Search NASA⌕ Search

SEARCH · Search NASA

Results for “Test methods”

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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2025

Can Restoring Tidal Wetlands Reduce Estuarine Nuisance Flooding of Coasts Under Future Sea‐Level Rise?

Wetland restoration is an increasingly popular nature‐based method for flood risk mitigation in coastal communities. In this study, we present a novel method using hydrodynamic modeling and harmonic analysis to quantify wetlands' ability to reduce future nuisance flooding. The method leverages a hydrodynamic model calibrated to present day data and was run for a range of future sea‐level rise (SLR) and wetland restoration scenarios to quantify changes to tidal harmonic amplitudes and phases. The harmonic constituents are used to generate water surface elevations over a time period of interest (e.g., one year) and compared to critical exceedance thresholds such as levee elevations. Then, changes to nuisance flooding are calculated by counting the number of hours critical thresholds are exceeded under different SLR and wetland restoration scenarios. We applied the method to Coos Bay, Oregon, USA as a test case. We found restoration reduces the number of hours nuisance flooding occurs in downtown Coos Bay from 15 hr (present day conditions) to 0 hr (fully restored condition) under median SLR (82 cm by 2100). Restoration had spatially variable impacts on reducing peak flood elevations with minimal impacts near the estuary mouth and greatest impact 32 km inland. The effectiveness of restoration was heavily dependent on future SLR. Restoration was maximally effective in 2050 under all SLR scenarios, less effective in 2100 under median SLR, and not effective under high SLR. Modeling results suggest increased tidal prism and accommodation space are driving restoration‐associated reductions in tidal amplitudes.

Brand, Matthew W. [Louisiana State Univ., Baton Ro↗
DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗
DOE OSTI2024

When should PIC simulations be applied to atmospheric pressure plasmas? Impact of correlation heating

Abstract Molecular dynamics simulations are used to test when the particle-in-cell (PIC) method applies to atmospheric pressure plasmas. It is found that PIC applies only when the plasma density and macroparticle weight are sufficiently small because of two effects associated with correlation heating. The first is the physical effect of disorder-induced heating (DIH). This occurs if the plasma density is large enough that a species (typically ions) is strongly correlated in the sense that the Coulomb coupling parameter exceeds one. In this situation, DIH causes ions to rapidly heat following ionization. PIC is not well suited to capture DIH because doing so requires using a macroparticle weight of one and a grid that well resolves the physical interparticle spacing. These criteria render PIC intractable for macroscale domains. The second effect is a numerical error due to artificial correlation heating (ACH). ACH is like DIH in that it is caused by the Coulomb repulsion between particles, but differs in that it is a numerical effect caused by a macroparticle weight larger than one. Like DIH, it is associated with strong correlations. However, here the macroparticle coupling strength is found to scale as Γ w 2 / 3 , where Γ is the physical coupling strength and w is the macroparticle weight. So even if the physical coupling strength of a species is small, as is expected for electrons in atmospheric pressure plasmas, a sufficiently large macroparticle weight can cause the macroparticles to be strongly coupled and therefore heat due to ACH. Furthermore, it is shown that simulations in reduced dimensions exacerbate these issues.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Pseudomonas aeruginosa : One Health approach to deciphering hidden relationships in Northern Portugal

Abstract Aims Antimicrobial resistance in Pseudomonas aeruginosa represents a major global challenge in public and veterinary health, particularly from a One Health perspective. This study aimed to investigate antimicrobial resistance, the presence of virulence genes, and the genetic diversity of P. aeruginosa isolates from diverse sources. Methods and results The study utilized antimicrobial susceptibility testing, genomic analysis for resistance and virulence genes, and multilocus sequence typing to characterize a total of 737 P. aeruginosa isolates that were collected from humans, domestic animals, and aquatic environments in Northern Portugal. Antimicrobial resistance profiles were analyzed, and genomic approaches were employed to detect resistance and virulence genes. The study found a high prevalence of multidrug-resistant isolates, including high-risk clones such as ST244 and ST446, particularly in hospital sources and wastewater treatment plants. Key genes associated with resistance and virulence, including efflux pumps (e.g. MexA and MexB) and secretion systems (T3SS and T6SS), were identified. Conclusions This work highlights the intricate dynamics of multidrug-resistant P. aeruginosa across interconnected ecosystems in Northern Portugal. It underscores the importance of genomic studies in revealing the mechanisms of resistance and virulence, contributing to the broader understanding of resistance dynamics and informing future mitigation strategies.

