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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

New method for fitting coefficients in standard model effective theory

We present an alternative method for carrying out a principal-component analysis of Wilson coefficients in standard model effective field theory (SMEFT). The method is based on singular-value decomposition (SVD). The SVD method provides information about the sensitivity of experimental observables to physics beyond the standard model that is not accessible in the Fisher-information method. In principle, the SVD method can also have computational advantages over diagonalization of the Fisher information matrix. We demonstrate the SVD method by applying it to the dimension-6 coefficients for the process of top-quark decay to a b quark and a W boson and use this example to illustrate some pitfalls in widely used fitting procedures. We also outline an iterative procedure for applying the SVD method to dimension-8 SMEFT coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Field expedient stool collection methods for gut microbiome analysis in deployed military environments

ABSTRACT Field expedient devices and protocols for the collection, storage, and shipment of stool samples in deployed settings are needed for the advancement of microbiome research in military health. Relevant assessments include the evaluation of microbiome signatures associated with susceptibility to travelers’ diarrhea and recovery of gut function following infection. However, inherent biases in microbial measurements due to preservatives and sampling methods are unclear and should be assessed for an accurate evaluation of the microbiome. We performed shotgun metagenomic sequencing and compared the microbiome composition in paired fecal samples collected using Flinters Technology Associates (FTA) cards and OMNIgene (OG) Gut tubes, prior to and during international travel, from 49 adult participants, 39 of whom remained asymptomatic and 10 experienced travelers’ diarrhea. Higher concentrations of nucleic acid and sequencing libraries were observed in OG samples. A majority of genera (82.9%) were detected with both methods, and detections of genera limited to one collection method were not highly prevalent across samples and were present in extremely low relative abundances (<0.01%). Differences in beta diversity were largely explained by inter-individuality of microbiome composition, followed by the effect of collection method and timepoint-disease states. Differential abundance analysis indicated that Corynebacterium and Blautia were consistently higher in abundance across all groups with FTA and OG collection, respectively. The observed differences in microbiome composition between methods suggest the need for consistent and standardized protocols within a study. Overall, the data presented here could help guide the future design of fecal microbiome study protocols in field and military deployment settings. IMPORTANCE The assessment of field-deployable methods for fecal sample collection and storage is required to reliably capture samples collected in remote and austere locations. This study describes a comparative metagenomics analysis between samples collected by two different commercially available methods in a military-deployed setting. The results presented here are foundational for the future design of fecal microbiome study protocols in an operational context.

field study↗
DOE OSTI2018

Stochastic Least-Squares Petrov--Galerkin Method for Parameterized Linear Systems

We consider the numerical solution of parameterized linear systems where the system matrix, the solution, and the right-hand side are parameterized by a set of uncertain input parameters. We explore spectral methods in which the solutions are approximated in a chosen finite-dimensional subspace. It has been shown that the stochastic Galerkin projection technique fails to minimize any measure of the solution error [A. Mugler and H.-J. Starkloff, ESAIM Math. Model. Numer. Anal., 47 (2013), pp. 1237--1263]. As a remedy for this, we propose a novel stochatic least-squares Petrov--Galerkin (LSPG) method. The proposed method is optimal in the sense that it produces the solution that minimizes a weighted $\ell^2$-norm of the residual over all solutions in a given finite-dimensional subspace. Moreover, the method can be adapted to minimize the solution error in different weighted $\ell^2$-norms by simply applying a weighting function within the least-squares formulation. In addition, a goal-oriented seminorm induced by an output quantity of interest can be minimized by defining a weighting function as a linear functional of the solution. We establish optimality and error bounds for the proposed method, and extensive numerical experiments show that the weighted LSPG method outperforms other spectral methods in minimizing corresponding target weighted norms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Low-Memory, Discrete Ordinates, Discontinuous Galerkin Methods for Radiative Transport

The discrete ordinates discontinuous Galerkin (SN-DG) method is a well-established and practical approach for solving the radiative transport equation. In this paper, we study a low-memory variation of the upwind SN-DG method. The proposed method uses a smaller finite element space that is constructed by coupling spatial unknowns across collocation angles, thereby yielding an approximation with fewer degrees of freedom than the standard method. Like the original SN-DG method, the low-memory variation still preserves the asymptotic diffusion limit and maintains the characteristic structure needed for mesh sweeping algorithms. While we observe second-order convergence in the scattering dominated, diffusive regime, the low-memory method is in general only first-order accurate. To address this issue, we use upwind reconstruction to recover second-order accuracy. Finally, for both methods, numerical procedures based on upwind sweeps are proposed to reduce the system dimension in the underlying Krylov solver strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters

Convolutional Neural Networks (CNNs) are one of the most extensively used tools in machine learning, but they are still not well understood and in many cases they are over-parameterized, leading to slow inference and impeding their deployment on low-power devices. In the last few years, many methods for decreasing the number of parameters in a network by pruning its output channels have been suggested, but a very recent work has argued that random pruning of channels performs on-par with state-of-the-art pruning methods. While random and other pruning methods might be effectively used for lowering the number of parameters in a CNN, none of these methods can be used to gain any further understanding of the model that the CNN has built. In this work, we propose a novel method for pruning a network, that at the same time can lead to a better understanding of what the individual filters of the network learn about the data. The method proposed aims to keep only the filters that are "important" for a class. We define a filter as important for a class if its removal has the highest negative impact on the accuracy for that class. We demonstrate that our method is better than random pruning on two networks used on the EMNIST and CIFAR10 datasets. By analyzing the important filters, we find that the important filters in the pruned networks learn features which are more general across classes. We demonstrate the importance and applicability of that observation in two transfer-learning tasks.

Dimovska, Mihaela↗
DOE OSTI2018

Automated J-R Curve Analysis Based on the ASTM E1820 Normalization Method

The J-integral vs. crack growth resistance curve (J-R curve) is a useful tool for evaluating material fracture toughness in the ductile condition. A widely accepted practice for conducting tests to obtain J-R curves is ASTM standard E1820, in which the normalization method was introduced as an alternative method for determining the J-R curve. The normalization method requires solely force-displacement data, together with initial and final crack size measurements from a specimen to derive the J-R curve. Because of the elimination of the compliance measurement, the normalization method significantly simplifies testing requirements to obtain J-R curve. Despite these advantages, the analysis procedures of the normalization method are quite complicated to follow, which may limit the wide application of this method. To address this, we have developed a software with user-friendly graphic interface to perform automated normalization analysis. Any users, even without detailed knowledge of the normalization analysis, can use this software to derive J-R curves based on the ASTM E1820 normalization method.

Chen, Xiang↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2020

Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model

The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improved node culture methods for rapid vegetative propagation of switchgrass (Panicum virgatum L.)

Abstract Background Switchgrass ( Panicum virgatum L.) is an important bioenergy and forage crop. The outcrossing nature of switchgrass makes it infeasible to maintain a genotype through sexual propagation. Current asexual propagation protocols in switchgrass have various limitations. An easy and highly-efficient vegetative propagation method is needed to propagate large natural collections of switchgrass genotypes for genome-wide association studies (GWAS). Results Micropropagation by node culture was found to be a rapid method for vegetative propagation of switchgrass. Bacterial and fungal contamination during node culture is a major cause for cultural failure. Adding the biocide, Plant Preservative Mixture (PPM, 0.2%), and the fungicide, Benomyl (5 mg/l), in the incubation solution after surface sterilization and in the culture medium significantly decreased bacterial and fungal contamination. In addition, “shoot trimming” before subculture had a positive effect on shoot multiplication for most genotypes tested. Using the optimized node culture procedure, we successfully propagated 330 genotypes from a switchgrass GWAS panel in three separate experiments. Large variations in shoot induction efficiency and shoot growth were observed among genotypes. Separately, we developed an in planta node culture method by stimulating the growth of aerial axillary buds into shoots directly on the parent plants, through which rooted plants can be generated within 6 weeks. By circumventing the tissue culture step and avoiding application of exterior hormones, the in planta node culture method is labor- and cost-efficient, easy to master, and has a high success rate. Plants generated by the in planta node culture method are similar to seedlings and can be used directly for various experiments. Conclusions In this study, we optimized a switchgrass node culture protocol by minimizing bacterial and fungal contamination and increasing shoot multiplication. With this improved protocol, we successfully propagated three quarters of the genotypes in a diverse switchgrass GWAS panel. Furthermore, we established a novel and high-throughput in planta node culture method. Together, these methods provide better options for researchers to accelerate vegetative propagation of switchgrass.

