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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Institute for High Heat Flux Removal (IHHFR). Phases I, II, and III

The IHHFR focused on interdisciplinary applications as it relates to high heat flux engineering issues and problems which arise due to engineering systems being miniaturized, optimized, or requiring increased high heat flux performance. The work in the IHHFR focused on water as a coolant and includes: (1) the development, design, and construction of the high heat flux flow loop and facility; (2) test section development, design, and fabrication; and, (3) single-side heat flux experiments to produce 2-D boiling curves and 3-D conjugate heat transfer measurements for single-side heated test sections. This work provides data for comparisons with previously developed and new single-side heated correlations and approaches that address the single-side heated effect on heat transfer. In addition, this work includes the addition of single-side heated circular TS and a monoblock test section with a helical wire insert. Finally, the present work includes: (1) data base expansion for the monoblock with a helical wire insert (only for the latter geometry), (2) prediction and verification using finite element, (3) monoblock model and methodology development analyses, and (4) an alternate model development for a hypervapotron and related conjugate heat transfer controlling parameters.

2-D Boiling Curves↗
DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗
DOE OSTI2014

A Model-Based Approach to Scintillator/Photomultiplier System Characterization

Scintillator/photomultiplier systems are prevalent in many instruments to measure a variety on nuclear events. When gamma energy from a nuclear event irradiates the scintillator, event radiation interacts with the scintillator material generating photons which are detected by the photomultiplier tube (PMT). The PMT photoelectrons or photocathode current is amplified in the PMT by a number of dynode stages producing a total charge out for a given radiation flux into scintillator. The resulting current is converted to a voltage when passed through a typical load impedance. This voltage is attenuated and provided as input to a digitizer through a variety of signal conditioners (filters, amplifiers, receivers) before analog-to-digital conversion. In this report, we concentrate on the development of analytic models for the scintillator/photomultiplier system and develop both a (1) statistical simulator; and (2) a model-based signal processor to extract the desired signal information while rejecting the noise and uncertainty. We develop the model analytically, transform it to the model-based framework (state-space), and then use actual experimental data to fit the model and apply it to our processing problem.

42 ENGINEERING↗
DOE OSTI2023

CRISPR-COPIES: Web Tool

CRISPR/Cas system has emerged as a powerful genome-editing tool for metabolic engineering and human gene therapy. However, the conundrum of where to integrate heterologous genes on the chromosome using the CRISPR/Cas system remains an open question. Selecting a site for gene integration requires incorporation of complex criteria such as factors involved in CRISPR/Cas-mediated integration, genetic stability, and gene expression and therefore, usually requires strenuous characterization of sites on particular or different chromosomal locations. To address these issues, we developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. The tool applies ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. This submission contains the code we developed to create a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate CRISPR-COPIES will serve as a useful tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, rapid strain construction to produce valuable biochemicals, and human gene and cell therapy.

Bioinformatics↗
DOE OSTI2023

Dark Field Proton Radiography (Los Alamos LDRD Report)

Proton radiography at LANL (pRad) takes advantage of the high flux, high proton energy, and flexible timing structure of LANSCE's 800-MeV proton beam to visualize dense, dynamic systems under extreme strain. With recent needs identified for the development of the W93, the pRad team has been actively searching for ways to increase the facility's radiographic capabilities. Recently, we discovered that utilizing two magnetic lenses (one upstream and one downstream of the object), and specific combinations of collimator choices within the two lenses, that the signal-to-noise ratio was improved by more than a factor of two. However, we did not fully understand the mechanism, and found that results did not match forward modeling predictions. This work specifically sought to implement an engineering solution to solve this problem, through the development of an actuated collimator that reduced the time required to swap collimator settings from a typical 4-8 hours, to less than 30 seconds. This allowed, in combination with a unique multi-material, multi-resolution stepwedge, to acquire an unprecedently enormous dataset, with in total 24 collimation combinations, and multiple diffuser settings and pulse patterns, equivalent to almost a full run cycle's worth of data in a few days. This massive dataset is currently being harnessed to make a fully-predictive, ultra-fast Python-based model of the system, to dial in specific settings for any future experiments that fully optimize that radiographic sensitivity to the material of interest.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2016

