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NASA NTRS2016

A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

big data↗

DOE OSTI2021

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗

DOE OSTI2022

Ensuring Transient Stability With Guaranteed Region of Attraction in DC Microgrids

DC microgrids have promising applications in renewable integration due to their better energy efficiency when connecting DC components. However, they might be unstable since many loads in a DC microgrid are regulated as constant power loads (CPLs) that have a destabilizing negative impedance effect. As a result, the state trajectory displacement caused by abrupt load changes or contingencies can easily lead to instability. Many existing works have been devoted to studying the region of attraction (ROA) of a DC microgrid, in which the system is guaranteed to be asymptotically stable. Nevertheless, existing work either focuses on using numerical methods for ROA approximations that generally have no performance guarantees or cannot ensure a desired ROA for a general DC microgrid. To close this gap, this paper develops an innovative control synthesis algorithm to make a general DC microgrid have a theoretically guaranteed ROA, for example, to cover the entirety of its operating range regarding state trajectories. Here, we first study the nonlinear dynamics of a DC microgrid to derive a novel transient stability condition to rigorously certify whether a given operating range is a subset of the ROA; then, we formulate a control synthesis optimization problem to guarantee the condition’s satisfaction. This condition is a linear constraint, and the optimization problem resembles an optimal power flow problem and has a good computational behavior. Simulation case studies verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Multiphysics Modeling of Microreactors with NEAMS codes, and Validation Based on KRUSTY Reactivity Insertion

The NEAMS Multiphysics Applications team continues to assess code usability and functionality for microreactor design and safety analyses, while demonstrating that NEAMS tools capture both steady-state and transient behavior across distinct microreactor concepts. In FY2025, the team advanced full-core, high-fidelity, multiphysics models that solve more complex problems and strengthen verification/validation for several microreactor systems: heat-pipe microreactor (HPMR), gas-cooled microreactor (GCMR), and the KRUSTY experiment. These models employ the MOOSE MultiApp/Transfers architecture with Griffin for neutronics, BISON for heat conduction/thermomechanics, Sockeye for heat pipes, SAM/THM for coolant channels and loops, and SWIFT for hydride behavior, with meshes generated via the MOOSE Reactor Module. The graphite models available in the Grizzly code were also investigated for future analyses. For the HPMR, a Na-HPMR variant was constructed to align with recently validated heat-pipe experiments and Sockeye’s LCVF capability, enabling mechanistic heat-pipe transients and startup modeling. The Na-HPMR will serve as the primary model for HPMR investigations in upcoming tasks. The load-following and single heat-pipe failure scenarios (Griffin/BISON/Sockeye), which were previously modeled for the K-HPMR, were replicated for the Na-HPMR, showing strong negative temperature feedback and highly localized thermal effects, respectively, while the startup case captured vapor-front progression and heat-removal activation. Solid mechanics was added to the previously built K-HPMR full-core model in BISON, showing minimal impact on steady-state reactivity yet enabling stress-field predictions that prepare the path for full-core TRISO performance analyses. For the GCMR, automated steady-state and four transient scenarios were executed using Griffin/BISON/SAM/SWIFT. Results confirm robust inherent safety: power collapses promptly in loss-of-cooling events, the inlet-temperature drop settles to a new equilibrium, and a single-channel blockage yields only a ~30 K local fuel-temperature rise with <0.4% power decrease. SWIFT-predicted hydrogen redistribution affects reactivity during both steady-state and transient conditions, underscoring its importance. A Brayton-cycle balance of plant (BOP) model in SAM/THM demonstrated stable startup behavior, and xenon-driven reactivity during load following was analyzed. To improve TRISO-compact temperature fidelity, a fast multiscale Heat Source Decomposition (HSD) treatment was implemented. Against heterogeneous benchmarks, HSD reduces underprediction of kernel temperatures and lowers predicted peak powers in reactivity-insertion transients compared to previous homogenized models. KRUSTY warm-critical validation progressed from FY2024 baselines: the 15Ȼ insertion shows excellent agreement in peak power (~2% high) and temperature trends, and the 30Ȼ case was automated via a feedback controller that maintained power near 3 kW for ~150 s with close agreement to data. The successful modeling of the warm critical tests has laid a strong foundation for simulating more complex nuclear system tests in the years ahead. Throughout FY2025, developer feedback was provided (e.g., MOOSE batch mesh generation, distributed pre-split meshes, Griffin sweeper on displaced meshes), several new models were contributed to the Virtual Test Bed, and an OECD-NEA WPRS multiphysics benchmark based on the HPMR was initiated to enable broader cross-comparison and best-practice development with the nuclear community at large.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Computational design and performance prediction of creep-resistant ferritic superalloys

