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Excavator Design Validation

The Excavator Design Validation tool verifies excavator designs by automatically generating control systems and modeling their performance in an accurate simulation of their expected environment. Part of this software design includes interfacing with human operations that can be included in simulation-based studies and validation. This is essential for assessing productivity, versatility, and reliability. This software combines automatic control system generation from CAD (computer-aided design) models, rapid validation of complex mechanism designs, and detailed models of the environment including soil, dust, temperature, remote supervision, and communication latency to create a system of high value. Unique algorithms have been created for controlling and simulating complex robotic mechanisms automatically from just a CAD description. These algorithms are implemented as a commercial cross-platform C++ software toolkit that is configurable using the Extensible Markup Language (XML). The algorithms work with virtually any mobile robotic mechanisms using module descriptions that adhere to the XML standard. In addition, high-fidelity, real-time physics-based simulation algorithms have also been developed that include models of internal forces and the forces produced when a mechanism interacts with the outside world. This capability is combined with an innovative organization for simulation algorithms, new regolith simulation methods, and a unique control and study architecture to make powerful tools with the potential to transform the way NASA verifies and compares excavator designs. Energid's Actin software has been leveraged for this design validation. The architecture includes parametric and Monte Carlo studies tailored for validation of excavator designs and their control by remote human operators. It also includes the ability to interface with third-party software and human-input devices. Two types of simulation models have been adapted: high-fidelity discrete element models and fast analytical models. By using the first to establish parameters for the second, a system has been created that can be executed in real time, or faster than real time, on a desktop PC. This allows Monte Carlo simulations to be performed on a computer platform available to all researchers, and it allows human interaction to be included in a real-time simulation process. Metrics on excavator performance are established that work with the simulation architecture. Both static and dynamic metrics are included.

Pholsiri, Chalongrath↗

Moderate Resolution Imaging Spectroradiometer (MODIS) MOD21 Land Surface Temperature and Emissivity Algorithm Theoretical Basis Document

This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a 'living' version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS bands: 20, 22, 23, 29, 31-33.

Hulley, G.↗

MODIS MOD21 Land Surface Temperature and Emissivity Algorithm Theoretical Basis Document

This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a ‘living’ version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS bands: 20, 22, 23, 29, 31–33

Hughes, Christopher↗

Cloud-Resolving Model and GPM

Over the past twenty years, rainfall retrieval algorithms have been developed to retrieve rainfall and vertical hydrometeor structures from passive microwave observations by making use of the fact that weighting functions for various frequencies peak at different levels within a rainy atmosphere. GPROF is one of two TMI rainfall algorithms. It is physically based retrieval that finds the vertical hydrometeor profile that best fits the brightness temperatures in the available passive radiometer channels. Matching is achieved using a library of hydrometeor profiles generated by cloud-resolving models (CRMs). The hydrometeor profiles have a corresponding surface precipitation rate. The algorithm retrieves the hydrometeor profiles and associated surface rainfall using a Bayesian approach that gives the estimated expected values. The ability of CRMs to produce cloud structures that are reliable and representative of observed storms is crucial for the success of GPROF. The cloud mycrophysics are one of the keys to achieving this. In addition, CRMs have been a very useful tool for GPM-algorithm developers through Cloud-Radiation Simulations (CRS), one of the nine GPM disciplinary research themes. This paper will discuss how to generate consistent and comprehensive 4D cloud datasets from an improved (i.e., in regard to bulk and multi-moment microphysics) CRM for TRMM and GPM rainfall retrieval algorithm developers. These cloud datasets include CRM-simulated clouds and cloud systems from different geographic locations in the tropics and midlatitudes. By linking the CRM with a passive microwave radiative-transfer model and using satellite and airborne data, the performance of the "cloud physics" can be assessed and in turn modified and improved. This paper will also address how to assess and improve the performance of various latent and diabatic heating algorithms and develop an algorithm to retrieve the vertical structure of apparent moistening (Q2). Considering that the GPM will produce high (temporal and spatial) resolution heating and rainfall data, these algorithms will be used to obtain the temporal and spatial distributions of surface rainfall and the associated vertical heating and moistening profiles throughout the subtropical and midlatitudes.

