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NASA NTRSDate not supplied

Autonomous Lunar PNT Simulator (ALPS)

In this paper we present a simulation tool that has been used to develop an architecture for a low-cost Position, Navigation and Time (PNT) system to provide PNT services on and around the Moon using non-dedicated low-cost orbital and ground assets. The simulation tool has been developed to be flexible and is capable of modeling and analyzing the many different capabilities and configurations that the non-dedicated assets could support. The tool models the creation of an ad hoc swarm, the localization of this swarm and the subsequent provision of PNT services from this swarm. We present results from several studies of select configurations chosen to reflect existing and future real-world needs and capabilities. Over the next few decades there is expected to be a substantial increase in Lunar missions supporting and inspired by NASA’s Artemis Program. It is expected that a large fraction of these missions will be low cost, utilizing rideshares and CubeSats, just as has seen in Earth orbit over the past decade. Many of these missions will need navigation capabilities but may be unable to support the large power, mass and weight that a weak GNSS or DSN based navigation solution would entail. As Lunar PNT service demand is not likely to be needed over the entire lunar surface 24/7, the creation of a dedicated Lunar GNSS constellation cannot be justified. Hence, our proposed architecture envisions utilizing existing Lunar science and exploration assets to create ad hoc and on demand Lunar PNT swarms capable of providing PNT services to low cost missions. The simulation reflects the two distinct parts of the architecture: the creation of a Lunar PNT swarm using non-dedicated existing assets, and the quantitative modeling of the quality of the PNT services that this swarm provides. To support the former, the simulation supports various swarm localization techniques, including centralized and distributed EKFs both of which support pluggable dynamics models. In modeling the PNT service performance the simulation adopts standard techniques from the GNSS community, including providing degree of precision (DOP) estimates for theoretical end users. Crucially, all asset capabilities, including clock accuracy, independent location self-knowledge and timing measurement precision can be set independently for each asset, reflecting the key concept of utilizing non-dedicated assets. The simulation is predominantly implemented in MATLAB, with GMAT being used for the propagation of orbital assets. The paper will present results from the simulation reflecting the tool’s flexibility and focusing on scenarios that match real-world proposed missions, including scenarios designed to provide PNT support to lunar surface missions similar to NASA Ames' forthcoming VIPER mission. The performance of centralized and decentralized swarm configuration and localization techniques will be compared. Finally, performance of the PNT service provided by an autonomous Lunar PNT swarm will be compared to existing radiometric and weak GNSS methods.

Kelley Elizabeth Hashemi↗

DOE OSTI2026

The AWAKEN wind farm benchmark, Part 2: Modeling results

Accurately modeling wind farm performance in complex atmospheric flows remains a challenge. This paper presents the modeling results of the American WAKE experimeNt (AWAKEN) wind farm benchmark, a collaborative effort involving 16 research groups from academia and industry within the International Energy Agency Wind Technology Collaboration Programme Task 57. The study evaluates a diverse suite of simulation tools, ranging from fast-running engineering wake models to high-fidelity large-eddy simulations, against a diurnal case study observed during the AWAKEN campaign. The benchmark utilized a three-phase structure to progressively assess model performance as observational data availability increased. Initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools. A distinct spatial bias was observed where models struggled to resolve the interplay between a low-level jet, wakes, and terrain-induced flow acceleration. In subsequent phases, leveraging additional measurements for model improvement led to a reduction in mean absolute error across the model ensemble; however, this effect was most pronounced in engineering wake models, where targeted calibration reduced error by up to 40~\%. Overall, the study demonstrates that inflow characterization remains a primary prerequisite for accuracy, particularly for models relying on coarse forcing datasets. While the limited ability to resolve local terrain-flow interactions under single-day conditions represent a recognized constraint, the overall findings on wake modeling and real-world validation still provide valuable guidance for model application and for mitigating this limitation.

