Laboratory-Generated Aerosols as Transfer Standards to

Laboratory-Generated Aerosols as Transfer Standards to Characterize

Smoke Detector Performance Xiaoliang Wanga, Judith C. Chowa, and John G. Watsona,

Marit E. Meyerb, Gary Ruffb, and David L. Urbanb, John Eastonc, Gordon Bergerd, Paul D. Mudgette

aDesert Research InstitutebNASA Glenn Research Center

cCase Western Reserve UniversitydUniversities Space Research Association

eNASA Johnson Space Center

38thAAAR Conference October 5-9, 2020

Background

• Spacecraft fire is a catastrophic threat– Causes component and system failure– Releases toxic compounds – Consumes oxygen– Increases temperature and pressure in

closed space – Limited options to fight fires

• Smoke detection is critical, requiring– Early detection– Sensitivity and selectivity– Low false alarms

Mir space station fire on 2/23/97

https://sma.nasa.gov/docs/default-source/safety-messages/safetymessage-2011-11-07-spacestationmironboardfire-vits.pdf?sfvrsn=a3ae1ef8_4

Motivation

• Underwriters Laboratories Standard for Smoke Alarms (UL217)– Specific test facilities– Smoldering and flaming tests with different fuels– Time and labor intensive– Limited repeatability in smoke conditions

• Alternative: Laboratory Aerosols– Known composition and optical properties– Controllable size distributions and concentrations

Study Objectives

• Evaluate a method to test smoke detectors using reference aerosols

• Characterize smoke detector responses to reference and combustion aerosols

• Determine the feasibility of using reference aerosols to predict smoke detector responses to different smoke aerosols

Reference Aerosols

Material Density(g/cm3)

Refractive index

(@589 nm)Size Generator

PSL 1.05 1.590 300, 500, and 900 nm TSI 3076 and 9306

DOS 0.915 1.448

100, 200, 300, 400, 500, and 700; polydisperse distributions (1.5%, 5%, 40%, and 100% solutions)

TSI 3076

Rudol®

mineral oil 0.861 1.470 100, 300, and 500 nm; polydisperse distributions Gemini 501B

PSL: Polystyrene latex sphereDOS: Dioctyl sebacate

Smoke Aerosols

PMMA: Poly(methyl methacrylate)

Experimental Setup

TSI 9306 Six-Jet Atomizer

Gemini 501BSmoke Detector

Tester

TSI 3076 Atomizer

Smoke Chamber

PressureBalance

Mixing Fan

Smoke Detectors

DustTrak DRX (TSI Model 8534 ; PM1, PM2.5,

PM4, PM10, PM15)

SMPS (TSI Model 3938; Size distribution:

~0.01-0.7 µm)

WCPC (TSI Model 3787; Particle number concentration)

Electrostatic Classifier

(TSI Model 3080)

Photo-diode

ObscurationMeter

Dryer

DryerVentFilter

Vent

Valve

Vent to Roof

KiddeScatter

KiddeIonization

CSP

Dilution Bridge

Neutralizer

Tube Furnace for Smoke Aerosol

Generation

ISS ScatteringSmoke Detector

STS Ionization Smoke Detector

IonZG

HEPAPurge

Air

HEPA CSP: Commercial Space Prototype smoke detectorIonZG: A commercial Kidde ionization smoke detectorISS: International Space StationSTS: Space Transport System

Reference Aerosol Size Distributions

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 100 1000

Parti

cle

Num

ber S

ize

Dis

tribu

tion

Nor

mal

ized

by

Tota

l Con

cent

ratio

n dN

/dlo

gDp,

uni

tless

Particle Diameter Dp, nm

5/14/2019

5/17/2019

Mineral Oil

0.0

0.5

1.0

1.5

2.0

2.5

10 100 1000

Parti

cle

Num

ber S

ize

Dis

tribu

tion

Nor

mal

ized

by

Tota

l Con

cent

ratio

n dN

/dlo

gDp,

uni

tless

Particle Diameter Dp, nm

1.5% DOS5% DOS40% DOS100% DOS

Dioctyl sebacate (DOS)

Mineral oil particles generated by the smoke tester on two separate days had similar size distributions.

Atomizing DOS solutions by the TSI 3076 atomizer allowed adjusting particle size distribution.

