Search NASA⌕ Search

SEARCH · Search NASA

Results for “low order model”

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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Analytical estimates of radial segregation in Bridgman growth from low-level steady and periodic accelerations

Approximate analytical solutions are developed that are mathematically tractable but provide the elements of CFD models for estimating convective flows subjected to low-level accelerations. The estimates are applicable to dilute systems that have horizontal temperature gradients in the vertical Bridgman configuration. The Navier-Stokes momentum equation is solved in 1D and subsequently in 2D by the first-order perturbation method. The analysis used is the case of plane-front solidification of a dilute system in the vertical thermally stable configuration. The model is found to predict the degree of radial segregation within a factor of two for a range of material and processing parameters. The method provides detailed information on the effects of processing on solute distribution in the grown crystal which are of interest in the orbital experiments emphasizing the control of radial segregation.

Naumann, Robert J.↗
DOE OSTI2025

Low‐dimensional manifold learning for uncertainty quantification in complex multi‐scale stochastic systems

Broadly speaking, the goals of the project are to develop techniques to use manifold learning to develop reduced‐order and surrogate models for "hyper‐reduction" of very high‐dimensional complex multi‐scale systems. This is being achieved by employing a newly proposed form of manifold projection and learning that leverages recent advancements in computational geometry and data‐driven modeling. In particular, we are applying a manifold projection technique to project the solutions of very high‐dimensional systems onto the so‐called Grassmannmanifold, a Reimannian manifold comprised of orthonormal matrices. We then apply data‐driven machine learning techniques to classify the solutions on the manifold (e.g. clustering techniques) according to their proximity on the manifold and leverage a further nonlinear dimension reduction to organize the structured data on the manifold. Finally, we are developing novel techniques that enable us to directly interpolate the hyper‐reduced data such that we can predict the solution of the complex, high‐ dimensional system without need to call the full expensive computational model. Given their adherence to the underlying structure of the solution of the physical system, it is expected that these approximate solutions will be sufficiently constrained so as to (approximately) adhere to physical principles.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

A laboratory model of a hydrogen/oxygen engine for combustion and nozzle studies

A small laboratory diagnostic thruster was developed in order to evaluate approaches for the use of temperature and pressure sensors for the investigation of low thrust rocket flowfields. Tests were performed at chamber pressures of about 255 kPa, 370 kPa, and 500 kPa with oxidizer/fuel mixture ratios between 4.0 and 8.0. Two gaseous hydrogen/gaseous oxygen injector designs were tested with 60 and 75 fuel film cooling. The results of hot-wire tests showed the thruster and instrumentation designs to be effective. Azimuthal temperature distributions were found to be a function of operating conditions and hardware configuration. Results indicated that small differences in injector design can result in dramatically different thruster performance and wall temperature behavior. However, the importance of these injector effects may be decreased by operating at a high fuel film cooling rate.

Morren, Sybil H.↗
NASA NTRS2020

An Evaluation of Clouds and Radiation in a Large-Scale Atmospheric Model Using a Cloud Vertical Structure Classification

We revisit the concept of the cloud vertical structure (CVS) classes we have previously employed to classify the planet's cloudiness (Oreopoulos et al., 2017). The CVS classification reflects simple combinations of simultaneous cloud occurrence in the three standard layers traditionally used to separate low, middle, and high clouds and was applied to a dataset derived from active lidar and cloud radar observations. This classification is now introduced in an atmospheric global climate model, specifically a version of NASA's GEOS-5, in order to evaluate the realism of its cloudiness and of the radiative effects associated with the various CVS classes. Such classes can be defined in GEOS-5 thanks to a sub column cloud generator paired with the model's radiative transfer algorithm, and their associated radiative effects can be evaluated against observations. We find that the model produces 50% more clear skies than observations in relative terms and produces isolated high clouds that are slightly less frequent than in observations, but optically thicker, yielding excessive planetary and surface cooling. Low clouds are also brighter than in observations, but underestimates of the frequency of occurrence (by ~20% in relative terms) help restore radiative agreement with observations. Overall the model better reproduces the longwave radiative effects of the various CVS classes because cloud vertical location is substantially constrained in the CVS framework.