Wang, Yongqin↗
DOE OSTI2017

Non-Aqueous Titration Method for Determining Suppressor Concentration in the MCU Next Generation Solvent (NGS)

A non-aqueous titration method has been used for quantifying the suppressor concentration in the MCU solvent hold tank (SHT) monthly samples since the Next Generation Solvent (NGS) was implemented in 2013. The titration method measures the concentration of the NGS suppressor (TiDG) as well as the residual tri-n-octylamine (TOA) that is a carryover from the previous solvent. As the TOA concentration has decreased over time, it has become difficult to resolve the TiDG equivalence point as the TOA equivalence point has moved closer. In recent samples, the TiDG equivalence point could not be resolved, and therefore, the TiDG concentration was determined by subtracting the TOA concentration as measured by semi-volatile organic analysis (SVOA) from the total base concentration as measured by titration. In order to improve the titration method so that the TiDG concentration can be measured directly, without the need for the SVOA data, a new method has been developed that involves spiking of the sample with additional TOA to further separate the two equivalence points in the titration. This method has been demonstrated on four recent SHT samples and comparison to results obtained using the SVOA TOA subtraction method shows good agreement. Therefore, it is recommended that the titration procedure be revised to include the TOA spike addition, and this to become the primary method for quantifying the TiDG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Feasibility Evaluation of Potential Stimulation Methods for Collab Experiment 3. Recommendation Report

In support of the Collab Project Milestone 1.4, INL is to submit a recommendation report describing feasible methods to investigate during Experiment 3 to DOE. This report fulfils this FY18Q1 SMART milestone. Twenty one potential methods were consideration for testing as part of Collab Experiment 3. The potential methods have been categorized as; Fracture Initiation, Stimulation Fluids, Stimulation Methods and Flow Control. The categories have been further divided into three general groups (e.g. high, medium, low) based on applicability to the scale of measurement at Collab (~10 meter) and it’s applicably to FORGE. The highest relevant methods (not necessary in order) are: 1) fracture initiation within the borehole, 2) use of slickwater fracturing fluids, 3) use of viscous fracturing fluids, 4) simultaneous stimulation of multiple wells, 5) use of energetic fluids to initiate larger fractures or connect a borehole to existing fractures, 6) injection of diverters to control short circuits and working fluid loss, 7) testing of borehole isolation techniques to control water flow pathways in the reservoir, and 8) use of multiple injection and production wells to characterize a fracture. Fractures created in Experiments 1 and 2 offer the opportunity to test engineering solutions to issues encountered in the stimulation process or during the fracture flow testing. An advantage of using these sites is that there would be a wealth of baseline characterization data for the site, and the borehole monitoring system and experimental infrastructure is already established and could possibly be reused. This suggested potential method list should be considered preliminary due to the timing of this report and the status of Collab Experiments 1 and 2 as well as the need for preliminary modeling to assess each of the proposed methods effectiveness.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Field Demonstration of The Krauklis Seismic Wave In A Novel Mva Method For Geologic CO 2 Storage