Smoke Detector Responses to Mineral Oil Particles

y = 0.0094x + 0.954y = 0.0112x + 0.9316y = 0.011x + 0.9615

0.8

1.2

1.6

2.0

2.4

2.8

3.2

0 50 100 150 200

CSP

, V

DRX PM2.5 Mass Concentration, mg/m3

(a) Polydisperse Mineral Oil Particles: CSP vs. PM2.5

5/14/20195/17/20195/23/2019

y = 0.1356x - 0.1486y = 0.1492x - 0.0388y = 0.1329x - 0.2125

-2

2

6

10

14

18

22

26

30

0 50 100 150 200

Obs

cura

tion,

%/m


(b) Polydisperse Mineral Oil Particles: Obscuration vs. PM2.5

5/14/20195/17/20195/23/2019

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 50 100 150 200

Ion

ZG, V


(c) Polydisperse Mineral Oil Particles: IonZG vs. PM2.5

5/14/20195/17/20195/23/2019

0.0

2.0

4.0

6.0

8.0

0 50 100 150 200

ISS

Scat

ter,

V


(d) Polydisperse Mineral Oil Particles: ISS Scatter vs. PM2.5

5/17/20195/23/2019

Scattering detectors (CSP, obscuration meter, and ISS scatter detector) signals increased with DRX concentration (before saturation). Ionization detector (IonZG) signal decreased with DRX concentration. Responses were repeatable on three test days.

Smoke Detector Responses to DOS Particles

The smoke detector vs. DRX linear regression slope increased slightly with particle size for CSP and obscuration meter. The IonZG is more sensitive to smaller particles.

0.9

1.2

1.5

1.8

2.1

2.4

0 20 40 60 80

CSP

, V


(a) Polydisperse DOS Particles : CSP vs. PM2.5

100% DOS40% DOS5% DOS1.5% DOS

-2

2

6

10

14

18

22

26

30

0 20 40 60 80

Obs

cura

tion,

%/m


(b) Polydisperse DOS Particles: Obscuration vs. PM2.5


2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 20 40 60 80

Ion

ZG, V


(c) Polydisperse DOS Particles: IonZG vs. PM2.5


Smoke Evolution

Example time series of a lamp wick test. One gram of lamp wick was added to the tube furnace heated to 200 °C. All instruments responded to the smoke concentration changes. The fuel boat was taken out at ~1500 s and the smoke chamber was purged slowly starting from ~1560 s.

Smoke Detector vs. DRX Linear

Regression Slopes• Mineral oil particles had

the lowest slopes among all tests;

• DOS particles from the 4 solution concentrations covered the slope range for most tested smoke aerosols;

• No single reference aerosol predicted all detector responses to smoke aerosols within ±10% error.

Particle Source

DOS 1.5%

DOS 5%

DOS 40%

DOS 100%

Mineral o

il

Lamp w

ick

Kapton

NomexTefl

onPMMA

Wire In

sulat

ionISS

Scat

ter D

etec

tor v

s. D

RX

PM2.

5

Con

cent

ratio

n Sl

ope,

V·m

3 /mg

0.0

0.1

0.2

0.3

0.4

Obs

cura

tio v

s. D

RX

PM2.

5 C

once

ntra

tion

Slop

e, %

·m2 /m

g0.10

0.15

0.20

0.25

0.30

0.35

0.40

(b)

(c)

CSP

Vol

tage

vs.

DR

X PM

2.5

Con

cent

ratio

n Sl

ope,

V·m

3 /mg

0.008

0.010

0.012

0.014

0.016

0.018

(a)

Reference Aerosols Smoke Aerosols

Error bars represents the linear regression slope uncertainty (standard deviation).

Regression Slope Ratios of Smoke over Reference Aerosols(Green indicates differences within ±10%)

(a) CSP vs. DRX LampWick Kapton Nomex Teflon PMMA Wire

InsulationDOS 1.5% 1.20 1.06 1.14 0.89 1.03 1.14DOS 5% 1.09 0.96 1.04 0.81 0.94 1.04

DOS 40% 0.88 0.78 0.84 0.65 0.76 0.84DOS 100% 0.94 0.83 0.90 0.70 0.81 0.90Mineral Oil 1.35 1.19 1.29 1.00 1.17 1.29

(b) Obscuration vs. DRX

LampWick Kapton Nomex Teflon PMMA Wire



(c) ISS Scatter vs. DRX LampWick Kapton Nomex Teflon PMMA Wire



Summary• Mineral oil particles had reproducible size

distributions, but underestimated smoke aerosol responses.

• Different DOS particle sizes covered the slope range for most tested smoke aerosols.

• No single reference aerosol predicted all detector responses to smoke aerosols within ±10% error.

• Conversion factors can be developed to predict smoke aerosol responses by reference aerosols

Acknowledgements

Ø NASA Saffire ProgramØ Universities Space Research Association (USRA)

Contract No. NNC13BA10BØ HX5 Contract No. DRIN20D03Ø NASA EPSCoR Research CAN Grant No.

80NSSC19M0152.

Reference:

Wang et al. (2020). "Spacecraft Smoke Detector Characterization with Reference and Smoke Aerosols." 50th International Conference on Environmental Systems, July 12-16, 2020. https://ttu-ir.tdl.org/handle/2346/86366

https://ttu-ir.tdl.org/handle/2346/86366

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Laboratory-Generated Aerosols as Transfer Standards to