Ferritic superalloys containing the B2 phase with the parent L21 phase precipitates in a disordered solid-solution matrix, also known as a hierarchical-precipitate-strengthened ferritic alloy (HPSFA), had been developed for high-temperature structural applications in fossil-energy power plants. These alloys were designed by adding Ti into a previously-studied NiAl-strengthened ferritic alloy (denoted as FBB8 in this study). Following with the concept of HPSFAs, in the present research, a systematic investigation on adding other elements, such as Hf and Zr, and optimizing the Ti content within the alloy system, has been conducted, in order to further improve the creep resistance of the model alloys. Studies include advanced experimental techniques, first-principles calculations on thermodynamic and mechanical properties, and numerical simulations on precipitation hardening, have been integrated and conducted to characterize the complex microstructures and excellent creep resistance of alloys. The experimental techniques include transmission-electron microscopy (TEM), scanning-electron microscopy (SEM), neutron diffraction (ND), and atom-probe tomography (APT), which provide the detailed microstructural information of the model alloys. Systematic tension/compression creep tests have also been conducted in order to verify the creep resistance of the potential alloy compositions. The results show that when replacing Ti with Hf and Zr, it does not form the L21 phase. Instead, the hexagonal Laves phase forms and distributes majorly along the grain boundary, or large segregation within grains. Since the Laves phase does not form parent to the B2-phase precipitates, it cannot bring the strengthening effect of HPSFAs. As a result, the FBB8 + 2 wt. % Hf and FBB8 + 2 wt. % Zr alloys have similar mechanical properties to the original FBB8. The FBB8 + Ti series alloys had also been studied, from the creep tests and microstructural characterizations, the FBB8 + 3.5 wt.% Ti possesses the greatest creep resistance, with the L21/B2 phase ratio of 4 (80% of the precipitates is the L21 phase, and 20% is the B2 phase). First-principles calculations include thermodynamics, elastic properties, and interfacial properties, which have been conducted for the understanding of the thermodynamic and mechanical properties of HPSFAs. In addition to the systematic experimental approach and first-principles calculations, a series of numerical tools and algorithms, which assist in the optimization of creep properties of ferritic superalloys, are utilized and developed. These numerical simulation results are compared with the available experimental data and previous first-principles calculations, providing the deep insight of creep mechanisms of the creep-resistant ferritic superalloys. To conclude the present research, we’ve found that (1) only FBB8 + Ti alloys have the potential of forming HPSFA, and FBB8 + Hf and FBB8 + Zr do not work. Therefore, only FBB8 + Ti alloys have desirable creep resistance, (2) the optimum composition for the FBB8 + Ti alloys is FBB8 + 3.5% Ti, which has the greatest creep resistance (218.8 MPa as the threshold stress at 700 oC), (3) first-principle calculations obtained results that could not be obtained in experiments, which are relevant to develop ferritic superalloys with the improved creep resistance, and (4) two-dimensional dislocation-dynamics simulations investigate effects of factors like precipitate volume fractions and precipitate radii in the alloy systems, which helps in developing the most desirable microstructure with greatest strengthening.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2019

Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks

Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. Here, a widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Identifying structural changes with unsupervised machine learning methods

In this work, unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems.

