Advanced laser modeling with BLAZE multiphysics · Advanced laser modeling with BLAZE Multiphysics ... Simulations of DPAL, XPAL, ElectricOIL ... The diode pumped alkali laser (DPAL)

Fluid Flow w/ Vorticity &Turbulence

Multi-Fluid HeatExchangers

BLAZEMULTIPHYSICS

, _Reacting Fiows and Lasers

PlasmaGas Discharge

Advanced laser modeling with BLAZE Multiphysics Andrew D. Palla, David L. Carroll, Michael I. Gray, and Lui Suzuki

CU Aerospace, 301 N. Neil St. – Suite 502, Champaign, Illinois, 61820, United States

ABSTRACT The BLAZE Multiphysics™ software simulation suite was specifically developed to model highly complex multiphysical systems in a computationally efficient and highly scalable manner. These capabilities are of particular use when applied to the complexities associated with high energy laser systems that combine subsonic/transonic/supersonic fluid dynamics, chemically reacting flows, laser electronics, heat transfer, optical physics, and in some cases plasma discharges. In this paper we present detailed cw and pulsed gas laser calculations using the BLAZE model with comparisons to data. Simulations of DPAL, XPAL, ElectricOIL (EOIL), and the optically pumped rare gas laser were found to be in good agreement with experimental data. Keywords: multiphysics, modeling, gas laser, plasma discharges, EOIL, DPAL, XPAL, OPRGL, ANGL

1. INTRODUCTION The BLAZE Multiphysics™ Simulation Suite [Palla, 2007; Palla, 2011] (Figure 1) was developed using the C++ programming language using modern data abstraction and encapsulation techniques to simplify extensions of the model, at the most basic level, to any problem of interest. BLAZE is comprised of a number of inter-operable and highly scalable parallel finite-volume models for the analysis of complex physical systems dependent upon laminar and turbulent fluid-dynamic (incompressible and compressible subsonic through hypersonic regimes), non-equilibrium gas- and plasma-dynamic, electrodynamic, thermal, and optical physics (radiation transport and wave optics) using any modern computational platform (Windows, Mac, Unix/Linux). BLAZE is compatible with a number of free, open-source, yet commercial quality grid generation and post-processing software packages, which greatly reduces training and operating costs. Further, BLAZE users can create, compile, and include their own complete models in BLAZE simulations with limited knowledge of parallel programming, input/output, grid/mesh formats, sparse linear system solution schemes, etc. as this functionality is provided to the user by the simulation engine via an easy to use application programming interface. Versions of BLAZE have an extensive record of use in state of the art plasma/electric discharge and kinetically active post-discharge flow simulation studies [Carroll, 2003; Palla, 2006; Palla, 2007; Palla, 2010; Palla, 2011, Zimmerman, 2016]. In this paper we present detailed cw and pulsed gas laser calculations using the BLAZE model with comparisons to data.

2. DPAL MODELING

The diode pumped alkali laser (DPAL) has the potential for both high electrical-to-optical efficiency and high beam quality for high energy systems. Investigations began over a decade ago [Krupke, 2003] using static alkali cells, and an excellent topical review of DPAL is provided by Krupke [Krupke, 2012]. Several important studies of this three-level laser system followed the initial DPAL demonstration including theoretical and multidimensional modeling [Beach, 2004; Oliker, 2014;

Fig. 1. BLAZE Multiphysics™ software suite for detailed multidimensional (3D) modeling and simulation of complex flows, plasmas, multi-phase flows, and other multiphysics phenomena.

Invited Paper

XXI International Symposium on High Power Laser Systems and Applications 2016, edited by Dieter Schuoecker, Richard Majer, Julia Brunnbauer, Proc. of SPIE Vol. 10254, 102540J

© 2017 SPIE · CCC code: 0277-786X/17/$18 · doi: 10.1117/12.2260487

Proc. of SPIE Vol. 10254 102540J-1

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/22/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx

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Barmashenko, 2014]. Recently the Air Force Research Laboratory (AFRL) reported a 1.5 kW flowing Potassium DPAL with a 50% slope efficiency [Pitz, 2016]. In this section we present multi-dimensional high-fidelity modeling of some classic DPAL experiments using the BLAZE Multiphysics software. Simulations of the classic Beach et al. DPAL data [Beach, 2004] were performed with BLAZE Multiphysics using the included pressure-based coupled Navier-Stokes, molecular transport, and radiation transport models. The axisymmetric computational domain was comprised of 10,000 finite volume (FV) cells and contained 100,000 total degrees of freedom (DOFs) based on the simulation of gas temperature, five species’ mass fractions, and left and right running (w.r.t. the optical axis) pump intensities at 852.3 nm and laser intensities at 894.6 nm. Second order flux schemes were utilized throughout. The calculations were run on an i7-based Windows desktop with 8 cores and required only ~10 seconds each. Broadening dynamics of the pump and laser transitions were calculated dynamically and varied spatially. The kinetics were taken from [Beach, 2004] (not shown for brevity). The pump has a Gaussian radial spatial profile with a 262.5 µm FWHM (which matches the value from Table 3 of [Beach, 2004]) and a Gaussian spectral profile with a 3×1010 Hz FWHM. An axisymmetric surface plot of steady state gas temperature for the case with an outcoupler reflectivity of ROC = 0.9, a total CW pump power of 0.872 W, and a constant wall temperature is shown in Figure 2; note the higher temperature to the left side of plot where the pump beam enters the alkali cell and is in part absorbed at its peak intensity. A parametric study based on similar calculations was constructed in which outcoupler reflectivities of 0.3, 0.5, 0.7, and 0.9 were modeled given pump powers between 0.35 and 0.88 W. The results, which are in good agreement with [Beach, 2004] data, are presented in Figure 3. Note that the two dimensional model captures the slight non-linearity of the power curves at low power (near threshold) which is not captured by zero dimensional models such as the one presented in [Beach, 2004]. Figure 4 shows BLAZE predictions of outcoupled laser power as a function of outcoupler reflectivity and cell temperature using [Beach, 2004] data at 383 K as the baseline; not surprisingly, performance decreases with increased temperature above the nominal 383 K as predicted by 1D models [Beach, 2004].

Figure 3. BLAZE Cs DPAL calculated outcoupled laser power (lines) compared to [Beach, 2004] data (points) as a function of outcoupler reflectivity ROC and pump power.

Figure 4. BLAZE predictions of outcoupled laser power as a function of outcoupler reflectivity and cell temperature using [Beach, 2004] data at 383 K as the baseline.

3. XPAL PULSED MODELING

A DPAL variant, the exciplex pumped alkali laser (XPAL) system invokes a molecular interaction to allow one to pump away from the atomic resonance in a broadband absorption blue satellite created by naturally occuring collision pairs such as Cs-Ar. XPAL was first demonstrated by Readle et. al. [Readle 2008; Readle, 2010] in mixtures of Cs vapor and Ar

Figure 2. BLAZE calculated axisymmetric temperature plot for ROC = 0.9 for a 0.872 W pump power case. Pump beam enters cell from the left.



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(with and without ethane), by pumping Cs-Ar atomic collision pairs and subsequent dissociation of diatomic, electronically-excited CsAr molecules (exciplexes or excimers). The four- and five-level variations of the pulsed XPAL system have also been modeled by Palla et. al. [Palla, 2011] using the BLAZE Multiphysics model for detailed time-dependent pulsed studies of XPAL data [Readle, 2008]. The model uses a kinetic set including nine species and 12 reactions for four- and five-level XPAL calculations [Palla, 2011] (not shown for brevity). All relevant thermodynamic data for the included species is contained in the model. A more detailed discussion of XPAL, as well as differences and similarities to DPAL systems may be found in [Readle 2008; Readle, 2010; Heaven, 2010b; Carroll, 2012].

XPAL simulations of the pulsed Readle et al. data [Readle, 2008] were revisited using the latest BLAZE Multiphysics (v.7) software pressure-based coupled Navier-Stokes, molecular transport, and radiation transport models. The 3D computational domain was comprised of 100,000 finite volume (FV) cells and contained 16,000,000 total DOFs based on the simulation of gas temperature, nine species’ mass fractions, and left and right running (w.r.t. the optical axis) pump intensities at 836.7 nm and laser intensities at 894.6 and 852.3 nm. Second order flux schemes were utilized throughout. The calculations were run on CU Aerospace’s CentOS 7 / AMD-6272 based HPC cluster using 32 cores and required ~5 minutes. Broadening of the pump and laser transitions were calculated dynamically and varied spatially. The calculations simulated 30 ns of transient pulsed operation using 0.5 ns time steps. The pump beam was modeled as Gaussian in time (4.3 ns FWHM), Gaussian in first dimension orthogonal to the laser axis (5 mm FWHM) and Gaussian in the second dimension orthogonal to laser axis (7 mm FWHM); values that matched the parameters from the experiments [Readle, 2008]. These simulations are similar to the case run by [Palla, 2011], except these new calculations include modeling of left and right running intensity waves on the 852 nm lasing line and include an automated broadening calculation for all photo-kinetic processes.

