Search NASA⌕ Search

SEARCH · Search NASA

Results for “engine simulation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering

If clean energy pathways are to harness massive increases in wind power, innovations with broad geographic viability will be needed to support buildout in diverse locations. However, geodiversity in impact potential is seldom captured in technology assessment. Here we propose a scalable approach to plant-level optimization using artificial intelligence to evaluate land sparing and economic benefits of wake steering at more than 6,800 plausible onshore wind locations in the USA. This emerging controls strategy optimizes plant energy production by directing turbine wakes. On the basis of estimates from our artificial intelligence model trained on engineering wind flow simulations, co-optimizing plant layouts with wake steering can reduce land requirements by an average of 18% per plant (site-specific benefits range from 2% to 34%), subject to errors and uncertainties in the flow model, wind resource estimates, buildout scenario and geographic factors. According to model estimates, wake steering is predicted to increase power production during high-value (relatively low wind) periods, boosting the annual revenue of individual plants by up to US$3.7 million (equivalent to US$13,000 MW-1 yr-1) but producing negligible gains in some settings. Consideration of wake steering’s geographic potential reveals divergent nationwide prospects for improved economics and siting flexibility.

deployment↗
DOE OSTI2020

Studying impacts of communication system performance on dynamic stability of networked microgrid

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time‐critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter‐dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB‐D simulation environment, along with a representative multi‐traffic, multi‐channel, multi‐protocol communication system model, developed in the network simulator (ns‐3). Second, a hierarchical engine for large‐scale infrastructure co‐simulation framework is developed to co‐simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use‐cases. While the example use‐cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

On the effect of mixing-driven vaporization in a homogeneous relaxation modeling framework

The homogeneous relaxation model (HRM) is one of the most widely used models to describe the liquid–gas phase transition in multiphase flows due to the occurrence of cavitation. However, in its original formulation, the HRM does not account for the presence of ambient gas species, which generally limits its applicability to the injector's internal flow where ambient gases are negligible. In this work, a mixing-driven vaporization (MDV) model was developed to extend the capability of the HRM in handling the mixing effect in the regions external to the nozzle, where vapor–liquid equilibrium for multi-species mixtures of fuel and ambient gas is considered. Herein, to assess the model performance, simulations of the Engine Combustion Network's Spray G injector were performed with the HRM and the MDV model under both flash-boiling and evaporating conditions. It was found that the MDV model led to a better match against x-ray measurements of fuel density in the near-nozzle region. In contrast to the HRM, the MDV model was able to reproduce the vaporization process in the mixing zone at the edge of the fuel jet, which aligns with the expected physics. This resulted in substantial differences in the prediction of other flow characteristics such as mixture temperature and pressure. Furthermore, this work demonstrates that evaporation timescales have a considerable effect on the MDV model's predictions, as shown by a parametric study in which a time factor was introduced to mimic the effect of different timescales due to different phase change mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A newly proposed isotherm model to predict Cs exchange with crystalline silicotitanate in tank waste simulants

The Zheng Anthony Miller (ZAM) computer model, a multicomponent ion exchange model used to predict the exchange of Group I metals onto crystalline silicotitanate (CST), has historically been used to predict Cs distribution coefficients from Hanford and Savannah River Site (SRS) tank waste simulants. Comparison of experimentally determined Cs distribution coefficients from tank waste simulants with ZAM isotherm model predictions indicate overprediction of Cs and K distribution coefficients for simple and complex simulants with the engineered form of CST. Additionally, recent changes in chemical composition/manufacturing of IONSIV TM R9140-B have resulted in increased Cs capacity from high-salt, highly alkaline solutions. Here, this work served to assess different isotherm models and refine equilibrium parameters to develop a model that can be applied to Hanford and SRS tank waste Cs removal efforts. Toward this goal, the Campbell Westesen Peterson (CWP) model was developed. This model utilized the experimentally determined Cs capacity, and simplified ZAM equilibria expressions to include only the binary substitution of Cs + or K + on the Na + sites. Equilibrium constants for these equations were refined using experimentally determined distribution coefficients. Overall, the CWP model significantly improved our ability to predict both Cs and K loading capacity from complex matrices.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Internal Radiation Dose Evaluation For An Unruptured Post Release Tristructural Istropic Fuel Particle for Advanced and Micro-reactor Applications