The Energy & Environmental Research Center (EERC) conducted a feasibility study of a new, low-impact geophysics monitoring method designed to incrementally track the saturation front of CO 2 injected into a reservoir. The method leverages a new way of transmitting energy from the surface to the reservoir and employs a new subsurface signal called the Krauklis wave (K-wave) and other guided waves for seismic monitoring. The EERC partnered with Seismos, Inc., the company that holds the exclusive technology license for this method, to advance development of K-wave technology, and Denbury Onshore, LLC, to field-test the system. The system was field-tested at Denbury’s Bell Creek oil field, currently undergoing CO 2 -based enhanced oil recovery (EOR) in southeastern Montana. The K-wave system uses a wellhead-mounted source to propagate energy down the wellbore in the form of a tube wave and through the well perforations into the reservoir. Part of the energy transmitted through the perforations into the reservoir converts to slow moving K-waves and/or other guided waves that are expected to propagate through the formation to neighboring wells. The propagated signal would induce tube waves in the neighboring wells that return to the surface to be recorded with a wellhead-mounted receiver. The propagation time of the signal is directly affected by the presence of CO 2 between the wells, so the difference in transit time of the returning signal between pre- and postinjection data sets could be used to help define the lateral extent of a CO 2 saturation front. Objectives of the study were to demonstrate, validate, and evaluate the K-wave method’s ability to monitor the morphology of injected CO 2 and estimate saturation distribution. Results from the demonstration were to be compared to conventional 4-D surface seismic methods, dynamic reservoir simulations, and CO 2 breakthrough data to validate the technology and provide useful information that may result in improvements. Prior to mobilizing for the full-scale K-wave baseline survey, a field test was conducted to better understand wellhead hardware configurations and test the response from the Bell Creek reservoir using three wells. Useful operational information resulted but signal from the first-generation gas release/gas injection source was not recorded in neighboring wells. After the initial field test, numerical modeling of the K-wave method was initiated using parameters specific to the Bell Creek Field. Modeling results indicated that the fundamental concepts behind the K-wave method exhibited problems and revealed a significantly greater attenuation of the signal at the wellbore perforations than expected, totaling on the order of 10-8 from source well to receiver well over a 400-m well spacing. Modeling also showed that greater source frequency bandwidth was needed to produce guided waves within a 30-foot-thick reservoir layer such as that at Bell Creek and that reservoir layer seismic velocity would need to be slower than the velocities of the bounding layers in order to act as a wave guide opposite to what exists at Bell Creek. Modeling results prompted a second field test with a stronger, second-generation displacement source developed in the interim. Concurrent with the second field test, a 3-D seismic survey was acquired to serve as a baseline for project validation and to serve as a monitor data set for the portion of the project area where CO 2 injection had already commenced. The new source was tested using a four-well geometry and failed to produce a detectable signal, which appeared to confirm modeling results and indicated that the Bell Creek Field is not an ideal site for the K-wave test. It was concluded that a source with sufficient strength and bandwidth could not be built to overcome the factors discovered by the modeling within a reasonable amount of time. Several other sites undergoing CO 2 -based EOR were also evaluated; however, none of these sites was amenable because of geology, limitations of source and receiver hardware, or the advanced state of the CO 2 flood. In addition, two other configurations of the source and receiver layout were considered; however, these design modifications either changed the scope of the project or did not meet the goals of the Carbon Storage Program. In consultation with the U.S. Department of Energy (DOE) Project Manager and Technology Manager, a no-go decision was invoked to forego additional expenditures as the technology would not meet current project objectives. Project activities to date have moved the technology forward by developing modeling equations that can be applied elsewhere, advanced the state of wellhead sensors and sources, and spurred improvements to field operations. The 3-D seismic survey in the project area and the dynamic reservoir simulations done as part of this study have provided value for ongoing and future synergistic activities at Bell Creek related to DOE carbon storage goals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Development of a Novel Accelerator for Neutron Transport Solution Using the Galerkin Spectral Element Methods (Final Report)