Fig. 5. Calculated normalized pump and laser intensity at the detector as a function of time compared to data for the 423 K, 6 cm cell, five-level, 836.7 nm pump baseline case [Readle, 2008].

Fig. 6. 3D surface plots of the sum of calculated left and right running XPAL laser intensities through the cell in time progression. Note: pump pulse is input from the left.

The revisited BLAZE Multiphysics (v.7) simulations were in excellent agreement with data, Fig. 5. Surface plots showing the sum of the left and right running laser intensities are illustrated in Fig. 6 (from top to bottom at time steps of 19.6, 20.1, 20.6, 21.1, and 21.6 ns) and show the increase followed by decrease in intensity of the laser pulse. Plots of calculated system powers and species concentrations as a function of time are very similar to those presented by Palla et al. [Palla, 2011], but are not shown here for brevity.

4. ELECTRIC OXYGEN-IODINE LASER (EOIL)

The electrically driven oxygen–iodine laser (EOIL) was first demonstrated by Carroll et al. [Carroll, 2005]. The lasing state I* is produced by near resonant energy transfer with the singlet oxygen metastable O2(a). Since the first reporting of a viable electric discharge driven oxygen–iodine laser system (also often referred to as ElectricOIL or DOIL in the literature), there have been a number of other successful demonstrations of gain and laser power; Ionin [Ionin, 2007] and Heaven [Heaven, 2010a] provide comprehensive topical reviews of discharge production of O2(a) and various EOIL

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studies. The highest EOIL output power reported to date is > 500 W and the technology shows superlinear scaling with g0L [Benavides, 2012]. Computational modeling of the discharge and post-discharge kinetics [Stafford, 2004; Palla, 2006; Palla, 2007; Palla, 2010] has been an invaluable tool in EOIL development, allowing analysis of the production and depletion of various discharge species [such as O2(a), O2(b1S), O-atoms, and O3] and determination of the influence of NOX species on system kinetics. 4.1 Multi-Fluid Heat Exchanger Modeling The discharge effluent consists of the desired O2(a), but also undesired heat and O-atoms. During the evolution of the EOIL technology development it became clear that it was critical to create post-discharge heat exchangers (HXs) that would significantly cool the gas flow while simultaneously (i) minimizing any loss of the critical O2(a), (ii) significantly reducing the detrimental O-atoms, and (iii) minimizing pressure drop for downstream pressure recovery reasons. This led to a series of BLAZE studies in which different geometries of cross-flow HXs were tested to guide the design of the HX to be utilized in the 7th generation EOIL cavity (CAV7) [Benavides, 2012; Benavides, 2014]. While a diamond shaped tube provided the best characteristics in the original survey [Benavides, 2014], a simple circular tube was found to provide adequate performance and was much more affordable to manufacture. This study was revisited with a focus on the impact of different tube diameters upon performance of the HX. The new BLAZE Multiphysics (v.7) simulations were run using the pressure-based coupled Navier-Stokes and molecular transport models and included both volume and surface reactions. The 2D computational domain was comprised of 77,504 FV cells having a total of 775,040 degrees of freedom (four Navier-Stokes variables and six species’ mass fractions for each cell). Cases were run as time-dependent for 0.15 s of operational time using 0.1 ms time steps. The calculations were run on the University of Illinois’ National Center for Supercomputing Applications (NCSA) Taub cluster using 96 cores and required ~7 hours each. Multiple cylindrical tube sizes ranging from 0.22 – 0.34 cm radius were simulated having flow conditions given in [Benavides, 2014] and using cryo-cooled Syltherm as the cross-flow coolant inside the cylindrical tubes. Figure 7 shows the simulations for temperature and O2(a) yield for three of the tube radii (0.28, 0.3175, and 0.34 cm), as well as the corresponding plot of average flow temperature and pressure change from the entry to the exit plane of the HX region of simulation. The calculations indicate that the gas flow temperature drop increases as the tube diameter increases, Figs. 7a and 7c, and the simulation is in good agreement with measured data; note that the HX performs very well in that it reduces the gas flow temperature by nearly 200 K in a compact 8.5 cm distance. The predicted O2(a) fraction quenched for all diameters was marginal, Fig. 7b, on average <4% of the magnitude of the O2(a) concentration. The pressure drop is predicted to increase as the tube diameter increased and the BLAZE prediction is in reasonably good agreement with the experimental data (which has a ±0.5 Torr error bar), Fig 7c.