Bodini, Nicola↗

DOE OSTI2019

Moisture Transfer in Commercial Buildings Due to Air Leakage: A New Feature in the Online Airtightness Savings Calculator

Air leakage through the building envelopes is responsible for a large amount of energy use. The US Department of Energy Windows and Building Envelope Research and Development Roadmap for Emerging Technologies states that, in 2010, air infiltration was responsible for 20% of primary energy consumption attributable to the fenestration and building envelopes of commercial buildings. Despite this fact, improving airtightness is not always recognized by commercial building owners, as they have been slow in acknowledging and diminishing the detrimental effects of air leakage on energy use, comfort, indoor air quality, and building material durability.The design and construction industry would benefit from a credible, easy-to-use tool that estimates potential energy and financial savings in a standardized manner so designers and contractors can give building owners compelling reasons to invest in reducing air leakage. In 2016–2017, Oak Ridge National Laboratory, the National Institute of Standards and Technology, the Air Barrier Association of America, and the US-China Clean Energy Research Center for Building Energy Efficiency collaborated to develop an online calculator. This user-friendly calculator is free to the public and uses the simulation results of the whole building energy simulation tool EnergyPlus and the airflow simulation tool CONTAM. In 2018–2019, the calculator was expanded to add moisture transfer calculations given that air leakage through the building envelope can have a significant impact on moisture transfer and associated impacts. Four more commercial building types were also added to the existing database of three building types as part of this update. This paper describes the procedure used to calculate moisture transfer due to air leakage and provides examples that demonstrate the moisture transfer for each of the seven commercial building types that are currently part of the calculator.

Shrestha, Som↗

DOE OSTI2020

GADRAS Batch Inject Tool User Guide

Gamma Detector Response and Analysis Software (GADRAS) is used by the radiation detection and emergency response community to perform modeling and spectral analysis for gamma detector systems. Built into GADRAS is the ability to define a detector, geometry, background characteristics and source composition to generate synthetic spectra for drills and exercises (injects). Consequence Management is currently in development of a sample result data simulator tool in which a deposition model is probed for source conditions at moments in time and locations in space. These values are used to generate realistic sample results for use in drills and exercises. In addition to sample results, there is a need to simulate the actual spectra that would be observed in the field by downlooking HPGe instruments given a deposition activity. This way, the FRMAC Gamma Spectroscopist can practice their process of generating quantified results from spectra on realistic data as well. Recognizing the decades of work done in GADRAS to accurately generate synthetic spectra, this team decided to build a link between the new simulator and GADRAS to generate these spectra quickly and easily. The simulator tool will generate a file that specifies the name of the spectra, its location, date/time of measurement, duration of measurement, height off the ground, and the deposition activity and age for every radionuclide in the simulation. Then, a new tool within the Inject Tab of GADRAS was developed to read in this file given a detector selection and generate In-Situ spectra for each row in the file in any file format the user chooses. This way, simulation cell staff can take these files and then upload them to the appropriate data system (RAMS or RadResponder) for use during drills and exercises. An advanced feature of this tool allows for generating any spectra given an appropriate model and mapping of source to model layer in the batch inject tool. This way, spectra from field sample counts, mobile laboratories, or even fixed laboratories can be generated in bulk given an estimate of the radioactivity concentration or total radioactivity in an import file. This expands the capabilities of this tool a great deal and will make it a more useful tool for CM and others to help estimate detector response for nearly any situation. This user guide will explain the steps needed to perform a batch inject file generation.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRSDate not supplied

