SEARCH · Engineering Papers

Results for “measurement data”

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 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2021

System and method for characterization of retrofit opportunities in building using data from communicating thermostats

Systems and methods for characterization of retrofit opportunities are described. The methods may comprise computing, using at least one computing device disposed remote from a building and based at least in part on heating, ventilation and air conditioning (HVAC) runtime data associated with the building, one or more thermal characteristics of the building. In some embodiments, a model-predicted indoor temperature may be fitted against thermal data measured by a thermostat at the building. The thermal characteristic of the building may comprise a thermal insulation, an air leakage rate and/or an HVAC efficiency. The method may be used to determine, using the at least one computing device, suitability of the building for a retrofit opportunity to improve energy efficiency of the building. Determining the suitability may comprise evaluating the one or more thermal characteristics. The HVAC runtime data may be computed based on data received from a thermostat or a meter, such as an electric or a gas meter.

Zeifman, Michael↗

DOE OSTI2021

System and method for characterization of retrofit opportunities in building using data from interval meters

Zeifman, Michael↗

DOE OSTI2022

Fast Reactor Physics Model Verification Studies using ARC and PyARC Workflows

PyARC was recently developed at Argonne National Laboratory to automate many of the tasks required in the ARC (Argonne Reactor Computation) fast reactor simulation workflow, from input file generation, code execution, data transfer between ARC codes, and output postprocessing. PyARC will likely be the path forward to train new users of the ARC codes with the goal of wide adoption by the national laboratories, academia, and industry. In particular, for the ANL-JAEA collaboration under the Civil Nuclear Working Group (CNWG) project agreement NE-01, PyARC will be used to model the Joyo and EBR-II reactors for comparisons with measured data and calculated results from JAEA (Task 3: Fast Reactor Fuel and Core). As an additional avenue for verification and validation, this report investigates the use of PyARC towards a variety of existing ARC-based reactor models, in order to understand its efficacy in replicating the behavior of base ARC codes and better understand any limitations within modeling realistic fast reactor problems. To this end, PyARC was used to model the Joyo MKI, RBEC Benchmark-M, PRISM Mod-B, and EBR-II Run 138B cores, and its results were compared to those from existing ARC-based models. It was found that for hexagonal-based geometries PyARC was able to replicate the behavior of ARC codes to within 10 pcm for small reactor cores, and ~150pcm difference in eigenvalue for larger cores. These discrepancies are attributed primarily to differences in local mesh refinement options between ARC and PyARC, which currently cannot be resolved with PyARC’s latest version (1.6.0). In some of these cases, PyARC was used to model steady-state problems with initial core compositions originating from a prior REBUS depletion calculation. While PyARC was not designed to support such steady-state calculations, workarounds were applied to replicate the behavior of ARC-based calculations as closely as possible. Thus, these results demonstrate the wide extent to which they can be applied to fast reactor problems while still providing immense benefit to the user in terms of automating and standardizing common routines within the fast reactor analysis workflow. This study concluded that PyARC will be suitable for modeling the steady-state conditions of the EBR-II and Joyo fast reactors as part of the CNWG project agreement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Reorganization Energies for Interfacial Proton-Coupled Electron Transfer to a Water Oxidation Catalyst

The reorganization energy (λ) for interfacial electron transfer (ET) and proton-coupled electron transfer (PCET) from a conductive metal oxide (In 2 O 3 :Sn, ITO) to a surface-bound water oxidation catalyst were extracted from kinetic data measured as a function of the thermodynamic driving force. Visible light excitation resulted in rapid excited-state injection (k inj > 10 8 s -1 ) to the ITO, which photo-initiated the two interfacial reactions of interest. The rate constants for both reactions increased with the driving force, −ΔG°, to a saturating limit, k max , with rate constants consistently larger for ET than for PCET. Marcus-Gerischer analysis of the kinetic data provided the reorganization energy for interfacial PCET (0.90 ± 0.02 eV) and ET (0.40 ± 0.02 eV), respectively. The magnitude of k max for PCET was found to decrease with pH, behavior that was absent for ET. Both the decrease in k max and the larger reorganization energy for an unwanted competing PCET reaction from the ITO to the oxidized catalyst showcases a significant kinetic advantage for driving solar water oxidation at high pH. Computational analysis revealed a larger inner-sphere reorganization energy contribution for PCET than for ET arising from a more significant change in the Ru–O bond length for PCET. Extending Marcus-Gerischer theory to PCET by including the excited electron-proton vibronic states and the proton donor-acceptor motion provided an apparent reorganization energy of 1.01 eV. Furthermore, this study demonstrates that the Marcus-Gerischer theory initially developed for ET can be reliably extended to PCET for quantifying and interpreting reorganization energies observed experimentally.

