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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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DOE OSTI2025

Closed-Form Approximation of the Total Variation Proximal Operator

Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using proximal methods, but the way in which they are applied is constrained by the absence of a closed-form expression for the proximal operator of the TV function. A closed-form approximation of the TV proximal operator has previously been proposed, but its accuracy was not theoretically explored in detail. Here, we address this gap by making several new theoretical contributions, proving that the approximation leads to a proximal operator of some convex function, it is equivalent to a gradient descent step on a smoothed version of TV, and that its error can be fully characterized and controlled with its scaling parameter. We experimentally validate our theoretical results on image denoising and sparse-view computed tomography (CT) image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the convergence and convergence order of finite volume gradient schemes for oblique derivative boundary value problems

This work is an improvement of the previous note (Bradji in: Fuhrmann et al., Finite volumes for complex applications VII–methods, theoretical aspects. Proceedings of the FVCA 7, Berlin, 2014) which dealt with the convergence analysis of a finite volume scheme for the Poisson’s equation with a linear oblique derivative boundary condition. The formulation of the finite volume scheme given in Bradji (in: Fuhrmann et al., Finite volumes for complex applications VII–methods, theoretical aspects. Proceedings of the FVCA 7, Berlin, 2014) involves the discrete gradient introduced recently in Eymard et al. (IMA J Numer Anal 30(4):1009–1043, 2010). In this paper, we consider the convergence analysis of finite volume schemes involving the discrete gradient of Eymard et al. (IMA J Numer Anal 30(4):1009–1043, 2010) for elliptic and parabolic equations with linear oblique derivative boundary conditions. Linear oblique derivative boundary conditions arise for instance in the study of the motion of water in a canal, cf. Lesnic (Commun Numer Methods Eng 23(12):1071–1080, 2007). We derive error estimates in several norms which allow us to get error estimates for the approximations of the exact solutions and its first derivatives. In particular, we provide an error estimate between the gradient of the exact solutions and the discrete gradient of the approximate solutions. Convergence of the family of finite volume approximate solutions towards the exact solution under weak regularity assumption is also investigated. In the case of parabolic equations with oblique derivative boundary conditions, we develop a new discrete a priori estimate result. The proof of this result is based on the use of a discrete mean Poincaré–Wirtinger inequality. Thanks to the stated a priori estimate and to a comparison with an appropriately chosen auxiliary finite volume scheme, we derive the convergence results. This work can be viewed as a continuation of the previous work (Bradji and Gallouët in Int J Finite Vol 3(2):1–35, 2006) where a convergence analysis for a finite volume scheme, based on the admissible mesh of Eymard et al. (In: Ciarlet and Lions, Handbook of numerical analysis, North-Holland, Amsterdam, 2000), for the Poisson’s equation with a linear oblique derivative boundary conditions is given. The obtained convergence results do not require any relation between the mesh sizes of the spatial and time discretizations. Some numerical tests are presented for both elliptic and parabolic equations. In particular, we present three methods to compute the discrete solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Regularized Finite Element Formulations for Shear Band Instabilities in Metals

In my research I have addressed several fundamental issues of dynamic fracture: 1. established the role of heat conduction as a natural regularizing mechanism in shearband problems (i.e. using the correct physics for this problem), 2. developed a unified model to incorporate fracture through a modified phase field formulation, 3. developed stability analysis tools to determine the onset of localization, 4. developed discretization methods and solvers to solve this problem (on parallel computers) that lead to mesh insensitive results and fast convergence. For such difficult problems, the physics and numerics are strongly coupled. To accurately model the physics, one must develop reliable numerics but reliable numerics depends on correct description of the physics. To this end, we have already made significant progress in the field showing that a fully-coupled implicit solution with heat conduction is the key to obtain reliable results for shear bands (see the next sections for more details). This research fits well within the mission of DOE ASCR program on all aspects: from governing equations to discretization to solvers to parallel computers to applications problems. It also relies on the computational tools developed through the SciDAC institutes FASTMath and SDAV, such as PETSc (and SLEPc), ParaView, Trilinos and Masquite.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Poster - 27: Incident Learning Practices in Ontario

