Results for “Test Driven Development” — Page 32

NASA NTRS1982

An experimental study of heat induced surface-tension driven flow

Laser Doppler anemometry, radiation thermometry, and time-lapse photography have been used to study heat induced surface-tension driven flows in silicone oil and Fluorinert FC-43 in large open containers. For silicone oil, the effect of the surface tension gradient on the velocity field was not apparent enough to be detected. Because of its high viscosity, the oil was insensitive to outside disturbances, and the flow was steady. In the case of FC-43, the fluid was noticeably influenced by outside disturbances, and the flow was unsteady. The work reported here is part of an effort to develop experimental techniques and select test fluids for future space experiments on fluid motion in materials processing.

Kamotani, Y.↗

NASA NTRS2000

Design and Testing of the X-38 Spacecraft Primary Parafoil

In August of 1995 the NASA began testing large ram-air parafoils for potential use in the landing phase of spacecraft recovery. This effort eventually became a part of the X-38 project, a technology demonstrator for the International Space Station Emergency Crew Return Vehicle. This paper traces how the original parafoil, used in the U.S. Army Graded Precision Air Delivery System (GPADS), was modified to improve the initial deployment dynamics. A discussion of how test experience with 750 sq ft parafoils has been used to scale up to the full-scale 5500 sq ft and 7500 sq ft parafoils is presented. In particular, the development of two techniques that have greatly improved the repeatability of the parafoil initial deployment and decreased the first stage deployment dynamics are discussed: a first stage upper surface energy modulator and floor vents. Comparisons of trends and results from the various scale parachutes are presented as well as constraints that at times have driven the direction that the design has taken.

DESIGN OPTIMIZATION↗

NASA NTRS2015

System Noise Prediction of the DGEN 380 Turbofan Engine

The DGEN 380 is a small, separate-flow, geared turbofan. Its manufacturer, Price Induction, is promoting it for a small twinjet application in the emerging personal light jet market. Smaller, and producing less thrust than other entries in the industry, Price Induction is seeking to apply the engine to a 4- to 5-place twinjet designed to compete in an area currently dominated by propeller-driven airplanes. NASA is considering purchasing a DGEN 380 turbofan to test new propulsion noise reduction technologies in a relevant engine environment. To explore this possibility, NASA and Price Induction have signed a Space Act Agreement and have agreed to cooperate on engine acoustic testing. Static acoustic measurements of the engine were made by NASA researchers during July, 2014 at the Glenn Research Center. In the event that a DGEN turbofan becomes a NASA noise technology research testbed, it is in the interest of NASA to develop procedures to evaluate engine system noise metrics. This report documents the procedures used to project the DGEN static noise measurements to flight conditions and the prediction of system noise of a notional airplane powered by twin DGEN engines.

Aircraft Design↗

NASA NTRS2015

System Noise Prediction of the DGEN 380 Turbofan Engine

The DGEN 380 is a small, separate-flow, geared turbofan. Its manufacturer, Price Induction, is promoting it for a small twinjet application in the emerging personal light jet market. Smaller, and producing less thrust than other entries in the industry, Price Induction is seeking to apply the engine to a 4- to 5-place twinjet designed to compete in an area currently dominated by propeller-driven airplanes. NASA is considering purchasing a DGEN 380 turbofan to test new propulsion noise reduction technologies in a relevant engine environment. To explore this possibility, NASA and Price Induction have signed a Space Act Agreement and have agreed to cooperate on engine acoustic testing. Static acoustic measurements of the engine were made by NASA researchers during July, 2014 at the Glenn Research Center. In the event that a DGEN turbofan becomes a NASA noise technology research testbed, it is in the interest of NASA to develop procedures to evaluate engine system noise metrics. This report documents the procedures used to project the DGEN static noise measurements to flight conditions and the prediction of system noise of a notional airplane powered by twin DGEN engines.

Aircraft Design↗

DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗

DOE OSTI2024

Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements

Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.

42 ENGINEERING↗

DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗

NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗

NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗

NASA NTRSDate not supplied

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗

DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗

NASA NTRS1974

Multicyclic jet-flap control for alleviation of helicopter blade stresses and fuselage vibration

Results of wind tunnel tests of a 12 meter-diameter-rotor utilizing multicyclic jet-flap control deflection are presented. Analyses of these results are shown, and experimental transfer functions are determined by which optimal control vectors are developed. These vectors are calculated to eliminate specific harmonic bending stresses, minimize rms levels (a measure of the peak-to-peak stresses), or minimize vertical vibratory loads that would be transmitted to the fuselage. Although the specific results and the ideal control vectors presented are for a specific jet-flap driven rotor, the method employed for the analyses is applicable to similar investigations. A discussion of possible alternative methods of multicyclic control by mechanical flaps or nonpropulsive jet-flaps is presented.

Mccloud, J. L., III↗

NASA NTRS1974

Multicyclic jet-flap control for alleviation of helicopter blade stresses and fuselage vibration

Results of wind tunnel tests of a 12-meter-diameter rotor utilizing multicyclic jet-flap control deflection are presented. Analyses of these results are shown, and experimental transfer functions are determined by which optimal control vectors are developed. These vectors are calculated to eliminate specific harmonic bending stresses, minimize rms levels (a measure of the peak-to-peak stresses), or minimize vertical vibratory loads that would be transmitted to the fuselage. Although the specific results and the ideal control vectors presented are for a specific jet-flap driven rotor, the method employed for the analyses is applicable to similar investigations. A discussion of possible alternative methods of multicyclic control by mechanical flaps or nonpropulsive jet-flaps is presented.

