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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Case studies of robots and automation as health/safety interventions in small manufacturing enterprises

This article reviews the experiences of 63 case studies of small businesses (<250 employees) with manufacturing automation equipment acquired through a health/safety intervention grant program. The review scope included equipment technologies classified as industrial robots (n = 17), computer numerical control (CNC) machining (n = 29), or other programmable automation systems (n = 17). Descriptions of workers' compensation (WC) claim injuries and identified risk factors that motivated the acquisition of the equipment were extracted from grant applications. Other aspects of the employer experiences, including qualitative and quantitative assessment of effects on risk factors for musculoskeletal disorders (MSD), effects on productivity, and employee acceptance of the intervention were summarized from the case study reports. Case studies associated with a combination of large reduction in risk factors, lower cost per affected employee, and reported increases in productivity were CNC stone cutting system, CNC/vertical machining system, automated system for bottling, CNC/routing system for plastics products manufacturing, and a CNC/Cutting system for vinyl/carpet. Six case studies of industrial robots reported quantitative reductions in MSD risk factors in these diverse manufacturing industries: snack foods; photographic film, paper, plate, and chemical; machine shops; leather goods and allied products; plastic products; and iron and steel forging. Furthermore, this review of health/safety intervention case studies indicates that advanced (programmable) manufacturing automation, including industrial robots, reduced workplace musculoskeletal risk factors, and improved process productivity in most cases.

42 ENGINEERING↗
DOE OSTI2022

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Surface Temperature Effect on Convective Heat Transfer Coefficients for Jet Impingement Cooling of Electric Machines with Automatic Transmission Fluid: Preprint

In this study, the results of NREL's continued work on experimental characterization of the thermal performance of free-surface jets of automatic transmission fluid impinged on a heated target surface are presented. The measured heat transfer coefficients are useful for understanding factors influencing performance of driveline fluid-based cooling systems for electric machines and help designers in developing high-performance, power-dense and reliable machines. Experiments were carried out for different fluid and target surface temperatures (50 degrees C, 70 degrees C, and 90 degrees C for the fluid and 90 degrees C, 100 degrees C, 110 degrees C, and 120 degrees C for the target surface). Impinging jet velocities (0.5 m/s to 7.5 m/s) and the jet position on the target surface (center versus edge) were also varied. The impinging angle was kept at 90 degrees relative to the target surface. It was found that higher target surface temperature increased heat transfer coefficients, namely, increasing surface temperature from 90 degrees C to 120 degrees C enhanced heat transfer coefficient values at higher impinged jet velocities (7.5 m/s) by up to 15%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Avoiding Communication in Primal and Dual Block Coordinate Descent Methods

Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations of these methods have become popular in analyzing large machine learning datasets. However, existing implementations communicate at every iteration, which, on modern data center and supercomputing architectures, often dominates the cost of floating-point computation. Recent results on communication-avoiding Krylov subspace methods suggest that large speedups are possible by re-organizing iterative algorithms to avoid communication. We show how applying similar algorithmic transformations can lead to primal and dual block coordinate descent methods that only communicate every $s$ iterations---where $s$ is a tuning parameter---instead of every iteration for the regularized least-squares problem. We show that the communication-avoiding variants reduce the number of synchronizations by a factor of $s$ on distributed-memory parallel machines without altering the convergence rate and attain strong scaling speedups of up to $6.1\times$ over the “standard algorithm" on a Cray XC30 supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Learning concave-convex profiles of data transport over dedicated connections

Dedicated data transport infrastructures are increasingly being deployed to support distributed big-data and high-performance computing scenarios. These infrastructures employ data transfer nodes that use sophisticated software stacks to support network transport among sites, which often house distributed file and storage systems. Throughput measurements collected over such infrastructures for a range of round trip times (RTTs) reflect the underlying complex end-to-end connections, and have revealed dichotomous throughput profiles as functions of RTT. In particular, concave regions of throughput profiles at lower RTTs indicate near-optimal performance, and convex regions at higher RTTs indicate bottlenecks due to factors such as buffer or credit limits. We present a machine learning method that explicitly infers these concave and convex regions and transitions between them using sigmoid functions. We also provide distribution-free confidence estimates for the generalization error of these concave-convex profile estimates. Throughput profiles for data transfers over 10 Gbps connections with 0–366 ms RTT provide important performance insights, including the near optimality of transfers performed with the XDD tool between XFS filesystems, and the performance limits of wide-area Lustre extensions using LNet routers. A direct application of generic machine learning packages does not adequately highlight these critical performance regions or provide as precise confidence estimates.

