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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Effect of Ionic Core on the Properties of Non-Isothermal Plasmas

This study investigates the effects of ionic cores on non-isothermal plasmas using a novel ion-ion interaction potential that incorporates screening effects from both ion cores and exchange-correlation interactions. Our findings indicate that with increasing distance, the effective potential approaches a Yukawa-like screening potential, while at shorter distances, strong electron binding weakens screening. The different values of the cutoff radius ' cut and core edge steepness α significantly influence the potential behavior and radial distribution functions (RDFs). Higher coupling parameters (Γ i ) strengthen the electron-ion interactions, leading to deeper potential wells and more pronounced non-ideality corrections. Increasing Θ decreases the absolute values of non-ideality corrections, indicating fewer interactions in the system. A larger cutoff radius r cut due to weaker screening effects. As Γ i at a fixed parameter also reduces corrections increases, non-ideality corrections grow, reflecting stronger coupling. The results show the importance of taking into account the ion core effects in dense non-isothermal plasma research.

dense plasmas↗
DOE OSTI2022

Overview of recent SCALE activities for Non-LWR inventory and decay heat analysis

In 2019, the US Nuclear Regulatory Commission initiated a project for the development and assessments of non-light-water reactor (non-LWR) accident progression using the SCALE and MELCOR simulation tools. SCALE simulations are used to generate nuclide inventories, full-core power distributions, decay heat, and kinetics parameters to initialize MELCOR simulations of severe accident scenarios. Five non-LWR concepts were studied: high-temperature gas-cooled reactor (HTGR), heat pipe reactor (HPR), high-temperature fluoride salt-cooled reactor (FHR), molten salt-fueled reactor (MSR), and sodium-cooled fast reactor (SFR). This paper summarizes the SCALE results obtained in 2021 for the first three non-LWR concepts, compares characteristics and results to common LWRs, and provides the strategy for the analysis of the remaining two non-LWRs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

KiDS-1000 cosmology: Combined second- and third-order shear statistics

Aims.In this work, we perform the first cosmological parameter analysis of the fourth release of Kilo Degree Survey (KiDS-1000) data with second- and third-order shear statistics. This paper builds on a series of studies aimed at describing the roadmap to third-order shear statistics. Methods.We derived and tested a combined model of the second-order shear statistic, namely, the COSEBIs and the third-order aperture mass statistics 〈ℳ ap 3 〉 in a tomographic set-up. We validated our pipeline withN-body mock simulations of the KiDS-1000 data release. To model the second- and third-order statistics, we used the latest version of HMCODE2020 for the power spectrum and BIHALOFITfor the bispectrum. Furthermore, we used an analytic description to model intrinsic alignments and hydro-dynamical simulations to model the effect of baryonic feedback processes. Lastly, we decreased the dimension of the data vector significantly by considering only equal smoothing radii for the 〈ℳ ap 3 〉 part of the data vector. This makes it possible to carry out a data analysis of the KiDS-1000 data release using a combined analysis of COSEBIs and third-order shear statistics. Results.We first validated the accuracy of our modelling by analysing a noise-free mock data vector, assuming the KiDS-1000 error budget, finding a shift in the maximum of the posterior distribution of the matter density parameter, ΔΩ m < 0.02 σ Ω m , and of the structure growth parameter, ΔS 8 < 0.05 σ S 8 . Lastly, we performed the first KiDS-1000 cosmological analysis using a combined analysis of second- and third-order shear statistics, where we constrained Ω m = 0.248 −0.055 +0.062 andS 8 = σ 8 √(Ω m /0.3 )= 0.772 ± 0.022. The geometric average on the errors of Ω m andS 8 of the combined statistics decreases, compared to the second-order statistic, by a factor of 2.2.

Astronomy & Astrophysics↗
DOE OSTI2020

Completion Effects on Diagnosing Multistage Facture Treatments with Distributed Temperature Sensing

