Dissertation Defense - Final

University of Maryland, College Park

Modeling of Solar Particle Receivers for

Hydrogen Production and Thermochemical

Energy Storage

Andrew S. OlesDecember 11th, 2014

Committee: Professor Greg Jackson, Chair

Professor Ken Kiger

Professor Amir Riaz

Professor Peter Sunderland

Professor Michael Zachariah


How Concentrating Solar Works

Electricity

Heliostat Field

Solar Receiver

Storage Generation

Hot Storage

Cold

Storage

• Central receiver designs

− High outlet temperatures for efficient

power cycles or chemical processes

− Amenable to high solar concentrations

for cost effective

2


• Concentrating solar power require new receiver and storage

technologies to meet DOE targets for cost of solar-thermal electricity

(SunShot Initiative)

• Solid particle receivers have potential as next-generation design

– Outlet temperatures > 600 °C for higher-efficiency power-cycles or high-

temperature chemistry (like H2O splitting for renewable H2)

Motivation

3


Falling Particle Receivers

• Low-stress on solid materials for high

temperature solar absorption

– Low-cost construction

– Extended material life

• Potential for effective energy storage

– High heat capacity

– Stable materials for high-T storage

• Potential as a reactor

– High temperature redox chemistry

– Potential for fuel, chemicals, or even

metals production

Conc. Solar

Radiation

Cold Particle

Flow In

Hot Particle

Flow Out 4


Thermochemical Fuel Production

• Oxide reduction can be used for thermochemical energy storage or

fuel productions

• Ceria is a common material studied for solar fuel production (Kodama

et al., Haile et al., Steinfeld et al., Abanades et al., Davidson et al.)

Particle

Receiver

5


• Background

• Inert Particle Receiver Simulations

– Model description

– Prototype-scale results

– Commercial-scale results

• Reactive Particle Receiver Simulations

– Reactive particle modeling

– Ceria particle results

– Perovskite particle results

• Reactive Particle Receiver CFD Simulations



– Comparison of simplified and CFD model

Outline

• Background













6


• Inert particle receivers can achieve most SunShot performance

requirements with proper design

– Integrated storage with high-Cp particles

– Low-cost materials stable in air over large temperature range

– Work with next-gen (supercritical Rankine) power cycles with firing

temperatures above 650 ºC

• Challenges in inert particle receiver design

– Difficult to design with complex interaction of radiation-driven heat

transfer and multi-phase particulate flow

– Tradeoffs between receiver “solar-absorption” efficiency ηsolar and

particle outlet temperatures Tp,out needed for high-efficiency power

cycles or high-temperature chemistry.

Inert Particle Receivers

7


Gas and Particle Dynamics ModelSide View of

Receiver

Sheath Gas

Particle

Curtain

Non-

participant

gas

• Particle momentum solved in Lagrangian

frame

• Solid-gas mass and momentum coupling

• Air entrainment adapted from semi-

empirical approach of Liu[1]

– Gaussian gas-phase velocity profile, uy,g

– Entrainment proportional to mean uy,g

• Empirical particle spreading of curtain

thickness (Δzcurt) based on Kim et al.[2]

[1]: Liu, Z. (2003). University of Wollongong Thesis Collections.

[2] Kim, K., et al. (2009). Sol. Energy. 83, 1784-1793.

g

ρ

ρρ

d

uuCC

ρ

ρ

dt

du

p

gp

p

2gy,py,

SD

p

gpy,

4

3

uz,g,entrained

= auy,g

8


Heat-Transfer Model

• Particle curtain transport adapted from the

approach of Röger et al.[3]

– Particle temperatures and energy balance

solved on Eulerian grid

– Gas-particle heat transfer modeled with

Ranz-Marshall correlation:

– Improved internal curtain heat-exchange

derived between 2 semi-transparent surfaces

[3] Röger, M. et al. (2011). J. of Sol. Energy Eng., 133.