A Solid of Revolution Time Study using COG11.1 and MCNP6.1

The performance of two continuous energy Monte Carlo transport codes, COG11.1 and MCNP6.1, has been compared for the {sup 239}Pu Jezebel Benchmark using cross sections based on the ENDF/B-VII.1 evaluated nuclear data library. The purpose of this study is to determine the length of time it takes each code to complete a series of criticality calculations, using two different methods to model a solid of revolution. A solid of revolution is essential in many fields of engineering and mathematics, and has been a standard feature in computer-aided design and manufacturing since the 1970's. In the field of criticality safety, a solid of revolution can be used to reproduce and simplify complex engineering drawings and systems that would otherwise require excessive use of geometric primitives and computational resources to achieve a comparable level of fidelity. There are two basic methods to model a solid of revolution: Method 1 (COG) specifies a set of x-y pairs or polar coordinates and rotate the curve about the x-axis. Method 2 (COG and MCNP) creates a set of end-to-end conical frustums and/or cylinders. Method 1 is only available in COG and can be implemented using one surface and sector specification. Method 2 can be implemented in both COG and MCNP. This time study was initially conceived as a result of criticality safety analysis performed for a complex system with a high degree of rotational symmetry. Each COG input deck typically took 1-2 days to run in serial mode. COG input decks were then converted to MCNP format and run on the same computer cluster. Instead of expected 1-2 day runtimes, it took MCNP6.1 more than one week to complete each run. Since MCNP typically runs faster than many codes, these lengthy runtimes were considered unusual. This study attempts to investigate the runtime discrepancy between the two codes. The methods and benchmark in this time study were selected to provide a consistent, generalized approach to modeling an arbitrary solid of revolution. While there are many different ways to model the {sup 239}Pu Jezebel Benchmark, the focus of this study is on investigating the practical difference between the two basic methods to model a surface of revolution. Throughout this study, the only code that maintains reasonably low runtimes is COG11.1 (Method 1). Based on the observed performance of each code and method, it is likely that the divergent runtimes are the result of tracking particles across planes that lie between conical frustums, as opposed to defining the object boundary as a single surface. Slight fluctuations in graphed results were also observed, due to random sampling and CPU loading. This was confirmed by re-running input decks and comparing average runtimes to a preliminary curve fit. From the perspective of a criticality safety analyst, the results of this study are expected to contribute towards understanding one of the practical trade-offs between codes and methods as they apply to real-world analysis. Ultimately, it's about choosing the right tool for the job.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Block-oriented modeling of superstructure optimization problems

We present a novel software framework for modeling large-scale engineered systems as mathematical optimization problems. A key motivating feature in such systems is their hierarchical, highly structured topology. Existing mathematical optimization modeling environments do not facilitate the natural expression and manipulation of hierarchically structured systems. Rather, the modeler is forced to "flatten" the system description, hiding structure that may be exploited by solvers, and obfuscating the system that the modeling environment is attempting to represent. To correct this deficiency, we propose a Python-based "block-oriented" modeling approach for representing the discrete components within the system. Our approach is an extension of the Pyomo library for specifying mathematical optimization problems. Through the use of a modeling components library, the block-oriented approach facilitates a clean separation of system superstructure from the details of individual components. This approach also naturally lends itself to expressing design and operational decisions as disjunctive expressions over the component blocks. By expressing a mathematical optimization problem in a block-oriented manner, inherent structure (e.g., multiple scenarios) is preserved for potential exploitation by solvers. In particular, we show that block-structured mathematical optimization problems can be straightforwardly manipulated by decomposition-based multi-scenario algorithmic strategies, specifically in the context of the PySP stochastic programming library. We illustrate our block-oriented modeling approach using a case study drawn from the electricity grid operations domain: unit commitment with transmission switching and N - 1 reliability constraints. Finally, we demonstrate that the overhead associated with block-oriented modeling only minimally increases model instantiation times, and need not adversely impact solver behavior. (C) 2013 Elsevier Ltd. All rights reserved.