Tao, Wei-Kuo↗

Combined Radiometer-Radar Microphysical Profile Estimations with Emphasis on High Frequency Brightness Temperature Observations

Information about the vertical microphysical cloud structure is useful in many modeling and predictive practices. Radiometers and radars are used to observe hydrometeor properties. This paper describes an iterative retrieval algorithm that combines the use of airborne active and wideband (10 to 340 GHz) passive observations to estimate the vertical content and particle size distributions of liquid and frozen hydrometeors. The physically-based retrieval algorithm relies on the high frequencies (greater than 89 GHz) to provide details on the frozen hydrometeors. Neglecting the high frequencies yielded acceptable estimates of the liquid profiles, but the ice profiles were poorly retrieved. Airborne radar and radiometer observations from the third Convection and Moisture EXperiment (CAMEX-3) were used in the retrieval algorithm as constraints. Nadir profiles were estimated for a minute each of flight time (approximately 12.5 km along track) from an anvil, convection, and quasi- stratiform rain. The complex structure of the frozen hydrometeors required the most iterations for convergence for the anvil cloud type. The wideband observations were found to more than double the estimated frozen hydrometeor content as compared to retrievals using only 90-GHz and below. The convective and quasi-stratiform quickly reached convergence (minimized difference between observations and calculations using the estimated profiles). A qualitative validation using coincident in situ CAMEX-3 observations shows that the retrieved particle size distributions are well corroborated with independent measurements.

Gail Skofronick-Jackson↗

A cell-centered AMR-ALE framework for 3D multi-material hydrodynamics. Part I: Lagrangian and indirect Euler AMR algorithms

Many applications of physics and engineering involve wide ranges of time and spatial scales. The numerical simulation of localized small scales such as shock waves and material interfaces requires a large number of computational cells in these regions. For these applications, Lagrangian and Arbitrary-Lagrangian-Eulerian (ALE) related methods are engaging since the moving mesh feature naturally brings mesh cells on shock discontinuities and material interfaces are carefully captured. In addition, Adaptive-Mesh-Refinement (AMR) strategies aim to optimize computational resources by concentrating finer mesh cells only in areas of interest while using coarser cells elsewhere. A key but challenging AMR requirement consists in efficiently distributing the computational effort to achieve high accuracy without the prohibitive computational costs associated with uniformly fine grids. Here, in this document, the coupling of the p4est AMR library with a cell-centered Lagrangian scheme is presented with the goal to perform reliable 3D Lagrangian-AMR and indirect Euler-AMR multi-material simulations. In particular, it is shown that starting from a 3D indirect ALE code, the memory management and load balancing requirements can be delegated to an external library (here the p4est library) to unlock ALE-AMR capabilities. First, we present a strategy to transcribe the octant-based connectivity of the 3D AMR framework with that of an unstructured mesh of polygonal cells used in Lagrangian hydrodynamics. Then, we show how refinement and coarsening operations must be adapted to the particular Lagrangian framework to ensure the conservation of volume during those steps. Finally, several numerical test cases are presented that demonstrate the capabilities of the Lagrangian-AMR and indirect Euler-AMR algorithms.

3D cell-centered Lagrangian numerical scheme↗

The WRF Lightning Forecasting Algorithm: Sensitivities to Microphysics and Boundary Layer Physics Schemes

As with WRF model simulated convection in general, LFA output is sensitive to cloud and PBL physics. WRF convection is also sensitive to model initialization uncertainties and unresolved errors.  Large sensitivity exists in HRRR-like Thompson microphysics and PBL, for which original LFA calibration constant needs to be multiplied by 1.13 to give proper FRD amplitudes. Thompson microphysics scheme also shows poorest correlations relative to reference data, suggesting low predictability of HRRR LFA output. Need to validate HRRR LFA against GLM observations.

weather prediction↗

Hyperspectral Sounder Spectral Fingerprinting: Using Machine Learning Techniques to Enhance Model-Based Physical Inversion

