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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A new approach to approximate equation-of-motion coupled cluster with triple excitations

Accurate methods for excited, ionized, and electron-attached states are critical to the study of many chemical species such as reactive intermediates, radicals, and ionized systems. The equation-of-motion coupled cluster singles, doubles, and triples (EOM-CCSDT) family of methods is very accurate (roughly similar in accuracy as for CCSDT calculations of the ground state), but the computational cost scales iteratively as the eighth power of the system size. Many approximations already exist, although most either correct only the excited state or require an iterative $\mathscr{O}$(n 7 ) procedure which can also be prohibitively expensive. In this paper, new methods, termed EOM-CCSD(T)(a) and EOM-CCSD(T)(a)*, are proposed which correct both the ground and excited states based on a shared effective Hamiltonian, and the latter of which includes only non-iterative corrections to both the CCSD and EOM-CCSD energies. These methods are found to significantly improve the description of excited and ionized potential energy surfaces, equilibrium geometries, and harmonic frequencies; the accuracy is very close to that of full EOM-CCSDT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

I Know I'm Right, But Does My Phone?

Transportation is the largest source of green-house gas emissions in the United States. Reducing transportation emissions depends on human travel behavior, which relies on local land use and planning. Travel diaries, consisting of sequences of trips between places for a particular individual, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of GPS tracking surveys. Mode inference algorithms in particular have been well represented in literature. However, these algorithms have typically been validated using prompted recall of pre-segmented trips, which doesn't account for segmentation error, thus disregarding the continuity of mode inference. Furthermore, phone operating systems and applications have adopted battery-conserving techniques, but we are not aware of prior work that has characterized the resulting data collection errors or evaluated procedures to mitigate them. We introduce a framework to evaluate accuracy of trip length computations and mode inference. We develop a temporal alignment procedure in analyzing continuous mode-segmented trajectories for groups of trips. We then apply our framework to evaluate an example set of travel diary algorithms from the open-source OpenPATH travel diary platform against MobilityNet, a public dataset containing information from three artificial timelines that cover 15 different travel modes. Our results show that inference based on an integration with map features results in weighted F_1 scores of 0.60 (iOS) and 0.74 (android). We also show that OpenPATH tends to under count trip length, with mean of signed relative error of -0.0438 on android and -0.0704 on iOS. We hope that other travel diary algorithms will be evaluated using this standardized process, and that the results used to understand and improve the state-of-the-art in this field.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2015

Radiation Exposure of Abdominal Cone Beam Computed Tomography

PurposeTo evaluate patients radiation exposure of abdominal C-arm cone beam computed tomography (CBCT).MethodsThis prospective study was approved by the institutional review board; written, informed consent was waived. Radiation exposure of abdominal CBCT was evaluated in 40 patients who underwent CBCT during endovascular interventions. Dose area product (DAP) of CBCT was documented and effective dose (ED) was estimated based on organ doses using dedicated Monte Carlo simulation software with consideration of X-ray field location and patients’ individual body weight and height. Weight-dependent ED per DAP conversion factors were calculated. CBCT radiation dose was compared to radiation dose of procedural fluoroscopy. CBCT dose-related risk for cancer was assessed.ResultsMean ED of abdominal CBCT was 4.3 mSv (95 % confidence interval [CI] 3.9; 4.8 mSv, range 1.1–7.4 mSv). ED was significantly higher in the upper than in the lower abdomen (p = 0.003) and increased with patients’ weight (r = 0.55, slope = 0.045 mSv/kg, p < 0.001). Radiation exposure of CBCT corresponded to the radiation exposure of on average 7.2 fluoroscopy minutes (95 % CI 5.5; 8.8 min) in the same region of interest. Lifetime risk of exposure related cancer death was 0.033 % or less depending on age and weight.ConclusionsMean ED of abdominal CBCT was 4.3 mSv depending on X-ray field location and body weight.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights

Technologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large-scale computational screening can be extremely helpful for this task. This work explores the methods and details associated with such screening procedures and recommends best practices. We also shed light on the limitations of traditionally used and inexpensive to compute prescreening approaches involving geometric and energetic features to predict water adsorption behavior of porous materials. Such approaches can provide general trends to predict adsorption behavior but may lead to the overlook of potentially important structures due to the complex nature of water adsorption. Finally, this study offers insights for future water adsorption simulations to facilitate the development of optimal water adsorbents.