Summary Distributed temperature sensing (DTS) is a valuable tool to diagnose multistage hydraulic fracture treatments. When a stage interval is shut in, the clusters that take more fluid during pumping warm up more slowly. Therefore, the fluid volume injected into each cluster can be quantitatively interpreted by numerical inversion of the warm-back temperature behavior. This general concept assumes that the different warm-back behavior is controlled by only the injected fluid volume; however, recent observations of DTS data indicate that completion configurations significantly influence the warm-back behavior. This paper investigates the completion effects on the DTS interpretation. In ideal conditions, when a stage is fractured, the upstream stage intervals should show an almost uniform temperature that is close to the injected fluid temperature. This is due to the high fluid velocity of injected fluid in the wellbore, and the upstream intervals have not been perforated (noncommunicating intervals), so the only heat transfer is heat conduction between the wellbore fluid and the surrounding reservoir. But the field DTS data show considerably irregular variations in temperature along the upstream stage intervals. These variations are caused by the completion effects. The nonuniform temperature profile is caused by different heat transfer behavior induced by completion hardware along the production casing string, such as joints, clamps, and blast protectors, and by the sensing cable location in the cement, as well as the cement quality. Because the varying heat transfer behavior impacts the warm-back behavior as well as the temperature profile, the completion effects need to be considered in DTS interpretation. A method of DTS interpretation considering the completion effects to diagnose multistage fracture treatments was developed. Because the heat transfer between a wellbore and a reservoir depends on the overall heat transfer coefficient describing heat conduction through the completion in a forward model, this parameter needs to be tuned all along the wellbore. To calibrate the completion effect, the temperature inversion is conducted using the temperature measured at a stage interval that is upstream of a stage interval currently being treated. Because the interpreted stage interval is not perforated at that time, the thermal behavior at the noncommunicating interval is governed by only the heat conduction through the completion environment. Once the effective values of the overall heat transfer coefficient are estimated along the interpreted stage interval, they can be assumed to be constant physical parameters. Then, the fluid volume distribution is interpreted by using the effective overall heat transfer coefficient profile along each interval. This study provides a field application of the developed interpretation method. The new interpretation method provides more accurate diagnosis of fracture treatments by DTS interpretation.

Engineering↗
DOE OSTI2020

Complications in the interpretation of the charge-asymmetry-dependent π flow for the chiral magnetic wave

The charge asymmetry ($A_ch$) dependence of the $π^₋$ and $π^+$ elliptic flow difference, $Δv_2(A_{\text{ch}})$, has been regarded as a sensitive observable for the possible chiral magnetic wave (CMW) in relativistic heavy ion collisions. In this work, we first demonstrate that, due to nonflow backgrounds, the flow measurements by the Q-cumulant method using all charged particles as reference introduce a trivial linear term to $Δv_2(A_{\text{ch}})$. The trivial contribution to the slope of triangle flow difference $Δv_3( A_{\text{ch}})$ can be negative if the nonflow is dominated by back-to-back pairs. After eliminating the trivial term, we find that the nonflow between like-sign pairs gives rise to an additional positive slope to $Δv_2(A_{\text{ch}})$ because of the larger multiplicity dilution to $π^+$ ($π^₋$) at positive (negative) $A_{\text{ch}}$. We further find that the competition between different π sources can introduce another nontrivial linear-$A_{\text{ch}}$ term due to their different multiplicity fluctuations and anisotropic flows. We then study the effect of neutral cluster (resonance) decays as a mechanism for local charge conservation on the slope parameter of $Δv_2(A_{\text{ch}})$. We find that the slope parameter is sensitive to the kinematics of those neutral clusters. Light resonances give positive slopes while heavy resonances give negative slopes. Local charge conservation from continuum cluster mass distribution can give a positive slope parameter comparable to experimental data. Our studies indicate that many non-CMW physics mechanisms can give rise to a $A_{\text{ch}}$-dependent $Δv_2(A_{\text{ch}})$ and the interpretation of $Δv_2(A_{\text{ch}})$ in terms of the CMW is delicate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Process and feedstock driven microstructure for laser powder bed fusion of 316L stainless steel

Here in the pursuit of improving additively manufactured (AM) component quality and reliability, fine-tuning critical process parameters such as laser power and scan speed is a great first step toward limiting defect formation and optimizing the microstructure. However, the synergistic effects between these process parameters, layer thickness, and feedstock attributes (e.g. powder size distribution) on part characteristics such as microstructure, density, hardness, and surface roughness are not as well-studied. In this work, we investigate 316L stainless steel density cubes built via laser powder bed fusion (L-PBF), emphasizing the significant microstructural changes that occur due to altering the volumetric energy density (VED) via laser power, scan speed, and layer thickness changes, coupled with different starting powder size distributions. This study demonstrates that there is not one ideal process set and powder size distribution for each machine. Instead, there are several combinations or feedstock/process parameter ‘recipes’ to achieve similar goals. This study also establishes that for equivalent VEDs, changing powder size can significantly alter part density, GND density, and hardness. Through proper parameter and feedstock control, part attributes such as density, grain size, texture, dislocation density, hardness, and surface roughness can be customized, thereby creating multiple high-performance regions in the AM process space.