Catalysts Kinetic parameters↗

DOE OSTI2023

A Reconfigurable Planar Capacitor Sensor for Thru the Jacket Dielectric Constant Measurement of Cable Insulation

Planar capacitor sensors have been proposed to measure the aging-related dielectric constant variation of the jacket or insulation materials of cables. Although capacitor sensors have been used to monitor the aging of cable insulation by removing the jacket, through the jacket measurement of cable insulation dielectric constant variation is difficult because once the sensor is placed on the jacket it would either measure the aging of the jacket or that of the jacket and insulation together. A reconfigurable sensor along with innovative cable-specific models could be used to determine the jacket and insulation aging using the same sensor. For the proposed novel reconfigurable sensor, the electric field penetration depth is changed by activating and deactivating PIN diode switches. In one instance, the sensor measures the dielectric constant variation of the jacket, while in the next, it measures the dielectric constant variation of both the jacket and the insulation. Here, by leveraging previously developed capacitance estimation models from large-scale finite element simulations for a specific cable, these two sets of measurement data are used to evaluate the aging-related dielectric constant variation of the insulation. Experimental data from aged cable specimens are presented to demonstrate the operation of the sensor.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Evaluation and optimization of flow boiling frictional pressure drop correlations using the data from traditional and next-generation refrigerants in a micro-fin tube

This study presents an experimental evaluation and optimization of flow boiling frictional pressure drop correlations for conventional and next-generation refrigerants in a horizontal micro-fin tube, with particular emphasis on the newly emerging refrigerant blends R-454C and R-455A, for which pressure-drop data in enhanced tubes remain limited. Experiments were conducted with R-410A, R-454C, R-455A, R-134a, R-1234yf, and R-1234ze(E) in a copper micro-fin tube with an inner diameter of 8.468 mm, over mass fluxes ranging from 100 to 300 kg/(m²·s) depending on the refrigerant, and evaporation temperatures of 7, 12, and 14 °C. Frictional pressure gradients were determined from measured total pressure drops after subtracting acceleration pressure drop, and the resulting database was used to assess four existing models: Kuo and Wang (1996), Cavallini et al. (1997), Goto et al. (2001), and Diani et al. (2014). The measured frictional pressure gradient increased with vapor quality and mass flux for all refrigerants and increased further at lower evaporation temperatures, with the overall trend strongly related to liquid viscosity. Among the four correlations, the Goto et al. (2001) model provided the best overall agreement with the measured data before optimization. To further improve prediction accuracy, the Kuo and Wang (1996) and Goto et al. (2001) models were optimized using the complete experimental database. After optimization, both models reduced the overall mean absolute deviation to below 15%, while the optimized Goto et al. (2001) model maintained the best and most consistent overall performance. The results provide new pressure-drop data for next-generation refrigerants and demonstrate that parameter optimization can significantly enhance the applicability of existing micro-fin-tube correlations.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗

DOE OSTI2026

PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE

This dataset is an open-source repository of spectral data measured at Pacific Northwest National Laboratory (PNNL). This database provides quantitative values for the complex index of refraction for seven polycyclic aromatic hydrocarbon (PAH) solids. A list of the chemicals is available in the readme file. These spectra consist of the optical constants, i.e., the real, n(ν), and imaginary, k(ν), refractive indices, over the spectral range from 7,800 to 400 cm-1 (1.28 – 25 μm). The conditions under which the individual data were acquired are described in the associated metadata files, and the user is strongly encouraged to read and understand this information to ensure the data are used appropriately for your application. Recommended Citation for Dataset Jessica M Salcido, Jeremy D. Erickson, Ashley M. Bradley, Russell G. Tonkyn, Timothy J. Johnson and Tanya L. Myers. 2026. PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE. [Data Set] PNNL DataHub. INSERT DOI License Information This work is marked with CC0 1.0: https://creativecommons.org/publicdomain/zero/1.0/. The authors do request that you appropriately cite the dataset when referencing or using the dataset.

Salcido, Jessica Marie Ortola↗

DOE OSTI2021

Two-dimensional retrieval methods for ultrafast imaging of molecular structure using laser-induced electron diffraction

Molecular structural retrieval based on electron diffraction has been proposed to determine the atomic positions of molecules with sub-angstrom spatial and femtosecond temporal resolutions. Given its success on small molecular systems, in this work, we point out that the accuracy of structure retrieval is constrained by the availability of a wide range of experimental data in the momentum space in all molecular systems. To mitigate the limitations, for laser-induced electron diffraction, here we retrieve molecular structures using two-dimensional (energy and angle) electron momentum spectra in the laboratory frame for a number of small molecular systems, which have previously been studied with 1D methods. Compared to the conventional single-energy or single-angle analysis, our 2D methods effectively expand the momentum range of the measured data. Besides utilization of the 2D data, two complementary methods are developed for consistency check on the retrieved results. Further, the 2D nature of our methods also offers a way of estimating the error from retrieval, which has never been explored before. Comparing with results from prior experiments, our findings show evidence that our 2D methods outperform the conventional 1D methods. Paving the way to the retrieval of large molecular systems, in which their tunneling ionization rates are challenging to obtain, we estimate the error of using the isotropic model in place of including the orientation-dependent ionization rate.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Magnetic moments and spin structure in single-phase B20 Co 1+x Si 1-x (x = 0.043)