42 ENGINEERING↗

DOE OSTI2023

In-Situ Transmission Electron Microscopy Study of the Evolution of Extended Defects in Oxide Nuclear Fuels

In-situ transmission electron microscopy (TEM) with simulated extreme environments is an effective tool for understanding and tracking microstructural changes down to the atomic scale. The objective of this research is to study the effect of temperature on the evolution of extended defects in ThO2 and UO2. Here, we present in-situ TEM isochronal thermal annealing experiments using a micro-electro-mechanical-system (MEMS)-based heating holder. ThO2 and UO2 single crystal specimens were grown inside an inert silver ampoule using hydrothermal synthesis. Both samples were irradiated using 2 MeV protons at 600oC up to 0.1 dpa at Texas A&M University’s Accelerator Laboratory. Fig. 1a shows a weak-beam dark field TEM image of ThO2 after irradiation, indicating the presence of faulted 1/3 <111> type dislocation loops. This presentation will discuss the effect of annealing temperature (600oC, 800oC, 1000oC, 1100oC, etc.) on dislocation loop density, size and distribution, loop nature (interstitial/vacancies) and Burgers vector, as well as the formation of voids in both ThO2 and UO2. This work will also discuss the interaction between defects during annealing. The in-situ TEM annealing cycle is shown in Fig 1b. This research significantly improves the understanding of defect behavior in oxide nuclear fuels with temperature and will aid computational modeling efforts. This work was supported as part of the Center for Thermal Energy Transport under Irradiation (TETI) Energy Frontier Research Center, funded by the U.S. Department of Energy Office of Science.

36 MATERIALS SCIENCE↗

NASA NTRS2016

Photospheric Current Spikes and Their Possible Association with Flares - Results from an HMI Data Driven Model

A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate per unit volume Q. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel using a band pass filter. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and that it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists. Above an AR dependent threshold value of Q, the number of events N(Q) with heating rates greater than or equal to Q obeys a scale invariant power law distribution for each AR given by N(Q) varies Q(sup -s), where 0.40 less than or equal to S less than or equal to 0.53, with a mean and standard deviation across the 14 ARs of 0.47 and 0.045, showing there is little variation of S from one AR to another. These properties of N(Q) are in close agreement with those of the distribution N(E) for the total energy E of solar flares, determined from observations to be N(E) = constant x E(sup -alpha). From observations of nanoflares in the 0.7 to 4 MK range, and from observations of flares in hard X-rays, it is found that 0.51 less than or equal to alpha less than or equal to 0.57, and 0.4 less than or equal to alpha less than or equal to 0.6, respectively (Crosby et al. 1993, Sol. Phys., 143, 275; Aschwanden & Parnell 2002, ApJ, 572, 1048). Observations also show that, as is found here for the exponent S, there is little variation of alpha with AR (Wheatland 2000, ApJ, 532, 1209), indicating N(E) and N(Q) are largely independent of individual properties of ARs such as area, total magnetic flux, and distribution of current density (i.e. non-potentiality). Therefore the power law scaling of the photospheric heating rate Q computed here on granulation scales is essentially identical to that found for coronal observations of flare energies on scales 1-2 orders of magnitude larger. This suggests the physical mechanisms that cause Q and coronal flares are closely related. It seems likely that Q is the signature of a magnetic reconnection process in an energy range and volume orders of magnitude smaller than those of flares. In this context, at least the larger spikes in Q might be signatures of UV photospheric or lower chromospheric bombs in which plasma is heated to temperatures approximately 10(exp -5) K (Peter et al. 2014, Science 346, 1255726; Judge 2015, ApJ, 808, 116). In addition, lattice based avalanche simulations of flare energy release predict 0.4 less than or equal to alpha less than or equal to 0.5, while analytic, fractal-diffusive self-organized criticality models predict 0.4 less than or equal to alpha less than or equal to 0.67, in excellent agreement with observations, and the results presented here (Aschwanden & Parnell 2002, ApJ, 572, 1048; Aschwanden 2012, A&A, 539, A2; Aschwanden 2013, in "Self Organized Criticality Systems"; Aschwanden et al. 2016, SSR, 198, 47).