316L stainless steel↗
DOE OSTI2022

Quantifying electron temperature distributions from time-integrated x-ray emission spectra

K-shell x-ray emission spectroscopy is a standard tool used to diagnose the plasma conditions created in high-energy-density physics experiments. In the simplest approach, the emissivity-weighted average temperature of the plasma can be extracted by fitting an emission spectrum to a single temperature condition. It is known, however, that a range of plasma conditions can contribute to the measured spectra due to a combination of the evolution of the sample and spatial gradients. In this work, we define a parameterized model of the temperature distribution and use Markov Chain Monte Carlo sampling of the input parameters, yielding uncertainties in the fit parameters to assess the uniqueness of the inferred temperature distribution. Here we present the analysis of time-integrated S and Fe x-ray spectroscopic data from the Orion laser facility and demonstrate that while fitting each spectral region to a single temperature yields two different temperatures, both spectra can be fit simultaneously with a single temperature distribution. We find that fitting both spectral regions together requires a maximum temperature of $1310^{+90}_{-70}$ eV with significant contributions from temperatures down to 200 eV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Information divergences to parametrize astrophysical uncertainties in dark matter direct detection

Astrophysical uncertainties in dark matter direct detection experiments are typically addressed by parametrizing the velocity distribution in terms of a few uncertain parameters that vary around some central values. Here we propose a method to optimize over all velocity distributions lying within a given distance measure from a central distribution. We discretize the dark matter velocity distribution as a superposition of streams and use a variety of information divergences to parametrize its uncertainties. With this, we bracket the limits on the dark matter–nucleon and dark matter–electron scattering cross sections, when the true dark matter velocity distribution deviates from the commonly assumed Maxwell-Boltzmann form. The methodology pursued is general and could be applied to other physics scenarios where a given physical observable depends on a function that is uncertain.

particle astrophysics↗
DOE OSTI2022

Edaphic controls on genome size and GC content of bacteria in soil microbial communities

Nutrient limitation has been shown to reduce bacterial genome size and influence nucleotide composition; however, much of this work has been conducted in marine systems and the factors which shape soil bacterial genomic traits remain largely unknown. Here, for this work, we determined average genome size, GC content, codon usage, and amino acid content from 398 soil metagenomes across a broad geographic range and used machine-learning to determine the environmental parameters that most strongly explain the distribution of these traits. We found that genomic trait averages were most related to pH, which we suggest is primarily due to the correlation of pH with several environmental parameters, particularly soil carbon content. Low pH soils had higher carbon to nitrogen ratios (C:N) and tended to have communities with lower GC content and larger genomes, potentially a response to increased physiological stress and a requirement for metabolic diversity. Conversely, communities in high pH and low soil C:N had smaller genomes and higher GC content—indicating potential resource driven selection against AT base pairs, which have a higher C:N than GC base pairs. Similarly, we found that nutrient conservation also applied to amino acid stoichiometry, where bacteria in soils with low C:N ratios tended to code for amino acids with lower C:N. Together, these relationships point towards fundamental mechanisms that underpin genome size, and nucleotide and amino acid selection in soil bacteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A minimally invasive, efficient method for propagation of full-field uncertainty in solid dynamics

In this work, we present a minimally invasive method for forward propagation of material property uncertainty to full-field quantities of interest in solid dynamics. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi-one-dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

42 ENGINEERING↗
DOE OSTI2021

Simulated effects of sample size and grain neighborhood on the modeling of extreme value fatigue response

Assessing the size of representative volume elements (RVEs) for fatigue-related applications is challenging. A RVE relevant to random microstructure requires a volume of material that is sufficiently large to capture the grain/phase heterogeneity that captures all statistical moments of the distribution of the driving force for fatigue crack formation at “hot spot” grains. Consequently, the large size of a microstructure RVE required to study fatigue phenomena is largely computationally intractable and difficult to explore. A more realistic objective in this work is to systematically study, as a function of the size of a statistical sample of microstructure, trends towards convergence of the simulated distribution of driving force for fatigue crack formation. Our present work accordingly leverages the recently developed open-source PRISMS-Fatigue framework to examine the trends in convergence of extreme value distributions (EVD) of Fatigue Indicator Parameters (FIPs) in progressively larger polycrystalline microstructure realizations of FCC Al alloy 7075-T6 using crystal plasticity finite element method simulations. The results are compared to the traditional method in which ensembles of statistical volume elements (SVEs) are simulated to build up statistics intended to approximate those associated with a larger volume of material. The convergence of EVDs with increase of size of a SVE of microstructure is closely related to the extent of grain nearest neighbor (NN) interactions. Accordingly, the sensitivity of the local micromechanical response at hot spot grains is quantitatively investigated by systematically varying the orientations of NN grains. Results indicate that SVEs with cubic crystallographic texture tend towards convergence of the EVD of FIPs with tens of thousands of grains while the random and rolled textures require larger volumes. Simple relationships based on microstructure parameters (e.g., Schmid Factor, grain size, NN misorientation) do not completely correlate to fatigue hot spot grains. Finally, the sensitivity of the extreme value fatigue response at hot spot grains extends to the 3rd NN when a single neighborhood grain orientation is altered.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Evolution of elastic and thermal properties of cementitious composites containing micro-size lightweight fillers after exposure to elevated temperature