(a)

(b)

(c) Fig. 7: BLAZE Multiphysics™ (v.7) 2-D computational fluid dynamic and kinetic results for three HX cylinder radii (0.28, 0.3175, and 0.34 cm). The results shown are a snapshot of the time dependent solution. Results for (a) temperature, (b) O2(a) yield, and (c) plot of temperature and pressure drop as a function of cooling tube outer radius.



Solid Temperature, K Coolant Temperature, K243.1 243.7 244.3 243.10 243.35 243.60

Reactive Flow Temp., K240.0 400.0 560.0

In an effort to more completely challenge BLAZE’s ability to handle multiple fluids, materials, and domains simultaneously, a 3D reactive flow case was set up to model the 3D HX including (i) the reacting gas flow, (ii) the solid aluminum HX and cooling tubes, and (iii) the actual Syltherm coolant inside the 0.3175 cm diameter cylindrical tubes, Fig. 8. The three coupled model domains used: 1) Gas reactive flow: 449,280 FV mesh, 4,942,000 DOFs, pressure-based coupled Navier-Stokes model, molecular

transport model, (w/ volume and surface reactions) 2) Coolant flow: 311,040 FV mesh, 1,555,200 DOFs,

pressure-based coupled Navier-Stokes model, Syltherm properties model, inner tube radius 0.19 cm, 243 K inlet coolant temperature

3) Solid aluminum: 124,416 FV mesh, 124,416 DOFs, general diffusion model

A grid split diffusion model was used at all solid-fluid interfaces to model conjugate heat transfer. The 3D case was run as time-dependent for 0.0225 s of operation time in 75 µs time steps. The calculation was run on the University of Illinois’ National Center for Supercomputing Applications (NCSA) iForge cluster using 192 cores and required 163 min. Figure 8 illustrates the 3D simulation showing how all of these different domains and materials (gas, liquid, and solid phases) are modeled simultaneously in an integrated fashion. The temperature drop of ~200 K is in good agreement with the data, Fig. 7c. Such a calculation illustrates the complexity of simulation that BLAZE Multiphysics is capable of performing.

4.2 Gain and Laser Modeling Versions of the BLAZE model have been used extensively over the years for modeling gain and laser performance of many high energy laser systems including EOIL. Simulations of early low-gain EOIL “Cav2” data [Palla, 2006] were revisited with the newest version of BLAZE using the pressure-based coupled Navier-Stokes and molecular transport models with volume and surface reactions. The 2D nozzle computational domain was comprised of 7,059 FV cells and included a total of 105,885 DOFs (four Navier-Stokes variables and eleven species’ mass fractions). Second order flux schemes were used with a Barth-Jespersen slope limiter applied to momentum flux in the streamwise direction. Computational domain inlet conditions were taken from [Palla, 2006]. The calculations were run on CU Aerospace’s HPC cluster using 32 cores and required ~2 minutes per case. The reaction set can be found in [Palla, 2010b] (not shown for brevity). Additional 3D non-lasing and lasing simulations were performed, with the lasing cases utilizing the radiation transport model to simulate left and right running transverse laser action in a Fabry-Perot resonator. The 3D computational domain was comprised of 225,888 FV cells and contained 3,614,208 and 4,065,984 DOFs in the non-lasing and lasing cases respectively and were otherwise similar to the 2D simulations. The 3D cases were run on NCSA’s “iForge” cluster using 192 cores requiring ~30 min each. Figure 9 shows the predicted small signal gain distribution through the EOIL nozzle as a function of molecular iodine flow rate derived from the 2D and 3D simulations compared to data; note that the 2D and 3D predictions are similar for the 14 µmol/s I2 case thereby supporting the belief that computationally less expensive 2D simulations can be used for parametric studies with reasonable accuracy. Figure 10 shows the predicted gain in a 3D nozzle. The corresponding 3D simulation with active lasing in a Fabry-Perot resonator using 0.75” diameter highly reflective optics is not shown for brevity. Classic shock diamonds are present inside the nozzle, Fig. 10; the simulations are in excellent agreement with the experimental data including the predicted total outcoupled power of 200 mW, which is in excellent agreement with the 220 mW measured experimentally. While these simulations were performed for relatively early EOIL experiments, the hardware has since been dramatically improved to attain a gain of 0.30% cm-1 and laser power > 500 W [Benavides, 2012]. Figure 1 shows plasma simulations of the concentric EOIL discharge tubes used in Cav7 [Benavides, 2012], as well as 3D nozzle simulations illustrating the O2(a) concentration and the lasing mode of an earlier Cav5 system [Zimmerman, 2009]. EOIL presently shows superlinear scaling with g0L and it is anticipated that any future work would result in significant performance enhancement, in part with the guidance of simulations from the sophisticated BLAZE Multiphysics™ plasma-fluids-laser model.

