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At least 649 records · Page 36

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NASA NTRS2016

Lightweight and High-Resolution Single Crystal Silicon Optics for X-ray Astronomy

We describe an approach to building mirror assemblies for next generation X-ray telescopes. It incorporates knowledge and lessons learned from building existing telescopes, including Chandra, XMM-Newton, Suzaku, and NuSTAR, as well as from our direct experience of the last 15 years developing mirror technology for the Constellation-X and International X-ray Observatory mission concepts. This approach combines single crystal silicon and precision polishing, thus has the potential of achieving the highest possible angular resolution with the least possible mass. Moreover, it is simple, consisting of several technical elements that can be developed independently in parallel. Lastly, it is highly amenable to mass production, therefore enabling the making of telescopes of very large photon collecting areas.

X-ray optics↗

NASA NTRS2019

Pixel-Based Smoke Detection with Neural Network

Exposure to biomass burning has been linked to respiratory and cardiovascular illnesses in humans. Traditional satellite based visual and multispectral smoke detection methods are not scalable to capabilities of new generations of remote sensing platforms. We develop a scalable, deep learning based detection model capable of identifying smoke pixels using GOES-16 shortwave reflectance data and present a operational web-based tool to visualize smoke predictions.

Ramasubramanian, M.↗

NASA NTRS2020

Multi-Agent Motion Planning using Deep Learning for Space Applications

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.

Madani, Ramtin↗

NASA NTRSDate not supplied

Design Development of a Stable, Lightweight, Tall and Self-Deploying Lunar Tower

Deployable composite booms with spaceflight heritage are being investigated at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) and the Massachusetts Institute of Technology (MIT) Space Resources Workshop for their potential to be vertically deployed in the lunar gravity field, in support of the NASA Artemis campaign. This paper reports new design development results—after the original presentation at the NASA 2020 BIG Idea Challenge—for a 16.5-meter-tall, compact, self-deploying composite tower intended to support the exploration of lunar permanently shadowed regions by nearby robotic assets or humans. Possible applications include vertical solar arrays and the provision of elevated lines-of-sight to science or engineering payloads, in support of nearby targets operating in areas of interest that may be hard to reach. Useful elevated payloads include radio repeaters, remote sensing and imaging, navigation and power beaming systems. However, while these lightweight rollable booms have an excellent height to mass ratio, they typically exhibit axial curvature upon deployment resulting in appreciable lateral dead-load deflection of the tip mass relative to the tower base. This static deflection increases with tower height and tip mass, not only constraining the value delivered by the tower but also endangering its integrity. To develop a competitive, lightweight deployable composite boom tower, a capability to correct static deflections during and after deployment will be required. In this paper, a deployable guy wire stability system will be presented for the MIT / LaRC self-erecting composite boom lunar tower that provides real time measurements, maintains tension both actively (during deployment) and passively (post-deployment), and can serve as a reconfigurable platform to test and trade alternative stability system configurations, such as with added spreaders inspired by sailing boat masts. Using a calibrated photogrammetry system, the natural lateral deflection of the boom tip relative to the boom base at different deployed heights was recorded for different configurations. With real-time force measurements it was found that tensioned guy wires can significantly reduce the static tip deflection of a deployable composite boom under dead load and can dampen a dynamic oscillation in under a minute. It was also found that control authority is greatest where it is needed most, i.e., for the lever arm closest to being opposite the direction of deflection. For a tower height of at least 11 m and spreader length of at least 60 cm, a solution of differential tension in all three arms exists and, in principle, provides sufficient control authority to correct or significantly reduce boom tip deflections. Notably, natural deflections occur almost entirely normal to the seams of the boom cross-section, but the natural boom tip lateral deflection under dead load upon deployment was approximately 5% of boom deployed length, exceeding the manufacturing acceptance specification of 1%. Ongoing and future work includes the further investigation towards mitigating manufacture-caused lateral deflection, trading of alternative guy wire system designs, as well as the design development of a second-generation tower incorporating a more capable boom design with the learnings from the proof-of-concept system presented here.

deployable composite boom↗

NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗

NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗

DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗

NASA NTRSDate not supplied

HydroSAR: A Cloud-based SAR Data Analysis Service to Monitor Hydrological Disasters and their Impact on Population and Agriculture