ṁp

hp(Tin,f)

ṁp

hp(Tin,b)

ṁp,f

hp(Tf)

ṁp,b

hp(Tb)

frad,Q

fconv,Q

fsol,Q

bsol,Q

curtQ

brad,Q

bconv,Q

iiiλiλ

M

m iλiλ

iλiλ

curt yxfTfTσρρ

εεQ ΔΔ∑

-1,

4i',

4i'

1 ',,

',,

mm

mm

mm

curtconvsolradp,inoutp, QQQQhhmp

3/12/1 PrRe6.02 gp

p

pp

k

dhNu

9


Radiation Transport Model

• Radiation balance solved via surface-to-surface radiation method

– Hottel’s zonal method[3] is employed for semi-transparent cells with view

factors calculated from Gaussian Integration

– Curtain transmittance τrad depends on particle

diameter dp and volume fraction fv:

curt

p

vrad z

d

fτ Δ

2

3exp

𝜹𝒌𝒋 𝒒𝒐𝒖𝒕,𝝀𝒎,𝒊′′ = 𝝆𝝀𝒎,𝒊 𝒒𝒊𝒏𝒄,𝝀𝒎,𝒊

′′ + 𝝉𝝀𝒎,𝒊 𝒒𝒊𝒏𝒄,𝝀𝒎,𝒊′′′ + 𝜺𝝀𝒎,𝒊𝒇𝝀𝒎,𝒊𝝈𝑻𝒊

𝟒 + 𝒒𝒔𝒐𝒍𝑹𝒆𝒇𝒍,𝝀𝒎,𝒊′′

𝑸𝒓𝒂𝒅,𝒊 = 𝑨𝒇

𝒎=𝟏

𝑴

𝜺𝝀𝒎,𝒊 𝒒𝒊𝒏𝒄,𝝀𝒎,𝒊′′ − 𝜺𝝀𝒎,𝒊𝒇𝝀𝒎,𝒊𝝈𝑻𝒊

𝟒

10


Ly,r

Ly,a

x

y

z

Prototype-Scale Receiver Model Parameters

Geometry Lx (m) Ly (m) Lz (m)

Receiver – r 1.85 5.00 1.50

Aperture – a 1.00 3.00 -

Curtain – c 1.00 5.00 Δzcurt

Property Units Baseline Range

dp μm 280 [100, 700]

ṁ’p kg s-1m-1 2.0 [1.0, 4.0]

εp[4] - 0.85 [0.1-1.0]

Tp,in K 600 [300, 1100]

𝒒𝑺𝒐𝒍𝒂𝒓′′ kW m-2 1000 [100, 1500]

ρp[4] kg m-3 3560 -

Cp,p[4] J kg-1K-1 264+2.07T-1.12e-3T2

[4] Siegel, N., et al. (2010). J. of Sol. Energy Eng., 132.

λ range (μm) εwall,λ[4]

0-4.5 0.20

4.5-∞ 0.80

11


ṁ'p = 4.0 kg s-1m-1, Tp,in = 600 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 600 μm

Left Wall Front Wall Right Wall

Bottom WallTop Wall Rear Wall

Curtain Front Curtain Rear









Prototype Receiver Wall and Curtain Temperatures

Wall Temperatures Particle Temperatures

12


• Smaller dp decreases curtain τrad

– Lower velocity due to greater

drag increases fv

• For smaller dp where τrad < 0.25,

ηsolar remains constant at ~84%

Impact of dp on Receiver Performance

0.65

0.70

0.75

0.80

0.85

440

460

480

500

520

540

100 200 300 400 500 600 700

ηso

lar

ΔT

p(K

)

dp (μm)

dp (μm)

13


Directly Irradiated

Zone

Directly Irradiated

Zone

ṁ'p(kg s-1m-1)

0.0

0.2

0.4

0.6

0.8

1.0

500

700

900

1100

1300

1500

0 10 20 30 40

ηS

ola

r

Ou

tlet

Tp

(K)

ṁ'p (kg s-1m-1)

Particle, rear

Particle, front

Efficiency

ṁ'p(kg s-1m-1)

• Increasing ṁ'p transmit reduces light to rear of the curtain and to

back walls.

• This increases ηsolar to maximum of ~ 88% at the expense of lower

Tp,out and higher T-gradients between front and rear of the curtain.

• Optimal flow-rate between 8 and 10 kg s-1m-1 achieve near maximum

ηsolar at higher Tp,out.

Impact of ṁ'p on Performance

14


• Prototype-scale results demonstrate need for high flow rates and

longer falls to achieve higher Tp,out while maintaining high ηsolar.

