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Results for “automated experimentation”

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.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An empirical investigation of organic software product lines

Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 20 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Malicious Cyber Activity Detection using Zigzag Persistence

In this study we synthesize zigzag persistence from topological data analysis with autoencoder-based approaches to detect malicious cyber activity, and derive analytic insights. Cybersecurity aims to safeguard computers, networks, and servers from various forms of malicious attacks, including network damage, data theft, and activity monitoring. We focus on the cybersecurity domain and investigate the detection of malicious activity using log data. We consider the dynamics of the log data and explore the changing topology of a hypergraph representation of this data to gain insights into the underlying activity. These hypergraphs capture complex interactions between processes, together with their temporal information. To study the changing topology we use zigzag persistence, which captures how topological features persist at multiple dimensions over time. We observe that this detects malicious activity in a cyber data set. To automate this detection we implement an autoencoder trained on a vectorization of the resulting zigzag persistence barcodes. Our experimental results demonstrate the effectiveness of the autoencoder in detecting malicious activity. Overall, this study highlights the potential of zigzag persistence and its combination with temporal hypergraphs for analyzing cybersecurity log data and detecting malicious behavior.

hypergraphs, temporal hypergraph, topological data↗
NASA NTRS1992

An experiment in vision based autonomous grasping within a reduced gravity environment

The National Aeronautics and Space Administration's Reduced Gravity Program (RGP) offers opportunities for experimentation in gravities of less than one-g. The Extravehicular Activity Helper/Retriever (EVAHR) robot project of the Automation and Robotics Division at the Lyndon B. Johnson Space Center in Houston, Texas, is undertaking a task that will culminate in a series of tests in simulated zero-g using this facility. A subset of the final robot hardware consisting of a three-dimensional laser mapper, a Robotics Research 807 arm, a Jameson JH-5 hand, and the appropriate interconnect hardware/software will be used. This equipment will be flown on the RGP's KC-135 aircraft. This aircraft will fly a series of parabolas creating the effect of zero-g. During the periods of zero-g, a number of objects will be released in front of the fixed base robot hardware in both static and dynamic configurations. The system will then inspect the object, determine the objects pose, plan a grasp strategy, and execute the grasp. This must all be accomplished in the approximately 27 seconds of zero-g.

Grimm, K. A.↗
NASA NTRS2014

An Experimental Study of the Effect of Transparency on Pilot Trust in the Emergency Landing Planner

This experimental study examined the effects of transparency (operationalized as increasing levels of explanation) on pilot trust of an automated emergency landing planner. A low-fidelity study was conducted where commercial pilots (N12) interacted with simulated recommendations from NASA's Emergency Landing Planner (ELP). These recommendations varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human-machine interface of the ELP.

transparency↗
DOE OSTI2025

Automation of debris removal and resurfacing of insulators in terawatt pulsed power systems using state of the art laser technology

The increasing complexity and scale of pulsed power systems necessitate effective maintenance strategies to ensure optimal performance and safety. This report investigates the automation of debris removal and resurfacing of insulators in terawatt pulsed power systems using advanced laser cleaning technologies. The motivation for this research stems from the limitations of current manual cleaning methods, which are labor-intensive and pose health risks due to hazardous materials. The study addresses the problem of surface flashover, a significant issue affecting insulator performance, by evaluating two innovative cleaning techniques: Pulsed Laser Cleaning (PLC) and Flash Lamp Annealing (FLA). Experimental results demonstrate that PLC effectively restores the dielectric strength of Rexolite insulators, while FLA shows limited success. The findings highlight the potential for automated cleaning solutions to enhance safety and efficiency in insulator maintenance, paving the way for future advancements in pulsed power technology.

42 ENGINEERING↗
DOE OSTI2021

A Simple, Cost-Effective, and Automation-Friendly Direct PCR Approach for Bacterial Community Analysis

Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples.

16S rRNA sequencing↗
NASA NTRS1985

Experimental Development of a Failure Detection Scheme for Large Space Structures

Large flexible spacecraft may require control systems consisting of large numbers of sensors and actuators. To assure a viable mission, the control system should tolerate failures of some of the control components. Hence, it is desirable to automate the process of failure detection, identification, and control system reconfiguration (FDI&R). Some of the opportunities to accommodate failure in the spacecraft design are reviewed. Some methods for FDI&R are presented in overview, and the method chosen for experimental testing is described. Finally, the experimental activities leading to the validation of the technique are presented.

Montgomery, R. C.↗
DOE OSTI2024

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Large-scale computational polymer solubility predictions and applications to dissolution-based plastic recycling

Dissolution-based plastic recycling is a promising approach to separate and recover high quality pure polymer resins from multicomponent plastic waste by exploiting differences in polymer solubility. The design of a dissolution-based polymer recycling process requires the selection of appropriate solvent systems and operating temperatures to dissolve only target polymers. Determining these parameters experimentally is challenging due to the wide range of solvents and temperatures possible for a given set of target polymers. In this work, we report a computational scheme that employs molecular dynamics simulations and the Conductor-like Screening Model for Realistic Solvents to predict polymer solubilities. Using this scheme, we established a computational solubility database for 8 common polymers and 1007 solvents at multiple temperatures and measured selected solubilities experimentally to validate computational predictions. Analysis of functional groups within this large database then provides chemical heuristics relating the molecular structures of good and non-solvents for selected polymers. We further developed a tool that automates the selection of solvents for all possible sequences in which target polymers can be selectively dissolved to guide the design of dissolution-based plastic recycling processes. Here, we demonstrate the application of these methods via multiple experimental case studies of representative dissolution-based polymer recycling processes in which pure polymer resins were successfully recovered from physical mixtures of polymers.