Fig. 8: BLAZE Multiphysics 3D calculation of an EOIL heat exchanger that simultaneously models 3 domains with 3 different materials (gas, liquid, and solid). (Partial cut-away of reacting gas for clarity.)



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Fig. 10: BLAZE 3D computational reacting flow results showing O2(a) yield and unsaturated laser gain in an EOIL nozzle for 14 µmoles/s iodine flow.

5. ADVANCED NOBLE GAS LASER (ANGL) MODELING

The optically-pumped plasma-excited advanced noble (rare) gas laser (ANGL gas laser, also referred to by the acronym OPRGL) was first demonstrated by Han and Heaven [Han, 2012], and is analogous in many ways to the classic DPAL system. The primary difference is the use of a noble gas in place of the alkali, with the addition of a discharge excitation step to bring the rare gas to a metastable 1s5 state as the ground state of the pumping and lasing transitions. There are major logistic advantages to having a noble gas in place of the alkali, therefore the ANGL system is of keen interest. However, one of the primary technical challenges is the creation of significant quantities of the rare gas metastable and being able to sustain it long enough for optical pumping prior to collisional quenching back down to unexcited rare gas. The Emory University group have demonstrated pulsed versions of the ANGL system using Ar*, Ne*, Kr*, and Xe* [Han, 2012; Han, 2013]. Rawlins et al. [Rawlins, 2015] demonstrated a CW Ar* configuration having a gain of ≈ 0.8 cm-1. Preliminary 2D and 3D modeling (2D results not presented for brevity) of one of the early ANGL experiments was performed with BLAZE, Fig. 11. For ease of model implementation, the experiment of Han et al. [2014] was simulated in which two 2.5 x 2.5 cm2 square electrodes separated by 0.5 cm were placed inside a chamber having a flowing Ar/He gas mixture with a partial pressure of 754 Torr of He and 15 Torr of Ar. An inlet flow rate of 1 mmol/s of the gas mixture at 300 K was assumed. 500 W of power was applied to the discharge electrodes driven by a 1522 V terminal voltage iteratively determined to achieve the specified target discharge power. For the purposes of 3D visualization, five parallel low power 100 mW (1 W/cm2 peak intensity) pump beams with a FWHM diameter of 1 mm and 30 GHz spectral linewidth were applied through the discharge region to examine the absorption of the beams by the 1s5 state. All fluid properties were calculated as a function of position and time. The 3D simulations used several BLAZE modules (models) including: Navier-Stokes, Molecular Transport (advection-diffusion for neutrals, drift-diffusion for charged particles), Poisson Electric Field, Electron Energy Transport (with optional non-equilibrium electron energy distribution function approach in which local mean electron energies are determined from the local energy distribution rather than from electron transport dynamics, and two-term spherical harmonic Bolztmann equation expansion EEDF solver module); and Radiation Transport. All fluxes were modeled as second order. The 3D computational domain was comprised of 739,896 finite volume cells and contained 10,358,544 DOFs (5 Navier-Stokes variables, He, Ar, Ar(1S5), Ar(2P9), Ar+, and e- mass fractions, two 811.754 nm pump intensity vectors, and electric scalar potential – 14 total DOFs per cell). Non-equilibrium Boltzmann tables were calculated and periodically updated on approximately 3115 Boltzmann cells which were derived from the multiphysics mesh using an



