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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

2024 Nasa Lunabotics University Competition: Site Preparation With Bulk Regolith

Introduction: Lunabotics provides accredited institutions of higher learning students (vocational-technical, college, university) an opportunity to apply the NASA systems engineering process to design and build a prototype robot. This robot would be capable of performing the proposed operations on the Lunar surface in support of future Artemis mission goals. Lunabotics features a systems engineering design challenge to engage students in the next phase of hu-man space exploration supporting the Artemis missions. This two-semester event encourages students to design and build an autonomous or telerobotic robot designed to traverse the simulated Lunar surface and complete the assigned construction tasks. The number of teams accepted into this challenge is not predetermined but is based on the scores and overall quality of the Project Management Plans received and other factors. The culmination of the Lunabotics virtual challenge will be the design, build and operation of a functional prototype Lunar robot. Teams are required to submit the following: (1) Project Management Plan, (2) Systems Engineering Paper, a (3) STEM Engage-ment Report, and a (4) Proof of Life Video. This is an optional item, but to qualify for the grand prize a team must also submit a: (5) Presentation and Demonstration. Background: The NASA Lunabotics University Competition was first held in 2010 as a follow on to the NASA Regolith Excavation Challenge [1]. The high level of interest and participation from over 50 universities each year has led to a sustained annual competi-ion cadence: it has been held every May for the past 14 years [2,3]. Over 6,000 students have participated and been inspired to pursue Science, Technology, En-gineering and Mathematics careers (STEM). 2024 Competition: The necessary lunar surface tasks are evolving to meet the NASA Artemis Mission requirements. In the past Lunabotics challenges we gathered data to support Lunar mining for consuma-bles in the Lunar regolith. Now, in 2024, the task is to gather data on Lunar site preparation and construction by designing and building a robot that will traverse the chaotic Lunar terrain and construct a regolith-based berm. The goal is to build a berm structure which would be useful to the Artemis Mission for blast and ejecta protection during lunar landings and launches, shading cryogenic propellant tank farms, providing radiation protection around a nuclear power plant and other mission critical uses. Lunabotics will consist of three separate events this year. The first event is NASA’s Lunabotics Project Development Challenge, where teams submit various deliverables to be scored by judges. The second event will be the University of Central Florida (UCF) Lunabotics Qualification challenge, in the Exolith laboratory, where teams will put their de-signs to the test. The top ten scoring teams from the Qualification challenge are then invited to the third and final event, NASA’s Lunabotics On-Site Challenge at Kennedy Space Center in Florida. This presentation will summarize the results and lessons learned from the NASA Lunabotics University Competition held in May 2024.

Lunabotics↗
NASA NTRS2016

Acoustic Analysis and Design of the E-STA MSA Simulator

The Orion European Service Module Structural Test Article (E-STA) Acoustic Test was completed in May 2016 to verify that the European Service Module (ESM) can withstand qualification acoustic environments. The test article required an aft closeout to simulate the Multi-Purpose Crew Vehicle (MPCV) Stage Adapter (MSA) cavity, however, the flight MSA design was too cost-prohibitive to build. NASA Glenn Research Center (GRC) had 6 months to design an MSA Simulator that could recreate the qualification prediction MSA cavity sound pressure level to within a reasonable tolerance. This paper summarizes the design and analysis process to arrive at a design for the MSA Simulator, and then compares its performance to the final prediction models created prior to test.

acoustics↗
DOE OSTI2020

Optimizing Simulation Parameters for Weak Lensing Analyses Involving Non-Gaussian Observables

We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in ray-tracing dark matter (DM)-only simulations, to two hyper-parameters that influence the accuracy as well as the computational cost of the predictions: the thickness of the lens planes used to build past light cones and the mass resolution of the underlying DM simulation. The statistics considered are the power spectrum (PS) and a series of non-Gaussian observables, including the one-point probability density function, lensing peaks, and Minkowski functionals. Counterintuitively, we find that using thin lens planes (< 60 h {sup −1} Mpc on a 240 h {sup −1} Mpc simulation box) suppresses the PS over a broad range of scales beyond what would be acceptable for a survey comparable to the Large Synoptic Survey Telescope (LSST). A mass resolution of 7.2 × 10{sup 11} h {sup −1} M {sub ⊙} per DM particle (or 256{sup 3} particles in a (240 h {sup −1} Mpc){sup 3} box) is sufficient to extract information using the PS and non-Gaussian statistics from weak lensing data at angular scales down to 1′ with LSST-like levels of shape noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2023

