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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗
DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Testing ΛCDM cosmology at turnaround: where to look for violations of the bound?

In ΛCDM cosmology, structure formation is halted shortly after dark energy dominates the mass/energy budget of the Universe. A manifestation of this effect is that in such a cosmology the turnaround radius—the non-expanding mass shell furthest away from the center of a structure— has an upper bound. Recently, a new, local, test for the existence of dark energy in the form of a cosmological constant was proposed based on this turnaround bound. Before designing an experiment that, through high-precision determination of masses and —independently— turnaround radii, will challenge ΛCDM cosmology, we have to answer two important questions: first, when turnaround-scale structures are predicted to be close enough to their maximum size, so that a possible violation of the bound may be observable. Second, which is the best mass scale to target for possible violations of the bound. These are the questions we address in the present work. Using the Press-Schechter formalism, we find that turnaround structures have in practice already stopped forming, and consequently, the turnaround radius of structures must be very close to the maximum value today. We also find that the mass scale of ∼ 10{sup 13} M{sub ⊙} characterizes the turnaround structures that start to form in a statistically important number density today —and even at an infinite time in the future, since structure formation has almost stopped. This mass scale also separates turnaround structures with qualitatively different cosmological evolution: smaller structures are no longer readjusting their mass distribution inside the turnaround scale, they asymptotically approach their ultimate abundance from higher values, and they are common enough to have, at some epoch, experienced major mergers with structures of comparable mass; larger structures exhibit the opposite behavior. We call this mass scale the transitional mass scale and we argue that it is the optimal for the purpose outlined above. As a corollary, we explain the different accretion behavior of small and larger structures observed in already conducted numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Prior research has shown that land use patterns and the spatial configurations of cities have a significant impact on residential energy demand. Given the pressing issues surrounding energy security and climate change, there is renewed interest in developing and retrofitting cities to make them more energy efficient. Yet deriving micro-scale residential energy footprints of metropolitan areas is challenging because high resolution data from energy providers is generally unavailable. In this study, a bottom-up model is proposed to estimate residential energy demand using datasets that are commonly available in the United States. The model applies novel machine learning methods to match records in the Residential Energy Consumption Survey with Public Use Microdata samples. This matching and machine learning produce a synthetic household energy distribution at a neighborhood scale. The model was tested and validated with data from the Atlanta metropolitan region to demonstrate its application and promise.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Collaborative Research: Using ARM Observations to Evaluate GCM Cloud Statistics for Development of Stochastic Cloud-Radiation Parameterizations

The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key step in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been an interdisciplinary collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen). The motivation and long-term goal underlying this work is the utilization of stochastic radiative transfer theory (Lane-Veron and Somerville, 2004; Lane et al., 2002) to develop a new class of parametric representations of cloud-radiation interactions and closely related processes for atmospheric models. The theoretical advantage of the stochastic approach is that it can accurately calculate the radiative heating rates through a broken cloud layer without requiring an exact description of the cloud geometry.

58 GEOSCIENCES↗
DOE OSTI2021

Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism

Abstract Background Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. Results We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10 −3 ), and combined dataset (p = 1.1 × 10 −4 ). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10 −35 , loss-of-function p = 2.26 × 10 −13 ) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10 −6 , OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. Conclusions In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2019

Avalanche statistics and the intermittent-to-smooth transition in microplasticity

Plastic flow at small scales is generally observed to be intermittent, whereas the stress-strain behavior of bulk crystals is mostly smooth. Here we find that when the external deformation rate of small-scale crystals approaches the speed of the crystallographic slip velocity, an intermittent-to-smooth transition of plastic flow is observed. By defining a rate-dependent intermittency parameter, this phenomenon can be captured with a power law covering 5.5 orders of magnitude for Au and Nb micron-sized single crystals with experiments and via simulations for Nb crystals. Here, our results indicate that the transition to smooth flow is driven by a gradual truncation of the underlying truncated power law that describes the intermittently evolving system. This is caused by a competition of internal and external rates, which aligns with the well-known transitions from serrated to nonserrated flow in metallic glasses or materials with dynamic strain aging.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Development of Ultra Small Scale Mechanical Testing and Localized He Implantation for Nuclear Applications

