Engineering Papers⌕ Search

Engineering topics

Perry, Bruce A.

Publications and source records attributed to Perry, Bruce A..

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Pele Simulation Suite for Reacting Flows at Exascale

In this work, we present the Pele suite of software tools for compressible and incompressible reacting flows. The Pele suite leverages several different libraries, notably AMReX and SUNDIALS, to achieve performance portability on heterogeneous computing architectures across the supercomputing landscape. The Pele suite is comprised of PeleC, a compressible reacting flow block-structured adaptive mesh refinement solver, PeleLMeX, a low-Mach number reacting flow block-structured adaptive mesh refinement solver, Pele-Physics, a library for transport, thermodynamics, finite rate chemistry, soot, spray and radiation physics. The objective of this paper is (i) to present the code development efforts necessary to achieve highly effective and scalable applications for exascale machines and (ii) to detail the performance results of the Combustion-Pele project applications on Oak Ridge National Laboratory's Frontier. We show good weak and strong scaling results for both PeleC and PeleLMeX up to more than 50 billion cells on more than 4096 Frontier graphics processing unit nodes. We also present a capability demonstration simulation of a dual-fuel pulse compression ignition engine (six adaptive mesh refinement levels, and 60 billion cells or 2.1 trillion degrees of freedom) on Frontier, to date one of the largest simulations performed on the first exascale-class supercomputer.

adaptive mesh refinement↗
DOE OSTI2023

Simulation of Methane Oxycombustion in Supercritical Carbon Dioxide

The Allam-Fetvedt Cycle (AFC) offers the potential for electricity generation with minimal or even negative carbon emissions by using oxycombustion in supercritical carbon dioxide (sCO2). Because of the extreme high temperatures and pressures encountered in these systems, simulation of oxycombustion requires use of a complex equation of state such as the modified Soave-Redlich-Kwong (SRK) equation, coupled with equally complex correlations for transport properties. The current work simulates a model combustor using both an ideal gas equation of state and the SRK equation of state. The model combustor features a jet of CH4 mixing with and reacting with O2 in the sCO2 ambient. Two jet configurations are considered: one of pure CH4, and one of CH4 mixed with sCO2. Comparison of results shows that key output properties such as combustor temperature and flow distribution are impacted by the choice of equations of state. Inspection of simulation results shows that mixing and reaction rates, as well as the uniformity of the flow, are impacted by the choice of equation of state. These results show that even though use of a complex equation of state greatly increases the computational costs of these simulations, it is critical to accurately capturing the physics of these systems.

computational fluid dynamics↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Improved Dynamic Modeling of the Cascade Distillation Subsystem and Analysis of Factors Affecting Its Performance

The Cascade Distillation Subsystem (CDS) is a rotary multistage distiller being developed to serve as the primary processor for wastewater recovery during long-duration space missions. The CDS could be integrated with a system similar to the International Space Station Water Processor Assembly to form a complete water recovery system for future missions. A preliminary chemical process simulation was previously developed using Aspen Custom Modeler® (ACM), but it could not simulate thermal startup and lacked detailed analysis of several key internal processes, including heat transfer between stages. This paper describes modifications to the ACM simulation of the CDS that improve its capabilities and the accuracy of its predictions. Notably, the modified version can be used to model thermal startup and predicts the total energy consumption of the CDS. The simulation has been validated for both NaC1 solution and pretreated urine feeds and no longer requires retuning when operating parameters change. The simulation was also used to predict how internal processes and operating conditions of the CDS affect its performance. In particular, it is shown that the coefficient of performance of the thermoelectric heat pump used to provide heating and cooling for the CDS is the largest factor in determining CDS efficiency. Intrastage heat transfer affects CDS performance indirectly through effects on the coefficient of performance.

Perry, Bruce A.↗
NASA NTRS2015

Analyses of Hubble Space Telescope Aluminized-Teflon Multilayer Insulation Blankets Retrieved After 19 Years of Space Exposure

Since its launch in April 1990, the Hubble Space Telescope (HST) has made many important observations from its vantage point in low Earth orbit (LEO). However, as seen during five servicing missions, the outer layer of multilayer insulation (MLI) has become increasingly embrittled and has cracked in many areas. In May 2009, during the 5th servicing mission (called SM4), two MLI blankets were replaced with new insulation and the space-exposed MLI blankets were retrieved for degradation analyses by teams at NASA Glenn Research Center (GRC) and NASA Goddard Space Flight Center (GSFC). The retrieved MLI blankets were from Equipment Bay 8, which received direct sunlight, and Equipment Bay 5, which received grazing sunlight. Each blanket was divided into several regions based on environmental exposure and/or physical appearance. The aluminized-Teflon (DuPont, Wilmington, DE) fluorinated ethylene propylene (Al-FEP) outer layers of the retrieved MLI blankets have been analyzed for changes in optical, physical, and mechanical properties, along with chemical and morphological changes. Pristine and as-retrieved samples (materials) were heat treated to help understand degradation mechanisms. When compared to pristine material, the analyses have shown how the Al-FEP was severely affected by the space environment. Most notably, the Al-FEP was highly embrittled, fracturing like glass at strains of 1 to 8 percent. Across all measured properties, more significant degradation was observed for Bay 8 material as compared to Bay 5 material. This paper reviews the tensile and bend-test properties, density, thickness, solar absorptance, thermal emittance, x-ray photoelectron spectroscopy (XPS) and energy dispersive spectroscopy (EDS) elemental composition measurements, surface and crack morphologies, and atomic oxygen erosion yields of the Al-FEP outer layer of the retrieved HST blankets after 19 years of space exposure.

Radiation↗
NASA NTRS2012

Analyses of Hubble Space Telescope Aluminized-Teflon Insulation Retrieved After 19 Years of Space Exposure

Since its launch in April 1990, the Hubble Space Telescope (HST) has made many important observations from its vantage point in low Earth orbit (LEO). However, as seen during five servicing missions, the outer layer of multilayer insulation (MLI) has become successively more embrittled and has cracked in many areas. In May 2009, during the 5th servicing mission (called SM4), two MLI blankets were replaced with new insulation pieces and the space-exposed MLI blankets were retrieved for degradation analyses by teams at NASA Glenn Research Center (GRC) and NASA Goddard Space Flight Center (GSFC). The MLI blankets were from Equipment Bay 8, which received direct sunlight, and Equipment Bay 5, which received grazing sunlight. Each blanket contained a range of unique regions based on environmental exposure and/or physical appearance. The retrieved MLI blanket s aluminized-Teflon (DuPont) fluorinated ethylene propylene (Al-FEP) outer layers have been analyzed for changes in optical, physical, and mechanical properties, along with space induced chemical and morphological changes. When compared to pristine material, the analyses have shown how the Al-FEP was severely affected by the space environment. This paper reviews tensile properties, solar absorptance, thermal emittance, x-ray photoelectron spectroscopy (XPS) data and atomic oxygen erosion values of the retrieved HST blankets after 19 years of space exposure.

deGroh, Kim K.↗