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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Automating Rabi & Ramsey Measurements via Machine Learning

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗

NASA NTRS1994

Technology demonstration of space intravehicular automation and robotics

Automation and robotic technologies are being developed and capabilities demonstrated which would increase the productivity of microgravity science and materials processing in the space station laboratory module, especially when the crew is not present. The Automation Technology Branch at NASA Langley has been working in the area of intravehicular automation and robotics (IVAR) to provide a user-friendly development facility, to determine customer requirements for automated laboratory systems, and to improve the quality and efficiency of commercial production and scientific experimentation in space. This paper will describe the IVAR facility and present the results of a demonstration using a simulated protein crystal growth experiment inside a full-scale mockup of the space station laboratory module using a unique seven-degree-of-freedom robot.

Morris, A. Terry↗

DOE OSTI2023

A critical review of existing and emerging technologies and systems to optimize solid waste management for feedstocks and energy conversion

Solid waste generation and its accumulation is increasing at an alarming pace due to population growth and urbanization posing severe risks to health, safety, and natural ecosystems. Herein this review strategically addresses the challenges and solutions to increasing the sustainability footprint of solid waste management (SWM) systems by revealing multipronged approaches that reduce solid waste and handling costs while generating revenue and reducing greenhouse gas and related emissions. For example, the United States sends ~150 million tons of waste to landfills, which is composed of over 75% organic and recyclable materials having a potential to be diverted to alternative scenarios. The emergence of an automated upstream and downstream sorting process for solid waste to increase material diversion from landfills is a promising approach for creating sustainable SWM. The utilization of artificial-intelligence-enabled smart and automated systems at the home and industrial scales, comprehensive public re-education including awareness of the adverse effects of landfilled waste on the ecosystem, and more eco-friendly product development are required to significantly reduce landfills and their negative footprint.

09 BIOMASS FUELS↗

NASA NTRSDate not supplied

Impact of Latency and Reliability on Separation Assurance with Remotely Piloted Aircraft in Terminal Operations

Remotely Piloted Aircraft (RPA) for cargo operations in the national airspace system will impact safety due to, among other factors, the latency and reliability of command & control, and of communication. This paper investigates the safety impact with increasing mix of RPA amidst manned traffic in a generic arrival pattern with three merging flows. Latency was modelled as the response time between air traffic control's determination of a resolution and the RPAs' initiation of the maneuver. Reliability was modelled as a message drop probability. The experiment was repeated with two different aircraft types having different performance characteristics as representatives of RPA for conducting automated cargo operations. Overall response time above thirty seconds and message drop probability over twenty percent caused losses of separation. Specific results depended on the RPA aircraft type. The detailed impacts of latency and reliability with increasing mix of RPA traffic are provided. Applications of the approach for further studies at increasing levels of automation are also discussed.

Vishwanath Bulusu↗

NASA NTRS2022

Impact of Latency and Reliability on Separation Assurance with Remotely Piloted Aircraft in Terminal Operations

Vishwanath Bulusu↗

NASA NTRS2021

TLSLEAF: Automatic Leaf Angle Estimates From Single-Scan Terrestrial Laser Scanning

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

3D↗

DOE OSTI2026

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗

DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗

DOE OSTI2022

Editorial: Themed issue on data-intensive water systems management and operation

The water sector increasingly looks at intensified instrumentation, data collection and automation as tools for daily use. Still, a massive opportunity remains to fully embrace emerging methods and technologies, such as artificial intelligence, data analytics, low-cost sensor hardware, and cloud computing. Indeed, sensing and automation technology has already infiltrated many facets of society. As such, the time is ripe to evaluate the role of novel technologies for systems monitoring, diagnostics, and automation of aquatic processes and large-scale water systems. When leveraged, the water sector will do more with less.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Bringing Fault Detection and Diagnostics (FDD) Tools into the Mainstream: Retro Commissioning and Continuous Commissioning of HVAC and Refrigeration Systems

HVAC systems in the commercial building sector consume about 3.4 quads of energy annually. Of all the HVAC systems, packaged rooftop air–conditioning units (RTUs) provide cooling and heating for over 60 percent of the commercial building floorspace (about 90 billion ft2) in the U.S. and they are a significant source of energy consumption and peak demand. All HVAC systems suffer from faults that impact thermal comfort and increase energy consumption. There are several commercially available Automated Fault Detection and Diagnostic (AFDD) tools on the market that can detect and diagnose faults, and if those faults are corrected, can save significant national energy. However, there are multiple market barriers for these tools including lack of independent verification of their performance in the field in terms of their technical capabilities, ease of use and installation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

The Role of Timing in Industrial Control Systems: A Primer

Accurate and synchronized time is an important dependency within an industrial control system. Manipulation or degradation of timing can result in varying impacts based on the critical infrastructure sector. As control systems continue to be digitized and automated, they require more precise timing elements which increases the potential impact of a cyber-attack on timing elements.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Spray System Documentation

The intent of this document is to provide background information on key design decisions for the Automatic Spray System, developed at LLNL. The document will overview mechanical, electrical, and toolpath critical decisions. The purpose of this design is to repeatably and uniformly spray parts with a variety of materials. The main aspects of the design are an atomizing air siphon spray nozzle and two rotary axes, C and B. Both axes are controlled by a NEMA17 stepper motor. The C axis is essentially a turntable that rotates the part to be sprayed. The B axis is where the atomizing spray nozzle is attached to the “crawler,” which is a stepper motor on an internal gear. Traditionally, spraying of hemispherical mandrels was done by a trained operator. Depending on the operator, there may be varying thickness in the spray coating. Moving towards an automated spraying system will reduce this variability and increase thickness consistency and uniformity.