de Sousa, Telma↗
DOE OSTI2026

Scattering phase shift in quantum mechanics on quantum computers

Here, we investigate the feasibility of extracting infinite volume scattering phase shift on quantum computers in a simple one-dimensional quantum mechanical model, using the formalism established in the work by Guo and Gasparian [Phys. Rev. D 108, 074504 (2023)] that relates the integrated correlation functions for a trapped system to the infinite volume scattering phase shifts through a weighted integral. The system is first discretized in a finite box with periodic boundary conditions, and the formalism in real time is verified by employing a contact interaction potential with exact solutions. Quantum circuits are then designed and constructed to implement the formalism on current quantum computing architectures. To overcome the fast oscillatory behavior of the integrated correlation functions in real-time simulation, different methods of postdata analysis are proposed and discussed. Test results on IBM hardware show that good agreement can be achieved with two qubits, but complete failure ensues with three qubits due to two-qubit gate operation errors and thermal relaxation errors.

Guo, Peng [Dakota State Univ., Madison, SD (United↗
DOE OSTI2024

DuraMAT: Building a Consortium to Accelerate the Photovoltaic Module Reliability Learning Cycle

Durable and reliable photovoltaic (PV) modules are critical to enabling an efficient transition to sustainable energy generation. The rate at which new module designs and materials are developed and deployed currently outpaces the rate at which we can identify failure mechanisms and understand degradation rates. Increasing the service life of PV modules, and our ability to predict performance over time, requires more durable materials and designs, better durability testing, more extensive material characterization, robust modeling, and methods to cross-examine historical performance data to extract meaningful results. This is a multidisciplinary challenge that requires expertise from a broad range of fields and, therefore, benefits significantly from a collaborative approach. In this Perspective, we outline the approach taken by the Durable Module Materials Consortium (DuraMAT), present a few case studies where our approach was successful, and provide an outlook on where this approach might be applied as the PV technology landscape continues to rapidly evolve. Published by the American Physical Society 2024

14 SOLAR ENERGY↗
DOE OSTI2026

Query Relaxation for LLM-Generated SPARQL Queries over Building Knowledge Graphs

When Knowledge Graph (KG) queries fail to match a pattern in a KG, they return no results. Identifying the statements causing these failures is tedious, especially for LLM-generated queries, which tend to be longer and more complex than queries written by hand. Query relaxation addresses this by systematically loosening query constraints until results are recovered. To evaluate the effectiveness of query relaxation against LLM generated queries, we propose a two-stage relaxation method combining triple deletion and path relaxation and test it against 1,823 failed queries for building KGs.

Paul, Lazlo↗
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

Thermal images collected during large-scale 3D printing with Ingersoll MasterPrint

This data set contains images produced to test the performance of anomaly and fault detection methods in the context of additive manufacturing. Two print jobs were executed using the Ingersoll MasterPrint with the Model 30 Strangpresse extruder. The material used was Techmer compounded polylactic acid (PLA) with wood flour as a filler (80/20 PLA/WF by weight). The first print job consists of the 3D printing of a hexagonal cylinder with a two-bead wall. This print was sliced at a gantry velocity of 3000 millimeter per minute, and an extruder screw speed of 68.14 rotations per minute. This are considered the normal operating conditions. A second hexagonal cylinder was printed using a reduced extruder screw speed, 15% lower than under normal operating conditions. The images were collected with a Teledyne FLIR Lepton 3.5 infra-red camera, a small form factor radiometric long-wave infrared camera with a spectral range of 8 µm to 14 µm. Sensor resolution was 160x120 pixels, with a pixel size of 12 µm, a temperature range of -10 - 450°C, and an accuracy of +/- 10°C in its low gain configuration. Images in this data set were collected with cameras oriented at the printer nozzle. The nozzle camera setup consisted of two cameras located 12.5cm from the nozzle center. These were mounted directly to the print head, so that the camera positions relative to the print direction would remain constant as it rotated around its C-axis to follow the print path. One camera was placed ahead of the nozzle to capture the previous layer immediately before being covered by the new layer of material, while the second camera was placed behind the nozzle and captured the freshly extruded bead. Images are collecting during each phase of the print: (a) idle (i.e., no material deposited), (b) extrusion (i.e., to prime the extruder), and (c) printing (deposition of material to manufacture the hexagonal cylinder).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Thermal images collected during 3D printing with Cincinnati BAAM

