Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “automated testing”

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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

NUM-DAT File Format Specification: Used in M-9 Gun Experiment Data Archiving

The M-9 Shock and Detonation Physics group executes experiments on gun and explosive platforms with large numbers of oscilloscopes used for data acquisition. The data acquisition from these oscilloscopes was automated many years ago using a custom piece of software called RunDig . The default save format from this software is a custom structure referred to as "NUM-DAT" format. This file format includes a text ".DAT" file which is a header file used to interpret the binary ".NUM" file which contains the oscilloscope data. The data save format was originally developed by John Vorthman and has been in use by M-9 personnel for over 20 years. This data format has been used for archiving data from experiments performed by M-9 personnel at TA-40, TA-39, and the TA-55 Impact Test Facility. Numerous custom analysis and visualization programs have also been developed, and continue to be used, that utilize this data format. This document describes the NUM-DAT format and provides code examples for reading the format and converting it to other formats.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

FY26 Mid-Year Report Self-Diagnostic Capabilities for Neutron Instruments

This year we will transfer the latest version of INCC6 with last year’s self-diagnostic features to the IAEA. In FY25 we completed the development of three automated self diagnostic features into INNC6. The completion of these features marked the final stable version of INCC 6.0 ready for release to the IAEA and other users. In the first half of the Fiscal Year 2026 we have worked with LANL’s Feynman Center and Export Control office to attain a license for exporting INCC6 to the IAEA. We have successfully attained the export license for INCC6, and contacts at the IAEA have been granted permission to receive INCC6. We are in the final stage of writing the version 6 user manual. Once this is complete, we will deliver INCC6 to the IAEA for testing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

NRIC Asset Suite Engineering and Operations Data Integration Plan

The National Reactor Innovation Center (NRIC), established by the U.S. Department of Energy (DOE) in August 2019, accelerates the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is a national program led by Idaho National Laboratory (INL), enabling collaborators to harness the world-class capabilities of the U.S. National Laboratory System. Committed to demonstrating advanced reactors by the end of 2025, NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the Nation’s most promising advanced nuclear reactors into commercial applications by 2030. To meet these needs, NRIC is developing two reactor demonstration test beds at Idaho National Laboratory (INL), the Laboratory for Operation and Testing in the United States (LOTUS) and the Demonstration and Operation of Microreactor Experiments (DOME) test bed. Each test bed involves the modification of existing facilities at INL’s Materials and Fuels Complex (MFC). Retrofitting these facilities to accommodate novel reactors, as well as subsequent but similar NRIC projects, is a non-trivial engineering task and is expected to generate a significant amount of new and revised documentation. Leveraging digital engineering practices, this documentation will be managed in a purpose-built data management tool to facilitate origination, review, and approval while coordinating with the design contractor. The subsequent upload and review of documentation to INL’s existing document control system comprises a long and unnecessarily manual task. An opportunity exists to automate this upload process and save job-hours while continuing to adhere to INL/MFC document management procedures.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Speeding-up fuzzing through directional seeds

Abstract Fuzzing is an automated process for discovering inputs in a program that may trigger unexpected behavior. Today, fuzzing has become a standard practice for the discovery of bugs and security vulnerabilities. However, the main issue with such practices is that the exploration of the input space of programs can often be prohibitively expensive. Therefore, several alternative fuzzing strategies have been introduced during the last few years. Some fuzzing techniques rely on human expertise to provide a plausible set of initial input examples, namely, seeds. However, the process of handcrafting seeds for fuzzing purposes often becomes strenuous for humans as it requires a deeper understanding of the Program-Under-Test (PUT). Also, the use of known inputs to programs often does not trigger vulnerable program behavior or may not reach potentially vulnerable code locations. To address those issues, we propose a seed generation framework that enables Human-In-The-Loop (HITL) directed fuzzing where the human assumes a more active role in the creation of seeds that can penetrate and assess desired locations of the PUT. Our proposed framework uses Symbolic Execution (SE) to generate seeds that exercise paths to target program locations. Moreover, our framework enables the visualization of the explored execution paths in the binary of the PUT for the generated seeds. We evaluated our approach on a set of 12 carefully designed C programs with diverse characteristics that mimic real-world programs. The experimental results show the effectiveness of the proposed approach in improving the performance of standard fuzzing tools such as the American Fuzzy Lop ("Image missing" <#comment/> ). Specifically, our solution can generate seeds that substantially enhance the performance of the fuzzer, achieving speedups ranging from $$1.46\times $$ 1.46 × to $$68.53\times $$ 68.53 × for branch conditions, $$1.39\times $$ 1.39 × to $$254.62\times $$ 254.62 × for branch depths, $$14,879.59\times $$ 14 , 879.59 × to $$30,295.88\times $$ 30 , 295.88 × for branch widths over traditional seeds. Additionally, the speedup increases with the number of target function ranging from $$12,260\times $$ 12 , 260 × to $$22,856.07\times $$ 22 , 856.07 × over traditional seeds while only requiring less than 15 seconds on average for the seed generation step.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ExaWorks software development kit: a robust and scalable collection of interoperable workflows technologies