There are unique benefits from advanced/micro reactor designs and fuel types that offer safety features in the case of an accident that may reduce environmental consequences compared to conventional reactors and fuels. Several micro reactor designs, for both civilian and military applications, are proposed using tristructural-isotropic (TRISO) fuel. TRISO particles are a robust advanced nuclear fuel type that leads to the unique question of how unruptured, activated TRISO particles will interact with humans. Human exposure to nuclear fuel, or radionuclides, includes all pathways of exposure. However, TRISO particles are 0.9 mm in size, and that particle size restricts internal dose assessment to the ingestion pathway; eliminating the inhalation and dermal absorption pathways. Furthermore, activity of the TRISO particle was established by High Temperature Engineering Test Reactor simulations assuming the TRISO particle was one day post-release from the reactor. International Commission of Radiological Protection publications 100 and 133 were used to develop the internal dosimetry model for an unruptured TRISO particle for the International Commission of Radiological Protection reference male and female dose assessment. The rectosigmoid colon had the highest internal organ dose for both reference male (2.1 Sv) and reference female (2.3 Sv). The internal dose from ingestion of the scenario-defined TRISO particle was 0.25 Sv for the reference male and 0.29 Sv for the refence female, which exceeds the annual occupational effective dose limit of 0.05 Sv in the code of federal regulations, 10.CFR.20 Subpart C.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Mixed-precision iterative refinement using tensor cores on GPUs to accelerate solution of linear systems

Double-precision floating-point arithmetic (FP64) has been the de facto standard for engineering and scientific simulations for several decades. Problem complexity and the sheer volume of data coming from various instruments and sensors motivate researchers to mix and match various approaches to optimize compute resources, including different levels of floating-point precision. In recent years, machine learning has motivated hardware support for half-precision floating-point arithmetic. A primary challenge in high-performance computing is to leverage reduced-precision and mixed-precision hardware. We show how the FP16/FP32 Tensor Cores on NVIDIA GPUs can be exploited to accelerate the solution of linear systems of equations Ax = b without sacrificing numerical stability. The techniques we employ include multiprecision LU factorization, the preconditioned generalized minimal residual algorithm (GMRES), and scaling and auto-adaptive rounding to avoid overflow. We also show how to efficiently handle systems with multiple right-hand sides. On the NVIDIA Quadro GV100 (Volta) GPU, we achieve a 4×-5× performance increase and 5× better energy efficiency versus the standard FP64 implementation while maintaining an FP64 level of numerical stability.

GMRES↗
DOE OSTI2024

GPU Accelerated Sparse Cholesky Factorization

The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky factorization of the coefficient matrix is performed using a right-looking approach and the resulting triangular factors are used to compute the solution. Sparse Cholesky factorization is compute intensive. In this work we investigate techniques for reducing the factorization time in sparse Cholesky factorization by offloading some of the dense matrix operations on a GPU. We will describe the techniques we have considered. We achieved up to 4x speedup compared to the CPU-only version.

Karsavuran, M Ozan↗
DOE OSTI2017

EnergyPlus

EnergyPlus(TM) is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption - for heating, cooling, ventilation, lighting and plus and process loads - and water use in buildings.