Purpose: The Radiation Incident and Safety Committee (RISC), established and supported by Cancer Care Ontario (CCO), is responsible for advising the Provincial Head of the Radiation Treatment program on matters relating to provincial reporting of radiation incidents with the goal of improved risk mitigation. Methods: The committee is made up of Radiation Incident Leads (RILs) with representation from each of the 14 radiation medicine programs in the province. RISC routinely meets to review recent critical incidents and to discuss provincial reporting processes and future directions of the committee. Regular face to face meetings have provided an excellent venue for sharing incident learning practices. A summary of the incident learning practices across Ontario has been compiled. Results: Almost all programs in Ontario employ an incident learning committee to review incidents and identify corrective actions or process improvements. Tools used for incident reporting include: paper based reporting, a number of different commercial products and software solutions developed in-house. A wide range of classification schema (data taxonomies) are employed, although most have been influenced by national guidance documents. The majority of clinics perform root cause analyses but utilized methodologies vary significantly. Conclusions: Most programs in Ontario employ a committee approach to incident learning. However, the reporting tools and taxonomies in use vary greatly which represents a significant challenge to provincial reporting. RISC is preparing to adopt the National System for Incident Reporting – Radiation Therapy (NSIR-RT) which will standardize incident reporting and facilitate data analyses aimed at identifying targeted improvement initiatives.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Numerical Analysis of Regular Material Point Method and its Application to Multiphase Flows

The material point method (MPM) is gaining wide popularity in engineering research to model and simulate complex multiphase flow dynamics. The method relies on solving the governing equations of motion and transport in a Lagrangian framework using particles also known as material points. The fluid and kinematic properties are stored on the material points while the spatial gradient calculation and temporal integration are performed on a background grid. This Lagrangian framework allows for large deformations, easy integration of constitutive models, and direct import of complex geometries as particles. However, despite their increasing popularity, very few studies have addressed the issues of numerical resolution and stability of MPM techniques. The presence of additional factors such as the number of material points-per-cell, the location of the material points, the CFL-like condition used in time update, and the grid shape functions also increase the complexity of the error analysis when compared to other finite element methods. In this presentation, we analyze the various forms of error incurred in the application of MPM to continuum mechanics and multiphase flows. The effect of the previously mentioned factors on the error dynamics is studied. The application of these principles to canonical and industrial problems is also presented.

high pressure reverse osmosis↗
DOE OSTI2021

A coupled discontinuous Galerkin-Finite Volume framework for solving gas dynamics over embedded geometries

Herein, we present a computational framework for solving the equations of inviscid gas dynamics using structured grids with embedded geometries. The novelty of the proposed approach is the use of high-order discontinuous Galerkin (dG) schemes and a shock-capturing Finite Volume (FV) scheme coupled via an hp adaptive mesh refinement (hp-AMR) strategy that offers high-order accurate resolution of the embedded geometries. The hp-AMR strategy is based on a multi-level block-structured domain partition in which each level is represented by block-structured Cartesian grids and the embedded geometry is represented implicitly by a level set function. The intersection of the embedded geometry with the grids produces the implicitly-defined mesh that consists of a collection of regular rectangular cells plus a relatively small number of irregular curved elements in the vicinity of the embedded boundaries. High-order quadrature rules for implicitly-defined domains enable high-order accuracy resolution of the curved elements with a cell-merging strategy to address the small-cell problem. The hp-AMR algorithm treats the system with a second-order finite volume scheme at the finest level to dynamically track the evolution of solution discontinuities while using dG schemes at coarser levels to provide high-order accuracy in smooth regions of the flow. On the dG levels, the methodology supports different orders of basis functions on different levels. The space-discretized governing equations are then advanced explicitly in time using high-order Runge-Kutta algorithms. Numerical tests are presented for two-dimensional and three-dimensional problems involving an ideal gas. The results are compared with both analytical solutions and experimental observations and demonstrate that the framework provides high-order accuracy for smooth flows and accurately captures solution discontinuities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