This data set contains images produced to test the performance of anomaly and fault detection methods in the context of additive manufacturing. A total of 10 print jobs were executed using a Cincinnati BAAM (model 606) with a Cincinnati medium compression screw. The material used was compounded polylactic acid (PLA) with wood flour as a filler produced by Jabil (80% NatureWorks Ingeo 6060D amorphous PLA, % 100 mesh pine flour from American Wood Fiber). Print sheets were 1/4in polycarbonate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Method for Real-Time Salt Aerosol Concentration and Size Measurements for Molten Salt Reactor Safety Assessments

Licensing a new nuclear reactor involves evaluating credible accident scenarios by developing models that simulate accident progression to predict possible outcomes and their impact on safety. The formation of radionuclide-bearing aerosols in the respirable size range can significantly affect dose consequence and threaten human health and is therefore a focus of nuclear reactor safety assessments. Recent reviews of the literature identified a lack of existing experimental data describing the mechanisms of formation and properties (size, concentration, and composition) of radionuclide-bearing aerosols that may be produced from molten salt reactor (MSR) facilities during postulated accident scenarios. Experiments on aerosol formation from molten salt systems are a high priority need that will support MSR licensing by indicating the radiological consequences of aerosol formation and providing the data required for model development and validation. The evolving conditions that occur during a MSR accident, such as a spill of molten fuel salt, may affect aerosol formation mechanisms and aerosol properties over time. There is a need to conduct experiments that simulate credible MSR accidents in a laboratory to generate aerosols with realistic characteristics and behaviors and a need to measure these aerosols in real time under accident-relevant conditions. This report describes the development of a method that can be used to quantify the size and concentration of salt aerosol particles that form from molten salt systems in real time. This method will be employed in future integral effects tests that are conducted at an engineering scale to simulate realistic MSR accidents and in future separate effects tests that will provide mechanistic insight into aerosol formation and properties to support process model development.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

High-Speed and High-Quality Field Welding Repair Based on Advanced Non-Destructive Evaluation and Numerical Modeling