Lee, Dongmin↗
NASA NTRS1991

Geomagnetic model investigations for 1980 - 1989: A model for strategic defense initiative particle beam experiments and a study in the effects of data types and observatory bias solutions

Two suites of geomagnetic field models were generated at the request of Los Alamos National Lab. concerning Strategic Defense Initiative (SDI) research. The first is a progression of five models incorporating MAGSAT data and data from a sequence of batches as a priori information. The batch sequence is: post 1979.5 observatory data, post 1980 land survey and selected aeromagnetic and marine survey data, a special White Sands (NM) area survey by Project Magnet with some additional post 1980 marine survey data, and finally DE-2 satellite data. These models are of 13th deg and order in their main field terms, and deg and order 10 in their first derivative temporal terms. The second suite consists of four models based solely upon post 1983.5 observatory and survey data. They are of deg and order 10 in main field and 8 in a first deg Taylor series. A comprehensive error analysis was applied to both series, which accounted for error sources such as the truncated core and crustal fields, and the neglected Sq and low deg crustal fields. Comparison of the power spectrum of the MGST (10/81) model with those of this series show good agreement.

Langel, Robert A.↗
DOE OSTI2021

First M87 Event Horizon Telescope Results. VIII. Magnetic Field Structure near The Event Horizon

Event Horizon Telescope (EHT) observations at 230 GHz have now imaged polarized emission around the supermassive black hole in M87 on event-horizon scales. This polarized synchrotron radiation probes the structure of magnetic fields and the plasma properties near the black hole. Here we compare the resolved polarization structure observed by the EHT, along with simultaneous unresolved observations with the Atacama Large Millimeter/submillimeter Array, to expectations from theoretical models. The low fractional linear polarization in the resolved image suggests that the polarization is scrambled on scales smaller than the EHT beam, which we attribute to Faraday rotation internal to the emission region. We estimate the average density n_e of order 10^4-7 cm-3, magnetic field strength B of order 1-30 G, and electron temperature Te of order (1-12) x 10^10 K of the radiating plasma in a simple one-zone emission model. We show that the net azimuthal linear polarization pattern may result from organized, poloidal magnetic fields in the emission region. In a quantitative comparison with a large library of simulated polarimetric images from general relativistic magnetohydrodynamic (GRMHD) simulations, we identify a subset of physical models that can explain critical features of the polarimetric EHT observations while producing a relativistic jet of sufficient power. The consistent GRMHD models are all of magnetically arrested accretion disks, where near-horizon magnetic fields are dynamically important. We use the models to infer a mass accretion rate onto the black hole in M87 of (3-20) x 10^-4 Msun yr-1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Development of Low-Cost, Crack-Tolerant Metallization Using Screen Printing

One of the ways to reduce the cost of solar electricity to 3/kWh, thus reaching parity with fossil-fuel-based generation, is to reduce the degradation rate of solar modules and extend their lifetime well beyond 30 years. The extended module lifetime in turn can positively influence the financial model and the bankability of utility-scale PV projects. Today, the highest-riskpriority solar module degradation mechanism is what is known as hot spots, often induced by cell cracks. In order to address this degradation mechanism, we make use of low-cost, multi-walled carbon nanotubes embedded in commercial screen-printable silver pastes. When the carbon nanotubes are properly functionalized and appropriately incorporated into commercial silver pastes, the resulting metal contacts on solar cells, after screen-printing and firing, show exceptional fracture toughness. These composite metal contacts possess increased ductility, electrical gap-bridging capability up to 50 um, and 'self-healing' to regain electrical continuity even after cycles of complete electrical failure under extreme strain.

14 SOLAR ENERGY↗
NASA NTRS2018

Modeling In-Space Aborts for NASA Human Exploration Missions

NASA is developing capabilities for crewed missions beyond Low Earth Orbit (LEO) for the first time in nearly 50 years. Given the greater distances from Earth that these missions will entail, it is prudent to develop in-space abort capabilities in order to save the crew in the event of critical life-threatening failures that may occur. NASA has developed a Cross Program PRA (XPRA) of the integrated vehicle, from pre-launch through landing and rescue of the crew. An ascent abort model has already been developed as part of this XPRA model to assess the risk associated with failures during prelaunch and ascent. The scope of the analysis discussed here is focused on aborts associated with the in-space portion of the mission up to and including the Trans-Lunar Injection (TLI) burn, which places the Orion spacecraft on a trajectory to the Moon.

