Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “automated experiments”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Dryland feedbacks to future climate change: how species mortality and replacement will affect coupled biogeochemical cycles and energy balance (Final Technical Report)

The overall goal of the dryland climate manipulation experiment is to improve our understanding and ability to forecast dryland responses to increasing temperatures and altered precipitation regimes. Drylands cover over 40% of Earth’s terrestrial surface and are predicted to increase in size 11-23% by 2100. Yet our understanding of this vast biome, which exchanges enormous amounts of CO 2 and energy with the atmosphere, is exceedingly poor. This project aims to transform and improve that understanding in order to reduce uncertainty and increase the confidence with which we can make global predictions of future climate. We focus on both above- and belowground processes, and explore temperature controls over critical aspects of carbon and nutrient cycling for dryland plants, soil, and microbes. We have 60 climate manipulation plots where plants (from above the canopy) and soils are warmed using infrared lamps and where precipitation has been altered using targeted watering regimes. We have measured myriad aspects of plant physiology, morphology, carbon exchange, energy exchange, and phenology, as well as analogous climate-induced changes to biological soil crusts and soil microbial communities. Our goals are to investigate temperature responses and acclimation potential of dryland plant and soil processes. Concurrent soil incubation experiments complement the in-situ soil warming component and enable more controlled mechanistic investigations of temperature response on microbial function. Automated soil CO 2 efflux chambers within the plots provide phenomenal opportunity to improve our understanding of dryland CO 2 exchange with the atmosphere and are coupled with plant CO 2 exchange measurements. The project has been exceptionally successful, with 35 peer-reviewed publications (and more in review), including in high-profile journals such as Nature Climate Change and PNAS. We have given over fifty presentations on the work and it has been featured in many high-profile media outlets, such as Discover Magazine and on NPR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Automating Orbit Correction In The Main Injector 8 GeV Line

The Main Injector 8 GeV line (MI8 line) transports beam from Fermilab s Booster accelerator to either the Booster Neutrino experiments (BNB), the Recycler or the Main Injector. Often the orbit of the beam through the MI8 line differs depending on the beam destination. The beam is collimated in the MI8 line, so increasing intensities and repetition rates make controlling orbits through the collimators a necessity. The current method of regulating the MI8 line orbit with DC corrector settings is insufficient. A system named MITUNE is being developed to sample and categorize all beams through the MI8 line and automatically calculate and apply proper dipole corrector ramps to maintain desired orbits for pulses to any destination.

Hazelwood, K. J. [Fermilab]↗
DOE OSTI2024

Towards non-contact pollution monitoring in sewers with hyperspectral imaging

Monitoring water quality in sewers is challenging, particularly because state-of-the-art technologies require contact with the raw wastewater. The presence of fat, oil, grease, and solids makes automated grab sampling difficult and causes sensor fouling. To overcome these limitations, non-contact methods based on light reflectance, such as hyperspectral imaging (HSI), are gaining attention. However, HSI has never been tested for raw wastewater. To assess its accuracy for measuring pollution, we developed a laboratory setup and performed targeted experiments with a combination of raw and diluted wastewater, as well as synthetic turbidity stock solutions. We measured seven pollution variables: chemical oxygen demand, turbidity, dissolved organic compounds, ammonium, total nitrogen, phosphate, and sulphates. We used automated pixel selection and partial least squares regression to retrieve pollution information from the hyperspectral images. Our results, based on 144 samples, suggest that HSI can estimate pollution levels with a precision in the range of state-of-the-art absorbance spectrophotometric methods. Additionally, we found that the combination of pixel and wavelength selection, enabled by the hyperspectral data structure, significantly influences the performance of partial least square modelling. Overall, our findings indicate that HSI is a promising technology for non-contact monitoring of water quality in raw wastewater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CYDRES: CYber Defense and REsilient System for securing grid-interactive efficient buildings