42 ENGINEERING↗
DOE OSTI2025

10-th order of accuracy for numerical solution of 3-D elasticity equations for heterogeneous materials on unfitted Cartesian meshes

We have developed the Optimal Local Truncation Error Method (OLTEM) with 10-th order of accuracy on unfitted Cartesian meshes for a system of 3-D elasticity equations with smooth irregular interfaces. 5 x 5 x 5 = 125-point stencils (similar to those for quadratic finite elements) for elastic heterogeneous materials are used for OLTEM. There are no unknowns at the interface points between different materials; the structure of the global discrete equations is the same for homogeneous and heterogeneous materials. The calculation of unknown stencil coefficients is based on the minimization of the local truncation error of the stencil equations and yields the optimal 10-th order of accuracy for OLTEM on unfitted Cartesian meshes, i.e., the increase by 7 orders in accuracy compared to quadratic finite elements on conformal meshes. A new post-processing procedure provides the 9-th order of accuracy for stresses in the 3-D case. Similar to basic computations it uses OLTEM with the 125-point stencils, the interface conditions and the elasticity equations. It was shown that the use of the elasticity equations for post-processing improves the accuracy of 0.1% stresses by 6 orders compared to post-processing without the use of PDEs. At an accuracy of for stresses, OLTEM with the new post-processing procedure reduces the number of degrees of freedom by 360 - 8000 times compared to quadratic finite elements with similar stencils. OLTEM with the 125-point stencils yields even more accurate results than high-order finite elements with much wider stencils. OLTEM provides accurate numerical results for compressible and nearly incompressible materials.

elasticity equations↗
DOE OSTI2017

Species-specific Susceptibility to Scale Loss: Using Simulated Shear to Understand Among Species Differences in Turbine Passage Injury

Hydropower is the largest source of utility-scale, renewable electricity (e.g., 44% of all renewable energy) in the USA to date (U.S. Energy Information Administration (USEIA), 2017); however, dam installation and operation have impacted natural upstream and downstream fish passage and altered connectivity of riverine fish communities throughout the USA (Čada, 1998; Čada et al., 2006; Pracheil et al., 2016a). Fish ladders and exclusion devices (among others) allow fish to safely circumvent and avoid passage through the turbines altogether (Čada, 2001), but are rarely 100% effective. At many dams, passage through turbines is unavoidable, and stressors like collision with structures or blade strike, barotrauma or cavitation, turbulence, and exposure to shear forces may cause injury or mortality to entrained fish (Čada, 2001; Neitzel et al., 2004; Pracheil et al., 2016a). Passage injury and mortality may be a consequence of exposure to the entire suite of stressors, while the exact role of each is difficult to ascertain and link to individuals (Colotelo et al., 2017). Contributing complicating factors to understanding mortality include turbine type, variation in fish size (i.e., life stages), and the marked morphological diversity of freshwater fishes (Pracheil et al., 2016a; Colotelo et al., 2017). Laboratory studies aimed to understand the effects of turbine passage have quantified each stressor separately to determine appropriate dose-response relationships based on fish size, species, etc. Establishing dose-response relationships by stressor for priority species that interact with hydropower dams is one of the primary objectives of the Biologicallybased Design and Evaluation (BioDE) project being conducted at Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL). At its core, BioDE is focused on empirically driven research that quantifies fish responses to stressors at exposure levels similar to those experienced during turbine passage so that Biological Performance Assessment (BioPA) models can be developed. The BioPA model informs hydropower turbine design modifications based on predicted injury and mortality in response to various stressor exposures, e.g., blade strike, shear, and barotrauma. Stress exposure is defined by computational fluid dynamics models of the turbine environment. Fish response relationships are determined through laboratory studies like the one reported in this document. The BioPA model will aid turbine manufactures, plant operators, and dam owners with design of lower impact turbines and modify operating procedures to maximize survival of entrained fishes.