Bigler, Mark A.↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2026

A Unidirectional Two-Compartment Neuron Circuit with On-chip STDP learning

Most neuromorphic chips implement the single-compartment point neuron model where synapse circuits connect directly to a leaky integrate and fire (LIF) soma circuit. However, when using a biologically plausible soma circuit (e.g., Hodgkin-Huxley neuron model), an interface circuitry, such as a current conveyor circuit, is needed to transmit synaptic current to the soma circuit. This is especially true for ultra-low power neuron circuits, where membrane capacitance is on the order of 20 fF. This need for an interface circuit arises because the parasitic capacitance and leakage current caused by fabrication mismatch and second-order effects of the output transistors in the synapse circuits can disturb the spiking dynamics of the soma circuit if connected without an interface. Using an interface circuit to isolate the soma’s membrane capacitor from synapses resolves this issue. We propose to use a unidirectional resistor (a transconductance circuit) to connect the synapse and soma circuits instead of conventional current conveyor circuits. Using a biologically plausible spike pattern detection model, we show that the on-chip spike-timing-dependent plasticity (STDP) learning performance of the proposed unidirectional two-compartment neuron circuit is similar to a single-compartment circuit (with a current conveyor as an interface) and additionally, it is more power-efficient and biologically plausible. The chip is fabricated in the Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm technology node and comprises a single neuron circuit.

Gautam, Ashish [ORNL]↗
NASA NTRS2018

The Importance of Conducting Life Sciences Experiments on the Deep Space Gateway Platform

Over the last several decades important information has been gathered by conducting life science experiments on the Space Shuttle and on the International Space Station. It is now time to leverage that scientific knowledge, as well as aspects of the hardware that have been developed to support the biological model systems, to NASA's next frontier - the Deep Space Gateway. In order to facilitate long duration deep space exploration for humans, it is critical for NASA to understand the effects of long duration, low dose, deep space radiation on biological systems. While carefully controlled ground experiments on Earth-based radiation facilities have provided valuable preliminary information, we still have a significant knowledge gap on the biological responses of organisms to chronic low doses of the highly ionizing particles encountered beyond low Earth orbit. Furthermore, the combined effects of altered gravity and radiation have the potential to cause greater biological changes than either of these parameters alone. Therefore a thorough investigation of the biological effects of a cis-lunar environment will facilitate long term human exploration of deep space.

microgravity↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Computational Aerodynamic Simulations of an 840 ft/sec Tip Speed Advanced Ducted Propulsor Fan System Model for Acoustic Methods Assessment and Development

Computational Aerodynamic simulations of an 840 ft/sec tip speed, Advanced Ducted Propulsor fan system were performed at five different operating points on the fan operating line, in order to provide detailed internal flow field information for use with fan acoustic prediction methods presently being developed, assessed and validated. The fan system is a sub-scale, lownoise research fan/nacelle model that has undergone extensive experimental testing in the 9- by 15- foot Low Speed Wind Tunnel at the NASA Glenn Research Center, resulting in quality, detailed aerodynamic and acoustic measurement data. Details of the fan geometry, the computational fluid dynamics methods, the computational grids, and various computational parameters relevant to the numerical simulations are discussed. Flow field results for three of the five operating conditions simulated are presented in order to provide a representative look at the computed solutions. Each of the five fan aerodynamic simulations involved the entire fan system, excluding a long core duct section downstream of the core inlet guide vane. As a result, only fan rotational speed and system bypass ratio, set by specifying static pressure downstream of the core inlet guide vane row, were adjusted in order to set the fan operating point, leading to operating points that lie on a fan operating line and making mass flow rate a fully dependent parameter. The resulting mass flow rates are in good agreement with measurement values. The computed blade row flow fields for all five fan operating points are, in general, aerodynamically healthy. Rotor blade and fan exit guide vane flow characteristics are good, including incidence and deviation angles, chordwise static pressure distributions, blade surface boundary layers, secondary flow structures, and blade wakes. Examination of the computed flow fields reveals no excessive boundary layer separations or related secondary-flow problems. A few spanwise comparisons between computational and measurement data in the bypass duct show that they are in good agreement, thus providing a partial validation of the computational results.