Goodman, Michael↗

NASA NTRS2017

Model Validation for Propulsion - On the TFNS and LES Subgrid Models for a Bluff Body Stabilized Flame

With advances in computational power and availability of distributed computers, the use of even the most complex of turbulent chemical interaction models in combustors and coupled analysis of combustors and turbines is now possible and more and more affordable for realistic geometries. Recent more stringent emission standards have enticed the development of more fuel-efficient and low-emission combustion system for aircraft gas turbine applications. It is known that the NOx emissions tend to increase dramatically with increasing flame temperature. It is well known that the major difficulty, when modeling the turbulence-chemistry interaction, lies in the high non-linearity of the reaction rate expressed in terms of the temperature and species mass fractions. The transport filtered density function (FDF) model and the linear eddy model (LEM), which both use local instantaneous values of the temperature and mass fractions, have been shown to often provide more accurate results of turbulent combustion. In the present, the time-filtered Navier-Stokes (TFNS) approach capable of capturing unsteady flow structures important for turbulent mixing in the combustion chamber and two different subgrid models, LEM-like and EUPDF-like, capable of emulating the major processes occurring in the turbulence-chemistry interaction will be used to perform reacting flow simulations of a selected test case. The selected test case from the Volvo Validation Rig was documented by Sjunnesson.

bluff body↗

DOE OSTI2019

Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated T&D Simulation: Preprint

This paper explores the advantages and challenges of simulating price-responsive load (PRL) interactions with large-scale power systems using integrated transmission and distribution (T&D) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture 'ISO-to-appliance' simulations using a synthetic T&D model built on the PJM 5 Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of PRLs. The results show substantial differences in the impacts of PRLs between integrated T&D and transmission-only (T-only) models. Similarly differences are observed between the price response of individual distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which cannot be captured in T-only simulations. Experiments include both traditional price responsive load reductions and price-driven load increases during low net demand periods driven by high daytime generation from distributed generation-based photovoltaics (DGPV).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Manipulating molecular order in nematic liquid crystal capillary bridges via surfactant adsorption: guiding principles from dissipative particle dynamics simulations

The ability of liquid crystals (LCs) to change orientational order is used in applications, ranging from sensors to displays. The aim of this work is to computationally investigate how surfactant adsorption on cylindrical LC bridges can be used to control such orientational order. Building from classical fundamental lessons, understanding the ordering of mesogens along a preferred axis with the help of molecular modelling contributes to investigations of systems that could be a platform for LC-based sensing applications. The coarse-grained dissipative particle dynamics (DPD) simulation method is implemented here, because it allows us to quantify the effect of molecular features on the properties of meso-scopic systems containing LC bridges, an aqueous solvent, and surfactants at various concentrations. Three surfactant types are modelled with short, medium, and long tail lengths, respectively. All surfactants adsorb at the LC–water interface. It is found that the length of the surfactant hydrophobic tail determines the effectiveness by which the LC order is affected. Short tails are not as effective as long ones. Surfactants with long tails affect the LC order, but, in agreement with experiments, predominantly only within a short distance from the LC–water interface. For these surfactants, the surface density at the LC–water interface is an important knob that can be used to control the order of the LCs. As the effective LC–surfactant interactions change, so does the distribution of the surfactants at the interface. Consistent with theoretical expectations, the results presented here elucidate the effect of molecular features on the anchoring mechanism between surfactants and mesogens within cylindrical bridges dispersed in aqueous systems and could be helpful for designing novel surface-active compounds in the development of advanced sensing devices based on LCs.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS2002

A New Method for Estimating the Single Scattering Phase Functions of Regolith Grains

Hapke's photometric model has been widely used in solar system remote sensing applications for nearly two decades. Recently, Hapke extended his model to describe the coherent-backscatter opposition effect and multiple-scattering by particles with anisotropic single particle phase functions (SPPF's). A practical difficulty for retrieving Hapke's model parameters from typical planet, satellite, and asteroid photometry data sets is that the model employs a large number of adjustable parameters (at least eight) that can be reliably constrained only for a small number of planetary data sets in which both disk-resolved and whole-disk observations are available from opposition to very large phase angles. The present work aims to reduce the number of adjustable parameters and preserve (or even enhance) the model's accuracy and usefulness by expressing Hapke's parameters in terms of more fundamental physical properties on which they mutually depend. The most difficult part of this task, described here, is to develop a simple method for computing the effective SPPF for structurally complex regolith grains from optical constants, grain-size distribution, and average regolith porosity. The development of light-scattering models for irregularly shaped particles is a large, complex subject and many sophisticated methods, such as Discrete Dipole Approximation (DDA) and Monte-Carlo simulations, have been explored elsewhere. Many of these methods are computationally intensive and probably impractical for routine substitution in Hapke's model. Here, progress is reported in developing a practical, semi-empirical method for estimating the directional scattering behavior (i.e. SPPF) of irregular regolith grains. The method employs Optical Transfer Function (OTF) techniques to model how the structural complexity of regolith particles broaden and attenuate the angular distribution of scattered light relative to that expected from ideal spherical particles of equivalent size and composition.