Smart buildings, especially Grid-interactive Efficient Buildings (GEBs), suffer from cyber-attacks and physical faults due to the integration of a large number of sensors and controls, connected devices, and associated communication networks. This study demonstrated a real-time advanced building resilient platform, called CYber Defense and REsilient System (CYDRES), which is deployable for existing and emerging Building Automation Systems (BASs). CYDRES aims to empower GEBs with cyber-attack-immune capabilities through multi-layer prevention and adaptation mechanisms to monitor, detect, and respond to cyber-attacks and physical operational faults. CYDRES is demonstrated through real-time experiments in a Hardware-in-the-Loop (HIL) testbed.

Building automation system, Cyber-attacks, Physica↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
DOE OSTI2021

Toward In Situ Synchrotron Mapping of Crystal Selection Processes during Crystal Growth

In this work, we present an automated and rapid method for non-destructive mapping of crystal grains in a rod-shaped sample. The approach was designed for application to in situ float-zone crystal growth experiments at an x-ray synchrotron source, but could be useful in other applications. The methods have been tested on a TiO 2 boule grown in an optical float zone furnace. The approach applies a statistical filter to polycrystalline diffraction patterns on 2D detectors to rapidly determine the degree of powder-quality 1of the signal. When larger crystals emerge in the growth, their position, size and shape can be tracked using an automated blob-tracking algorithm that follows individual Bragg peaks as a function of position in a grid-scan, even when multiple crystals are contributing spots to diffraction images. This method is found to be robust as the same crystal shape can be independently reconstructed using different sets of Bragg reflections. Image segmentation methods are then used to map out the polycrystalline grains. We also note that other information about crystal quality, such as mosaicity or strain state, may be inferred and mapped from the intensity variation of the Bragg peaks at different locations within the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2023

Production of zirconium-88 via proton irradiation of metallic yttrium and preparation of target for neutron transmission measurements at DICER

Abstract A process for the production of tens to hundreds of GBq amounts of zirconium-88 ( 88 Zr) using proton beams on yttrium was developed. For this purpose, yttrium metal targets (≈20 g) were irradiated in a ~16 to 34 MeV proton beam at a beam current of 100–200 µA at the Los Alamos Isotope Production Facility (IPF). The 88 Zr radionuclide was produced and separated from the yttrium targets using hydroxamate resin with an elution yield of 94(5)% (1σ). Liquid DCl solution in D 2 O was selected as a suitable 88 Zr sample matrix due to the high neutron transmission of deuterium compared to hydrogen and an even distribution of 88 Zr in the sample matrix. The separated 88 Zr was dissolved in DCl and 8 µL of the obtained solution was transferred to a tungsten sample can with a 1.2 mm diameter hole using a syringe and automated filling station inside a hot cell. Neutron transmission of the obtained 88 Zr sample was measured at the Device for Indirect Capture Experiments on Radionuclides (DICER).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

Abstract A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP’s saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence

The traditional development of novel metal–organic frameworks (MOFs) is often hindered by challenges such as synthetic accessibility and time- and resource-intensive experimentation. High-throughput, automated experimental and computational techniques have enabled rapid chemical space exploration and theoretical MOF design. When combined with artificial intelligence (AI), these methods can be used to lead autonomous laboratories to new frontiers for MOF discovery, where these materials can be designed for a specific application, efficiently synthesized, characterized, and evaluated. Here, this perspective highlights the role of AI in advancing automated MOF synthesis and characterization, computational MOF design and screening, and the integration of these approaches within autonomous workflows to ultimately enable the MOF laboratories of the future.