Tweedt, Daniel L.↗
DOE OSTI2025

Temperature and Strain Sensing Characteristics of a 128° YX-Cut LiNbO 3 Rayleigh-Mode SAW Sensor From Room to Cryogenic Temperatures

Accurate, passive, and wireless monitoring of cryogenic hardware is essential for high-energy physics, space propulsion, and biomedical instrumentation. Here, this study quantifies the coupled temperature-strain behavior of Rayleigh-mode surface acoustic-wave (SAW) delay-line sensors fabricated on 128° YX-cut LiNbO 3 . A nonlinear finite element (FE) model incorporating Varshni-based elastic constants, higher-order thermal expansion, and temperature-dependent piezo- and dielectric coefficients was developed and validated experimentally between 280K and 80K. Free-standing (first test condition) and bonded/wired (second test condition) devices exhibited indistinguishable thermal responses; the average temperature coefficient of delay (TCD) in the critical cryogenic range from 130K down to 80K differed by only 0.15 ppm/K (0.32%), confirming that bonding-induced stress is negligible. Over 280-80K the measured TCD was 61.77 ppm/K, while the FE model predicted an equivalent temperature coefficient of frequency (TCF) of −62.74 ppm/K with an overall coefficient of determination R2 = 0.998. In the critical cryogenic interval 130-80K the TCD fell to 47.66 ppm/K, indicating improved thermal stability at low temperature. Controlled loading (0-300 με) revealed a strain coefficient of delay (SCD) that rises from 0.53 ± 0.02 ppm/με at 300K to 1.05 ± 0.02 ppm/με at 80K. This modest sensitivity confirms that, for temperature sensing, strain is a second-order perturbation above 135K but must be compensated at deeper cryogenic levels. Overall, this work establishes a predictive multiphysics model together with repeatable wired measurements that confirm the suitability of SAW sensors for temperature and strain monitoring in extreme cryogenic environments, while also providing a baseline for future wireless implementations.

25 ENERGY STORAGE↗
DOE OSTI2025

Precision e + e − hemisphere masses in the dijet region with power corrections

We derive high-precision results for the e + e − heavy jet mass (HJM) dσ/dρ and dihemisphere mass (DHM) d 2 σ/(ds 1 ds 2 ) distributions, for s 1 ~ s 2 , in the dijet region. New results include: i) the N 3 LL resummation for HJM of large logarithms ln n (ρ) at small ρ including the exact two-loop non-global hemisphere soft function, the 4-loop cusp anomalous dimension and the 3-loop hard and jet functions, ii) N 3 LL results for DHM with resummation of logarithms ln(s 1,2 /Q 2 ) when there is no large separation between s 1 and s 2 , iii) profile functions for HJM to give results simultaneously valid in the peak and tail regions, iv) a complete two-dimensional basis of non-perturbative functions which can be used for double differential observables, that are needed for both HJM and DHM in the peak region, and v) an implementation of renormalon subtractions for large-angle soft radiation to $\mathcal{O}$ (α$^{3}_{s}$) together with a resummation of the additional large ln(Qρ/Λ QCD ) logarithms. Here Q is the e + e − center-of-mass energy. Our resummation results are combined with known fixed-order $\mathcal{O}$ (α$^{3}_{s}$) results and we discuss the convergence and remaining perturbative uncertainty in the cross section. We also prove that, at order 1/Q, the first moment of the HJM distribution involves an additional non-perturbative parameter compared to the power correction that shifts the tail of the spectrum (where 1 ≫ ρ ≫ Λ QCD /Q). This differs from thrust where a single non-perturbative parameter at order 1/Q describes both the first moment and the tail, and it disfavors models of power corrections employing a single non-perturbative parameter, such as the low-scale effective coupling model. In this paper we focus only on the dijet region, not the far-tail distribution for ρ ≳ 0.2 beyond which the trijet factorization and resummation become important.

Factorization↗
DOE OSTI2023

The Intrinsic 150‐Day Periodicity of the Southern Hemisphere Extratropical Large‐Scale Atmospheric Circulation

Abstract The variability of the Southern Hemisphere (SH) extratropical large‐scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state‐of‐the‐art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150‐day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal‐mean circulation, and in hemispheric‐scale precipitation and ocean surface wind stress. The 150‐day period is consistent with the predictions of a new reduced‐order model for the SAM, which suggests that this periodicity is associated with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM's variability connect to the models' ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