Helfenstein, P.↗

NASA NTRS2021

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign would be a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR would, for the first time ever, present a need to communicate with multiple surface assets that are co-located on Mars in a coordinated effort to accomplish the unified objective of fetching, transporting, and returning samples from Mars. Currently, Mars surface assets relay data to and from Earth using a number of orbiters in what’s known as the Mars Relay Network (MRN). This network is characterized by a small number of surface assets distributed across the Martian globe and a larger number of orbiters to provide relay services. As of June 2020, there are two surface assets for which five orbiters are providing relay. During the MSR Campaign, there would be two rovers and a lander that all would require relay communication from a small number of Mars orbiters to meet the aggressive MSR timeline. The inversion of the current MRN paradigm, a system of many surface assets requiring relay and few orbiters to provide relay, necessitates the unique challenge of optimally allocating relay passes to maximize the operational capability of all assets. The allocation must consider a large number of trade variables including Mars asset operational requirements and Earth ground system constraints, including staffing schedules, operations planning across time zones, and more. To address these telecommunication challenges, the Mars Asset Relay Mission Link Allocation Design Environment (MARMLADE) tool was developed. It is a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets and quantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbiter geometry and telecommunication capabilities provided by JPL’s Telecom Orbit Analysis and Simulation Tool (TOAST) software to compute which asset should get each pass based on a series of heuristics and predictions of all assets’ states. Within MARMLADE, the user can provide inputs including the option for time-based pass splitting, fixed FWD data rate capabilities, DFE communication capabilities, and link parameters allowing for the assessment of complex operations and hardware trades using surface mission operational efficiency as a primary figure of merit. As the MSR mission concepts continue to mature, MARMLADE is being used to assess ability of all MSR elements to meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.This paper will describe the motivation and design of the MARMLADE tool and how it is being used to perform campaign and mission level trades, generate requirements, and support development of the MSR surface mission scenarios.

Lee, Charles↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Distributed sensing↗

DOE OSTI2018

DeStager: feature guided in-situ data management in distributed deep memory hierarchies

In-situ analytics have been increasingly adopted by leadership scientific applications to gain fast insights into massive output data of simulations. With the current practice, systems buffer the output data in DRAM for analytics processing, constraining it to DRAM capacity un-used by the simulation. The rapid growth of data size requires alternative approaches to accommodating data-rich analytics, such as using solid-state disks to increase effective memory capacity. For this purpose, this paper explores software solutions for exploring the deep memory hierarchies expected on future high-end machines. Leveraging the fact that many analytics are sensitive to data features (regions-of-interest) hidden in the data being processed, the approach incorporates the knowledge of the data features into in-situ data management. It uses adaptive index creation/refinement to reduce the overhead of index management. In addition, it uses data features to predict data skew and improve load balance through controlling data distribution and placement on distributed staging servers. The experimental results show that such feature-guided optimizations achieve substantial improvements over state-of-the-art approaches for managing output data in-situ.

Computer Science↗

DOE OSTI2021

Hardware-in-the-Loop Testbed for Cyber-Physical Security of Photovoltaic Farms

In the last decades, modem grids with distributed energy resources, such as photovoltaic (PV) farms, are increasingly vulnerable to cyber-attacks that seriously affect the stability and performance of the power system. While cyber-physical security of smart grids is extensively studied, most of the existing work focuses on the grid level and neglects the modeling and features of device-level power electronics converters (PECs). Furthermore, establishing a high-fidelity simulation testbed that can simulate harmonic frequencies of the PV farm is in urgent need. In this paper, a high-fidelity and real-time hardware-in- the-loop testbed is built to simulate the harmonics of power electronics converters for cyber-physical security of PEC-enabled PV farms. Based on this testbed, the impact of typical cyber-attacks and physical faults on the PV converter can be analyzed, thus providing a foundation for cyber-attack detection, root cause diagnosis, and resilient control to mitigate the adverse effects of cyber-attacks.

14 SOLAR ENERGY↗