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automated cell-agglomeration scheme internal to the model. Reconstruction of Boltzmann results on the multiphysics mesh utilized a second order approach along with a Gaussian spatial filter to eliminate any minor spatial discontinuities resulting from highly spatially parallelized operation. Boltzmann tables were constructed on a discrete reduced electric field (E/N) domain between 10-23 and 10-20 V-m2, with individual non-equilibrium EEDFs modeled using an electron energy domain between 0.1 and 200 eV. The 3D cases were run on NCSA’s “iForge” cluster using 384 cores requiring ~20 min each. Several interesting characteristics are shown in Figure 11. First, the anticipated concentration increases of the excited species at the edges of the electrodes are visible in Figs. 11a-c. The gas flow temperature between the electrode plates is around 610 K, while there are localized hot spots at the corners that are approximately 900 K, Fig. 11d. The desired 1s5 metastable state has a concentration of approximately 1019 m-3 (1013 cm-3) and is shown to be reasonably uniform between the electrode plates, Fig. 11b and 11f; this predicted number density of the 1s5 state is in good agreement with the estimates of Han [Han, 2014]. The electric field lines are shown in Fig. 11e. The effect of the 5 pump beams at 811.75 nm are illustrated in Fig. 11f; the pumps themselves are low intensity (only 1 W/cm2), so there is no observable effect on the 1s5 state, but there is clear pumping of the 2p9 state. These ANGL gas laser simulations show some of the detailed 3D effects that occur in real plasma discharges, including coupling of the discharge and optical physics, that are impossible to adequately capture with lower dimensional models.

(a)

(b)

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(d)

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Figure 11. BLAZE Multiphysics 3D simulations of an ANGL discharge pumped by 5 Gaussian beams showing: (a) Ar+ concentration, (b) desired plasma created Ar(1s5) state, (c) electron number density, (d) gas flow temperature, (e) the electric field lines, and (f) concentrations and intensity plot along centerline plane.

6. CONCLUDING REMARKS

The BLAZE Multiphysics™ software simulation suite was specifically developed with the ability to handle highly complex multiphysical problems in a computationally efficient manner. This capability is of particular use for the



complexities associated with high energy laser systems that combine subsonic/transonic/supersonic fluid dynamics, chemically reacting flows, laser electronics, heat transfer, and in some cases plasma discharges. 2D and 3D simulations of DPAL, XPAL, ElectricOIL (EOIL), and the optically pumped noble gas laser (ANGL) were found to be in good agreement with experimental data. 3D simulations were run showing how multiple different domains and materials (gas, liquid, and solid phases) can modeled simultaneously in an integrated fashion. A wave optics module has also been added to BLAZE; this module can be implemented in the future for laser resonator simulations and beam propagation studies. These calculations illustrate the complexity of simulation that BLAZE Multiphysics™ is capable of performing.

ACKNOWLEDGMENTS

This work with BLAZE Multiphysics™ (Version 7) was supported by CU Aerospace Internal R & D funds.

REFERENCES

Barmashenko B D, Rosenwaks S, and Waichman K (2014). “Kinetic and fluid dynamic processes in diode pumped alkali lasers: semi-analytical and 2D and 3D CFD modeling,” SPIE Vol. 8962, 89620C.

Beach, R. J., Krupke, W. F., Kanz, V. K., and Payne, S. A., J. Opt. Soc. Am. B, 21, pp. 2151-2163 (2004). Benavides, G.F., Woodard, B.S., Zimmerman, J.W., Palla, A.D., Day, M.T., King, D.M., Carroll, D.L., Verdeyen, J.T.,

and Solomon, W.C. (2012). IEEE J. Quant. Elect., 48 (6) 741-753. Benavides G F, Palla A D, Zimmerman J W, Woodard B W, Carroll D L, and Solomon W C (2014) “Oxygen atom density

and thermal energy control in an electric oxygen-iodine laser”, Proc. SPIE 8962 89620G. Carroll D L, Verdeyen J T, King D M, Woodard B S, Skorski L W, Zimmerman J W, and Solomon W C (2003) IEEE J.

Quant. Elect., 39 (9), 1150.

