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The facilities and services of the GEOmetry LABoratory (GEOLAB) at the NASA Langley Research Center are described. Included in this description are the laboratory functions, the surface modeling and grid generation technologies used in the laboratory, and examples of the tasks performed in the laboratory.
One of the biggest barriers to the scale up of mini-grids in Sub Sahara Africa ('SSA') is access to finance. While there are plenty of government and donor-led initiatives, private investment in the mini-grid sector has been slow to take off because of the high level of risk to reward and the relatively high transaction costs compared to larger grid-tied projects. Specific challenges for mini-grids include policy and regulatory risk, a lack of proven business models, and uncertainty about demand and the ability and willingness of customers to pay. Bundling or aggregation of mini-grids is one potential solution to these challenges. At its simplest, bundling means combining projects or different aspects of projects (eg. financial, operational and service) into a portfolio to create economies of scale (eg. lower costs, mitigation of risks, reaching an investment size that is attractive to more financiers.) There are two main types of bundling: Operational bundling - aggregation of similar projects (eg. similar business model, geographical area, technology or customer type) in order to reduce development and operating costs, - Financial bundling - aggregation of projects into a portfolio in order to attract more private investors through the larger ticket size and portfolio de-risking. This report looks at the different mini-grid business and financing models, and the opportunities and challenges around financial bundling. It looks at techniques for bundling through donors and national governments such as tenders, auctions and results based financing. It considers financial pooling mechanisms, including funds for mini-grid growth and end user financing, and financial products for de-risking mini-grid investments. Finally, there are three developer case studies of financial bundling of mini-grids.
Propellant storage and transfer during future long-duration missions will involve fuel depot operations in which a donor depot tank is used to fill a receiver spacecraft tank with a cryogenic propellant. Prior to the on-Orbit filling operation both the receiver tank and the transfer line must be cooled. The line chilldown process involves transition between boiling regimes in microgravity that will be quite different from their 1g ground-based counterparts. Since the cryogenic propellant itself will be used to perform the chilldown process, the time constants to cool the wall and the amount of fuel that will be used become important design considerations. In this light, the focus of the present work is to understand the flow boiling characteristics of a cryogenic fluid, namely, liquid nitrogen, during the chill-down of a transfer line using CFD modelling and simulations. The cryogenic chill-down process involves different flow boiling regimes: film boiling, transition boiling and nucleate boiling. The prediction of transition between these regimes in a CFD framework is new and challenging. The present work addresses this challenge by employing a volume-of-fluid (VOF) based methodology with Lee phase change model to predict the film boiling regime of the chill-down process in ANSYS Fluent®. The transition and nucleate boiling regimes are predicted by incorporating a sub-grid model developed by Craft Tech that accounts for bubble nucleation, growth, shedding frequency, and departure diameter. The sub-grid model is implemented into Fluent via a user-defined function for wall-fluid heat flux calculations. The sub-grid model is similar in formulation to the well-known Rensselaer Polytechnic Institute (RPI) boiling model. The model constants are tested for different operating conditions and designated values are reported. The CFD model is validated against published experimental data for liquid nitrogen chill-down of a heated stainless-steel pipe in 1g. Predicted results showing good agreement of wall temperature, rewetting temperature, and transition between film and nucleate boiling with the experimental measurements are presented and discussed.
Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.
