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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Analysis in Motion Initiative – Human Machine Intelligence

As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Mammalian nonmuscle myosin II comes in three flavors

Highlights: • Nonmuscle myosin II executes numerous mechanical tasks in cells including cytoskeleton organization and cell migration. • Three mammalian nonmuscle myosin II paralogs have distinct kinetic and dynamic properties in vitro and in vivo. • Mammalian nonmuscle myosin II paralogs mix and match their abilities to perform both individual and joint tasks in the cell. Nonmuscle myosin II is an actin-based motor that executes numerous mechanical tasks in cells including spatiotemporal organization of the actin cytoskeleton, adhesion, migration, cytokinesis, tissue remodeling, and membrane trafficking. Nonmuscle myosin II is ubiquitously expressed in mammalian cells as a tissue-specific combination of three paralogs. Recent studies reveal novel specific aspects of their kinetics, intracellular regulation and functions. On the other hand, the three paralogs also can copolymerize and cooperate in cells. Here we review the recent advances from the prospective of how distinct features of the three myosin II paralogs adapt them to perform specialized and joint tasks in the cell.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

PV Performance Modeling Methods and Practices: Results from the 4th PV Performance Modeling Collaborative Workshop.

In 2014, the IEA PVPS Task 13 added the PVPMC as a formal activity to its technical work plan for 2014-2017. The goal of this activity is to expand the reach of the PVPMC to a broader international audience and help to reduce PV performance modeling uncertainties worldwide. One of the main deliverables of this activity is to host one or more PVPMC workshops outside the US to foster more international participation within this collaborative group. This report reviews the results of the first in a series of these joint IEA PVPS Task 13/PVPMC workshops. The 4th PV Performance Modeling Collaborative Workshop was held in Cologne, Germany at the headquarters of TÜV Rheinland on October 22-23, 2015.

14 SOLAR ENERGY↗
DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗
DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗
DOE OSTI2015

WE-EF-207-01: FEATURED PRESENTATION and BEST IN PHYSICS (IMAGING): Task-Driven Imaging for Cone-Beam CT in Interventional Guidance

Purpose: This work introduces a task-driven imaging framework that utilizes a patient-specific anatomical model, mathematical definition of the imaging task, and a model of the imaging system to prospectively design acquisition and reconstruction techniques that maximize task-based imaging performance. Utility of the framework is demonstrated in the joint optimization of tube current modulation and view-dependent reconstruction kernel in filtered-backprojection reconstruction and non-circular orbit design in model-based reconstruction. Methods: The system model is based on a cascaded systems analysis of cone-beam CT capable of predicting the spatially varying noise and resolution characteristics as a function of the anatomical model and a wide range of imaging parameters. Detectability index for a non-prewhitening observer model is used as the objective function in a task-driven optimization. The combination of tube current and reconstruction kernel modulation profiles were identified through an alternating optimization algorithm where tube current was updated analytically followed by a gradient-based optimization of reconstruction kernel. The non-circular orbit is first parameterized as a linear combination of bases functions and the coefficients were then optimized using an evolutionary algorithm. The task-driven strategy was compared with conventional acquisitions without modulation, using automatic exposure control, and in a circular orbit. Results: The task-driven strategy outperformed conventional techniques in all tasks investigated, improving the detectability of a spherical lesion detection task by an average of 50% in the interior of a pelvis phantom. The non-circular orbit design successfully mitigated photon starvation effects arising from a dense embolization coil in a head phantom, improving the conspicuity of an intracranial hemorrhage proximal to the coil. Conclusion: The task-driven imaging framework leverages a knowledge of the imaging task within a patient-specific anatomical model to optimize image acquisition and reconstruction techniques, thereby improving imaging performance beyond that achievable with conventional approaches. 2R01-CA-112163; R01-EB-017226; U01-EB-018758; Siemens Healthcare (Forcheim, Germany)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers

Here, sequence labeling aims at assigning a label to every sample of a signal (or pixel of an image) while considering the sequentiality (or vicinity) of the samples. To perform this task, many works in the literature first filter and then label the data. Unfortunately, the filtering, which is performed independently from the labeling, is far from optimal and frequently makes the latter task harder. In this paper, a novel approach that trains a Gaussian process classifier and estimates the coefficients of an optimal filter jointly is presented. The new approach, based on Bayesian modeling and alternating direction method of multipliers (ADMMs) optimization, performs both tasks simultaneously. All unknowns are treated as stochastic variables, which are estimated using variational inference and filtering and labeling are linked with the use of ADMM. In the experimental section, synthetic and real experiments are presented to compare the proposed method with other existing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗
DOE OSTI2018

Using Contact to Increase Robot Performance for Glovebox D and D Tasks - 18227

Glovebox decommissioning tasks usually require manipulating relatively heavy objects in a highly constrained environment. Thus, contact with the surroundings becomes inevitable. In order to allow the robot to interact with the environment in a natural way, we present a contact-implicit motion planning framework. This framework enables the system, without the specification in advance of a contact plan, to make and break contacts to maintain stability while performing a manipulation task. In this method, we use linear complementarity constraints to model rigid body contacts and find a locally optimal solution for joint displacements and magnitudes of support forces. Then, joint torques are calculated such that the support forces have the highest priority. We evaluate our framework in a 2.5D, quasi-static simulation in which a humanoid robot with planar arms manipulates a heavy object. Our results suggest that the proposed method provides the robot with the ability to balance itself by generating support forces on the environment while simultaneously performing the manipulation task. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

42 ENGINEERING↗
DOE OSTI2020

Advanced Wind Turbine Drivetrain Topology for Improving System Reliability (CRADA CRD-10-400 Final Report)

Alstom Power has a desire to locate its latest entrance into the U.S. market at a location that truly tests the durability of the machine in a turbulent environment as well as verify the basic operating characteristics of the machine on a 60 Hz grid. The DOE/NREL National Wind Technology Center (NWTC) is well suited for this goal. The DOE/NREL Wind Program is working on improving drivetrain reliability through its Gearbox Reliability Collaborative (GRC). Alstom is also interested in performing research on their innovative drivetrain that is aligned with the objectives of the GRC. The tasks outlined in this Joint Work Statement are intended to accomplish these goals to the mutual satisfaction of both parties and the advancement of the understanding of wind turbine drivetrain behavior.

17 WIND ENERGY↗
DOE OSTI2019

Robotic Solutions for Remote Access and Retrieval of High-Level Radioactive Waste - 19544

This paper describes the ongoing development of robotic solutions for accessing and retrieving high-level radioactive waste from underground storage bins as part of the Calcine Retrieval Project at the INL Site. The waste is in the form of a solid powder, referred to as calcine, stored in a set of stainless steel bins within an underground concrete vault. Due to the radiation field produced by the calcine, all operations must be performed remotely. The Calcine Retrieval Project is currently carrying out proof-of-concept testing of methods for the various stages of calcine retrieval. The bins were designed with no means for access, so retrieval operations will require welding access riser pipes to the bins, followed by cutting through the bin walls from within the risers. These tasks are complicated by the fact that four of the risers must be placed off center on a domed surface, resulting in a weld joint that is curved and tilted relative to the riser axis. The Access Riser Connection robot is being developed to perform these operations using interchangeable welding and plasma cutting attachments. The majority of the calcine will be removed using a vacuum system and pneumatically transferred to storage. This system is not described in this paper. However, the bins contain several internal features that are expected to retain calcine, most notably circumferential stiffening rings on the walls and radial ribs on the floors. One of the solutions being developed for the task of retrieving this residual calcine is a vacuum crawler, a wheeled robot using vacuum to adhere to the interior walls of the bins. The vacuum crawler would use a separate vacuum system to remove residual calcine or would use compressed air to move calcine to a location from which it can be retrieved more easily. While comparable welding and wall-climbing robots have been developed before, this particular application has several constraints that are not satisfied by existing systems. For this reason, both the access riser welder and the vacuum crawler, although still under development, may be of interest to other projects faced with similar constraints. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Fujitsu and BEA Collaboration on Cyber Enabled Smart Grid Performance (CRADA Final report)