Mechanical properties degradation of components deployed in nuclear applications has been a concern since the deployment of first generation nuclear power plants. Radiation damage along with environmental degradation, elevated temperatures, and long term deployment of nuclear structures pose a challenging environment for all materials in these applications. Traditionally, the mechanical properties are assessed by standardized macroscopic testing methods. While those are providing engineering relevant data these techniques do have limitations, especially on reactor irradiated materials, due to the large size of the samples. Limitations on the amount of radioactivity of large samples must be applied for research applications as they are conducted at universities. In addition it can be difficult to retrieve sufficiently large amounts of material from an operating reactor component. Therefore smaller sample sizes are desirable and reduce the amount of radioactive material which needs to be handled or material which needs to be retrieved. In addition, the number of tests which can be performed on materials irradiated in a test reactor program is limited due to limited space in specific dose and temperature positions. Downsizing of the mechanical test can overcome this issue and perform more tests on the same amount of irradiated materials, reducing the statistical uncertainties. In addition, smaller scale samples allow access to specific regions of interest in a material or component, be it the zone in front of a crack tip or the sampling of very small components like springs or welded areas. In addition ion beam irradiation has become an integral part of nuclear materials research to enhance the understanding of radiation damage in materials in a time and cost effective fashion. The drawback of ion beam irradiation is the small penetration depth of the ions into the material making localized microstructural characterization as well as small scale mechanical testing necessary. Another more fundamental motivation is the fact that since the beginning of electron microscopy, one has been concerned about imaging and quantifying defects in materials. Multiscale tools to image a large range of radiation induced defects have been established starting from atom probe tomography to transmission electron microscopy, up to scattering techniques. Mechanical testing, however, has always been performed on a large scale, and an attempt to match the same scale where a defect is observed with the materials' mechanical property response has not yet been realized. However, the question of separate effects testing on specific radiation induced defects remains and is an additional driving force for small scale mechanical testing. In this work we present an approach for small scale mechanical testing in irradiated materials and the variety of possible test methods developed today. We also present recent efforts of performing localized ion beam implantation to achieve a number of different doses in the same region of interest enhancing comparability and quantification of observed phenomena. It is the intention to give the reader an overview of the different microscale mechanical testing techniques possible today as well as an introduction of a novel ion beam irradiation method using a newly established capability. In this paper we can't discuss all aspects of all small scale mechanical testing of irradiated materials However, It does give an overview over the techniques enabled due to recent effort and enhanced instrument capability. While indentation or micro compression testing has been performed previously tensile testing and ion beam irradiation using the He ion beam microscope are novel approaches not attempted previously. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2024

The 3D Lyman- α forest power spectrum from eBOSS DR16

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Screening and qualification methodology for SiC end plug processing methods

Deployment of SiC-ceramic-based fuel cladding for light water reactors requires a hermetic end plug–to–cladding joint that can withstand neutron irradiation during normal operation and maintain integrity during design-basis accidents. Reactor experiments have shown that some SiC composite tubes with SiC end plugs can retain hermeticity after irradiation. However, achieving consistent joint performance under irradiation remains a key challenge. Resolving this issue is essential to enable integral irradiation testing and to demonstrate fuel integrity under commercial-reactor irradiation conditions. This report aims to: (1) provide guidance for designing radiation-tolerant end plug joints for SiC cladding; (2) demonstrate experimental methods to detect processing defects that are unstable under neutron irradiation at light-water-reactor-relevant temperatures and doses; and (3) outline a step-by-step approach for designing and conducting reactor experiments to screen joining methods. The resulting data will be used to improve joint processing and to define critical defect types and sizes that must be detected and eliminated through non-destructive evaluation for quality assurance. Based on prior irradiation experiments at the High Flux Isotope Reactor, differential swelling among the cladding, bonding layer, and end plug was identified as an underlying mechanism for irradiation-induced joint degradation. Accordingly, this effect must be considered in the design of radiation-tolerant joining techniques. In this work, miniature SiC end plug joint specimens irradiated during the previous project were analyzed using X-ray computed tomography to characterize the joint microstructure. Digital volume correlation of the tomography data quantified radiation-induced microstructural changes and enabled evaluation of defect-related risks. Finally, ongoing neutron irradiation efforts using larger specimen volumes are presented. These efforts aim to statistically assess joint performance and to build a microstructure–performance (e.g., leak-tightness) dataset to inform processing improvements and quality control.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Bridging the length scales on mechanical property evaluation (Final Report)