Rao, Nageswara↗
DOE OSTI2019

Erratum: 'Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading'

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. The results were compared to currently available models for contact resistance, and one model was adapted for prediction of TCR in future motor designs.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Wound Field and Hybrid Synchronous Machines for EV Traction with Brushless Capacitive Rotor Field Excitation (Final Report)

This project focused on the development of wound field synchronous machines (WFSMs) and hybrid excitation synchronous machines (HESMs) with brushless capacitive power transfer for the field excitation. The target application for the machines developed is the main traction motor in electric vehicles. The magnetization in these types of machines is provided by a field winding on the rotor which is excited with DC current. The magnetization level in the machine can be varied by changing the magnitude of the field current. The variable magnetization or field is one of the key features of WFSMs. WFSMs are commonly used as generators however they have several attractive features for automotive traction applications. 1) No use of permanent magnet: Rare earth permanent magnets are primarily mined and processed in China. They have been subject to large price and supply variations and their export may be restricted during times of geopolitical tension. 2) Easy field weakening: Wound field synchronous machines have complete control of their field excitation. With proper design, this type of machine can electromagnetically have an infinite constant power speed range. 3) High power factor: With proper choice of the field excitation, WFSMs may be operated with high or even unity power factor. This potentially allows for the inverter connected to the stator winding to be downsized. In comparison induction machines and interior permanent magnet synchronous machines must supply reactive power to the stator increasing the kVA rating and cost of the inverter. 4) Reduced iron losses at high speed: By reducing the field excitation, the iron loss in the stator can be reduced. This is in comparison to interior permanent magnet synchronous machines which must use stator current to buck or reduce the flux produced by the permanent magnets. Generally, WFSMs have their highest efficiency at high speed. 5) Torque output at high temperatures: The magnetization provided by the field winding only depends on the field current and not on the field winding temperature. This is in contrast to permanent magnet machines where the permanent magnet flux decreases as the magnet temperature increases. Historically a number of approaches have been developed to provide DC current to the rotating field winding in WFSM’s including brushes and slip rings, low frequency brushless exciters, and high frequency rotary transformers and rectifiers. In this project, a different approach was used: brushless capacitive power transfer. Brushless capacitive power transfer uses two sets of rotating capacitors or electrodes in which an AC electric field is established by a high frequency inverter. A displacement current can flow through the airgap in the rotating capacitors which is rectified on the rotor using a diode bridge. The potential advantage of capacitive power transfer is that there is no need for heavy iron to guide magnetic flux. The electric flux lines terminate on the charges on the rotary capacitor surfaces. This should also limit the electric field outside the rotary capacitor airgaps. The main challenge with capacitive power transfer is that the capacitance and surface area of the rotating capacitors is small. Because capacitive power transfer systems are essentially a dual of a magnetic system, an Ampere per Hertz relationship is characteristic versus a Volts per Hertz relationship. A very high frequency power inverter must be used to provide sufficient excitation to the field winding. The concept of using capacitive power transfer to excite a high performance WFSM was initially developed in a previous U.S.A. Dept. of Energy project, DE-EE0006829. This project focused on increasing the power density of the WFSMs and reducing the cost and manufacturing complexity of the capacitive power transfer system. This project has demonstrated that WFSMs with brushless capacitive field power transfer can provide a high-power density and low-cost automotive powertrain technology.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. Furthermore, the results were compared to currently available models for contact resistance, and one model was adapted for prediction of TCR in future motor designs.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Review of Axial Flux Permanent Magnet Machine Technology

Axial flux permanent magnet machines (AFPM) are popular for applications that benefit from high torque density and an axially compact form factor, such as in-wheel traction drives. Although the radial flux permanent magnet machine (RFPM) and the AFPM work based on the same underlying principle, the differences in their geometry introduce complexities in analysis of the AFPM. In this paper, the different AFPM design variants, their sizing approaches, computationally efficient design optimization techniques, and manufacturing techniques reported in literature are reviewed. In addition to classical AFPM machines, emerging variants and research opportunities with potential to push the boundaries of electric machine technology are reviewed. Furthermore, these include bearingless AFPMs, magnetically geared AFPMs, and combined radial-axial flux machines.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Recent Advances in Analysis and Design of Axial Flux Permanent Magnet Electric Machines

Axial flux permanent magnet machines (AFPM) are popular for applications that benefit from high torque density and an axially compact form factor, such as in-wheel traction drives. Although the radial flux permanent magnet machine (RFPM) and the AFPM work based on the same underlying principle, the differences in their geometry introduce complexities in analysis of the AFPM. In this paper, the different AFPM design variants, their sizing approaches, computationally efficient design optimization techniques, and manufacturing techniques reported in literature are reviewed. In addition to classical AFPM machines, emerging variants and research opportunities with potential to push the boundaries of electric machine technology are reviewed. These include bearingless AFPMs, magnetically geared AFPMs, and combined radial-axial flux machines.

Nishanth, FNU↗
DOE OSTI2020

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient’s hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient’s vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics – pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multi-cyclone analysis and machine learning model implications of cyclone effects on forests

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R 2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Improving the wear resistance of machine parts

Various types of operational faults in critical machine parts are analyzed. The possibility of eliminating the factors responsible for such faults at the manufacturing stage is considered. Thermodynamic analysis reveals the relation between the bulk wear rate and the surface quality.

42 ENGINEERING↗
DOE OSTI2022

Causality guided machine learning model on wetland CH 4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

54 ENVIRONMENTAL SCIENCES↗