Building-to-Grid Modeling Framework with a Case Study of Battery Systems

Coordinating electricity load shifting operations of multiple buildings has the potential to achieve significant benefits for both building owners and grid operation. In support of this research, this paper develops a novel building-to-grid modeling framework, which uses EnergyPlus to develop physics-based building energy models and GridLAB-D to develop distribution-level grid models. The data exchanges between buildings and distribution-level grid are conducted using Python script and HELICS co-simulation platform. Then, by using this modeling framework, this paper conducts a case study of battery systems in a connected community, which consists of 10 multi-family buildings with appropriately sized batteries. The three scenarios, baseline operation, tolerant to price fluctuations, and opportune usage, are studied. The result displays that, by adopting onsite batteries, both electricity costs for building owners and aggregated peak power demands are reduced.

Building-to-grid, connected community, co-simulati↗
DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗
DOE OSTI2025

Automated model generation and parameter estimation of building energy models using an ontology-based framework

This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Chemical and architectural intricacy from nanoscale tetrahedra and their analogues

The tetrahedron, as the simplest platonic shape, is a profound building block with the potential to create intricate superstructures. Noteworthy designs utilizing tetrahedral building blocks include the Sierpiński tetrahedron (the most fundamental three-dimensional fractal), a one-dimensional helical structure known as the tetrahelix, and various crystalline and quasicrystalline packings. Historically, the practicality of tetrahedral superstructures has been evident, providing stable, well-defined frameworks for various constructions, including truss bridges, tower cranes, and electricity transmission line pylons. In the field of self-assembled nanocrystal superlattices, tetrahedral nanocrystals, as building blocks, occupy a unique place among all the possible nanoscale particles. Mathematical models, simulation work, and experimental studies using nanocrystals in the laboratory have suggested that self-assembled structures derived from nanoscale tetrahedral building blocks are notably intricate, giving rise to new horizons of high-entropy nanocrystal superlattices. An important implication from previous works is that such tetrahedral nanocrystal superlattices form through highly delicate interparticle interactions, emphasizing the importance of the fine features of these nanocrystals. In this article, we summarize the advances in superlattices assembled from tetrahedral nanocrystals. Here, we first define the tetrahedron and tetrahedron analogues based on Conway’s transformation and graph theory, underscoring their relevance to the crystallization process producing tetrahedral nanocrystals. Then, we showcase previous reports on the synthesis of tetrahedral nanocrystals and the resulting nanocrystal superstructures. Finally, we conclude by offering insights and perspective into the chemical and architectural intricacy that could emerge from tetrahedral nanocrystals.

Nanostructure↗
DOE OSTI2024

Driving forces for particle-based crystallization: From experiments to theory and simulations

The multi-step crystallization processes involving the formation of stable building blocks that subsequently assemble into a crystal are ubiquitous in mineral formation and biomineralization and are particularly attractive in materials synthesis. Utilizing these pathways offers the approach to overcoming the restrictions on the expression of various crystal faces imposed by the interfacial energy during monomer-by-monomer growth to unlock the breadth of architectures with unique properties. Further, controlling particle-based crystallization proved challenging despite its promise due to the complex interdependence of interfacial forces and their non-linear dependence on synthesis parameters. Here, the status of the development of state-of-the-art approaches to measuring interparticle forces and predictive theoretical models of particle-based crystallization are reviewed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2 , CH 4 ) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3 ) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

Pau, G. S. H.↗
NASA NTRSDate not supplied

Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky

The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.