Weather-related hazards are ubiquitous around the world including: 1) hurricane storm surges, 2) rapid snowmelt and heavy rainfall, 3) severe weather leading to flash floods, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements and has the potential to impact agricultural productivity. In each setting, end-users in disaster management need access to data processing tools helpful in mapping past and current disasters. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future. Having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data are particularly useful for these activities due to their all-weather 24/7 monitoring capabilities. In this effort we present HydroSAR, a cloud-based SAR data analysis service for the mapping of meteorological and hydrological disasters as well as their impact on population and agriculture. As part of this project we have developed a series of SAR-based value added products for the monitoring of surface hydrology (image time series, change detection, flood extent, flood depth) and the assessment of impacts on population (flood depth) and agriculture (active agriculture, inundated agriculture, flood duration). We also developed a cloud-based platform for generating these products over affected areas and are working with end-users to integrate derived product into decision-making workflows The paper will briefly introduce the SAR-based products that were developed for this effort. We describe the cloud-based production pipeline that was built to automatically generate these products in near-real time over extended regions. The integration of SAR-based information into hazard preparation and response activities is described for a number of recent disasters including the 2019 forest fires in Alaska, 2019 flooding in the U.S. Midwest, the 2020 U.S. severe weather easter outbreak, 2020 tropical storm Christobal, 2020 cyclone Amphan, 2020, Alaska Spring breakup flooding, and the 2020 flood season in Eastern India, Bangladesh, and Nepal.

Franz Josef Meyer↗

NASA NTRS1982

A computer simulation approach to measurement of human control strategy

Human control strategy is measured through use of a psychologically-based computer simulation which reflects a broader theory of control behavior. The simulation is called the human operator performance emulator, or HOPE. HOPE was designed to emulate control learning in a one-dimensional preview tracking task and to measure control strategy in that setting. When given a numerical representation of a track and information about current position in relation to that track, HOPE generates positions for a stick controlling the cursor to be moved along the track. In other words, HOPE generates control stick behavior corresponding to that which might be used by a person learning preview tracking.

Green, J.↗

DOE OSTI2025

Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy

The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment.

17 WIND ENERGY↗

NASA NTRSDate not supplied

AstroAmpSeq: Microbial Bioinformatics Education with NASA GeneLab’s Amplicon Pipeline

The prevalence and importance of large sequencing datasets in microbiology has led to a movement to share microbial ecology experimental data through open-access databases. This is particularly true of experiments that are difficult to replicate, such as those conducted in the spaceflight environment and shared via NASA GeneLab. It is now possible and indeed valuable for students to access and re-analyze these shared datasets for educational and research purposes. To provide students with experience utilizing microbial bioinformatics tools, GeneLab for Colleges and Universities (GL4U) has designed AstroAmpSeq, a week-long, virtually implemented project-based learning (PBL) minicourse to instruct undergraduate students on 16S amplicon sequencing. AstroAmpSeq was created to be accessible to students without prior bioinformatics or microbial ecology experience. During the minicourse students work in teams to process, analyze, and visualize a subsample of GeneLab dataset GLDS-280 using GeneLab’s standard amplicon processing pipeline, which is based in R. Students develop a hypothesis related to the dataset then generate and analyze figures to evaluate their hypothesis. Formative assessment of student learning is determined via pre- and post-evaluations, peer feedback, and self-reflection. Project and presentation rubrics serve as a summative assessment of student learning. GL4U AstroAmpSeq not only meets American Society for Microbiology Curriculum Guidelines, but also incites student interest in research by an inquiry-based approach and can be made part of a larger semester-long curriculum. GL4U AstroAmpSeq raises awareness of space microbiology and bioinformatics as a field and career path among undergraduates. Further, by using a GeneLab dataset and nesting microbiology techniques into the real-world application of space biology, AstroAmpSeq enforces deeper and longer-lasting student learning.

microbiology↗

DOE OSTI2025

Automated Nanocrystal Synthesis: Lessons from 25 Years of Robots, Microfluidics, and Machine Learning