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simple and Accurate One-Body Energy and Dipole Moment Surfaces for Water and Beyond

Water is often the testing ground for new, advanced force fields. While advanced functional forms for intermolecular interactions have been integral to the development of accurate water models, less attention has been paid to a transferable model for intramolecular valence terms. In this work, we present a one-body energy and dipole moment surface model, named 1B-UCB, that is simple yet accurate and can be feasibly adapted for both standard and advanced potentials. 1B-UCB for water is comparable in accuracy to those with much more complex functional forms, despite having drastically fewer parameters. The parametrization protocol has been implemented as part of the Q-Force automated workflow and requires only a quantum mechanical Hessian calculation as reference data, hence allowing it to be easily extended to a variety of molecular systems beyond water, which we demonstrate on a selection of small molecules with different symmetries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Improving to the neutron fluence rate monitor measurement system at the Advanced Test Reactor [Poster]

The existing fluence monitor wire scanning system at the Advanced Test Reactor (ATR) was designed and installed for use in the Engineering Test Reactor (ETR) when it began operation in 1958. The wire scanner was operated in ETR for over 20 years until ATR began operation, when it was moved to the ATR west canal area in 1971 and subsequently moved to the west canal in 2006 where it presently resides. With a continued service life of 65 years the system is well beyond the typical design life of 20 years for these types of systems. The need to update the data acquisition and control system was identified, and the benefits of replacing the existing sodium iodide (NaI) detector with an electronically cooled high-purity germanium (HPGe) detector are discussed. The wirescanner system in the ATR canal is utilized after every reactor cycle by the ATR Radiation Measurements Laboratory (RML) to assess the activation of cobalt and nickel dosimeter wires during the cycle. These wires become activated through exposure to thermal and fast neutrons respectively during the irradiation cycle and are highly radioactive upon shutdown. It is for this reason that the wirescanner is used in the ATR canal rather than transporting the dosimeters to another facility. A scoping study was performed to develop a base-line design to ensure that existing capabilities could be replaced with a new system. The new hardware will enable automated measuring of several flux monitor holders without necessitating the removal of the flux wires. In this way, flux wire measurements will be performed with minimal dose to the technicians and will not be limited by canal operations as is presently the case. The new control and acquisition software will be based on commercially available and supported systems that have a wide user-base to provide long-term stability. An electronically cooled HPGe detector will be used to provide high-resolution gamma-ray measurements, an improvement from the low-resolution sodium-iodide detector that is presently deployed. The electronic cooler eliminates the need for liquid nitrogen to cool the detector head. A new collimator has been designed to house the new detector and allow for sufficient counting rates.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

The Application of Coordination to Magnetic Measurement Automation: An SSW System Example

Magnetic measurements are an integral part of the development cycle of accelerator magnets, a part which provides necessary feedback to drive design and manufacturing improvements and corrections. Automation of these measurements allows for reduction of human errors and reproducibility of measurements. Realization of automation via coordination that is separated at the architectural level from the functional code provides flexibility in defining measurement procedures and substantial extensibility. The described Single Stretched Wire (SSW) system herein is an implementation of such a solution with the use of a component-based framework designed to build a family of measurement systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2020

Implementation Plan for Combined Heat and Power Systems VOLTTRON Controller: Performance Monitoring and Real-Time Commissioning Algorithm Verification

Building-integrated cooling, heating, and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV, and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of clean, efficient, reliable, and affordable next-generation integrated energy system. Previous report listed the components for which PM, real-time CxV, and AFDD algorithms will be developed, how the algorithms will be tested, and the metrics that will be used to validate the algorithms and their ease of deployment. Deployment of these algorithms in the field will result in a reduction in energy consumption of between 10% and 20% (for both CHP and conventional building systems). This report builds upon the previous report by detailing the process by which PNNL will implement performance monitoring and real-time commissioning algorithms for CHP systems in conjunction with the use of the VOLTTRON CHP economic dispatch agent in host facilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A high throughput assay to detect enzymatic polyethylene oxidation