• Sandia National Labs[5] have been studying large, commercial-scale

receivers at their solar field facility.

• It is important to assess performance trade-offs at these larger

commercial scales before large-scale investments can be made for

plants using particle receivers.

• Commercial-scale receiver design requires evaluation of important

operating parameters for further development

– Impact of εp on performance

– Advantages of selective absorption, with εp in solar spectra and low εp at

longer wavelength

Commercial-scale Particle Receiver Simulations

[5] Ho,C. (2014). Personal Communication.

15


Ly,r

Ly,a

x

y

z

Commercial-Scale Receiver Model Parameters


Receiver – r 12 21 15

Aperture – a 11 20 -

Curtain – c 11 21 tcurt

Property Units Baseline

dp μm 280

ṁ’p kg s-1m-1 40

ρp[4] kg m-3 3560

Cp,p[4] J kg-1K-1 264+2.07T-1.12e-3T2

Tp,in K 600

𝒒𝑺𝒐𝒍𝒂𝒓′′ kW m-2 1000

16

λ range (μm) εp,λ εwall,λ[4]

0-2.5 0.1-0.9 0.2

2.5-4.5 0.1-0.9 0.2

4.5-∞ 0.1-0.9 0.8


• ηsolar and Tp,out both increase

monotonically with εp

• Due to high ṁ'p, minimal

solar irradiation reaches rear

of curtain.

Impact of Grey Particle Emissivity on Performance

0.0

0.2

0.4

0.6

0.8

1.0

500

700

900

1100

1300

1500

0.1 0.3 0.5 0.7 0.9

ηS

ola

r

Tp

(K)

εp (-)

Front Temperature

Rear Temperature

Efficiency

17


Solar Irradiance and Particle Emittance

0

300

600

900

1,200

1,500

1,800

250 750 1250 1750 2250 2750 3250 3750

Sp

ectr

al

Irra

dia

nce /

Em

itta

nce (

W m

-2n

m -

1)

Wavelength (nm)

Solar Source 5600 K Source

1000 K Blackbody 1300 K Blackbody

1600 K Blackbody 1900 K Blackbody

0

100

200

300

1750 2250 2750 3250 3750

18


ṁ'p = 40 kg s-1m-1, Tp,in = 600 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 280 μm

εp,λ<2.5μm=0.90, εp,λ>2.5μm=0.50


εp,λ<2.5μm=0.90, εp,λ>2.5μm=0.90


εp,λ<2.5μm=0.90, εp,λ>2.5μm=0.10

Impact of Particle IR Emissivity on Temperatures

19


Performance

MeasureUnits

IR emissivity (λ > 2.5 μm)

ελ = 0.1 ελ = 0.3 ελ = 0.5 ελ = 0.7 ελ = 0.9

ηSolar (-) 0.892 0.888 0.884 0.881 0.878

ηgas (-) 0.005 0.005 0.005 0.005 0.005

ηrad,lost (-) 0.090 0.094 0.098 0.101 0.104

ηconv,lost (-) 0.012 0.012 0.012 0.013 0.013

Tp,out (front) K 1307 1303 1300 1296 1294

Tp,out (rear) K 648 648 648 648 648

Impact of Particle IR Emissivity on Receiver Performance

Results for Inlet Tp,in = 600 K with ελ<2.5 = 0.9

20


Outline

• Background













21


• Undoped and doped ceria has been proposed by many authors[6-10]

for solar thermochemical fuel production because it:

– Preserves its (flourite) crystal structure under large degrees of

reduction, δ

– Maintains thermal stability with melting temperature >2800 K

– Exhibits high catalytic activity for H2O and CO2 reduction

• Lab-scale tests have demonstrated the capability to reliably yields H2

or CO, but have had trouble identifying practical receiver geometries

Ceria as a Solar Material

Parameter Value

ρpart (kg/m3)7215 (Ce2O4)

6200 (Ce2O3)

cp,part (J/kg-K) ~460[11]

kpart (W/m-K) 12.0[11]

λ range

(μm)

frad (%)

Solar

εp,λ[10]

Solar

frad (%)

1600 K

εp,λ[10]

1600K

0-0.6 31 0.57 0 0.36

0.6-1.25 54 0.26 7 0.17

1.25-3.5 15 0.09 64 0.08

3.5-∞ 0 0.51 29 0.34

[6] Chueh, W, & Haile, S. (2010) Phil. Trans. Roy. Soc A, 368.