Friedman, Z↗
DOE OSTI2026

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2023

Real-Time Monitoring of Fracture Dynamics with a Contrast Agent-Assisted Electromagnetic Method

In collaboration with the Advanced Energy Consortium, our team has previously demonstrated that the placement of electrically active proppants (EAPs) in a hydraulic fracture surveyed by electromagnetic (EM) methods can enhance the imaging of the stimulated reservoir volumes during hydraulic fracturing. That work culminated in constructing a well-characterized EAP-filled fracture anomaly at the Devine field pilot site (DFPS). In subsequent laboratory studies, we observed that the electrical conductivity of our EAP correlates with changes in pressure, salinity, and flow. Thus, we postulated that the EAP could be used as an in-situ sensor for the remote monitoring of these changes in previously EAP-filled fractures. This paper presents our latest field data from the DFPS to demonstrate such correlations at an intermediate pilot scale. We conducted surface-based EM surveys during freshwater (200 ppm) and saltwater (2,500 ppm) slug injections while running surfaced-based EM surveys. Simultaneously, we measured the following: 1) bottomhole pressure and salinity in five monitoring wells; 2) injection rate using high-precision data loggers; 3) distributed acoustic sensors in four monitoring wells; and 4) tiltmeter data on the survey area. We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, by comparing the electrical field traces with the bottomhole pressure, flow rate, and salinity, we concluded that the observed electric field in our study is influenced by fracture dilation and flow rate. Salinity effect was observed when saltwater was injected. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce all of the measured electric field magnitudes. Preliminary estimates showed that including streaming potential in our geophysical model may be needed to reduce the simulation mismatch. The methods developed and demonstrated during this study will lead to a better understanding of the extent of fracture networks, formation stress states, fluid leakoff and invasion, characterizations of engineered fracture systems, and other applications where monitoring subsurface flow tracking is deemed important.

02 PETROLEUM↗
DOE OSTI2011

Survival and Passage of Juvenile Chinook Salmon and Steelhead Passing through Bonneville Dam, 2010

Pacific Northwest National Laboratory (PNNL) and subcontractors conducted an acoustic-telemetry study of juvenile salmonid fish passage and survival at Bonneville Dam in 2010. The study was conducted to assess the readiness of the monitoring system for official compliance studies under the 2008 Biological Opinion and Fish Accords and to assess performance measures including route-specific fish passage proportions, travel times, and survival based upon a single-release model. This also was the last year of evaluation of effects of a behavioral guidance device installed in the Powerhouse 2 forebay. The study relied on releases of live Juvenile Salmon Acoustic Telemetry System tagged smolts in the Columbia River and used acoustic telemetry to evaluate the approach, passage, and survival of passing juvenile salmon. This study supports the U.S. Army Corps of Engineers continual effort to improve conditions for juvenile anadromous fish passing through Columbia River dams.

13 HYDRO ENERGY↗
DOE OSTI2017

Additively Manufactured IN718 Components with Wirelessly Powered and Interrogated Embedded Sensing

A methodology is described for embedding commercial-off-the-shelf sensors together with wireless communication and power circuit elements using direct laser metal sintered additively manufactured components. Physics based models of the additive manufacturing processes and sensor/wireless level performance models guided the design and embedment processes. A combination of cold spray deposition and laser engineered net shaping was used to fashion the transmitter/receiving elements and embed the sensors, thereby providing environmental protection and component robustness/survivability for harsh conditions. By design, this complement of analog and digital sensors were wirelessly powered and interrogated using a health and utilization monitoring system; enabling real-time, in situ prognostics and diagnostics.