42 ENGINEERING↗

DOE OSTI2026

PV Operations Software Transparency: A PVMAC Industry Snapshot

The rapid growth of photovoltaic (PV) deployment has increased reliance on software platforms for monitoring, workflow automation, diagnostics, and performance analytics. As these tools play a central role in asset management and operations and maintenance (O&M), greater transparency in methodologies, data handling, and validation practices benefits the broader PV ecosystem. To better understand current practices and identify opportunities for improved clarity and interoperability, 24 software providers contributed detailed responses through the PV O&M Analytics Collaborative (PVMAC) initiative, the first structured questionnaire of its kind in the industry, covering onboarding, interoperability, data quality, diagnostics, AI/ML, and other operational categories. These providers represent over 1.1 TW of solar assets under management. The analysis shows broad adoption of digital twins, AI/ML, and API integrations, but also highlights challenges in onboarding processes, inconsistent definitions and methodologies, variability in key performance indicator (KPI) calculations, and limited independent validation. Greater standardization, clearer documentation, and stronger validation frameworks could improve transparency, comparability, and trust across PV operations software platforms.

14 SOLAR ENERGY↗

DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗

NASA NTRS1981

Meteorology impact on ATC system design

The impact of meteorology on air traffic control (ATC) system design for designs, and for cost benefit evaluations is discussed. The myriad of choices for implementation is a problem of great magnitude, given the economic climate of today. Cost versus benefit requires greater emphasis. Expanding and improving weather data acquisition, increasing the speed of weather data transmission and automating those actions that lend themselves to standardization for automated data processing are outlined. Three programs are mentioned: (1) automated weather observations, (2) weather radar and improvements to the national airspace system as related to the handling of weather data; and (3) products.

Vandemark, F. E.↗

NASA NTRS1995

Cognitive models of pilot categorization and prioritization of flight-deck information

In the past decade, automated systems on modern commercial flight decks have increased dramatically. Pilots now regularly interact and share tasks with these systems. This interaction has led human factors research to direct more attention to the pilot's cognitive processing and mental model of the information flow occurring on the flight deck. The experiment reported herein investigated how pilots mentally represent and process information typically available during flight. Fifty-two commercial pilots participated in tasks that required them to provide similarity ratings for pairs of flight-deck information and to prioritize this information under two contextual conditions. Pilots processed the information along three cognitive dimensions. These dimensions included the flight function and the flight action that the information supported and how frequently pilots refer to the information. Pilots classified the information as aviation, navigation, communications, or systems administration information. Prioritization results indicated a high degree of consensus among pilots, while scaling results revealed two dimensions along which information is prioritized. Pilot cognitive workload for flight-deck tasks and the potential for using these findings to operationalize cognitive metrics are evaluated. Such measures may be useful additions for flight-deck human performance evaluation.

Jonsson, Jon E.↗

NASA NTRS1997

2195 Aluminum-Copper-Lithium Friction Plug Welding Development

Technology developments and applications of friction plug welding is presented. This friction repair welding technology is being studied for implementation on the Space Transportation System's Super Light Weight External Tank. Single plug repairs will be used on a vast majority of weld defects, however, linear defects of up to several inches can be repaired by overlapping plug welds. Methods and results of tensile, bend, simulated service, surface crack tension and other tests at room and cryogenic temperatures is discussed. Attempts to implement Friction Plug Welding has led to both tool and process changes in an attempt to minimize expansive tooling and lengthy implementation times. Process control equipment and data storage methods intended for large scale production will also be addressed. Benefits include increased strength and toughness, decreased weld repair time, automated and highly reliable process, and a lower probability of having to re-repair defect locations.

Takeshita, Rike P.↗

NASA NTRS1997

NASA Advanced Transportation Technologies Program

The primary objective of the NASA Advanced Air Transportation Technologies (AATT) program is to fully explore the possibilities of the "Free Flight" concept. AVIATION products will enable substantial increases in the effectiveness of national and global air transportation systems. These increases will be achieved by developing and testing automation aids that can assist in the decision-making process among pilots, air traffic controllers, and dispatchers. The AATT program is responsible for defining, exploring, and developing advanced air traffic system concepts to a level suitable for preproduction prototype assessment by the FAA which, if successful, will result in full-scale deployment. These decision support tools will allow all airspace users to choose the best flight path for their own purpose within the constraints of safety and the needs of other users. To do this, several goals must be met: allow users to minimize operating costs by making trade-offs between time and routing; improve the effectiveness of high-density operations on the ground and in the air, enable safe operation in a smooth and efficient manner across boundaries of free-flight and capacity-constrained flight regions; provide system improvements that are easily deployable anywhere in the world; and improve the ability to simulate advanced capabilities in the airspace system.

Dudley, Michael R.↗