[7] Scheffe, J., Steinfeld, A. (2012) Energy & Fuels, 26.

[8] Lapp et al. (2012) Energy, 37.

[9] Le Gal et al. (2011). Energy & Fuels, 25.

[10] Marabelli & Wachter. (1987) Phys. Rev. B., 36.

[11] Mogensen et al. (2000). Sol. State. Ion., 129.

22


Ceria Modeling

δb

δsb

δs

Diff. R1 R2

• Species fractions related to δ:

Diffusion

Ce2O4(b) + Ce2O3(sb) ↔ Ce2O3(b) +Ce2O4(sb)

D∞ = 1.0 e-4 m2/s [12] Ea,diff = 333.4 kJ/kmol [12]

δρ

δρ

2VOCe

21OOCe

0O32

0O42

-

dr

μd

TR

ρDj OOOdiff

0

23

Reverse Incorporation

Ce2O4(sb) + VO(s) ↔ OO(s) + Ce2O3(sb)

kfwd,R1 = 3e6 kmol/s[13] βR1 = 0.5[13]

Surface Exchange

2 OO(s) ↔ O2(g)+2 VO(s)

σO2 = 0.75[14] βR2 = 0.5[13]

TRXk

TRXkn

exssbred

exssbred

,R1(sb)OCeO(s)R1rev,

,R1(sb)OCe(s)VR1fwd,R1

exp +

1exp

32

42O

TRTRW

P

TRkn

exsred,R22

(s)V

O

OO

exsred,R22

O(s)R2fwd,R2

exp2

1exp2

O

2

2

2

[12] Giordano et al. (2011). Energy & Fuels, 25. [13] DeCaluwe et al. (2010). J. Phys. Chem, 114.

[14] Leistner et al. (2012) Appl. Cat. B, 415.


0.0001

0.001

0.01

0.1

1

1.E-321.E-281.E-241.E-201.E-161.E-121.E-081.E-041.E+00

δin

CeO

2-δ

1773

1673

1573

1473

1373

1273

1173

1073

973

873

• Zinkevich et al. (2010) model incorrectly accounted for δ dependence

– Corrected Zinkevich model correctly models T >1000 K

– Corrected Zinkevich model has reasonable low-T performance

• Surface thermodynamics fit ∆𝒉𝒓𝒆𝒅,𝒔𝟎 − ∆𝒉𝒓𝒆𝒅,𝒃

𝟎 and ∆𝒔𝒓𝒆𝒅,𝒔𝟎 − ∆𝒔𝒓𝒆𝒅,𝒃

𝟎

by using in-situ XPS data of DeCaluwe et al. (2011)

Thermodynamic Model

Equilibrium PO2 (atm) compared to experimental values[12]

24


• Thermodynamics must capture temperature dependence of ideal-

state and excess properties under partially reduced conditions.

– Ideal-state temperature dependence captured with SGTE polynomial

– Ideal entropy of mixing by dilute solution (thermodynamically consistent)

– Non-ideal bulk excess free energy calculated with Redlich-Kister terms

• Chemistry based on reversible mass action kinetics with rates and

excess free energy term modeled as in DeCaluwe et al. (2011)

Ceria Thermochemistry for Reactive Particle Model

25

4

0,

2

32ln,ΔOCe

OCeexred

X

XRTδTμ

TRkk

0red

R1fwd,R1rev, exp

TR

μμμ

TRWπ

Pσk

0O(s)

0O

0(s)V

O

0

OR2fwd,2O

2

2

5.0exp

2

δTμδTμTμδTμ exred

exredredred ,Δ,ΔΔ,Δ 0,0


Ly,r

Ly,a

x

y

z

Prototype-Scale Receiver Model Parameters


Receiver – r 1.85 5.00 1.50

Aperture – a 1.00 3.00 -

Curtain – c 1.00 5.00 tcurt


dp μm 300 [200, 700]

ṁ’p kg s-1m-1 2.0 [1.0, 4.0]

Tp,in K 1100 [1000, 1400]

𝒒𝑺𝒐𝒍𝒂𝒓′′ kW m-2 1000 -

PO2,in atm 1·(10-5) -

26

λ range (μm) εwind,λ[15] ρwind, λ

[15] εwall,λ[4]

0-0.6 0.00 0.073 0.20

0.6-1.25 0.00 0.071 0.20

1.25-3.5 0.046 0.068 0.20

3.5-∞ 0.91 0.011 0.80

[15] Heraeus. (2007).