Science Performance Comparison Between a Spaceborne HSRL and CALIOP

NASA operates airborne and spaceborne lidar systems to answer aerosol and cloud related science questions. NASA Langley Research Center has extensive experience operating lidar systems in both regimes. These include High Spectral Resolution Lidar (HSRL) systems, which have been operating on airborne systems,and CALIOP, the spaceborne elastic backscatter lidar system on board CALIPSO. In support of NASA’s ACCP Study Plan to address the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Designated Observables called out in the 2017 Earth Science Decadal Survey, LaRC is using lidar simulation tools to evaluate the performance of spaceborne systems using both the HSRL and elastic backscatter techniques. The LaRC high-fidelity simulator tool models both HSRL and elastic backscatter lidar systems by modeling the effects of the instrument specifications and producing backscatter signals generated from molecules, aerosols, clouds, ocean surface, and ocean subsurface. It derives the solar background signals from the scene specific aerosol and cloud characteristics, surface type, and sun elevation. The tool models both random and systematic uncertainties in the retrieved geophysical parameters. In this study, we will present simulated results that compare and contrast the performance of spaceborne HSRL systems to the performance of CALIOP. As recommended by the Decadal Study, ACCP is seeking advances over performance that has been achieved by A-Train. This study will provide a description of the HSRL and elastic backscatter techniques and demonstrate how and why the performance of these HSRL systems exceeds the performance of CALIOP.

Kathleen A Powell↗

DOE OSTI2022

NEAMS Technical Area Support in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities outlined in this report include a factor of 10^4 improvement in dependency resolution speed, sorting of user objects, ability to compute residuals and Jacobians together, transfer fixes, support for the mortar method in finite volume discretizations, addition of generalized advection schemes for fluid simulations, 10^2 speedup in some Griffin simulations due to a new matrix-only solve type, and much more.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

Numerical Model of Flame Spread Over Solids in Microgravity: A Supplementary Tool for Designing a Space Experiment

The recently developed numerical model of concurrent-flow flame spread over thin solids has been used as a simulation tool to help the designs of a space experiment. The two-dimensional and three-dimensional, steady form of the compressible Navier-Stokes equations with chemical reactions are solved. With the coupled multi-dimensional solver of the radiative heat transfer, the model is capable of answering a number of questions regarding the experiment concept and the hardware designs. In this paper, the capabilities of the numerical model are demonstrated by providing the guidance for several experimental designing issues. The test matrix and operating conditions of the experiment are estimated through the modeling results. The three-dimensional calculations are made to simulate the flame-spreading experiment with realistic hardware configuration. The computed detailed flame structures provide the insight to the data collection. In addition, the heating load and the requirements of the product exhaust cleanup for the flow tunnel are estimated with the model. We anticipate that using this simulation tool will enable a more efficient and successful space experiment to be conducted.

Shih, Hsin-Yi↗

DOE OSTI2022

User-Oriented Improvements in the MOOSE framework in support of Multiphysics Simulation

The MOOSE Framework is a foundational capability used by NEAMS to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms including optimization, improved user experience, streamlined APIs, parallelism, and new capability. The work transcribed in the report was in direct support of the simulation tools and is already deployed or will be deployed in the coming months. The capabilities implemented were, in the same order as this report, chainable execution objects or executors, support for transfers between applications at the same level in a coupling scheme, support for boundary/subdomain restricted transfers, support for transfers between applications with different coordinate or unit systems, support for MOOSE applications in the NEAMS workbench, deployment of MOOSE application of the INL HPC OnDemand platform, addition of a triangular meshing library in libMesh and increased support of face variables.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

User-oriented Improvements in the MOOSE Framework in Support of Multiphysics Simulation

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed or will be deployed in the coming months. The capabilities were implemented in the same order as they are covered in this report: chainable execution objects or executors, support for transfers between same-level applications in a coupling scheme, support for boundary/subdomain restricted transfers, support for transfers between applications with different coordinate or unit systems, support for MOOSE applications in the NEAMS Workbench, deployment of the MOOSE application of the Idaho National Laboratory (INL) high-performance computing (HPC) OnDemand platform, the addition of a triangular meshing library in libMesh, and increased support of face variables.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗

DOE OSTI2023

NEAMS Technical Area Support in MOOSE

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

NEAMS Technical Area Support in MOOSE

The MOOSE framework is a foundational capability used by the NEAMS program to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and other new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities outlined in this report include enabling selective polynomial basis refinement, implementing a custom convergence system, building a scalable preconditioner for saddle-point problems, and much more.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