Here, this perspective highlights the evolution of techniques for automating the synthesis of colloidal nanocrystals. Over the past 25 years, microfluidic reactors and robotic workflows have been developed to enhance the reproducibility of nanocrystal synthesis, facilitate rapid screening of reaction conditions, optimize material properties, and perform multistep syntheses of high-quality nanoparticles with complex heterostructures. Modern automated systems are now valued for their ability to generate robust data sets for validating physical models, supporting chemical mechanisms, training machine learning models, and for directing autonomous experimentation. We discuss the early challenges and limitations of these technologies and present key lessons for effectively utilizing automated and ML-guided tools to accelerate nanocrystal discovery for the next 25 years.

Nanocrystals↗

DOE OSTI2026

U.S. Department of Energy Solar District Cup Collegiate Design Competition

The Solar District Cup is a multidisciplinary collegiate competition that challenges student teams to design and model standalone solar and solar-plus-storage systems for multiple buildings on a local electrical distribution network, such as a college campus, across a development, or in a district. Students learn cross-cutting skills that prepare them to become part of the next generation of the distributed energy workforce. Student teams act as renewable energy developers proposing to solar and solar-plus-storage system requests. Students, faculty, and prospective employers can benefit from knowing about this program and what students learn--either immediately as participants, mentors, or judges, or as potential employers of Solar District Cup alumni. RE+ is also one of our competition partners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2016

Overview of Proposed ISRU Technology Development

ISRU involves any hardware or operation that harnesses and utilizes in-situ resources (natural and discarded) to create products and services for robotic and human exploration: Assessment of physical, mineral chemical, and volatile water resources, terrain, geology, and environment (orbital and local). Production of replacement parts, complex products, machines, and integrated systems from feedstock derived from one or more processed resources. Civil engineering, infrastructure emplacement, and structure construction using materials produced from in situ resources. Radiation shields, landing pads, roads, berms, habitats, etc. Generation and storage of electrical, thermal, and chemical energy with in situ derived materials. Solar arrays, thermal wadis, chemical batteries, etc. ISRU is a disruptive capability: Enables more affordable exploration than todays paradigm. Allows more sustainable architectures to be developed. Understand the ripple effect in the other Exploration Elements: MAV: propellant selection, higher rendezvous altitude (higher DV capable with ISRU propellants). EDL: significantly reduces required landed mass. Life Support: reduce amount of ECLSS closure, reduce trash mass carried through propulsive maneuvers. Power: ISRU drives electrical requirements, reactant and regeneration for fuel cells for landers, rovers, and habitat backup. Every Exploration Element except ISRU has some flight heritage (power, propulsion, habitats, landers, life support, etc.) ISRU will require a flight demonstration mission on Mars before it will be included in the critical path. Mission needs to be concluded at least 10 years before first human landed mission to ensure lessons learned can be incorporated into final design. ISRU Formulation team has generated a (still incomplete) list of over 75 technical questions on more than 40 components and subsystems that need to be answered before the right ISRU system will be ready for this flight demo.

Mars exploration↗

DOE OSTI2025

Hydropower Market Game

SF-25-121 The Hydropower Market Game is an interactive educational software developed to teach fundamental concepts of hydropower generation, operations, and its role within electricity markets. Developed in Python and powered by Pygame, the game combines narrative-driven learning with progressive, hands-on levels where players explore topics such as the relationship between water flow and power generation, dam operations, pumped-storage systems, market interactions, and environmental constraints.Designed to support outreach and workforce development initiatives, the game provides an intuitive and engaging way to build energy literacy and raise awareness of hydropower’s contribution to the nation’s energy system.

Ploussard, Quentin [Argonne National Laboratory (A↗

NASA NTRS1981

Analogical acts as conceptual strategies in science, engineering and the humanities

The composing models which operate by means of analogy are identified. The importance of analogical acts in the prewriting stage of the composing process is discussed. The relations between analogical acts and concept formation are explored. A basic correspondence between the analogical thinking employed in successful learning and analogical thinking as a composing strategy is discussed. Teaching analogical acts as conceptual strategies for exploring problems and generating the form and content of discourse is presented in support of the contention that writing is a unique mode of learning.

Winkler, V. M.↗

DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