Different retrieval algorithms have been developed to process top-of-atmosphere (TOA) spectral radiance data provided by hyperspectral infrared sounder missions. Those algorithms are either optimal estimation method (OEM) based schemes with radiative transfer calculation involved in the retrieval process, or machine learning based methods that allow ultra-efficient data procession but lack of radiometric consistency validation based on the directly measured information. Combining both approaches leverages their respective technical advantages, leading to more accurate results. This study introduces a hyperspectral sounder fingerprinting algorithm to explore this hybrid approach. This approach involves the use of a spectral information-based classification method to identify an reference geophysical state and the corresponding radiative kernel. This enables the efficient retrieval of geophysical variables of interest through a radiative kernel-based linear inversion procedure. The fingerprinting method has been applied to analyze a decade-long hyperspectral sounder data record.

Wan Wu↗

Improved Passive Microwave Algorithms for North America and Eurasia

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

Foster, James↗

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗

Elastic Model Transitions: a Hybrid Approach Utilizing Quadratic Inequality Constrained Least Squares (LSQI) and Direct Shape Mapping (DSM)

A method for transitioning linear time invariant (LTI) models in time varying simulation is proposed that utilizes both quadratically constrained least squares (LSQI) and Direct Shape Mapping (DSM) algorithms to determine physical displacements. This approach is applicable to the simulation of the elastic behavior of launch vehicles and other structures that utilize multiple LTI finite element model (FEM) derived mode sets that are propagated throughout time. The time invariant nature of the elastic data for discrete segments of the launch vehicle trajectory presents a problem of how to properly transition between models while preserving motion across the transition. In addition, energy may vary between flex models when using a truncated mode set. The LSQI-DSM algorithm can accommodate significant changes in energy between FEM models and carries elastic motion across FEM model transitions. Compared with previous approaches, the LSQI-DSM algorithm shows improvements ranging from a significant reduction to a complete removal of transients across FEM model transitions as well as maintaining elastic motion from the prior state.

Jurenko, Robert J.↗

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

Kirschbaum, Dalia↗

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

sensory feedback↗

Satellite Optical Remote Sensing of Clouds and Aerosols: From Particle Single-Scattering and Gaseous Absorption Through Radiative Transfer to Retrieval Products

Clouds and aerosols are fundamental regulators of Earth’s radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. The connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO₂ slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral, and infrared split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multi-spectral and/or multi-angle radiometric and polarimetric measurements, and active-passive sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR). Furthermore, the unique strengths of active remote sensing techniques based on spaceborne lidar observations are briefly elaborated in the context of studying ice clouds composed of randomly and horizontally oriented ice crystals, which is a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Furthermore, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.

Aerosols↗

Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (r(e): R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of r(e) (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

SPECTRAL-RESOLUTION LIDAR↗

thornado+FLASH-X: A Hybrid Discontinuous Galerkin–Implicit-explicit and Finite-volume Framework for Neutrino-radiation Hydrodynamics in Core-collapse Supernovae

We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement–based multiphysics simulation framework FLASH-X. thornado, developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to $\mathcal{O}(v/c)$, and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino–matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino–electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved through a hybrid DG-FV representation of the fluid variables and operator-split evolution within FLASH-X. The implementation is verified using basic transport tests with idealized opacities and relaxation and deleptonization problems with tabulated microphysics. Spherically symmetric CCSN simulations demonstrate accuracy and robustness of the coupled scheme, including close agreement with the CCSN simulation code Chimera. An axisymmetric CCSN simulation further demonstrates the viability of DG-based neutrino transport for multidimensional supernova modeling within FLASH-X. thornado’s neutrino-transport solver is GPU-enabled using OpenMP offloading or OpenACC, and all CCSN applications included in this work use the GPU implementation. Together, these results establish a foundation for future enhancements in physics fidelity, numerical algorithms, and computational performance, for increasingly realistic large-scale CCSN simulations.

Endeve, Eirik [Oak Ridge National Laboratory (ORNL↗

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗

Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction

Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes.

X-ray crystallography↗