The development of small-scale mechanical testing in combination with microstructural investigation is of great interest to the nuclear materials community for both materials development and monitoring applications. Dramatically reducing the sample sizes to reduce radioactivity and obtaining mechanical properties of irradiated samples is truly intriguing. Moreover, such studies promise a range of benefits including cost reduction, fundamental insight in structure-property relationships, increased statistics on less sample material, and reinvestigation of prior irradiated and tested reactor samples while simultaneously enabling the generation of mechanical test data on ion beam irradiated materials with limited penetration depths. Small scale materials testing on sub-sized samples has been studied for several decades, though it has only been after the development of micro-testing based on Focused Ion Beam (FIB) sample manufacturing in that orders of magnitude smaller samples could really be investigated in a quantitative manner. In recent years, small scale mechanical testing techniques at a number of length scales has been developed for both unirradiated and irradiated (ion and neutron) materials. Technological advances made in this field have enabled ex-situ and in-situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) examination, thus leading to more accurate measurements as well as additional mechanistic information. A recent review of the benefits of these techniques show that these techniques are at a stage to tackle multi-scale ranges of materials investigations and can be utilized to obtain fundamental science-based understanding of nuclear materials. Considering the tremendous advances made, one can see how small-scale mechanical testing techniques combined with modeling can enable true small scale to bulk scale mechanical property correlations. However, for the engineering community to adapt this approach fully, one needs to demonstrate that a) that these techniques can produce results with high fidelity and reproducibility, b) generate engineering stress-strain data that one can utilize to understand bulk behavior, and c) generate new insight into relevant phenomena fostering the true understanding of radiation damage and microstructure in materials for nuclear applications. It is the objective of this proposal to bridge the length scale between macro- and micro- scale mechanical testing of unirradiated and irradiated materials. This involves the development and demonstration of procedures for multi-scale mechanical testing that enable high fidelity reproducibility of data and the generation bulk property data from small-scale mechanical tests. Through this, the proposal aims to enhance the confidence in the obtained data at the smaller length scales and enhance the insight provided from these techniques for bulk scale applications on both unirradiated and irradiated materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Merging flux-variance with surface renewal methods in the roughness sublayer and the atmospheric surface layer

In this article, Two micrometeorological methods utilizing high-frequency sampled air temperature were tested against eddy covariance (EC) sensible heat flux (𝐻) measurements at three sites representing agricultural, agro-forestry, and forestry systems. The two methods cover conventional and newly proposed forms of the flux-variance (FV) and surface renewal (SR) schemes of differing complexities. The sites represent measurements in surface, roughness, and roughness to surface transitional layers. Regression analyzes against EC show that the most reliable FV and SR forms estimate 𝐻 with slopes within ±10% from unity and coefficient of determination 𝑅2 > 0.9 across all the three sites. The best performance of both FV and SR was found at the agricultural site with measurements well within the surface layer, while the worst was found for the tall forest with measurements within the roughness sublayer where its thickness needed to be additionally estimated. The main variable driving 𝐻 in FV is the temperature variance, whereas in SR, it is the geometry of ramp-like structures. Since these structures are also responsible for most of the temperature variance, a novel FV-SR approach emerging from combining the methods is proposed and evaluated against EC measurements and conventional FV and SR schemes. The proposed FV-SR approach requiring only a single fast response thermocouple is potentially independent of calibration and ameliorates some of the theoretical objections that arise when combining ramp statistics with similarity arguments. The combination of methods also provides new insights into the contribution of coherent structures to the temperature variance and its dependence on atmospheric stratification. Other potential utility of the new method is to include it in multi-tool assessments of surface energy fluxes, since a convergence or divergence of the results has a high diagnostic value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Effective electric field: Quantifying the sensitivity of searches for new -odd physics with EuCl 3 ∙ 6H 2 O

Laboratory-scale precision experiments are a promising approach to searching for physics beyond the standard model. Noncentrosymmetric solids offer favorable statistical sensitivity for efforts that search for new fields, whose interactions violate the discrete parity and time-reversal symmetries. One example is the cosmic axion spin precession experiment (CASPEr), which can be sensitive to the defining interaction of the quantum chromodynamics (QCD) axion dark matter with gluons in atomic nuclei. The effective electric field is the parameter that quantifies the sensitivity of such experiments to new physics. We describe the theoretical approach to calculating the effective electric field for noncentrosymmetric sites in ionic insulating solids. We consider the specific example of the crystal, which is a particularly promising material. The optimistic estimate of the effective electric field for the isotope in this crystal is . The calculation uncertainty is estimated to be two orders of magnitude, dominated by the evaluation of the europium nuclear Schiff moment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Passive Neutron Non-Destructive Assay for Remediation of Radiological Waste at Hanford Burial Grounds- 13189

The Hanford burial grounds contains a broad spectrum of low activity radioactive wastes, transuranic (TRU) wastes, and hazardous wastes including fission products, byproduct material (thorium and uranium), plutonium and laboratory chemicals. A passive neutron non-destructive assay technique has been developed for characterization of shielded concreted drums exhumed from the burial grounds. This method facilitates the separation of low activity radiological waste containers from TRU waste containers exhumed from the burial grounds. Two identical total neutron counting systems have been deployed, each consisting of He-3 detectors surrounded by a polyethylene moderator. The counts are processed through a statistical filter that removes outliers in order to suppress cosmic spallation events and electronic noise. Upon completion of processing, a 'GO / NO GO' signal is provided to the operator based on a threshold level equivalent to 0.5 grams of weapons grade plutonium in the container being evaluated. This approach allows instantaneous decisions to be made on how to proceed with the waste. The counting systems have been set up using initial on-site measurements (neutron emitting standards loaded into surrogate waste containers) combined with Monte Carlo modeling techniques. The benefit of this approach is to allow the systems to extend their measurement ranges, in terms of applicable matrix types and container sizes, with minimal interruption to the operations at the burial grounds. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