Mccloud, J. L., III↗

DOE OSTI2025

Performance-Limiting Factors of Hydrocarbon Ionomeric Binders for Fuel Cells and Electrolyzers

Here, the move toward nonfluorinated hydrocarbon ionomers for fuel cells and electrolyzers is driven by potential restrictions on polyfluoroalkyl substances such as Nafion. This study examines the key limitations of hydrocarbon ionomers through half- and single-cell experiments with model hydrocarbon ionomers. Half-cell tests reveal three major performance barriers: undesirable adsorption, electrochemical oxidation, and low gas permeability. Competitive sulfate adsorption helps counteract ionomer adsorption and oxidation. These findings align with single-cell performance data, which further reveal additional oxygen mass transport limitations likely caused by localized electrode flooding. Together, these findings offer valuable insights to guide the development of high-performance, fluorine-free hydrocarbon ionomers for next-generation fuel cells and electrolyzers.

Choi, Jong-Ho [Los Alamos National Laboratory (LAN↗

NASA NTRS2021

Numerical Validation of Composite Panel Impact Tests

Researchers have met the challenge of modeling impact events involving composite targets for a variety of applications in a variety of ways. In this paper, the theory and implementation details of an orthotropic visco-elastic-visco-plastic material model with strain rate dependence are discussed. The model is driven by experimental data from quasi-static as well as high strain rate tests, and the data is used in defining the deformation, damage and failure sub-models. Validation data is generated by shooting a hollow 50 g Al-2024 projectile at different velocities against a flat panel target made of unidirectional composite material in a 16-ply [(0/90/45/-45)_(2)]_(S) layup. Explicit dynamic finite element analyses of four high speed tests involving one contained (projectile rebounded) and three uncontained (projectile penetrated) impact tests show that the developed material model and modeling techniques yield reasonable and acceptable predictions.

Orthotropic Composite↗

NASA NTRS2023

CFD Integrated Transition Modeling for High-Speed Flows via Coupled OVERFLOW-LASTRAC Analysis

This work details ongoing efforts at the NASA Langley Research Center to develop and validate a general-use CFD tool that includes built-in predictions of boundary-layer transition in high-speed flows. Existing tools for the coupling of the NASA OVERFLOW structured overset RANS solver and LASTRAC stability analysis code have been extended to capture boundary-layer transition in high-speed flows driven by either Mack’s first-mode or secondmode instabilities and/or crossflow instabilities. The efficacy of this coupled approach has been demonstrated by examining a variety of supersonic and hypersonic test cases for which experimental validation data is available. The intermittency prescription parameters inherited from prior low-speed applications are found to be suitable for some, but not all high-speed flow scenarios. The method is found to be robust for straight cone configurations and improvements necessary to accurately capture transition on geometries with spatially complex amplification factor envelopes with intermediate regions of slow N-factor variation are examined. The automated, coupled analysis is demonstrated for a 3d supersonic test case and found to perform well within the limits of the linear stability analysis on which it relies. Finally, a preliminary investigation of the method’s robustness to non-ideal CFD meshes is conducted.

High-speed flow↗

DOE OSTI2025

Computational Fluid Dynamics Simulations of Glass Vitrification Refractory Coupon Tests

The Waste Treatment and Immobilization Plant (WTP) at the Hanford site is nearing the start of the Direct-Feed Low-Activity Waste (DFLAW) operations. DFLAW is destined to convert a pretreated low activity waste portion of the 56 million gallons of tank waste into a stable solid glass. In the subsequent decade completion of the high-level waste (HLW) facility is anticipated. Sustained operational missions of both LAW and HLW melter facilities are expected over multiple decades. In high-temperature glass melters, the refractory lining corrodes over time, which could potentially be an issue for longer term operations, this refractory corrosion is higher at the level of the glass-air interface due to surface tension driven flow. The glass viscosity, melt pool temperature, and glass chemical composition can impact the rate at which the refractory corrodes. This rate is important to quantify for the various waste glasses to be produced at the WTP since the integrity of the refractory should not be a limiting factor affecting the lifetime of the melter. To this end, a series of glasses representative of the first batches of waste glass produced by the WTP will be melted in small-scale crucibles with Monofrax® K-3 coupons inserted. The corrosion of the K-3 will be measured in the melt and at the meltline (or neckline). A model for the corrosion rate will be constructed and implemented into a previously developed framework for a computational fluid dynamics (CFD) model of the full-scale WTP. To assist with experimental design and validate the implementation of the model in the full-scale melter, CFD simulations of the small-scale crucible tests were performed. The bubbling that occurs in the small-scale crucible is initially validated here with a model that uses silicone oil at room temperature. The viscosity of the oil ranges from 1 to 100 Pa•s, which corresponds to operating glass pool temperatures near 1150 °C down to idling temperatures near 950 °C. The simulation results show good agreement with the bubble sizes that form during experiments. CFD modeling of the crucible setup was used to determine bubbling characteristics to match the range of near-wall velocities expected in the full-scale WTP. This study presents the initial CFD modeling results, corrosion testing plan, and some preliminary corrosion samples with an outline for the next steps for the development of the corrosion model.

Abboud, Alexander W. [Idaho National Lab]↗

DOE OSTI2024

Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV

Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are measured at the relay location. The objective of this work is to develop a machine learning model that can identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of waveforms. The overall testing accuracy of the trained model exceeds 95%.

Ramesh, Meghana↗