Shan Zeng↗
DOE OSTI2017

Foresee: A user-centric home energy management system for energy efficiency and demand response

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Energy Conservation Through Cloned Execution Of Simulations

High-performance computing facilities used for scientific computing draw enormous energy, some of them consuming many megawatt-hours. Saving the energy consumption of computations on such facilities can dramatically reduce the total cost of their operation and help reduce environmental effects. Here, we focus on a way to reduce energy consumption in many ensembles of simulations. Using the method of simulation cloning to exploit parallelism while also significantly conserving the computational and memory requirements, we perform a detailed empirical study of energy consumed on a large supercomputer consisting of hardware accelerator cards (graphical processing units, GPUs). We build on previous insights from mathematical analysis and implementation of cloned simulations that result in computational and memory savings by several orders-of-magnitude. Using instrumentation to track the power drawn by thousands of accelerator cards, we report significant aggregate energy savings from cloned simulations.

Yoginath, Srikanth↗
DOE OSTI2026

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗
DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II - Clustering Extremely High-Dimensional Grid-Based Data

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Assessment of MELCOR 2.1: Containment Thermal-Hydraulic Tests in the Heissdampfreaktor (HDR) Facility

MELCOR is being developed at Sandia National Laboratories for the U.S. Nuclear Regulatory Commission. MELCOR is a fully integrated code (encompassing the reactor coolant system and the containment building) that models the progression of postulated accidents in light water reactor power plants. It provides a capability for independently auditing analyses submitted by reactor manufacturers and utilities. In order to assess the adequacy of containment thermal - hydraulic modeling incorporated in the MELCOR code, a key containment test facility was analyzed. This report documents MELCOR code calculations for simulating steam - water blowdown tests performed in the Heissdampfreaktor ( HDR) de-commissioned containment facility located near Frankfurt , Germany . These tests are a series of blowdown experiments in a large scaled test facility ; including some tests with the addition of hydrogen release which are intended to simulate a variety of postulated break s inside large containment buildings. The key objectives of this MELCOR assessment are to study: (1) the expansion and transport of high energy steam - water releases, (2) heat and mass transfer to structural passive heat sinks, and (3) containment gas mixing and stratification. Moreover, MELCOR results are compared to the CONTAIN code for the same tests.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Greenbuilt Retrofit Test House (Final Report)

The Greenbuilt house is a 1980's era house in the Sacramento area that was a prominent part of Sacramento Municipal Utility District's (SMUD) Energy Efficient Remodel Demonstration Program. The house underwent an extensive remodel, aimed at improving overall energy efficiency with a goal of reducing the home's energy use by 50%. NREL researchers performed a number of tests on the major systems touched by the retrofit to ensure they were working as planned. Additionally, SMUD rented the house from Greenbuilt Construction for a year to allow NREL to perform a number of tests on the cooling system and the water heating system. The goal of the space conditioning tests was to find the best ways to cut cooling loads and shift the summer peak. The water heating system, comprised of an add-on heat pump water heater and an integrated collector-storage solar water heater, was operated with a number of different draw profiles to see how varying hot water draw volume and schedule affected the performance of the system as a whole. All the experiments were performed with the house empty, with a simulated occupancy schedule running in the house to mimic the load imposed by real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Developing a Database of Bio-based Materials for Building Envelope Applications

Oak Ridge National Laboratory (ORNL) has been funded by the Department of Energy (DOE) to help accelerate the introduction of building envelope materials that would reduce the carbon footprint of the buildings sector. The DOE’s Building Technologies Office has historically sought to resolve the knowledge gaps regarding the energy efficiency and moisture durability of building envelope systems and to develop the data, guidance, and tools needed to facilitate rapid industry adoption of high-performance, moisture-managed envelope systems. This project will help accelerate the widespread acceptance of a new generation of building materials developed specifically with the intent of reducing the carbon footprint of buildings. We have produced a database of hygrothermal transport properties on low embodied carbon building materials that can be added to energy and durability simulation tools. Properties that were measured include density, heat capacity, thermal conductivity as a function of temperature and relative humidity, moisture dependent permeance, and sorption isotherms as a function of relative humidity. These data sets were measured following consensus national standards using state-of-the-art facilities. The data has been compiled and is being made available to building designers who require these data to assess these new materials in their designs. We will publish the data and seek its addition to reference databases such as the ASHRAE Handbook of Fundamentals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