Land surface heterogeneity has long been recognized and increasingly incorporated in the land surface modelling. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types. In this study, we developed a new subgrid classification method (SGC) that accounts for the topographic variability of the vegetation cover. Each model grid cell was represented with a number of elevation classes and each elevation class was further described by a number of vegetation types. The numbers of elevation classes and vegetation types were variable and optimized for each model grid so that the spatial variability of both elevation and vegetation can be reasonably explained given a pre-determined total number of classes. The subgrid structure of the Community Land Model (CLM) was used as an example to illustrate the newly developed method in this study. With similar computational burden as the current subgrid vegetation representation in CLM, the new method is able to explain at least 80% of the total subgrid PFTs and greatly reduced the variations of elevation within each subgrid class compared to the baseline method where a single elevation class is assigned to each subgrid PFT. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid with different perspectives of surface cover classification. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0° and 2.0°) with three maximum-allowed total number of classes N_class of 24, 18 and 12 representing different computational burdens over the North America (NA) continent, the new method showed variable performances compared to the SGC1 and SGC2 methods. However, the advantage of the SGC method over the other two methods clearly emerged at coarser model resolutions and with moderate computational intensity (N_class = 18) as it explained the most PFTs and elevation variability among the three subgrid methods. Spatially, the SGC method explained more elevation variability in topography-complex areas and more vegetation variability in flat areas. Furthermore, the variability of both elevation and vegetation explained by the new method was more spatially homogeneous regardless of the model resolutions and computational burdens. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.
We present a substantial update to the MESA Isochrones and Stellar Tracks (MIST) library, extending the MIST model grids and isochrones down the white dwarf (WD) cooling sequence with realistic physics for WD cooling timescales. This work provides a large grid of MESA models for carbon–oxygen core WDs with hydrogen atmospheres (spectral type DA/DC), descended from full prior stellar evolution calculations. The model tracks, isochrones, and WD cooling timescale contours are available on the MIST project website and at doi:10.5281/zenodo.15242047. Our WD models provide a very large, publicly available grid with detailed physics for WD cooling timescales: realistic interior and envelope compositions, with element diffusion and heavy-element sedimentation, nuclear burning at the base of the WD hydrogen envelope, core crystallization, and C/O phase separation. As a large grid of open-source stellar evolution models, these WD models provide both out-of-the-box model tracks for comparison with observations and a framework for building further WD models to investigate variations in WD physics.
An adaptive grid finite difference method for solving multi-dimensional, time-dependent, magnetohydrodynamic (MHD) equations is developed. The method is capable of solving problems that include high gradients due to geometry, propagation of shock waves and unsteady boundary conditions. The grid generation technique is based on variational principles with direct control over grid concentration, smoothness and skewness. An example for a two-dimensional MHD simulation of the propagation of a solar-flare-generated shock wave in solar wind flow in the heliographic equatorial plane is selected for illustration of this method.
The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.
Grid Enhancing Technologies Modeling Methodology Introduction -Grid Enhancing Technology -PLEXOS and TARA Modeling Methodology Overview -PLEXOS/TARA agreement -Flowgate identification -DLR and PFC Location Identification Key Lessons Learned and Possible Improvements
In this work, the multi-physics coupling of CTF-PARCS for both steady-state and transient simulations is evaluated focusing on the quantification of the importance of the modeling fidelity when applied to the Best Estimate Plus Uncertainty safety analysis. For the steady-state, a Picard iteration approach is adopted in the coupled code while for the transient an explicit approach has been developed as a first step. The CTF-PARCS coupling is applied to the REA at HFP BOC scenario proposed in the exercise I-2c of LWR-UAM benchmark Phase III. Two different modeling choices are investigated for the nominal calculations. The first consists of including the 3D burnup information in CTF, and the second of modeling the spacers grids in both PARCS and CTF. It is observed that due to the degradation of the fuel thermal conductivity, the 3D burnup inclusion in CTF leads to significant higher centerline temperatures than the base case, with a difference up to 300 K. The spacer grids modeling shows the expected power and temperature decrease in the axial positions of the spacer grids. For the uncertainty quantification study, the multi-physics modeling including the burnup information in CTF is used. The results show that the inserted reactivity has a relative standard deviation of 13.6% mainly attributed to the nuclear data. The maximum linear power has a relative standard deviation of 16.2% due to the nuclear data as well, while the maximum centerline temperature has a relative standard deviation of 3.5% primarily caused by the fuel thermal conductivity. (authors)
Immersed cylindrical tube arrays often are used as heat exchangers in gas-particle fluidized beds. In multiphase computational fluid dynamics (CFD) simulations of large fluidized beds, explicit resolution of small cylinders is computationally infeasible. Instead, the cylinder array may be viewed as an effective porous medium in coarse-grid simulations. The cylinders' influence on the suspension as a whole, manifested as an effective drag force, and on the relative motion between gas and particles, manifested as a correction to the gas-particle drag, must be modeled via suitable sub-grid constitutive relationships. In this work, highly resolved unit-cell simulations of flow around an array of horizontal cylinders, arranged in a staggered configuration, are filtered to construct sub-grid, or `filtered', drag models, which can be implemented in coarse-grid simulations. The force on the suspension exerted by the cylinders is comprised of, as expected, a buoyancy contribution, and a kinetic component analogous to fluid drag on a single cylinder. Furthermore, the introduction of tubes also is found to enhance segregation at the scale of the cylinder size, which, in turn, leads to a reduction in the filtered gas-particle drag.