Neutron powder diffraction (NPD) and x-ray magnetic circular dichroism (XMCD) spectroscopy are employed to investigate the magnetism and spin structure in single-phase B20 Co 1.043 Si 0.957 . The magnetic contributions to the NPD data measured in zero fields are consistent with the helical order among the allowed spin structures derived from group theory. The magnitude of the magnetic moment is (0.3 ± 0.1) μ B /Co according to NPD, while the surface magnetization probed by XMCD at 3 kOe is (0.18–0.31) μ B /Co. Both values are substantially larger than the bulk magnetization of 0.11 μ B /Co determined from magnetometry at 70 kOe and 2 K. Finally, these experimental data indicate the formation of a helical spin phase and the associated conical states in high magnetic fields.

36 MATERIALS SCIENCE↗

DOE OSTI2026

OC6 Phase III: Validation of Wind Turbine Aerodynamic Loading During Surge/Pitch Motion

The objective of Phase III of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was to validate the accuracy of aerodynamic load predictions by offshore wind modeling tools for a floating offshore wind turbine as it experiences large surge-translational and pitch-rotational motion, as would occur during normal operation. The test data considered were generated at Politecnico di Milano – a wind tunnel with a robotic excitation system to emulate wave loading on the wind turbine. Testing was performed using a scaled version of the DTU 10-MW reference model, and motion was prescribed as harmonic oscillations in the surge and pitch directions, independently. A variety of models were examined in the project, performing a three-way validation between engineering-level tools, higher-fidelity tools, and measurement data from two wind tunnel experimental campaigns. The Load Cases (LC) considered in this testing are as follows: LC 1.X - Steady Wind LC 2.X - Unsteady Wind - Surge Motion LC 3.X - Unsteady Wind - Pitch Motion Details on the results of the OC6 Phase III campaign can be found in the following reference: Bergua R., et al. OC6 project Phase III: validation of the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure, Wind Energ. Sci., 8, 465–485, https://doi.org/10.5194/wes-8-465-2023, 2023.

17 WIND ENERGY↗

DOE OSTI2020

Application of an Embedded Fracture and Borehole Modeling Approach to the Understanding of EGS Collab Experiment 1

EGS Collab is a series of meso-scale experiments and associated numerical simulation activities being funded by the United States Department of Energy, Geothermal Technologies Office (GTO) to investigate enhanced geothermal system processes under in-situ stress and slightly elevated temperature conditions. This project is designed to provide scientists and engineers with immediate access to impermeable rock at scales larger than possible in the laboratory, but generally smaller than those for commercial production. Immediate access to rock is provided via the existing drifts of the former Homestake Gold Mine, now operated as the Sanford Underground Research Facility in Lead, South Dakota. The objectives of the EGS Collab project are to develop well controlled fracture networks between injector and producer boreholes using normal and shear stimulation for permeability enhancement. The first experimental site was located off the West Access drift on the 4850 Level (4850 feet below ground surface) in phyllite of the Precambrian Poorman formation, and involved the creation of a fracture network comprising a combination of hydraulic and natural fractures. A second experimental site is now being considered near the battery alcove on the 4100 Level in amphibolite and rhyolite of the Yates Unit. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. Experiment 1 has comprised a series of successful tests, including a long-term chilled-water circulation test, but the testbed has two atypical EGS elements. Active ventilation in the adjacent drift over a 50-year period cooled the testbed rock mass and hydraulic fracturing intersected monitoring boreholes making them conduits for fluid flow. Numerical simulations executed in support of the design of the EGS Collab Experiment 1, computed a radial temperature distribution and stress gradient orthogonal to the drift axis, resulting in the forecast of an oblong hydraulic fracture geometry, extended in the direction of the drift from the stimulation borehole. This paper describes the numerical simulation of long-term chilled-water circulation test with an embedded fracture and borehole modeling approach. The principal objective of the simulation work is show agreement between experimental observations in terms of production fluid temperatures, monitoring borehole temperatures, injection and production pressures, and tracer recoveries using a single conceptual model for the fracture network, using characterization and monitoring data generally available for EGS. A secondary objective is to improve the numerical simulation result comparisons with additional data available from the broader monitoring equipment within the testbed versus deeper, hotter, and more remote EGS. One complicating factor for comparisons between the numerical simulation results and experimental observations, is the Joule-Thomson heating associated with large pressure drops across the fracture network.