Carroll, D. L., Verdeyen, J. T., King, D. M., Zimmerman, J. W., Laystrom, J. K., Woodard, B. S., Benavides, G. F., Kittell, K., Stafford, D. S., Kushner, M. J., and Solomon, W. C. (2005). Appl. Phys. Lett., 86, 111104.

Carroll D L and Verdeyen J T (2012). “XPAL theory and predictions”, SPIE Vol. 8238, 823804. Han, J., and Heaven, M.C. (2012). Opt. Lett. 37, 2157. Han, J., Glebov, L., Venus, G., and Heaven, M.C. (2013). Opt. Lett. 38, 5458 (2013). Han, J., Heaven, M.C., Hager, G.D., Venus, G.B., and Glebov, L.B. (2014a). “Kinetics of optically pumped Ar

metastables,” SPIE Vol. 8962, 896202. DOI: 10.1117/12.2045164. Heaven, M.C. (2010a). Laser & Photon. Rev., 4 (5) 671-683. Heaven, M.C., and Stolyarov, A.V. (2010b). “Potential Energy Curves for Alkali Metal – Rare Gas Exciplex Lasers,”

AIAA Paper 2010-4877. Ionin, A. A., Kochetov, I. V., Napartovich, A. P., and Yuryshev, N. N. (2007). J. Phys. D: Appl. Phys. 40 R25. Krupke, W. F., Beach, R. J., Kanz, V. K., and Payne, S. A. (2003). Opt. Lett., 28, 2336. Krupke, W. F. (2012). Prog. Quant. Electronics, 36, 4-28. Oliker, B.Q., Haiducek, J.D., Hostutler, D.A., Pitz, G.A., Rudolph, W., and Madden, T.J. (2014). “Simulation of

Deleterious Processes in a Static-Cell Diode Pumped Alkali Laser,” SPIE Vol. 8962, 89620B. Palla, A. D., Carroll, D. L., Verdeyen, J. T., and Solomon, W. C., “Mixing effects in post-discharge modeling of electric

discharge oxygen-iodine laser experiments,” J. Appl. Phys., 100(2), 023117 (2006). Palla, A. D., Zimmerman, J. W., Woodard, B. S., Carroll, D. L., Verdeyen, J. T., Lim, T. C., and Solomon, W. C. (2007).

J. Phys. Chem. A, 111, 6713. Palla, A. D., Carroll, D. L., Verdeyen, J. T., Zimmerman, J. W., Woodard, B. S., Benavides, G.F., Day, M.T., and Solomon,

W. C. (2010). “Modeling of Recent ElectricOIL Gain Recovery Data,” AIAA Paper 2010-5041. Palla, A.D., Carroll, D.L., Verdeyen, J.T., and Heaven, M.C. (2011). J. Phys. B: Atomic, Molec., Opt. Phys., 44, 135402. Pitz, G.A., Stalnaker, D.M., Guild, E.M., Oliker, B.Q., Moran, P.J., Townsend, S.W., and Hostutler, D.A. (2016). SPIE

Vol. 9729, 972902. doi: 10.1117/12.2217078. Rawlins W T, Galbally-Kinney K L, Davis S J, Hoskinson A, Hopwood J, and Heaven M C (2015). Opt. Exp. 23, 4804. Readle, J. D., Wagner, C. J., Verdeyen, J. T., Carroll, D. L., and Eden, J. G. (2008). Electronics Letters, 44, 25. Readle, J. D., Eden, J. G., Verdeyen, J. T., and Carroll, D. L. (2010). Appl. Phys. Lett., 97, 021104. Stafford, D. S., and Kushner, M.J. (2004). J. of Appl. Phys., 98 (5) 2451. Zimmerman J W, Benavides G F, Woodard B S, Carroll D L, Palla A D, Verdeyen J T, and Solomon W C (2009).

“Measurements of Improved ElectricOIL Performance, Gain, and Laser Power,” AIAA Paper 2009-4059. Zimmerman J W, Rajasegar R, Mitsingas C, Palla A D, King D, Carroll D L, and Lee T (2016). “Plasma-Assisted Methane

Combustion in Swirled-Flow Reactors: Experiments and Multiphysics Simulations,” AIAA Paper 2016-3664.



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Advanced laser modeling with BLAZE multiphysics · Advanced laser modeling with BLAZE Multiphysics ... Simulations of DPAL, XPAL, ElectricOIL ... The diode pumped alkali laser (DPAL)