Biological plastics deconstruction and upcycling have emerged as sustainable alternatives to traditional recycling technologies for plastics waste. The discovery and engineering of efficient thermostable poly(ethylene terephthalate) (PET) hydrolases have made biological PET recycling possible at scale; however, enzymes for non-PET plastics, which account for approximately 70% of all plastics produced, remain largely undiscovered. To accelerate the discovery of such enzymes, we develop a high-throughput screen to detect initial polymer oxidation, specifically that of the C-H bond to an aldehyde. We test 4-hydrazino-7-nitro-2,1,3-benxoxadiozole hydrazine (NBD-H), which reacts with generated aldehydes to form a fluorescent hydrazone on plasma oxidized low-density polyethylene (LDPE) films. Hydrazone generation correlated well with the area of aldehyde peaks as measured by Fourier Transform Infrared Spectroscopy (FTIR) (R2 = 0.92). Moreover, we demonstrate that the probe reliably identifies LDPE-active dye decolorizing peroxidases (DyPs) that generate aldehydes on LDPE films (1.7 – 3.0 fold change relative to background), serving as an effective screen as demonstrated by receiver operating characteristic area under the curve of 0.95. Furthermore, this assay offers an LDPE oxidation screening platform that can be readily parallelized and automated for accelerated discovery of enzymes involved in polyolefin deconstruction.

biocatalysis↗
DOE OSTI2023

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. Furthermore, this paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control: Preprint

This paper validates the effectiveness of an Artificial Intelligence (AI)-driven PV plant control and optimization approach, namely, the Automated Learner for Intermittency Control by Extrapolation (ALICE), in empowering PV plant as a dependable grid reliability service provider. The validation is performed in a realistic laboratory controller-hardware-in-the-loop (CHIL) environment, leveraging accurate PV plant modeling and standard industrial communication protocol. Simulation results, considering both varying weather conditions and active control scenarios, demonstrate the superior performance of ALICE in improving the grid service delivery precision and reducing the over-curtailment compared to a state-of-the-art approach, i.e., reference-control grouping based approach. Such a work could help mitigate risks and provide practical guidance during the field deployment of ALICE, while establishing a standardized testing framework for evaluating various PV active control strategies.

hardware-in-the-loop↗
DOE OSTI2024

A general Bayesian algorithm for the autonomous alignment of beamlines

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Voronoi Meshing to Accurately Capture Geological Structure in Subsurface Simulations

Mesh generation lies at the interface of geological modeling and reservoir simulation. Highly skewed or very small grid cells may be necessary to accurately capture the geometry of geological features, but the resulting poorly scaled or small grid cells can have a substantial negative impact on simulator accuracy and speed. One way to minimize numerical errors caused by gridding complex structures is to simulate on high-quality Voronoi meshes, which reduce grid orientation effects in fluid flow. This work presents a complete methodology to create Voronoi simulation grids, model fluid flow in complex geological systems, and visualize the results. A recently developed Voronoi meshing method that can automatically generate provably good unstructured meshes that conform to input surfaces creating closed volumes is used. Initially an analytical benchmark simulation is presented to validate the quality of the meshes and simulation results and demonstrate the superiority of simulation results using Voronoi meshes over flexed-hexahedral meshes on a domain with internal features. Next, meshes are created for test structures representing four of the most common geological features in the subsurface: layering, pinch-out, an interior lens that tapers to zero thickness on all sides and a fault with offset. Two benchmark flow simulations are run for each test structure. Finally, a realistic geological example for CO 2 injection into an anticline is simulated. Three realizations of the Voronoi mesh at the same resolution are generated for the simulations. Each mesh is highly refined near the injection wells and coarse in areas of less interest. These three meshes are used to model the CO 2 plume in the subsurface as it migrates to the top of the structure and then fills downward. Simulations on the meshes with randomly generated elements inside the input volumes each give slightly different fingering patterns for the viscous-unstable buoyant gas flow. The results presented in this work show a promising step towards utilizing fully automated Voronoi meshing for subsurface flow simulations in complex geology.

58 GEOSCIENCES↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