numerical simulation, embedded fractures and boreh↗

DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Theoretical study of the electronic, thermodynamic, and thermo-conductive properties of y-LiAlO2 with 6Li isotope substitutions for tritium production

Lithium aluminate has attracted researchers’ interests due to its wide applications. The structural, electronic, optical, lattice phonon thermodynamic, and thermo-conductive properties of ?-LiAlO2 with 6Li-isotope substitution are investigated by density functional theory and lattice phonon dynamics. The calculated results show that ?-LiAlO2 possesses a wide band-gap of 4.63 eV. The valence band is mainly formed by p orbitals of Li, O, and Al. The calculated lattice thermal conductivity of ?-LiAlO2 is found to be in good agreement over a wide range of temperatures with the available experimental data. The calculated dielectric matrix shows anisotropic behavior along z and x (or y) axes. Due to the wide band-gap, the optical conductivity of ?-LiAlO2 is mainly contributed from the electron hopping between valence-conduction bands. Substitution of 7Li with 6Li isotope in ?-LiAlO2 leads to observable differences in the lattice phonon frequencies at higher frequencies (>8THz) and to slight changes in corresponding thermal conductivity and the infrared and Raman spectra which are in good agreement with the measured data. The relevance of these properties for tritium production through 6Li absorbing neutron in nuclear reactors is discussed.

ab initio thermodynamics, ?-LiAlO2 and 6Li-isotope↗

DOE OSTI2025

Multiphysics Simulation of KRUSTY Warm Critical Experiments Using MOOSE Tools

A series of critical experiments were performed during the Kilopower Reactor Using Stirling TechnologY (KRUSTY) project, producing valuable and unique data for the validation of corresponding microreactor models. In this paper, the development of a high-fidelity multiphysics model of the KRUSTY microreactor within the MOOSE ecosystem is presented, with a focus on the microreactor configuration during the warm critical experiments. The model was established by coupling a deterministic neutronics model using the Griffin code, with a BISON thermomechanical model. The multiphysics model successfully simulated the KRUSTY 15 ¢ and 30 ¢ warm critical reactivity insertion experiments in satisfactory agreement with the experimental measurement data. In conclusion, the modeling results demonstrated the performance of MOOSE-based multiphysics tools in microreactor simulations, and spotlighted the useful applications of these tools in supporting microreactor design, qualification, and licensing activities.

KRUSTY↗

DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗

DOE OSTI2023

TRINIDI: Time-of-Flight Resonance Imaging With Neutrons for Isotopic Density Inference

Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging offers a potential approach by exploiting the characteristic neutron absorption spectra of each isotope. However, it is a major challenge to compute quantitatively accurate images due to a variety of confounding effects such as severe Poisson noise, background scatter, beam non-uniformity, absorption non-linearity, and extended source pulse duration. We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel. Both components are based on the inversion of a forward model that accounts for the highly non-linear absorption, energy-dependent emission profile, and Poisson noise, while also modeling the substantial spatio-temporal variation of the background and flux. Further, to do this, we formulate the non-linear inverse problem as two optimization problems that are solved in sequence. We demonstrate on both synthetic and measured data that TRINIDI can reconstruct quantitatively accurate 2D views of isotopic areal density that can then be reconstructed into quantitatively accurate 3D volumes of isotopic volumetric density.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Developing and Evaluating Metrics for Demand Flexibility in Buildings: Comparing Simulations and Field Data

Building demand flexibility (DF) has attracted significant attention in recent years among researchers, technology developers and control companies, aggregators, utilities, and many others. There are numerous challenges with today’s electricity systems such as managing peak demand capacity and integrating variable renewables into the grid. Flexible building loads can provide various grid services to help reduce electricity costs, smooth out renewables intermittency and balance supply and demand. Recognizing this, the US DOE is leading the Grid-interactive Efficient Buildings (GEB) initiative which includes research to evaluate the potential, availability and timing of flexible loads. In this paper we present load shed metrics for three building types – medium office, large office and retail store – and compare prototype simulation results with measured data from 12 actual buildings that participated in hot summertime utility demand response (DR) events. The DR strategies included varying zone temperature and reducing light levels. The magnitude of a key DF metric, “demand decrease intensity” (or “shed intensity”) (W/ft2), between the simulation results and field data are similar (14-32% differences) for both mean and median values, though the field data show much larger variation among DR events. The coefficient p-values from linear regression model tests showed that outside air temperature is a significant variable for the whole building shed intensity when the resetting zone temperature strategy is deployed. These findings support the concept of using prototype building simulation to estimate building DF and expanding future simulation research to additional building types and climate zones.

Liu, Jingjing↗

DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