User Manual for the PROTEUS Mesh Tools

PROTEUS is built around a finite element representation of the geometry for visualization. In addition, the PROTEUS-SN solver was built to solve the even-parity transport equation on a finite element mesh provided as input. Similarly, PROTEUS-MOC and PROTEUS-NEMO were built to apply the method of characteristics on unstructured finite element meshes. Given the complexity of real world problems, experience has shown that using commercial mesh generator to create rather simple input geometries is overly complex and slow. As a consequence, significant effort has been put into place to create multiple codes that help assist in the mesh generation and manipulation. There are three input means to create a mesh in PROTEUS: UFMESH, GRID, and NEMESH. At present, the UFMESH is a simple way to generate two-dimensional Cartesian and hexagonal fuel assembly geometries. The UFmesh input allows for simple assembly mesh generation while the GRID input allows the generation of Cartesian, hexagonal, and regular triangular structured grid geometry options. The NEMESH is a way for the user to create their own mesh or convert another mesh file format into a PROTEUS input format. Given that one has an input mesh format acceptable for PROTEUS, we have constructed several tools which allow further mesh and geometry construction (i.e. mesh extrusion and merging). This report describes the various mesh tools that are provided with the PROTEUS code giving both descriptions of the input and output. In many cases the examples are provided with a regression test of the mesh tools. The most important mesh tools for any user to consider using are the MT_MeshToMesh.x and the MT_RadialLattice.x codes. The former allows the conversion between most mesh types handled by PROTEUS while the second allows the merging of multiple (assembly) meshes into a radial structured grid. Note that the mesh generation process is recursive in nature and that each input specific for a given mesh tool (such as .axial or .merge) can be used as “mesh” input for any of the mesh tools discussed in this manual.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Refining a Novel Process Monitoring Method to Safeguard Continuously Cycling Designs Using Isotopic Ratios

In advanced reactor (AR) designs, a common feature is continuous chemical processing and circulation of the nuclear material. This work bridges a significant measurement gap in safeguarding reactors with circulating fuel or continuous refueling by leveraging and building on the isotope ratio method first developed by our team under an FY21 Advanced Reactors International Safeguards Engagement (ARISE) project (Uribe et al. 2021). In circulating fuel designs, the radioisotope inventory changes from traditional effects (e.g., radioactive decay, fission) but also includes material transport due to pressure and temperature gradients. Such designs may also require regular or continuous additions or removals during operation, which significantly increases the rate of inventory change compared to traditional pressurized water reactor (PWR)s. Thus, directly tracking the nuclear inventory is ineffective since the isotopes are continuously added and removed. The isotope ratio method instead focuses on detecting changes to the input and output flows of radioisotopes. Previous work showed that for well-chosen pairs of isotopes, the isotopic ratio provides a sensitive and lasting indicator of deviation from normal conditions (e.g., startup, shutdown, diversion). The isotope ratio method is a process monitoring method with potential for application in for forward-looking approaches to International Atomic Energy Agency (IAEA) safeguards. The original process monitoring method was developed for a specific case—the decay tank of a thorium-fueled molten salt breeder reactor. In this expanded work, we explored other types of reactors and processes with nonstationary (e.g., flowing) nuclear material, which are difficult to safeguard with traditional methods because of the transient nature of the systems. The goal of the present work is to generalize the isotope ratio method for use in processes with continuously flowing nuclear material. All continuous processes have an average time for isotopes to be replaced in the system. The isotope ratio method works by choosing radioisotopes with half-lives both above and below the processing time. The present work seeks to explore which isotopes are suitable for the method by simulating the nuclear inventory, radioisotope emissions, and detector responses for several classes of advanced reactors. While the method can in principle be applied to other processes (e.g., enrichment or reprocessing facilities), the present work limits scope to ARs with continuously flowing fuel. Section 2 details the mathematics supporting the isotope ratio method, and Section 3 introduces the representative ARs selected for this work. Section 4 discusses how each reactor was analyzed, and Section 5 showcases the results for each representative reactor. Finally, Section 6 provides concluding remarks and suggests pathways for further analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