Reactive particle wall temperatures

ṁ'p = 1 kg s-1m-1, Tp,in = 1300 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 300 μm, σstick=0.75

27


Directly Irradiated Zone

ṁ'p = 1 kg s-1m-1, Tp,in = 1300 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 300 μm, σstick=0.75ṁ'p = 1 kg s-1m-1, Tp,in = 1300 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 300 μm, σstick=0.10

Directly Irradiated Zone

• Ceria is rate-controlled by surface reaction

• Cooling outside directly irradiated zone by radiation loss and reaction

• Lower σstick cases do not reach equilibrium by exit

Impact of varying ceria kinetics

28


0

0.02

0.04

0.06

XC

e2O

3(2

δ)

Particle Flow Rate (kg s-1m-1)

1 2 3 4

0

0.1

0.2

0.3

ηto

t

1300

1500

1700

1900

1000 1100 1200 1300 1400

Tp

,ou

t(K

)

Tp,in (K)

Impact of varying Inlet Tp

29


• Smaller particles capture more energy chemically

– Greater surface area and Tp

• Reactive particles can achieve higher ηSolar than inert particles

• Particles much lower than 300 μm can have stability problems[4]

Impact of dp and reaction on performance

Chem

Solar

k

kSensible η

Q

hmhm

η

tot

1

kout,kout,kin,kin,

Solar

n

iipreac

O

ireactg

ChemQ

ThW

m

η

cells

1

,,,Δ

2

0

0.05

0.1

0.15

0.2

0.25

100 200 300 400 500 600 700

Eff

icie

ncy

dP (μm)

ηSensible ηChem ηInert

30


• Receiver design is not optimized for ceria production

– To achieve high Tp at this scale requires low ṁ'p

• Design requires evaluation in context of a full-system

– Strategies for power production or heat recovery

• Undoped ceria performance is low due to very high Tp and low εp

– Doping strategies being explored, but face challenges due to cycling

and slow oxidation kinetics. [6,8-10]

• Lower-Tp cycles with better optical properties can achieve higher

performance

• Perovskites are a class of materials with similar solid-structures and

high εp

– Favorable reduction thermodynamics at much lower temperatures

– Cannot be used for fuel production

Ceria conclusions and perovskite motivation

31


Surface Exchange

2 OO(s) ↔ O2(g)+2 VO(s)

ksurf,∞ = 0.109 m/s [18] Ea,surf =74.30 kJ/mol [18]

La0.1Sr0.9Co0.8Fe0.2O3-δ Particle Model

δb

δs

DiffusionSurf

Exch.

• Species fractions related to δ:

Diffusion

LSCFO3(b) + VO(s) ↔ LSCFO2(b) + OO(s)

D∞ = 1.01e-4 m2/s [18] Ea,diff = 55.96 kJ/mol [18]

dr

μd

TR

ρDan

OOO

partdiff

0

= ( )sseqsurfsurf kn δδρ -,

0=

Parameter Value

ρpart (kg/m3)6580[16] (LSCFO3)

6051[16] (LSCFO2)

cp,part (J/kg-K) 145[16]

ε (-) 0.90[17]

[16]: Beale, S. et al. (2011). ECS Transaction, 35: 935-943.

[17]: Guar, A. et al. (2013) Euro. Fuel Cell Conf.

[18]: Choi, M. et al. (2011). Sol. State Ionics, 11: 269-274.