This work studies the evolution of elastic and thermal properties of cementitious composites containing micro-size lightweight fillers after exposure to elevated temperatures (up to 800 °C). A multiscale investigation is carried out to study the thermal degradation of cementitious composite materials at multiple scale levels: at cement past level, the dehydration kinetics of cement hydrates (i.e., C–S–H, CH and aluminate hydrates) and development of microcracks are studied as functions of exposure temperatures. The damage mechanism of the inclusion phases and damages within the interfacial transition zone (ITZ) under elevated temperature are also investigated. In addition, the change of mechanical (i.e., elastic moduli, compressive strengths) and thermal (thermal conductivities) properties are measured as functions of the exposure temperature. Based on the experimental studies, a multiscale thermal degradation model is developed where the thermal degradations of cement paste (including water composition, porosity, and solid phase changes), inclusion phases, and the interfaces are captured through a multiscale sub-stepping homogenization scheme. The thermal degradation model is validated through multiple sets of experimental data. Finally, the influence of key parameters including the particle size distribution of the inclusions, the chemical composition, as well as the effects of ITZ damage are studied.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system

Bayesian finite element (FE) model updating using direct model evaluations of large-scale high-fidelity FE models is extremely computationally expensive. Surrogate models can be used as fast emulators of FE models to accelerate the model calibration process. The physics/mechanics-based FE models are still the underpinning behind the surrogate models. Here, this paper evaluates the loss in accuracy and the gain in computational time while performing Bayesian model updating by using surrogate model evaluations compared to using direct FE model evaluations. This evaluation is crucial before entirely relying on surrogate models in model updating for structural health monitoring (SHM) and damage prognosis (DP) purposes. This paper also demonstrates Bayesian updating and surrogate model construction of large-scale high-fidelity FE models of infrastructure systems. In this regard, the miter gate structural system is considered as the testbed structure. Three predominant damage modes (loss of contact between gate and wall, loss of thickness due to corrosion, and loss of tension in the diagonal rods) are considered for model updating purposes. Bayesian model updating is performed using direct FE evaluations by leveraging parallel computing. Two types of surrogates, namely polynomial chaos expansion (PCE) and Gaussian process regression (GPR), are developed for the miter gate. Model updating is performed again using the trained surrogate models, and the updating results are compared with their counterparts obtained using the direct FE evaluation results. The posterior distribution of the FE model parameters obtained using the trained surrogates are sufficiently accurate with respect to the posterior obtained utilizing the direct FE evaluations. In addition, an approximate 4-fold decrease in the computational time was observed when using surrogate model evaluations instead of direct FE evaluations for model updating.

42 ENGINEERING↗
DOE OSTI2022

Wire-coil insert optimization for high-heat-load/flux synchrotron components

Many synchrotron components require high levels of internal-flow, forced-convection heat transfer to minimize surface temperatures, thermal gradients, and thermally induced stress on high-power beam-interacting surfaces. Wire-coil inserts, physically similar to a common spring, are mechanically fitted inside of component cooling passages to optimize heat transfer performance. They are routinely used in Advanced Photon Source (APS) front end and beamline high-heat-load/flux components to significantly enhance convection heat transfer-up to 400% compared to plain open passages. This has the additional benefit of greatly reducing coolant flow requirements for these components. Using several cooling passage sizes, five different wire sizes, and a range of pitch values, an experimental investigation conducted at the APS has determined the average heat transfer coefficient and resulting pressure loss as a function of water flow rate for 65 different wire-coil inserts. Data from this study have been non-dimensionalized and generalized to yield relationships that can be used to determine the heat transfer performance and resulting pressure loss for any given wire-coil insert that may be used at the APS. Through data reduction, the wire-coil insert characteristic dimensions have also been optimized to yield the highest heat transfer enhancement while minimizing the coolant flow requirements. These generalized expressions for wirecoil inserts will be presented, and they can be used by scientists and engineers during the component design process to evaluate achievable heat transfer performance and associated pressure loss, aiding in the establishment of optimized operating parameters and cooling passage flow distribution schemes.

43 PARTICLE ACCELERATORS↗