Creep strength-enhanced ferritic (CSEF) steels such as Grade 91 (9Cr-1Mo-V) and Grade 92 (Fe-9Cr-2W-0.5Mo) steels are widely used in the fossil-fuel-fired and nuclear power plants. The weld integrity of these steels is crucial for power plants' safe and reliable operations. Due to harsh service conditions, the steel weld can become susceptible to environmental degradation. Field welding repair is used to restore the degraded weld’s performance where a controlled temper-bead welding technique is commonly used to temper the freshly formed martensite during welding. However, knowledge of weld repairability is limited and experimental trial and error optimization to achieve desired microstructure and joint properties is expensive and time-consuming. Many existing computational models, e.g., finite element models, are limited to solving heat conduction equation and ignoring convective heat transfer due to molten metal flow. These models can result in over-prediction of peak temperatures of weld pool and heat-affected zone (HAZ), which in turn can affect the accuracy of tempering prediction. Moreover, these finite element models require an input of the deposit profiles in advance and thus limits the usability of these models. Here, a molten pool-based, multi-pass multi-layer model has been developed based on computational fluid dynamics (CFD) approach with the Volume of Fluid (VOF) method. The model calculates the bead formation, thereby eliminating the need for pre-determined bead profiles required by finite element models. For computational efficiency, a coordinate system attached to the moving heat source is utilized. A subroutine is developed to convert the temperature profiles in the reference frame stationary to the heat source to that stationary to the workpiece. The converted thermal cycles are then imported into a microstructure model to compute the tempering kinetics and resultant hardness using a Johnson-Mehl-Avrami-Kolmogorov (JMAK), and modified Grange-Baughman parameter. The modeling approach is first developed and validated on single- and multi-pass deposition of stainless steel filler metal onto a SA-533 high strength steel substrate. The models are then applied to a multi-pass V-groove repair weld of Grade 91 steel plate as well as directed energy deposition of Grade 92 steel. Non-destructive characterization of microstructures was performed on Grade 91 and 92 steel welds. Two welding processes, cold metal transfer (CMT) and flux-cored arc welding (FCAW), were investigated for the Grade 91 steel weld samples. For the Grade 92 weld samples, three different heat inputs (low, medium, and high) of gas tungsten arc welding (GTAW) were utilized to replicate traditional field welding processes. The non-destructive evaluation (NDE) method used for this research was immersion ultrasonic testing (UT) using a micro-resolution ultrasonic imaging methodology specifically designed to operate in the through-transmission configuration operating at 20 MHz of frequency. The system used a focused ultrasonic beam spot size diameter between 250-300 μm, and a 6 μm laser vibrometer spot size for detection, to produce highly defined images with longitudinal and mode-converted shear waves. From the micro-resolution ultrasonic C-scan images, three microstructural regions, i.e., weld metal (WM), HAZ, and base metal (BM), were clearly identifiable. Various levels of ultrasonic amplitudes distributed over the three regions were correlated with electron beam backscattered diffraction (EBSD) images using grain size, grain boundaries, and dislocation densities. The results showed that areas with relatively higher ultrasonic amplitude levels were associated with smaller grains and higher dislocation densities, while areas with lower amplitude levels were associated with larger grains and lower dislocation densities. In addition, ultrasonic velocity data obtained across the three different weld microstructural regions of Grade 91 test samples were correlated with optical metallographic images and hardness measurements. The results showed distinctive decreases in ultrasonic velocity and hardness over the HAZ region, where weld failures often occur during service.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2022

Lithium-Ion Battery Design for Grid-Scale Energy Storage App

A software that delivers parameters from energy storage system (ESS) to container, rack, module and single cell design, as well as data analysis on arbitrage energy and frequency regulation of ESS in different regions, has been developed. The Lithium-ion Battery Design for Grid-scale Energy Storage App V1.0 has the capability to output the system, module and cell design with the energy, power, capacity, group method, cost of single cell, and single cell test protocol which break down from input energy storage system data in different regions. The default chemistry of the battery is LiFePO4 and graphite. The energy density of the graphite/LiFePO 4 pouch cell ranges from 100 Wh/kg to 200 Wh/Kg in the software. Graphite/LiFePO 4 pouch cell (up to 1Ah in lab) manufacturing line is also built and can be used to evaluate the test protocol, moreover, for electrolyte evaluation in other ESMI seedling projects. The software enables rapid prototyping to accelerate energy storage research, development, and manufacturing.

Liu, Dianying [Pacific Northwest National Laborato↗
DOE OSTI2026

The Optical and Infrared Are Connected

Galaxies are often modeled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model that exploits subtle correlations between physical processes to accurately predict infrared (IR) Wide-field Infrared Survey Explorer (WISE) photometry from a neural summary of optical Sloan Digital Sky Survey spectra. The model achieves accuracies of $χ^{2}_{N} ≈ 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of active galactic nuclei (AGN) and dust parameters such as q PAH . We also test whether current spectral energy distribution (SED) fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates (SFRs) and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (Ca II , Sr II , Fe I , [O II ], and Hα), which point to the complex chronology of star formation and chemical enrichment being incorrectly modeled.

Jespersen, Christian Kragh [Princeton Univ., NJ (U↗
DOE OSTI2025

Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale

Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