[ ] [ ] ( )

[ ] [ ] δρ

δρ

0

O20.20.80.90.1

0

O30.20.80.90.1

VOFeCoSrLa

1OOFeCoSrLa

==

== -

32


• Assume ΔHO(δ) and ΔSO(δ) are constant with temperature[19]

• Ideal thermodynamics fit to NASA Polynomial (Ref. state: δ0 =0.45)

LSCF Thermodynamics

ΔHO = -433.27δ - 55.835

ΔSO = -76.414δ - 168.78

1000 °C

950 °C

900 °C

800 °C

1000 °C

950 °C

900 °C

800 °C

OO

eqO

OeqOOLSCFOLSCFO μTμP

PRTTμPTμδTμδTμ Δ

2

1ln

2

1,

2

1,-, 0

0

,0, 2

2

22223

[19]: Choi, M. et al. (2012). Sol. State Ionics, 12: 22-27.

33


ṁ'p = 7.0 kg s-1m-1, Tp,in = 800 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 300 μm, k = ksurf

Directly irradiated zone

ṁ'p = 7.0 kg s-1m-1, Tp,in = 800 K, 𝒒𝑺𝒐𝒍𝒂𝒓′′ = 1000 kW m-2, dpart = 300 μm, k = 10* ksurf

Directly irradiated zone

Influence of Reaction Rate

• Process is kinetically limited by surface rates.

• Reduction is driven strongly by Tp, even at high PO2.

• Faster reduction decreases ΔTp and improves efficiency.

34


Influence of Reaction Rate

1100

1130

1160

1190

1220

1250

200 300 400 500 600

Tp

,ou

t(K

)

dP (μm)

k x 10 k x 1

• ηSolar is relatively constant at both kinetic rates

• ηChem increases while ηSensible decreases with faster kinetics

• Smaller dp particle curtains have lower τ, greater surface area, and

slower up

• With faster kinetics, ηSensible actually decreases with dp

0

0.2

0.4

0.6

0.8

1

200 300 400 500 600

Sto

rag

e E

ffic

ien

cy

dP (μm)

k x 10 - ηSolar k x 10 - ηChem

k x 1 - ηSolar k x 1 - ηChem

35


• LSCF transmits more than inert particles at higher ṁ'p due to higher ρ

• Transition in temperature curve ~1000 K due to reaction

• Tradeoff between higher ṁ'p and higher Tp shows inflection in O2

production around 20 kg s-1m-1

Commercial-scale, influence of ṁ'p

36

ṁ'p(kg s-1m-1)

ṁ'p(kg s-1m-1)ṁ'p

(kg s-1m-1)


• LSCF tests demonstrate significant potential to improve storage

density significantly via chemical reduction

• LSCF equilibrium show strong Tp dependence large ΔSO desirable

• Reactive particles require careful consideration of storage conditions

• Ideal operation requires evaluation in full-cycle context

Perovskite conclusions

0

400

800

1200

1600

0.0

0.2

0.4

0.6

0.8

1.0

20 30 40 50 60 70 80

Ou

tlet

Tp

(K)

Eff

icie

ncy

ṁ'p (kg s-1m-1)

37


Outline

• Background













38


• Validate simplified model assumptions

– Gas entrainment model developed for non-reactive, isothermal flow

– Curtain stability untested with simplified model

• Evaluate impact of gas-flow on performance

– Internal gas-flow impacts wall and particle temperatures through

recirculation

• Test alternative gas-flow conditions for improvements

– Opportunity to improve O2 injection in vicinity of reaction

– Improved thermal impacts of gas

CFD Model Motivations

39


• Lagrangian-frame particle tracking

• Particle temperatures and reaction integrated along the fall

• Gas-phase coupling

• Stochastic particle tracking to account for turbulent dispersions

Particle Model

44

1,, Δ pRppreacreacpgpp

N

m

pmpmp TTσεahnTTha

dt

dTcm

m

cell

mm

drops

pYm

V

tWn

n

NdS

Δ

cell

ipmmpipigpD

ppdrops

pMi

V

tuWnmuu

C

dρ

μ

n

NdS

Δ

24

Re18,,,2

cell

refmpmmgppp

drops

pT

V

tThThnTTha

n

NdS

Δ00

p

gpiipig

pD

pp

ip

ρ

ρρguu

C

dρ

μ

dt

du ,,2

,

24

Re18

40


• To implement in CFD framework, kinetic mechanism modified to

depend on degree of surface reduction (δs)

• Simplified model shows δs stays in equilibrium with δb.

• Optimization method calculates δs in equilibrium with δb.