13 HYDRO ENERGY↗
DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning

With the proliferation of mobile devices and smart cameras, detecting anomalies and predicting their mobility are critical for enhancing safety in ubiquitous computing systems. Due to data privacy regulations and limited communication bandwidth, it is infeasible to collect, transmit, and store all data from mobile devices at a central location. To overcome this challenge, we propose FedADMP, a federated learning based joint Anomaly Detection and Mobility Prediction framework. FedADMP adaptively splits the training process between the server and clients to reduce computation loads on clients. To protect the privacy of user data, clients in FedADMP upload only intermediate model parameters to the cloud server. We also develop a differential privacy method to prevent the cloud server and external attackers from inferring private information during the model upload procedure. Extensive experiments using real-world datasets show that FedADMP consistently outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Stochastic optimization of GeantV code by use of genetic algorithms

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2016

SU-G-206-05: A Comparison of Head Phantoms Used for Dose Determination in Imaging Procedures

Purpose: To determine similarities and differences between various head phantoms that might be used for dose measurements in diagnostic imaging procedures. Methods: We chose four frequently used anthropomorphic head phantoms (SK-150, PBU-50, RS-240T and Alderson Rando), a computational patient phantom (Zubal) and the CTDI head phantom for comparison in our study. We did a CT scan of the head phantoms using the same protocol and compared their dimensions and CT numbers. The scan data was used to calculate dose values for each of the phantoms using EGSnrc Monte Carlo software. An .egsphant file was constructed to describe these phantoms using a Visual C++ program for DOSXYZnrc/EGSnrc simulation. The lens dose was calculated for a simulated CBCT scan using DOSXYZnrc/EGSnrc and the calculated doses were validated with measurements using Gafchromic film and an ionization chamber. Similar calculations and measurements were made for PA radiography to investigate the attenuation and backscatter differences between these phantoms. We used the Zubal phantom as the standard for comparison since it was developed based on a CT scan of a patient. Results: The lens dose for the Alderson Rando phantom is around 9% different than the Zubal phantom, while the lens dose for the PBU-50 phantom was about 50% higher, possibly because its skull thickness and the density of bone and soft tissue are lower than anthropometric values. The lens dose for the CTDI phantom is about 500% higher because of its totally different structure. The entrance dose profiles are similar for the five anthropomorphic phantoms, while that for the CTDI phantom was distinctly different. Conclusion: The CTDI and PBU-50 head phantoms have substantially larger lens dose estimates in CBCT. The other four head phantoms have similar entrance dose with backscatter hence should be preferred for dose measurement in imaging procedures of the head. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Arms Control Opportunities for Inherently Safe and Secure Nuclear Command, Control, and Communications

The Department of Defense Science Board has stated that the United States is "not prepared to defend againsr cyber-attacks and that the military could lose "trust in the information and ability to control U.S. systems and forces [including nuclear forces]." One potential weak spot in cyber-security is storing encryption keys in computer memory. This paper explores the use of hardware devices (so-called Physical Unclonable Functions, or PUFs) to generate, in the nuclear weapon itself, unique encryption keys each time they are needed. Not only do we find that this has the potential to mitigate a number of cyberthreats, but such hardware has the potential to greatly diminish the total uncertainty associated with radiation-based warhead authentication procedures; a procedure many analysts feel will be key to future arms control regimes. After outline the use of PUFs in nuclear command, control, and communications--and indicating some of the areas that still require further research--we discuss their application to arms control and warhead authentication

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