In conclusion of this CRADA, this letter documents the actions taken in regard to this collaboration. While the statement of work overviews several joint tasks associated with this effort, the availability of resources on the FLA side prevented any collaboratory development of shared Intellectual Property. Therefore, no patents, copyrights or publications resulted from this effort. However, several meetings at the INL and FLA offices maintained a good relationship and sharing of other potential areas of collaboration in the area of building/community energy management and cyber security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

MPACT Verification and Validation Manual (rev 1)

The MPACT code provides the capability to calculate the steady-state and transient reactor core neutron distribution for operating nuclear power plant (PWR) conditions across multiple fuel cycles. As required in the CASL SQA plan, it is the responsibility of UM and ORNL as joint owners of MPACT to ensure that verification and validation activities are performed and documented in a V&V manual with supporting publications and CASL technical reports which can be distributed for reference and distribution within VERA. This document provides the first revision of the MPACT verification and validation (V&V) manual and describes the current state of MPACT V&V and updates the framework for future MPACT V&V activities. During FY16, MPACT V&V activities in the areas of code verification have continued to mature and work has progressed on code error evaluation using the Method of Manufactured Solutions (MMS). Several specific tasks have also been completed to improve MPACT code verification to include tasks to improve the unit and regression test coverage. A joint UM/ORNL task was completed to develop standardize coding standards and workflow for the continued collaborative development of MPACT. During the past year, MPACT validation work has continued in both the areas of measured data from critical experiments as well as measured data from operating nuclear power plants. Preliminary work was also performed in FY16 on the use of post-irradiation examinations for evaluation and validation of the isotopic depletion capability in MPACT. Based on the results of V&V efforts this past year, the confidence level has increased in the ability of MPACT to model an operational Pressurized Water Reactor. Areas were identified which require increased emphasis during FY17 to include updating all the V&V problems with the new 51-group library, the completion of cycle 2 of the BEAVRS benchmark, initial validation for BWR applications, the addition of Uncertainty Quantification results into the benchmark results, and the completion of specific validation tasks for the CASL Challenge problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computation of Direct Sensitivities of Spatial Multibody Systems With Joint Friction

Abstract Friction exists in most mechanical systems and may have a major influence on the dynamic performance of the system. The incorporation of friction in dynamic systems has been a subject of active research for several years owing to its high nonlinearity and its dependence on several parameters. Consequently, optimization of dynamic systems with friction becomes a challenging task. Gradient-based optimization of dynamical systems is a prominent technique for optimal design and requires the computation of model sensitivities with respect to the design parameters. The novel contribution of this paper is the derivation of the analytical methodology for the computation of direct sensitivities for smooth multibody systems with joint friction using the Lagrangian index-1 formulation. System dynamics have been computed using two different friction models; the Brown and McPhee, and the Gonthier et al. model. The methodology proposed to obtain model sensitivities has also been validated using the complex finite difference method. A case study has been conducted on a spatial multibody system to observe the effect of friction on the dynamics and model sensitivities, compare sensitivities with respect to different parameters and demonstrate the numerical and validation aspects. Since design parameters can have very different magnitudes and units, the sensitivities have been scaled with the parameters for comparison. Finally, a discussion has been presented on the interpretation of the case study results. Due to the incorporation of joint friction, ‘jumps’ or discontinuities are observed in the model sensitivities akin to those observed for hybrid dynamical systems.

Engineering↗
DOE OSTI2025

Evaluation of Seismic Artificial Intelligence with Uncertainty

Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still a lack of robust evaluation frameworks for evaluating and comparing DLMs. Here, we address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework’s ability to guard against misleading declarations of model superiority is demonstrated through the evaluation of PhaseNet (Zhu and Beroza, 2018), a popular seismic phase picking DLM, under three training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data.

58 GEOSCIENCES↗