Modified Ceria Reaction Mechanism

spsredbpbredsb δTμδTμμ ,Δ,ΔΔ ,,

Profiles of Tp and δ for bulk and surface along fall

41


• Solves for radiation intensity, Iλ, at every location and in specified

directions, θ and φ

• Directions determined by splitting Cartesian grid into Nθ x Nφ

discretizations in each octant

• Particle source terms determined by collecting contributions from

each injection

Discrete Ordinates (DO) Radiation Model

')'(',,, , ΩΦ4

4

0

2 dsssrIπ

σSrInasrIσaassrI

π

λ

pIpλλbλλppλλ

cell

pp

drops

pp

V

tεd

π

n

Nda

Δ

4

2

cell

pσp

drops

pp

V

tεfd

π

n

Nσd

Δ11

4

2

cell

pppλ

drops

pIpλ

V

tTεaf

n

NdS

m

Δ4,

Property Value

𝒒𝑺𝒐𝒍𝒂𝒓′′ (kW m-2) 917.8

Beam Direction [0, 0, -1]

Beam Width

Δθ x Δφ (deg)0.001 x 0.001

Diffuse Fraction 0.0

Property Value

Nθ x Nφ 9 x 5

𝑵𝜽𝒑 x 𝑵𝝋𝒑 7 x 7

Δλ1 (μm) [0, 4.5]

Δλ2 (μm) [4.5, 100]

42


Prototype-Scale Run Parameters

Lx (m) Ly (m) Lz (m)

Receiver – r 1.85 5.00 1.50

Aperture – w 1.00 3.00 -

Curtain – c 1.00 - 0.01

Gas Inlet - i 1.00 - 0.10


dpart μm 300 [200, 500]

ṁ'part kg s-1m-1 2.0 [2.0, 4.0]

Tin K 1100 -

εp - 0.3347 -

PO2,in atm 1·(10-5) -

ug,in m/s 1.0 -

𝒒𝑺𝒐𝒍𝒂𝒓′′ kW m-2 917 -

λ range (μm) εWall,λ[4]

0-4.5 0.20

4.5-∞ 0.80 Lc,z

Li,z

Lc,x

Lagrangian Particle Injection

Locations

43


Wall and Curtain Temperatures

44


• Gas recirculation cells form due to particle-entrainment and buoyancy

• Tg greater than Tp in early fall, less than Tp in later half

• Minimal backflow occurs around edges of curtain

Gas Profiles

45


• Higher ṁ'p reduces temperatures

– Lowers thermodynamic forcing

– Slows kinetics

• δeq falls due to lower Tp and

increasing PO2

• Higher ṁ'p releases more O2

despite lower δ

Impact of varying ṁ'p (kg s-1m-1)

0.025

0.020

0.015

0.010

0.005

0.0000 1 2 3 4 5

Mea

n δ

p(-

)

Distance from inlet (m)

0.04

0.03

0.02

0.01

0.000 1 2 3 4 5

PO

2(a

tm)


1900

0 1 2 3 4 51100

1300

1500

1700

Mea

n T

p(K

)


46


• Gas recirculation

– Pre-heats particles along first half

of fall

– O2 from exit recirculates to inlet

• Higher gas flow-rate around

particles in CFD

– Dampens PO2 rise from reaction

• Higher max Tp with CFD model

– δb reaches equilibrium before exit

– Cooling damped by reoxidiation

outside directly irradiated zone

Comparison of Simplified and CFD ModelsCFD Model

Simplified Model

47


• Higher ṁg impact gas absorption and wall temperatures

• Isotropic radiation reduces reflection out of window

• Higher Tp increases chemical storage

Comparison of Simplified and CFD Models

0.0

0.2

0.4

0.6

0.8

1.0

2.0 3.0 4.0ṁ'p (kg s-1m-1)

0.0

0.2

0.4

0.6

0.8

1.0

2.0 3.0 4.0

Fra

ctio

n Q

Sola

r

ṁ'p (kg s-1m-1)

CFD Model Simplified Model

48


• Gas injected at the bottom of the receiver near the front and rear wall

– Promotes curtain stability

– Pre-heats entrained gas

Impact of Alternative Gas Injection Strategies

49


Impact of Alternative Gas Injection Strategies

2100

2000

1900

1800

1700

1600

1500

1400

1300

1200

1100

Tem

peratu

re (K)