NEAMS Technical Area Support in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and other new capabilities. The work described in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities outlined in this report include implementing hash table matrix assembly for efficient sparsity pattern construction for contact in BISON, developing re-step testing infrastructure for ensuring the viability of overlapping domain coupling between SAM and Pronghorn, allowing unique preconditioners for single-input multi-system solves, supporting multi-system in MOOSE’s workhorse executioners, and many more smaller feature enhancements and bug fixes.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Performance Assessment of the Overall Building Envelope Thermal Performance—Building Envelope Performance (BEP) Metric

Today, to describe the thermal performance of the building envelope and its components we use a variation of metrics; such as, R-value, ACH (air exchange rate per hour), SHGC (solar heat gain coefficient) of windows, U-factor etc. None of these performance indicators is meant to represent the overall thermal performance. In this paper, such a metric is introduced, the BEP (building envelope performance) value. Unlike the thermal resistance, typically expressed as an R-value, the BEP-value considers additional elements of heat transfer that affect the energy demand of the building because of exterior and interior (solar) thermal loads: conductive and radiant heat transfer, and air infiltration. To demonstrate BEP’s utility, validation studies were carried out by comparing the BEP-value to theoretical results using whole building energy simulation tools such as EnergyPlus and WUFI Plus. Results show that BEP calculations are comparable to calculations made using these simulation tools and that unlike other similar metrics, the BEP-value accounts for all heat transfer mechanisms that are relevant for the overall energy performance of the building envelope. The BEP-value thus allows comparing envelopes of buildings with different use types in a fair and realistic manner.

42 ENGINEERING↗

DOE OSTI2022

Useability and Optimization Improvements in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed or will be deployed in the coming months. The capabilities were implemented in the same order as they are covered in this report: increased support of face variables, arbitrary spatial and temporal evaluation of material properties, and the addition of a triangular meshing library in libMesh.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Towards fully self-consistent optoelectronic simulation of planar devices

Photon recycling plays an important role in various optoelectronic devices, such as thin-film solar cells and intracavity double-diode structures presently studied for electroluminescent cooling. However, a complete description of photon recycling requires developing fully self-consistent simulation tools of photon and electronic charge transport in realistic device structures. In this paper, we describe a possible route towards such simulation tools in planar devices by combining the radiative transfer (RT) equation of photon transport with the drift-diffusion (DD) equations of electron and hole dynamics. We investigate the feasibility of the approach by selected proof-of-principle device studies. The results indicate that combining the RT and DD models not only yields the expected device characteristics, but also produces new insight into the pertinent local emission and absorption properties within the device structures. In particular, the model allows to accurately investigate how emission directivity and saturation affect the coupling coefficient in the double diode structures, showing that the main contribution to the coupling coefficient arises from the reflectivity of the top contact. For completeness, we also discuss how interference affects photon transport in resonant devices by an example calculation using the quantized fluctuational electrodynamics framework, paving way for self-consistent modeling tools that also account for wave-optical effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRSDate not supplied

CFD 2030 Grand Challenge: CFD-in-the-Loop Monte Carlo Flight Simulation for Space Vehicle Design