This report summarizes the results of a three-year research project sponsored by the U.S. Department of Energy (DOE) Nuclear Energy University Program (NEUP) to develop and implement advanced acceleration schemes for the DOE NEAMS neutronics code PROTEUSSN. The project team included the University of Massachusetts Lowell, The Ohio State University, University of Michigan, and Argonne National Laboratory. The PROTEUS code is a high-fidelity capable deterministic neutron transport code based on unstructured finite element meshes, which solves the steady-state and transient neutron transport problem using the 2nd-order discrete ordinate method (SN2ND), the method of characteristics (MOC), and the advanced nodal transport method (NODAL). The DSA scheme has been implemented in PROTEUS to speed up SN transport calculations. The existing DSA scheme employs a consistent finite element formulation on the same fine mesh structure as the SN solution While the use of consistent discretizations makes the DSA method effective, it does not necessarily make it efficient because the numerical solution of a discretized elliptic diffusion problem itself can be costly, particularly when the problem size becomes large. We developed a new discontinuous Galerkin (DG) discretization of the diffusion equation, called DG-DSA, which can effectively and efficiently accelerate the SN transport iterations. As compared with the previous work, the novelty of our method is that the diffusion equation is solved on a coarse-mesh grid using the DG methods, and the DG diffusion discretization incorporates local hp adaptation, i.e., local adaptation of mesh size and/or polynomial degree, based on local total cross section (or optical thickness). Therefore, the resulting number of degrees of freedom (DOF) of the DG discretization is much less than the conventional consistent DSA discretizations, and thus DG-DSA can achieve significant improvement in computational efficiency. We implemented this scheme in PROTEUS-SN. In addition, we developed a new stabilization scheme named linear prolongation CMFD (lpCMFD). A novel feature of this scheme is that the conventional flat flux ratio–based scaling approach is replaced with a linear interpolation of the scalar flux differences at the coarse-mesh cell edges between the neutron transport and CMFD calculations. Fourier convergence analysis and numerical results show that lpCMFD is unconditionally stable and more effective than other CMFD based on acceleration schemes such as pCMFD, odCMFD. We also developed a new nonlinear diffusion acceleration scheme for solving neutron transport equations. This scheme, called LR-NDA, employs a local refinement approach on the framework of CMFD by solving a local boundary value problem of the scalar flux on the coarse-mesh structure to replace the piecewise constant scalar flux obtained by CMFD. The refined flux is then used to update the scalar flux in the neutron transport source iteration. It has been demonstrated that that LR-NDA is much more effective and stable than CMFD for a wide range of optical thicknesses. LR-NDA is a local adaptive method, which means LR-NDA does not necessarily require local refinement for all the coarse-mesh cells on the problem domain, i.e., it can be used only for relatively optically thick regions where the standard CMFD scheme would encounter the convergence problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Preliminary description of a new creep-fatigue design method that reduces over conservatism and simplifies the high temperature design process

The report provides the initial description of a new creep-fatigue design method for structural components in high temperature nuclear service. The new method is based on an integrated elastic-perfectly plastic (EPP) analysis and Simplified Model Test (SMT) approach that reduces over conservatism, improves the treatment of elastic follow up, and simplifies the design procedure, when compared with the current creep-fatigue design methods in ASME Boiler and Pressure Vessel Code. Developing the design charts for the EPP-SMT design method requires extrapolating SMT test data as a function of hold time and follow up factor. The report develops the preliminary design charts for Alloy 617 at temperatures between 800°C and 950°C by combining two extrapolation approaches developed in a previous work. The report also presents a comparative analysis between the EPP-SMT design method and the current ASME creep-fatigue design methods by evaluating design life of two sample geometries under different loading conditions. Results from the comparative analysis verify the EPP-SMT design charts but suggest the requirement of additional test data in the low strain range regime for improving the extrapolation procedure that will further reduce the over conservatism in the creep-fatigue damage evaluation. The report also concludes that the EPP-SMT design procedure can account for effect of primary load on creep-fatigue life by using a fixed, bounding value of follow up in constructing the design charts. The conclusions to this report describe the future work required to complete this new design method so it can be codified through a nuclear Code Case.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict their performance in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors; yet in practice, their performance has led to neither high deployment nor high user satisfaction in any significant application, nor has it differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them have typically been loosely described and incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a well-described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, also additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recent published, well-described test methods for characterizing occupancy sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates the best techniques for characterizing sensor performance in indoor spaces and identifies test method gaps that need to be filled in order to evaluate emerging technologies and products.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Hand Calculation Methods for Nuclear Criticality Safety

This primer provides an overview of the most common hand calculation methods used for criticality safety calculations. The most widely used tools available to a nuclear criticality safety (NCS) practitioner are probably the common Monte Carlo or deterministic criticality safety codes, which can be used to model very complex systems. However, use of these codes can obscure the parameters to which a particular fissile system may be sensitive, whereas the hand calculation methods can be used to delve into the ways each parameter may affect the reactivity of a fissile material system. Furthermore, practitioners must avoid using computer codes as devices that take inputs and simply provide outputs (i.e., a “black box”). Many years ago, pioneers such as Joe Thomas, David Smith, and Hugh Paxton, among others in the field of nuclear criticality safety, took the time before the advent of high-speed desktop computers to create simple hand methods for criticality safety analyses. Some of the methods can be used for single fissile units; others are applicable to fissile units arranged into simple array configurations. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the various results. The NCS practitioner will need to spend time to master the methods that could be most useful; however, they can provide the practitioner with fast and accurate answers to criticality safety problems if they are used correctly and if critical data exist for the problem at hand. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information more quickly than using a criticality code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