Top

Injection

Bottom

Injection

.020

.018

.016

.014

.012

.010

.008

.004

.002

.000

δb

(-)

Top

Injection

Bottom

Injection

Curtain Temperatures Curtain Reduction

50


• Smaller particles are more efficient

– Below dp ~200 μm, minimal improvement in performance

• Trade-off between ηSolar, mean Tp, and Tp,front-Tp,rear with increasing ṁ'p

– Ideal ṁ'p of 8-10 kg s-1m-1 to balance ηSolar and mean Tp

• Ideal flow values relate to curtain τ, with optimal τ <25% to achieve

higher Tp with minimal changes in ηSolar

• Important to maximize εp, but ideal selectivity improves ηSolar < 2% for

Tp below 1300 K

– At temperature above 1600 K, selectivity can improve ηSolar ~ 5%

Conclusions – Inert Particles

0

0.2

0.4

0.6

0.8

1

600 800 1000 1200 1400 1600

ηS

ola

r

Tp,out (K)

dp

Tp,in

𝑞𝑆𝑜𝑙𝑎𝑟′′

εp

51


• General reactive-particle conclusions:

– Particle size is even more important due to lower τ and higher surface

area

– Best performance occurs when reaction cycle is properly scaled to

particle reaction-rate

– Reactors require analysis in context of a full-cycle to optimize

• Ceria operating Tp is too high and εp too low: Max ηChem ~ 7%

– At maximum ceria ηSolar ~ 35%, the ηChem < 1%

• Perovskite particles show promise due to low reduction Tp , high εp,

and ability to work above atmospheric PO2

• CFD simulations demonstrate the importance of capturing gas-flow

effects

Conclusions – Reactive Particles

52


• Test new materials

– Perovskites and other dark materials with fast kinetics, low reduction Tp,

and lower cost

• Receiver architectural changes

– Shorter particle drops

– Layered curtains

– Investigate more alternative gas-injection strategies

• Improvements to simplified model

– Improved gas-treatment to include influence of gas over larger range

– Semi-empirical gas flow along walls to capture recirculation

• Improvements to CFD model

– Improve particle-radiation coupling to allow for multi-bin particle

properties and anisotropic scattering

Future Work

53


• Presentations

– Concentrated Solar Thermal Energy for H2O and CO2 Splitting. Oles, Jackson, Thamire, Gibbons.

ASME-ES2012

– Simulation of High-Temperature Receivers Using Ceria Particles. Oles, Jackson, Gibbons. ASME-

ES2013

– Simulation of High-Temperature Receivers Using LSCF Particles. Oles, Jackson. ASME-ES2014

– Impacts of Spectral Selectivity in Directly Irradiated Particle Receivers. Oles, Jackson, Ho. ASME-

ES2014

• Publications

– Parametric design modeling of concentrated-solar falling-particle receivers. Oles, Jackson. WIP.

– Investigation of absorption selectivity on concentrated-solar falling-particle receiver performance. Oles,

Jackson, Ho. WIP.

– Modeling of a concentrated-solar falling-particle receiver for ceria reduction. Oles, Jackson. Solar

Energy.

– Modeling of storage enhancement in a falling-particle solar receiver utilizing reactive perovskite

particles. Oles, Jackson. WIP

– Modeling reactive ceria particles in a falling-particle solar receiver using CFD. Oles, Jackson. WIP.

Presentations and Publications

54


• Thank you to Dr. Gregory Jackson for his help and direction as my

Ph.D. advisor.

• Thank you to Warren Citrin for financial support through the Warren

Citrin Fellowship for Entrepreneurial Engineering Students

• Thank you to my dissertation committee – Dr. Kiger, Dr. Riaz, Dr.

Sunderland, and Dr. Zachariah – for your time and scrutiny of this

work.

• Thank you to Dr. Cliff Ho at Sandia Natl. Labs for his collaboration

and direction.

• Thank you to my lab-mates including Will Gibbons, Lei Wang, Josh

Pearlman, Babak Eslami, Danica Gordon, and Esteban Echeverria.

• Thanks to Amanda and my family for their support and

encouragement to make this possible.

Acknowledgements

55

Documents

Dissertation Defense - Final