embedded sensing↗
DOE OSTI2019

Particle/Ray Interaction Simulation Manager (PRISM) Assessment Report

The Particle/Ray Interaction Simulation Manager (PRISM) is a software tool developed by Triple Ring Technologies (TRT), http://www.tripleringtech.com/, under a Small Business Innovation Research Award from the Department of Homeland Security (DHS) to investigate the utility of simulations in X-ray-based explosive detection systems. The PRISM tool is intended to provide a standardized and intuitive user interface to control and configure realistic simulations of X-ray imaging systems. It includes a command-line interface that enables the user to configure simulated X-ray radiography and computed tomography (CT) systems. It allows the import of computer-aided design (CAD) models of objects and it supports Monte Carlo simulation engines such as GEANT4. PRISM is designed to simulate realistic X-ray projection images including scatter, noise, and various detector effects such as quantum efficiency, lag, and veiling glare. At the request of DHS, the authors assessed PRISM’s capabilities to simulate data generated by a Micro-CT test-bed system (MCT-TB) housed at the Lawrence Livermore National Laboratory (LLNL). This document describes the results of that assessment.

42 ENGINEERING↗
DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Continuous toolpaths for additive manufacturing

Toolpath generation for additive manufacturing systems involves operations on polygonal contours derived from a model for additively manufacturing a structure. One aspect involves modifying or creating a model to allow parts to be printed without starting and stopping the printing equipment by generating continuous toolpaths or toolpaths having a reduced number of isolated paths. Another aspect involves modifying a slicing engine to generate a continuous toolpath or toolpath having a reduced number of isolated paths based on a representation of an object to be additively manufactured. Another aspect involves selectively placing the gaps at alternating positions among the sliced layers to create a zippering effect.

Borish, Michael C.↗
DOE OSTI2014

MO-E-18C-01: Open Access Web-Based Peer-To-Peer Training and Education in Radiotherapy

Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties. Methods: We have developed a free of charge web site ( https://i.treatsafely.org ) using the Google App Engine and datastore (NDB, GQL), Python with AJAX-RPC, and Javascript. The site is a radiotherapy-specific hosting service to which user-created videos illustrating clinical or physics processes and other relevant educational material can be uploaded. Efficient navigation to the material of interest is provided through several RT specific search tools and videos can be scored by users, thus providing comprehensive peer review of the site content. The site also supports multilingual narration\translation of videos, a quiz function for competence assessment and a library function allowing groups or institutions to define their standard operating procedures based on the video content. Results: The website went live in August 2013 and currently has over 680 registered users from 55 countries; 27.2% from the United States, 9.8% from India, 8.3% from the United Kingdom, 7.3% from Brazil, and 47.5% from other countries. The users include physicists (57.4%), Oncologists (12.5%), therapists (8.2%) and dosimetrists (4.8%). There are 75 videos to date including English, Portuguese, Mandarin, and Thai. Conclusion: Based on the initial acceptance of the site, we conclude that this open access web-based peer-to-peer tool is fulfilling an important need in radiotherapy training and education. Site functionality should expand in the future to include document sharing and continuing education credits.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

A Soft-Switching Inverter for High-Temperature Advanced Hybrid Electric Vehicle Traction Motor Drives

The state-of-the-art hybrid electric vehicles (HEVs) require the inverter cooling system to have a separate loop to avoid power semiconductor junction over temperatures because the engine coolant temperature of 105°C does not allow for much temperature rise in silicon devices. The proposed work is to develop an advanced soft-switching inverter that will eliminate the device switching loss and cut down the power loss so that the inverter can operate at high-temperature conditions while operating at high switching frequencies with small current ripple in low inductance based permanent magnet motors. The proposed tasks also include high-temperature packaging and thermal modeling and simulation to ensure the packaged module can operate at the desired temperature. The developed module will be integrated with the motor and vehicle controller for dynamometer and in-vehicle testing to prove its superiority. This report will describe the detailed technical design of the soft-switching inverters and their test results. The experiments were conducted both in module level for the module conduction and switching characteristics and in inverter level for its efficiency under inductive and dynamometer load conditions. The performance will be compared with the DOE original specification.

33 ADVANCED PROPULSION SYSTEMS↗