42 ENGINEERING↗
NASA NTRS1991

Water droplet impingement on airfoils and aircraft engine inlets for icing analysis

This paper includes the results of a significant research program for verification of computer trajectory codes used in aircraft icing analysis. Experimental water droplet impingement data have been obtained in the NASA Lewis Research Center Icing Research Tunnel for a wide range of aircraft geometries and test conditions. The body whose impingement characteristics are required is covered at strategic locations by thin strips of moisture absorbing (blotter) paper and then exposed to an airstream containing a dyed-water spray cloud. Water droplet impingement data are extracted from the dyed blotter strips by measuring the optical reflectance of the dye deposit on the strips with an automated reflectometer. Impingement characteristics for all test geometries have also been calculated using two recently developed trajectory computer codes. Good agreement is obtained with experimental data. The experimental and analytical data show that maximum impingement efficiency and impingement limits increase with mean volumetric diameter for all geometries tested. For all inlet geometries tested, as the inlet mass flow is reduced, the maximum impingement efficiency is reduced and the location of the maximum impingement shifts toward the inlet inner cowl.

Papadakis, Michael↗
NASA NTRSDate not supplied

Validation of Delamination Growth Predictions under Fatigue Loading using the Single Leg Bending Specimen

The Single Leg Bending Specimen (SLB) was chosen for a study to compare experimentally determined delamination growth with analysis predictions. First, fatigue tests were performed on SLB specimens with several different starter delamination lengths. Specimens were cycled in load control and delamination length increase was continuously recorded with increasing number of cycles. Test results from different load levels were combined to capture the entire delamination growth law (Paris Law) over a wide range of energy release rates. Second, a recently developed interpolation scheme was used to predict the Paris Law associated with the SLB specimen. This interpolation scheme allows the calculation of the growth rates for a given mixed-mode ratio from known single-mode growth laws. Third, finite element analyses were performed using two-dimensional models of the SLB specimen. New Virtual Crack Closure Technique based analysis tools for automated delamination growth analysis under cyclic loading in Abaqus were used for growth predictions. Fourth, the predictions from analyses were compared to experimental fatigue data. Remaining analysis shortcomings were highlighted.

Ronald Krueger↗
NASA NTRS1977

Automated array assembly

A general technology assessment and manufacturing cost analysis was presented. A near-term (1982) factory design is described, and the results of an experimental production study for the large-scale production of flat-panel silicon and solar-cell arrays are detailed.

Daiello, R. V.↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spatiotemporal Route to Understanding Metal Halide Perovskitoid Crystallization

Here, a spatiotemporal experimental route is reported for the antisolvent vapor diffusion crystal growth of metal halide perovskitoids. A computational analysis combining automated image capture and diffusion modeling enables the determination of the critical concentrations required for nucleation and crystal growth from a single experiment. Five different solvent systems and ten distinct organic ammonium iodide salts were investigated with lead iodide, from which nine previously unreported compounds were discovered. Automated image capture of the mother liquor and antisolvent vials was used to determine changes in solution meniscus positions and detect the nucleation event location. Matching the observations to a numerical solution of Fick's second law diffusion model enables the calculation of reactant, solvent, and antisolvent concentrations at both the time and position of the first stable nucleation and crystal growth. A machine learning model was trained on the resulting data, and it reveals solvent- and amine-specific crystallization tendencies. Solvent systems that interact more weakly with dissolved lead species promote crystallization, while those with stronger interactions can prevent crystallization through increased solubilities. Organic amines that interact more strongly with inorganic components and exhibit greater rigidity are more likely to be incorporated into crystalline products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning

Here, we present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2022

BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators

The parameters in Monte Carlo (MC) event generators are tuned on experimental measurements by evaluating the goodness of fit between the data and the MC predictions. The relative importance of each measurement is adjusted manually in an often time-consuming, iterative process to meet different experimental needs. In this work, we introduce several optimization formulations and algorithms with new decision criteria for streamlining and automating this process. These algorithms are designed for two formulations: bilevel optimization and robust optimization. Both formulations are applied to the datasets used in the ATLAS A14 tune and to the dedicated hadronization datasets generated by the SHERPA generator, respectively. The corresponding tuned generator parameters are compared using three metrics. We compare the quality of our automatic tunes to the published ATLAS A14 tune. Moreover, we analyze the impact of a pre-processing step that excludes data that cannot be described by the physics models used in the MC event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Demonstration of Autonomous Emittance Characterization at the Argonne Wakefield Accelerator

Transverse beam emittance plays a key role in the performance of high-brightness accelerators. Characterizing beam emittance is often carried out using a quadrupole scan, which fits beam matrix elements to experimental measurements using first-order beam dynamics. Despite its simplicity at face value, this procedure is difficult to automate due to practical limitations. Key issues that must be addressed include maintaining beam size measurement validity by keeping beams within the radius of diagnostic screens, ensuring that measurement fitting produces physically valid results, and accurately characterizing emittance uncertainty. We describe a demonstration of the Bayesian exploration technique towards solving this problem at the Argonne Wakefield Accelerator, enabling a turn-key, autonomous quadrupole scan tool that can be used to quickly measure beam emittances at various locations in accelerators with limited operator input.

Roussel, Ryan↗