Flight qualification of space vehicles is markedly different from those typically employed for aircraft. The concept of an extensive flight test campaign for a space vehicle does not exist, and vehicle designers must look to alternative techniques for demonstrating robust and reliable performance of their vehicles prior to operational flight. A space vehicle may undergo only a handful of flight tests in its development cycle, with each flight representing a drastically different flight phase or flight configuration. For instance, NASA’s Space Launch System (SLS) launch vehicle and Orion spacecraft will only see a total of four flight demonstrations before flying a crew on its first operational mission, and each flight demonstrates a unique vehicle configuration and/or set of flight conditions. The SLS will be flown only one time before it becomes operational (Artemis 1). The Orion spacecraft Crew Module (CM) will have been tested twice, once on a Delta IV launch vehicle (Exploration Flight Test 1) and once as a fully integrated system with the SLS launch vehicle (Artemis 1). The Orion Launch abort system will have been tested twice, once in a pad abort scenario (Pad Abort 1) and once in an inflight abort scenario (Ascent Abort 2) on a modified Peacekeeper booster. Both of these latter tests involve only a boiler plate CM, not a functional Orion spacecraft. Thus, unlike aircraft, there is very little opportunity for engineers to assess and evaluate their preflight predictions. Instead, space vehicle designers rely on Monte Carlo flight simulations with detailed dispersions of predicted nominal flight behavior to determine how robust their design is to errors and uncertainties in the flight conditions their vehicle may encounter. These Monte Carlo analyses entail thousands of trajectory simulations to demonstrate that the vehicle can meet design requirements at a specified level of reliability. From an aerodynamics and aerothermodynamics perspective, these trajectory simulations are fueled by an extensive aerodynamic database that covers the complete range of expected flight conditions, vehicle configurations, and flight attitudes expected in a given mission. Today, these databases amount to a table of engineering parameters that can be quickly interrogated by the trajectory simulator. The aerodynamic and aerothermodynamic databases are assembled via a series of ground tests, empirical and analytical analysis, physics-based computational analysis, applicable past flight performance data, and in some cases, engineering judgment. These databases generally take years to assemble for a new space vehicle system and in the case of SLS/Orion, over a decade of test and analysis have been expended to develop the extensive databases required to cover the myriad of configurations and potential flight conditions required for the system. Recently, it has been proposed that Computational Fluid Dynamic (CFD) and computing capability may be reaching a point where it is foreseeable that CFD could be integrated directly into the production trajectory simulation tools used to design NASA’s space vehicles. To demonstrate this, NASA has embarked on two demonstrations of this type of capability, one where six degree of freedom flight trajectory simulation equations are embedded in an existing CFD solver and another where a production CFD solver is loosely coupled with a production trajectory simulation tool. These efforts represent an initial demonstration of a future approach to flight trajectory simulation, but they are a far cry from the capability required to perform a full-up CFD-in-the-loop Monte Carlo trajectory simulation. Therefore, this represents a viable grand challenge for computational methods addressing space vehicle design and development. The final paper/presentation will discuss the many hurdles, beyond simply raw computational power, to realizing this grand challenge and how they map directly to the CFD Vision 2030 ojectives. Among these are the wide range of flight conditions, including accelerating/decelerating flight, encountered by a space vehicle during launch and/or entry. The vehicle can also encounter numerous configuration changes, some of which can be quite drastic, during the course of its flight, so robust, automated geometry modeling, grid generation, and adaptation will play a huge role in reaching this goal. Multiply this by 1000’s of trajectory simulations occurring simultaneously in a given Monte Carlo analysis, and the problem readily scales to absorb virtually any size of supercomputer envisioned today. The concept of CFD-in-the-loop Monte Carlo trajectory simulation poses a formidable challenge for emerging and future computing systems, and it has the potential to shave years off the development cycle for aerodynamic and aerothermodynamic performance predictions as compared to today’s space vehicle design approach.

CFD 2030↗

DOE OSTI2021

Off-Road Vehicle Energy-Saving Potential

The U.S. Department of Energy’s (DOE’s) Vehicle Technology Office (VTO) has played a critical role in enabling electrification and reducing fuel consumption in the automotive sector. However, the off-road vehicle sector accounts for 8% of transportation fuel, and that share is likely to increase over time as onroad vehicles move towards electrification. DOE can again play a key role in the off-road vehicle market by providing tools that can be used by academia and the industry to evaluate the impact of advanced technologies. The need for a pre-competitive simulation tool was also identified during a 2019 DOE-led workshop at Argonne National Laboratory that included OEMs and suppliers to this market. Autonomie, a powerful and robust system simulation tool for vehicle energy consumption and performance analysis, was developed by Argonne National Laboratory in collaboration with General Motors. Its application covers energy consumption and performance analysis throughout the entire vehicle development process by leveraging its plug-and-play powertrain and vehicle model architecture and development environment

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