The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.
Increased coupling between critical infrastructure networks, such as power and communication systems, has important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several researchers have studied models of interdependent networks and reported that increased coupling can increase vulnerability. However, these conclusions come largely from models that have substantially different mechanisms of cascading failure, relative to those found in actual power and communication networks, and that do not capture the benefits of connecting systems with complementary capabilities. In order to understand the importance of these details, this paper compares network vulnerability in simple topological models and in models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid model shows a higher level of vulnerability, relative to the contagion model. Second, we compare a percolation model of topological cascading in coupled networks to three different models of power networks coupled to communication systems. Again, the more accurate models suggest very different conclusions than the percolation model. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the percolation model, in which zero coupling is optimal. Only in an extreme case, in which communication failures immediately cause grid failures, did we find that increased coupling can be harmful. Together, these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems.
Using the Gemini Planet Imager located at Gemini South, we measured the near-infrared (1.0–2.4 μm) spectrum of the planetary companion to the nearby, young star β Pictoris. We compare the spectrum obtained with currently published model grids and with known substellar objects and present the best matching models as well as the best matching observed objects. Comparing the empirical measurement of the bolometric luminosity to evolutionary models, we find a mass of 12.9 ± 0.2 M{sub Jup}, an effective temperature of 1724 ± 15 K, a radius of 1.46 ± 0.01 R{sub Jup}, and a surface gravity of logg=4.18±0.01 [dex] (cgs). The stated uncertainties are statistical errors only, and do not incorporate any uncertainty on the evolutionary models. Using atmospheric models, we find an effective temperature of 1700–1800 K and a surface gravity of logg=3.5–4.0 [dex] depending upon the model. These values agree well with other publications and with “hot-start” predictions from planetary evolution models. Further, we find that the spectrum of β Pic b best matches a low surface gravity L2 ± 1 brown dwarf. Finally, comparing the spectrum to field brown dwarfs, we find the the spectrum best matches 2MASS J04062677–381210 and 2MASS J03552337+1133437.
The simulation design and evaluation of a transactive energy system is contingent on the setting in which it would exist, which could vary significantly with the many drivers that impact the potential future states of the power system. This report summarizes the development of the Transactive Future Grid State (TFGS) model, which is a lightweight capacity expansion model that will support transactive energy research by allowing users to explore alternative future states. The TFGS model determines the long-term generation capacity needs of a given power system that satisfy specified constraints and support a least cost hourly dispatch. This is accomplished by solving a single optimization problem that simultaneously finds the generation fleet with capacity sufficient to meet peak load and the flexibility to meet hourly operational dispatch, all at lowest cost. The TFGS model was applied to a larger ongoing study taking place that will be simulating how distribution system operators (DSOs) could use a transactive energy system to manage distributed energy resources. The larger study, commonly referred to as the DSO+T study, involves two parallel analyses; one that represents the system today and one that represents a future system with a high degree of renewable energy penetration. The TFGS model provided the generation mix of the high renewable base case. As the TFGS model was being used to provide inputs to the DSO+T study, two issues became apparent. First, additional functionality would have been useful, and second, a larger-than-anticipated amount of user inputs and data were required to successfully use the TFGS model. These discoveries have lead to the recommendation of specific functionality that can be added in future development of the tool.
