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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 667 records · Page 37

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DOE OSTI2017

Torque ripple reduction in electric machines

An electric machine, such as an Internal Permanent magnet or Synchronous Reluctance machine, having X phases, that includes a stator assembly, having M slots, with a stator core and stator teeth, that is further configured with stator windings to generate a stator magnetic field when excited with alternating currents and extends along a longitudinal axis with an inner surface that defines a cavity; and a rotor assembly, having N poles, disposed within the cavity which is configured to rotate about the longitudinal axis, wherein the rotor assembly includes a shaft, a rotor core located circumferentially around the shaft. The machine is configured such that a value k=M/(X*N) wherein k is a non-integer greater than about 1.3. The electric machine may alternatively, or additionally, include a non-uniformed gap between the exterior surface of the rotor spokes and the interior stator surface of the stator.

42 ENGINEERING↗

DOE OSTI2019

Real-Time Machine Learning Inferencing with Edge Computing Devices from Google and Intel

Machine Learning (ML) has been demonstrated to accelerate discovery and improve the accuracy of many measurements in astrophysics and high energy physics research. As an increasingly commonplace tool, we have reached a new set of challenges in the deployment of ML, at the forefront of which is their computational complexity. The goal of this project is to explore the viability of dedicated machine learning hardware, such as Google’s Coral/Edge TPU and Intel’s Movidius Myriad devices, in astrophysics and high energy physics applications. ML algorithms, while faster than traditional methods, can become the bottleneck in data processing pipelines --- which is a critical issue in systems that require low latency. Within the context of high energy physics and astrophysics, we test this hardware in an attempt to find if it is able to reduce the computational cost of machine learning. We have benchmarked some of these devices, specifically the Google Coral Dev Board and th e Google AIY Vision Kit (Containing Intel’s Movidius Myriad 2).This was done via a strong gravitational lens finding application and a known data set of lenses, simulating a realistic astrophysics application of machine learning, and compare their results to that of traditional computer hardware.

Hawks, Benjamin↗

DOE OSTI2014

Methods, systems and apparatus for controlling operation of an electric machine in an overmodulation region

Embodiments of the present disclosure relate to methods, systems and apparatus for controlling operation of an electric machine in a vector controlled motor drive system when the electric machine operates in an overmodulation region. The disclosed embodiments can reduce variations/errors in the phase voltage command signals applied to the multi-phase machine so that phase current may be properly regulated thus reducing current/torque oscillation, which can in turn improve machine efficiency and performance, as well as utilization of the DC voltage source.

42 ENGINEERING↗

DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

DOE OSTI2020

Scroll-type machine

A scroll-type displacement machine, e.g. scroll compressor, has fixed and orbiting scrolls and an Oldham ring to prevent relative rotation between scrolls. A drive module positioned between the orbiting scroll and drive shaft incorporates an eccentric drive system and first and second counterweights for dynamic balancing. The motor, motor bearings, and motor shaft (i.e., drive shaft) are packaged as a separate, modular unit (e.g. “off-the-shelf” motor) that attaches to the compressor frame. The machine is configured as a high-side machine with a low-pressure port at a radial outer portion of the fixed scroll and a high-pressure port or discharge port located in the floor of the orbiting scroll at its axis, with high pressure flow passing over and around the eccentric drive bearing and drive mechanism. The Oldham ring is distributed on a lateral or radial plane of the scroll pair with a portion surrounding a portion of the fixed scroll and another portion surrounding a portion of the orbiting scroll. The ring may be formed as four arcuate members arranged on two levels, to occupy otherwise unused space in the scroll-type machine.

42 ENGINEERING↗

DOE OSTI2022

A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.

additive manufacturing↗

DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗

DOE OSTI2022

Generative memory for lifelong machine learning

Techniques are disclosed for training machine learning systems. An input device receives training data comprising pairs of training inputs and training labels. A generative memory assigns training inputs to each archetype task of a plurality of archetype tasks, each archetype task representative of a cluster of related tasks within a task space and assigns a skill to each archetype task. The generative memory generates, from each archetype task, auxiliary data comprising pairs of auxiliary inputs and auxiliary labels. A machine learning system trains a machine learning model to apply a skill assigned to an archetype task to training and auxiliary inputs assigned to the archetype task to obtain output labels corresponding to the training and auxiliary labels associated with the training and auxiliary inputs assigned to the archetype task to enable scalable learning to obtain labels for new tasks for which the machine learning model has not previously been trained.

Nadamuni Raghavan, Aswin↗

DOE OSTI2023

Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Monitoring cutting forces for process control may be challenging because force measurements typically require invasive instrumentation. To remedy this situation, two new methods were recently developed to estimate cutting forces in real time based on the use of on-machine accelerometer measurements. One method uses machine learning, while another uses a physics-inspired data-driven approach, to generate a model that estimates cutting forces from on-machine accelerations. The estimated forces from both approaches were compared against cutting force data collected during various milling operations on several machine tools. The results reveal the advantages and disadvantages of each model to estimate real-time cutting forces.

Vogl, Greg↗

DOE OSTI2023

Integrated electro-hydraulic machine

An integrated electromechanical and hydraulic system is disclosed which includes an electric machine, the electric machine includes a casing, a stator adapted to remain stationary within the casing, one or more electrically insulated windings coupled to the stator, a rotor separated from the stator and the one or more windings by a radial diamagnetic gap, electrical power couplings, and a first mechanical power couplings, and a hydraulic machine also disposed in the housing, the hydraulic machine having hydro-mechanical couplings as well as a second mechanical power couplings, wherein the second mechanical power couplings are coupled to the first mechanical power couplings.

Vacca, Andrea↗

DOE OSTI2023

Devices and methods for increasing the speed or power efficiency of a computer when performing machine learning using spiking neural networks

A method for increasing a speed and efficiency of a computer when performing machine learning using spiking neural networks. The method includes computer-implemented operations; that is, operations that are solely executed on a computer. The method includes receiving, in a spiking neural network, a plurality of input values upon which a machine learning algorithm is based. The method also includes correlating, for each input value, a corresponding response speed of a corresponding neuron to a corresponding equivalence relationship between the input value to a corresponding latency of the corresponding neuron. Neurons that trigger faster than other neurons represent close relationships between input values and neuron latencies. Latencies of the neurons represent data points used in performing the machine learning. A plurality of equivalence relationships are formed as a result of correlating. The method includes performing the machine learning using the plurality of equivalence relationships.

Vineyard, Craig Michael↗

DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. Here, this paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. Here, in this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.

Machine learning↗

DOE OSTI2020

Classical versus quantum models in machine learning: insights from a finance application

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Design of High Power Density 100 kW Surface Permanent Magnet Machine with No Heavy Rare Earth Material Using Current Source Inverter for Traction Application

Surface permanent magnet (SPM) machines are appealing candidates for traction applications because of high power density and high efficiency. Rare-earth magnets deliver high performance but raise concerns about material supply dependability, particularly if they use heavy rare-earth materials. This paper presents the design of a high-performance SPM machine without any heavy rare-earth material that is optimized specifically for a current-source inverter (CSI) traction drive. This machine is designed for a constant-power speed ratio (CPSR) of 3. A genetic algorithm has been performed to achieve a target of 50 kW/L active power density at speeds up to 20,000 rpm. Operation at a peak line-to-line voltage of 800 V can be achieved using 1200 V power devices with a safety margin. The predicted electromagnetic and rotor structural performance characteristics are presented using both analytical and finite element analysis (FEA) results.

(CPSR)↗