New, JoshuaR↗
DOE OSTI2018

Geocellular model of St. Peter Sandstone for University of Illinois at Urbana-Champaign DDU Feasibility Study

The geocellular model of the St. Peter Sandstone was constructed for the University of Illinois at Urbana-Champaign DDU feasibility study. Starting with the initial area of review (18.0 km by 18.1 km [11.2 miles by 11.3 miles]) the boundaries of the model were trimmed down to 9.7 km by 9.7 km (6 miles by 6 miles) to ensure that the model enclosed a large enough volume so that the cones of depression of both the production and injection wells would not interact with each other, while at the same time minimizing the number of cells to model to reduce computational time. The grid-cell size was set to 61.0 m by 61.0 m (200 feet by 200 feet) for 160 nodes in the X and Y directions. The top surface of the St. Peter Sandstone was provided by geologists working on the project, and the average thickness of the formation was taken from the geologic prospectus they provided. An average thickness of 68.6 m (225 feet) was used for the St. Peter Sandstone, resulting in 45 layers for the model. Petrophysical data was taken from available rotary sidewall core data (Morrow et al., 2017). As geothermal properties (thermal conductivity, specific heat capacity) are closely related to mineralogy, specifically the percentage of quartz, available mineralogical data was assembled and used with published data of geothermal values to determine these properties (Waples and Waples, 2004; Robertson, 1988). The St. Peter Sandstone was divided into facies according to similar geothermal and petrophysical properties, and distributed according to available geophysical log data and prevailing interpretations of the depositional/diagenetic history (Will et al. 2014). Petrophysical and geothermal properties were distributed through geostatistical means according to the associated distributions for each lithofacies. The formation temperature was calculated, based on data from continuous temperature geophysical log from a deep well drilled into the Precambrian basement at the nearby Illinois Basin Decatur Project (IBDP) where CO2 is currently being sequestered (Schlumberger, 2012). Salinity values used in the model were taken from regional studies of brine chemistry in the St. Peter Sandstone, including for the IBDP (e.g., Panno et al. 2018). After being reviewed by the project's geologists, the model was then passed onto the geological engineers to begin simulations of the geothermal reservoir and wellbores.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Geocellular Model of Mt. Simon Sandstone for University of Illinois at Urbana-Champaign DDU feasibility study

The geocellular model of the Mt. Simon Sandstone was constructed for the University of Illinois at Urbana-Champaign DDU feasibility study. Starting with the initial area of review (18.0 km by 18.1 km [11.2 miles by 11.3 miles]) the boundaries of the model were trimmed down to 9.7 km by 9.7 km (6 miles by 6 miles) to ensure that the model enclosed a large enough volume so that the cones of depression of both the production and injection wells would not interact with each other, while at the same time minimizing the number of cells to model to reduce computational time. The grid-cell size was set to 61.0 m by 61.0 m (200 feet by 200 feet) for 160 nodes in the X and Y directions. Within the model, 67 layers are represented that are parameterized with their sediment/rock properties and petrophysical data. The top surface of the Mt. Simon Sandstone was provided by geologists working on the project, and the average thickness of the formation was taken from the geologic prospectus they provided. An average thickness of 762 m (2500 feet) was used for the Mt. Simon Sandstone, resulting in 60 layers for the model. Petrophysical data was taken from available rotary sidewall core data (Morrow et al., 2017). As geothermal properties (thermal conductivity, specific heat capacity) are closely related to mineralogy, specifically the percentage of quartz, available mineralogical data was assembled and used with published data of geothermal values to determine these properties (Waples and Waples, 2004; Robertson, 1988). The Mt. Simon Sandstone was divided into three separate units (lower, middle, upper) according to similar geothermal and petrophysical properties, and distributed according to available geophysical log data and prevailing interpretations of the depositional/diagenetic history (Freiburg et al. 2016). Petrophysical and geothermal properties were distributed through geostatistical means according to the associated distributions for each lithofacies. The formation temperature was calculated, based on data from continuous temperature geophysical log from a deep well drilled into the Precambrian basement at the nearby Illinois Basin Decatur Project (IBDP) where CO2 is currently being sequestered (Schlumberger, 2012). Salinity values used in the model were taken from regional studies of brine chemistry in the Mt. Simon Sandstone, including for the IBDP (e.g., Panno et al. 2018). After being reviewed by the project's geologists, the model was then passed onto the geological engineers to begin simulations of the geothermal reservoir and wellbores.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

