibility of using LIBS to measure the cation composition of HLW (High Level Waste) streams. In addition, we focused on enabling continuous interrogation of those liquids using the LIBS system. For the first part, we investigated simple (sodium nitrate) solutions as well as various Hanford tank waste surrogate slurries. We showed that relevant cations (such as sodium, aluminum, magnesium, potassium) can be measured via LIBS under good conditions but the results – both quantitative and qualitative (aka reproducibility) – strongly depend on the kind and concentration of the material used. The dependence of signal intensity on concentration was also confirmed by other measurements conducted (sodium chloride and sodium nitrate in water). For the second part, we investigated the influence of varying depth and concentration of a liquid sample. We observed that for the concentrations and fill depths investigated, the liquid fill depth did not impact the emission intensity of the analyte, while concentration is confirmed to have significant effects. The results of the sample depth experiments influenced testing of waste surrogates. In addition, we focused on further developing and adapting data fitting tools in order to efficiently, accurately, and reproducibly identify the elements and materials present in a LIBS spectrum.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Unrolled Video Super-Resolution Network with Autoregressive Prior for the Case of Known Motion

Real-time detection and classification of distant objects is necessary for many national security applications. However, when objects are far from the sensor, they occupy only a small number of pixels in the captured video, limiting the amount of visual detail available for recognition. State-of-the-art classification methods typically rely on high-resolution (HR) video streams to capture characteristic object features, but obtaining such detail is challenging for distant objects that occupy only a few pixels. This motivates the development of video super-resolution (VSR) methods that enhance object classification by recovering fine details from low-pixel representations. Current VSR methods rely either on model-based optimization, which is interpretable but computationally expensive, or on learning-based approaches, which are efficient and high-performing but often lack flexibility and interpretability. In this report, we propose an end-to-end trainable unrolled VSR network, UVSRNet, which super-resolves each frame in a video by exploiting sub-pixel motion between neighboring low-resolution (LR) frames as well as incorporating high-frequency detail from previously super-resolved frames. In particular, by unrolling a plug-and-play (PnP) half-quadratic splitting (HQS) algorithm, we leverage a model-based data-fitting module alongside a learning-based autoregressive prior module. This combination yields a method that maintains the flexibility and interpretability of model-based methods while achieving the performance advantages of learning-based methods.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Measuring Intelligence with the Sandia Matrices: Psychometric Review and Recommendations for Free Raven-Like Item Sets

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. Further, to aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Mathematics↗

NASA NTRS1967

The theory and application of least squares

Least squares method theory and application to curve fitting, data smoothing, and solution of overdetermined sets of equations

Smith, G. A.↗

NASA NTRS1973

Role of gas-surface interactions in the reduction of OGO 6 neutral-particle mass spectrometer data

Data obtained with the quadrupole mass spectrometer aboard the OGO 6 satellite show the effects of significant surface interaction processes, including nearly complete recombination of incoming atomic oxygen on the walls of the instrument antechamber plus absorption and desorption of oxygen and carbon monoxide. The observed data are fit by solving the time-dependent continuity equations accounting for production and loss of atomic oxygen, molecular oxygen, and (in the case of mass 28) carbon monoxide. The surface parameters that best fit the data are selected and applied to the determination of ambient densities and their estimated errors.

Hedin, A. E.↗

NASA NTRS1973

Role of gas-surface interactions in the reduction of Ogo 6 neutral particle mass spectrometer data.

Data obtained with the quadrupole mass spectrometer aboard the Ogo 6 satellite show the effects of significant surface interaction processes, including nearly complete recombination of incoming atomic oxygen on the walls of the instrument antechamber plus adsorption and desorption of oxygen and carbon monoxide. The observed data are fit by solving the time-dependent continuity equations accounting for production and loss of atomic oxygen, molecular oxygen, and (in the case of mass 28) carbon monoxide. The surface parameters that best fit the data are selected and applied to the determination of ambient densities and their estimated errors.

Hedin, A. E.↗