Gaidimas, Madeleine A. [Northwestern University, E↗
DOE OSTI2023

Automated High‐Throughput Fatigue Testing of Freestanding Thin Films

Abstract Mechanical testing at small length scales has traditionally been resource‐intensive due to difficulties with meticulous sample preparation, exacting load alignments, and precision measurements. Microscale fatigue testing can be particularly challenging due to the time‐intensive, tedious repetition of single fatigue experiments. To mitigate these challenges, this work presents a new methodology for the high‐throughput fatigue testing of thin films at the microscale. This methodology features a microelectromechanical systems‐based Si carrier that can support the simultaneous and independent fatigue testing of an array of samples. To demonstrate this new technique, the microscale fatigue behavior of nanocrystalline Al is efficiently characterized via this Si carrier and automated fatigue testing with in situ scanning electron microscopy. This methodology reduces the total testing time by an order of magnitude, and the high‐throughput fatigue results highlight the stochastic nature of the microscale fatigue response. This manuscript also discusses how this initial capability can be adapted to accommodate more samples, different materials, new geometries, and other loading modes.

Barrios, Alejandro↗
DOE OSTI2021

Advanced calculations of X-ray spectroscopies with FEFF10 and Corvus

The real-space Green's function code FEFF has been extensively developed and used for calculations of X-ray and related spectra, including X-ray absorption (XAS), X-ray emission (XES), inelastic X-ray scattering, and electron energy-loss spectra. The code is particularly useful for the analysis and interpretation of the XAS fine-structure (EXAFS) and the near-edge structure (XANES) in materials throughout the periodic table. Nevertheless, many applications, such as non-equilibrium systems, and the analysis of ultra-fast pump–probe experiments, require extensions of the code including finite-temperature and auxiliary calculations of structure and vibrational properties. To enable these extensions, we have developed in tandem a new version FEFF10 and new FEFF -based workflows for the Corvus workflow manager, which allow users to easily augment the capabilities of FEFF10 via auxiliary codes. This coupling facilitates simplified input and automated calculations of spectra based on advanced theoretical techniques. The approach is illustrated with examples of high-temperature behavior, vibrational properties, many-body excitations in XAS, super-heavy materials, and fits of calculated spectra to experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Improvements to MOOSE user workflow through polyhedral elements, automation, and concise physics syntax

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 MOOSE's broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of NEAMS tools and includes: addition of support for polyhedral elements, incorporation of mesh smoothers for mesh repair, integration of the Physics and ActionComponents systems, expansion of the Convergence system, and exploration of automated input file generation. These five areas of development are fundamental capabilities that will be leveraged by many NEAMS applications.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2021

Automated Internal Energy Calibration by OnTheFly for AGR-5/6/7

The software program, OnTheFly, was developed at Idaho National Laboratory (INL) to keep high-purity germanium (HPGe) detectors energy calibrated during very long experiments. Over time, spectra produced from a HPGe detector will slowly stretch or contract, causing the energy calibration to change. If the energy calibration changes too much, it will cause energy lines to be misidentified. The higher the energy line the more affected they are by this change. OnTheFly tracks and adjusts the slowly changing energy calibrations so the energy lines will not be misidentified. OnTheFly was used with the Fission Product Monitoring System (FPMS) during the AGR-5/6/7 irradiation experiment performed at INL’s Advanced Test Reactor (ATR). Five HPGe detectors were used to monitor the five capsules in AGR-5/6/7. There were also two extra HPGe detectors that were maintained as spares. Before each ATR cycle of the experiment, the HPGe detectors were energy calibrated with a thorium radioactive source. During each ATR cycle, each detector would acquire data for 8 hours and then reset and acquire data for another 8 hours. After each 8-hour run, the spectra would be saved and analyzed autonomously. The results were then read by OnTheFly and used to produce a new energy calibration for each detector. The new energy calibrations were compared to the current energy calibrations and, if certain criteria were met, the new energy calibration would replace the current energy calibration. The new energy calibration would be used for future spectra. OnTheFly was run at least every couple of days but could be run as much as after each 8-hour run. OnTheFly kept the spectra energy calibrated during the ATR cycles over the 2 years and 5 months that the AGR-5/6/7 experiment ran.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