Presented By Hiroto TANAKA Thermal & Fluids Analysis Workshop TFAWS 2019 August 26-30, 2019 NASA Langley Research Center Hampton, VA TFAWS Interdisciplinary Paper Session Thermal Analysis of Spacecraft using Data Assimilation Hiroto TANAKA 1 , Hiroki NAGAI 1 and Takashi Misaka 2 1 Tohoku University, Japan 2 AIST, Japan
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Presented By
Hiroto TANAKA
Thermal & Fluids Analysis Workshop
TFAWS 2019
August 26-30, 2019
NASA Langley Research Center
Hampton, VA
TFAWS Interdisciplinary Paper Session
Thermal Analysis of Spacecraft
using Data Assimilation
Hiroto TANAKA1, Hiroki NAGAI1
and Takashi Misaka2
1Tohoku University, Japan2AIST, Japan
Table of Contents
1. Research Background
2. Objective
3. Methodology
4. Experiment
5. Result and Discussion
6. Conclusion / Future Work
TFAWS 2019 – August 26-30, 2019 2
Research Background
Thermal analysis of the Spacecraft
TFAWS 2019 – August 26-30, 2019 3
Temperature Prediction
✓ Temperature prediction of TMM has uncertainty due to
“model incompleteness” and “disturbance of boundary condition”
✓ In deep space missions, estimating thermal state of entire system is difficult
due to limited temperature data
Uncertainty of TMM
Maximum case
T [
K]
t [sec.]
Minimum case
Prediction
Research Background
Temperature Estimation using “Data Assimilation”
TFAWS 2019 – August 26-30, 2019 4
➣ By using flight temperature datasets, estimate the thermal
state in higher accuracy than conventional TMM analysis
Thermal Analysis by TMM Flight Data
Temperature
monitoring
Research Background
Data assimilation technique
TFAWS 2019 – August 26-30, 2019 5
✓ Statistic approach to combine observed data and simulated data
Data Assimilation
Simulation
Estimation of System State
Observation
Observed data
Simulated data
Data assimilation
T [
K]
t [sec.]
True Value
Objective
TFAWS 2019 – August 26-30, 2019 6
✓ Apply the data assimilation technique to the TMM in order to
improve the temperature estimation accuracy
✓ Confirm the availability of data assimilation assisted TMM and
compare its performance with conventional thermal analysis
Thermal Mathematical Model
Limited Temperature Datasets
Better Temperature Estimation?
Methodology
1. Thermal Mathematical Model (TMM)
2. Ensemble Kalman Filter (EnKF)
3. Data Assimilation / Ensenble Kalman Filter
TFAWS 2019 – August 26-30, 2019 7
Methodology
1. Thermal Mathematical Model (TMM)
8
…
4 4
1 1
( 1) ( ) ( ) ( ) ( ) ( ) ( )n n
i i i ij i j ij i j
j ji
tT t T t Q t C T t T t R T t T t
C
Governing equation
Heat balance between nodes
1 2 3
Node
Conductance : Cij
Prediction
STEP : 1 STEP : 2Update
Initial StateUpdate
STEP : 0
Prediction
TMM consists of…
✓Node : heat generation / temperature / heat capacity
✓Path : thermal conductance
Temperature distribution
Methodology
2. Ensemble Kalman Filter (EnKF)
9
Kalman Filtering
Xest = Xsimu + K × ( Xsimu – Y )
Xest : Estimated data
Xsimu : Simulated data
Y : Observed data
K : Kalman gain
Xest : Estimated data Xsimu : Simulated data Y : Observed data
✓ Simulated data is modified by difference between simulation and observation
✓ Kalman gain “K” is calculated from Variance of Xsimu
Estimation variance σ2 : System Noise σ2 : Observation Noise