IC Annual Report for w17_solarcells

We used first principles simulations to guide engineering of nanoparticle solar cells by considering effects of temperature and nanoparticle interactions

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Implementation of a Grid Connected Battery-Inverter Fleet Model

Batteries are designed to store electrical energy. The increasing variation in time value of energy has driven the use of batteries as controllable distributed energy resources (DER). This is enabled though low-cost power electronic inverters that are able to precisely control charge and discharge. This paper describes the software implementation of an open-source battery inverter fleet models in python. The Sandia BatterylnverterFleet class model can be used by scientists, researchers, and engineers to perform simulations of one or more fleets of similar battery-inverter systems connected to the grid. The program tracks the state- of-charge of the simulated batteries and ensures that they stay within their limits while responding to separately generated service requests to charge or discharge. This can be used to analyze control and coordination, placement and sizing, and many other problems associated with the integration of batteries on the power grid. The development of these models along with their python implementation was funded by the Grid Modernization Laboratory Consortium (GMLC) project 1.4.2. Definitions, Standards and Test Procedures for Grid Services from Devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Constrained Bayesian Optimization of Criticality Experiments at LLNL [Slides]

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗
DOE OSTI2025

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Real-Time Characterization of Salt Aerosols Generated from Static and Sparged Molten Salt

The formation of radionuclide-bearing aerosols in the respirable size range has the potential to significantly influence offsite dose consequences and is, therefore, an important consideration in nuclear facility safety assessments. Molten salt reactor (MSR) developers will likely need to demonstrate an understanding of the conditions under which radionuclide-bearing aerosols may be generated from their reactor under normal operating and accident conditions, as well as the characteristics and transport behavior of these aerosols, to demonstrate to the U.S. Nuclear Regulatory Commission (NRC) that the facility can be operated safely. Recent reviews of the literature identified a lack of experimental data describing the mechanisms of formation and properties (size, concentration, and composition) of salt aerosol particles that are produced from molten salts. Experiments that identify the conditions that lead to radionuclide-bearing salt aerosol releases and quantify the characteristics of salt aerosols formed by different mechanisms are high-priority needs to support MSR licensing. This report describes tests that were conducted within the Argonne Salt Aerosol Test Stand (a sealed vessel and measurement system) to generate salt aerosols from static and sparged molten salts and measure their size and concentration in real-time. The results provide insight into salt aerosol formation by the vapor condensation and bubble bursting mechanisms and inform the potential radiological consequences of aerosol formation from molten fuel salt. Videos of the salt surface were taken during salt sparge tests to observe surface bubble behavior. The data in this report can be used to develop mechanistic source term and accident progression models for MSRs. The real-time salt aerosol characterization technique used in this study will be employed in future integral effects tests that are conducted at an engineering scale to simulate realistic MSR accidents and in future separate effects tests to address additional variables that may impact salt aerosol characteristics (e.g., presence of fission products in salt and humidity in atmosphere).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Connecting Risk and Resilience for a Power System Using the Portland Hills Fault Case Study

Active seismic faults in the Pacific Northwest area have encouraged electric utilities in the region to deeply contemplate and proactively intervene to support grid resilience. To further this effort this research introduces Monte Carlo (MC)-based power system modeling as a means to inform the Performance Based Earthquake Engineering method and simulates 100,000 sample earthquakes of a 6.8 magnitude (M6.8) Portland Hills Fault (PHF) scenario in the Portland General Electric (PGE) service territory as a proof of concept. This paper also proposes the resilience metric Seismic Load Recovery Factor (SLRF) to quantify the recovery of a downed power system and thus can be used to quantify earthquake economic risk. Using MC results, the SLRF was evaluated to be 19.7 h and the expected economic consequence cost of a M6.8 PHF event was found to be $\$180$ million with an annualized risk of $\$90,000$ given the event’s 1 in 2000 year probability of occurrence. The MC results also identified the eight most consequential substations in the PGE system—i.e., those that contributed to maximum load loss. This paper concludes that retrofitting these substations reduced the expected consequence cost of a M6.8 PHF event to $117 million.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