ASC CSSE Level 2 Milestone #6362: Resilient Asynchronous Many Task Programming Model

This report is an outcome of the ASC CSSE Level 2 Milestone 6362: Analysis of Re- silient Asynchronous Many-Task (AMT) Programming Model. It comprises a summary and in-depth analysis of resilience schemes adapted to the AMT programming model. Herein, performance trade-offs of a resilient-AMT prograrnming model are assessed through two ap- proaches: (1) an analytical model realized by discrete event simulations and (2) empirical evaluation of benchmark programs representing regular and irregular workloads of explicit partial differential equation solvers. As part of this effort, an AMT execution simulator and a prototype resilient-AMT programming framework have been developed. The former permits us to hypothesize the performance behavior of a resilient-AMT model, and has undergone a verification and validation (V&V) process. The latter allows empirical evaluation of the perfor- mance of resilience schemes under emulated program failures and enabled the aforementioned V&V process. The outcome indicates that (1) resilience techniques implemented within an AMT framework allow efficient and scalable recovery under frequent failures, that (2) the abstraction of task and data instances in the AMT programming model enables readily us- able Application Program Interfaces (APIs) for resilience, and that (3) this abstraction enables predicting the performance of resilient-AMT applications with a simple simulation infrastruc- ture. This outcome will provide guidance for the design of the AMT programming model and runtime systems, user-level resilience support, and application development for ASC's next generation platforms (NGPs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Carbons with Regular Pore Geometry Yield Fundamental Insights into Supercapacitor Charge Storage

We conduct molecular dynamics simulations of electrical double-layer capacitors (EDLCs) using a library of ordered, porous carbon electrode materials called zeolite templated carbons (ZTCs). The well-defined pore shapes of the ZTCs enable us to determine the influence of pore geometry on both charging dynamics and charge storage mechanisms in EDLCs, also referred to as supercapacitors. We show that charging dynamics are negatively correlated with the pore-limiting diameter of the electrode material and display signatures of both progressive charging and ion trapping. However, the equilibrium capacitance, unlike charging dynamics, is not strongly correlated to commonly used, purely geometric descriptors such as pore size. Instead, we find a strong correlation of capacitance to the charge compensation per carbon (CCpC), a descriptor we define in this work as the average charge of the electrode atoms within the coordination shell of a counterion. A high CCpC indicates efficient charge storage, as the strong partial charges of the electrode are able to screen counterion charge, enabling higher ion loading and thus more charge storage within the electrode at a fixed applied voltage. We determine that adsorption sites with a high CCpC tend to be found within pockets with a smaller radius of curvature, where the counterions are able to minimize their distance with multiple points on the electrode surface, and therefore induce stronger local partial charges.

25 ENERGY STORAGE↗
DOE OSTI2021

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

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

A moving control volume approach to computing hydrodynamic forces and torques on immersed bodies

Here, we present a moving control volume (CV) approach to computing hydrodynamic forces and torques on complex geometries. The method requires surface and volumetric integrals over a simple and regular Cartesian box that moves with an arbitrary velocity to enclose the body at all times. The moving box is aligned with Cartesian grid faces, which makes the integral evaluation straightforward in an immersed boundary (IB) framework. Discontinuous and noisy derivatives of velocity and pressure at the fluid–structure interface are avoided and far-field (smooth) velo city and pressure information is used. We re-visit the approach to compute hydrodynamic forces and torques through force/torque balance equations in a Lagrangian frame that some of us took in a prior work (Bhalla et al., 2013 [13]). We prove the equivalence of the two approaches for IB methods, thanks to the use of Peskin's delta functions. Both approaches are able to suppress spurious force oscillations and are in excellent agreement, as expected theoretically. Test cases ranging from Stokes to high Reynolds number regimes are considered. We discuss regridding issues for the moving CV method in an adaptive mesh refinement (AMR) context. The proposed moving CV method is not limited to a specific IB method and can also be used, for example, with embedded boundary methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