The subsonic flow field was numerically simulated around several High Speed Research Reference H configurations at various pitch and yaw angles. A sequence of structured-viscous grids were generated; the first grid modeled the wing-body high-lift geometry, and the second grid incorporated the nacelles and the horizontal tail. The third grid modeled the full-span geometry for sideslip calculations, and was obtained by mirroring a coarser version of the second grid. The CFL3D code, a Reynolds averaged, thin-layer Navier-Stokes flow solver for structural grids, was used for the flow solver and modeled the free-air Reference H high-lift configuration at wind tunnel conditions of Mach number 0.24 and Reynolds number of 1.4 x 10(exp 5) per in. Pitch sweeps were performed at angles of attack from 6 deg to 15 deg. Sideslip angle sweeps at 0 deg <= Beta <= +18 deg were performed at an angle of attack of 8 deg. The lateral and longitudinal performance characteristics were well predicted and very good force and moment comparisons were obtained. A very complex multiple vortical system develops at the higher angles of attack, and detailed postprocessing of the solutions provided a comprehensive three-dimensional understanding of the flow which helps to correlate and interpret the wind tunnel data.
Magnetic fields can drastically change predictions of evolutionary models of massive stars via mass-loss quenching, magnetic braking, and efficient angular momentum transport, which we aim to quantify in this work. We use the MESA software instrument to compute an e xtensiv e main-sequence grid of stellar structure and evolution models, as well as isochrones, accounting for the effects attributed to a surface fossil magnetic field. The grid is densely populated in initial mass (3–60 M ), surface equatorial magnetic field strength (0–50 kG), and metallicity (representative of the Solar neighbourhood and the Magellanic Clouds). We use two magnetic braking and two chemical mixing schemes and compare the model predictions for slowly rotating, nitrogen- enriched (‘Group 2’) stars with observations in the Large Magellanic Cloud. We quantify a range of initial field strengths that allow for producing Group 2 stars and find that typical values (up to a few kG) lead to solutions. Between the subgrids, we find notable departures in surface abundances and evolutionary paths. In our magnetic models, chemical mixing is al w ays less efficient compared to non-magnetic models due to the rapid spin-down. We identify that quasi-chemically homogeneous main sequence evolution by efficient mixing could be prevented by fossil magnetic fields. We recommend comparing this grid of evolutionary models with spectropolarimetric and spectroscopic observations with the goals of (i) revisiting the derived stellar parameters of known magnetic stars, and (ii) observationally constraining the uncertain magnetic braking and chemical mixing schemes.
This report on a Virginia Institute for Marine Science project demonstrates that the sub-grid modeling technology (now as part of Chesapeake Bay Inundation Prediction System, CIPS) can incorporate high-resolution Lidar measurements provided by NASA Langley Research Center into the sub-grid model framework to resolve detailed topographic features for use as a hydrological transport model for run-off simulations within NASA Langley and Langley Air Force Base. The rainfall over land accumulates in the ditches/channels resolved via the model sub-grid was tested to simulate the run-off induced by heavy precipitation. Possessing both the capabilities for storm surge and run-off simulations, the CIPS model was then applied to simulate real storm events starting with Hurricane Isabel in 2003. It will be shown that the model can generate highly accurate on-land inundation maps as demonstrated by excellent comparison of the Langley tidal gauge time series data (CAPABLE.larc.nasa.gov) and spatial patterns of real storm wrack line measurements with the model results simulated during Hurricanes Isabel (2003), Irene (2011), and a 2009 Nor'easter. With confidence built upon the model's performance, sea level rise scenarios from the ICCP (International Climate Change Partnership) were also included in the model scenario runs to simulate future inundation cases.