48th International Conference on Environmental Systems ICES-2018-062 8-12 July 2018, Albuquerque, New Mexico
Copyright © 2018 Jet Propulsion Laboratory/California Institute of Technology
Structural, Thermal, and Optical Performance (STOP)
Modeling and Analysis for the Surface Water and Ocean
Topography Mission
Louis A. Tse1, Zensheu Chang2, Ruwan P. Somawardhana3, Eric Slimko4
Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, 91109
The Surface Water and Ocean Topography (SWOT) mission objectives include high-resolution measurement
of continental water levels and the topography of the ocean surface, which aids climate modeling and
predictions. The primary instrument is the Ka-band Radar Interferometer (KaRIn), which has stringent
requirements on overall phase error in order to achieve its design performance. We conducted extensive
structural, thermal, and optical performance (STOP) modeling in order to analyze the design viability to
meet performance requirements. In this paper, we present the modeling and results of our study. These
predictions will be used to project results of on-orbit performance. We also share lessons learned regarding
STOP design and analysis flow.
Nomenclature
CCHP = constant conductance heat pipe
CNES = French National Space Studies Center (Centre National D’Etudes Spatiales)
CTE = coefficient of thermal expansion
DAA = deployable antenna assembly
FEA = finite element analysis
FSS = feed support structure
KaRIn = Ka-band Radar Interferometer Instrument
km = kilometer
LHP = loop heat pipe
MLI = multi-layer insulation
NASA = National Aeronautics and Space Administration
PSD = power spectrum density
STOP = structural-thermal-optical
SWOT = Surface Water Ocean Topography
I. Introduction
he Surface Water Ocean Topography (SWOT) is a joint partnership between NASA, Centre National d-Etudes
Spatiales (CNES) and Canadian Space Agency, to conduct a comprehensive global survey of Earth’s surface
water and ocean topography, planned for launch in 2021. The principal objective of SWOT is to collect precise
measurements of surface water hydrology, observe details of ocean surface topography and circulation, and measure
how water bodies change over time.
The SWOT mission is composed of six payloads that include an altimeter, microwave radiometer, global positioning
systems, a laser retroreflector, and an interferometer. The primary instrument is the Ka-band Radar Interferometer
(KaRIn) which will make large swath measurements to measure the surface elevations of water bodies; one with
horizontal polarity (H-pol) and another with vertical polarity (V-pol). The precision measurements needed to meet the
primary science objectives impose challenging requirements for the engineering team to accommodate for the KaRIn
instrument design. One of the main thermal challenges is to maintain precise temporal stability requirements
1 Thermal Engineer, Spacecraft Thermal Engineering, MS 125-123, 4800 Oak Grove Dr, Pasadena, CA 91109 2 Mechanical Engineer, Electroactive Technologies, MS 67-119, 4800 Oak Grove Dr, Pasadena, CA 91109 3 Lead Thermal Engineer, Instrument And Payload Thermal Engineering, MS 125-123, 4800 Oak Grove Dr, Pasadena,
CA 91109 4 Lead Mechanical Engineer, Payload & Small Spacecraft Mechanical Engineering, MS 321-356, 4800 Oak Grove
Dr, Pasadena, CA 91109
T
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(<0.05°C/min) in a low earth orbit while managing large electronic dissipation (>1,000 W) that configurationally
requires co-location.
Figure 1: SWOT mission architecture [1].
II. KaRIn Overview and Performance Metrics
KaRIn is a bistatic synthetic aperture radar (SAR) system that utilizes near-nadir swaths on deployable antennas
on both sides of the satellite track, as shown in Figure 1. The deployable antenna assembly (DAA) is formed by two
5 m long and 0.3 m wide deployable antennae on opposite ends of a 10 m boom. Reflectarray technology is installed
on the ends of the booms, which consist of flat panels with etched elements on its surface which creates the necessary
phase change to emulate a parabolic reflector. The chain of these subsystems collectively serves as the principal
instrument on the mission, and each subsystem has several stringent design requirements in order to maintain earth
observation measurement performance. These requirements are dependent on the interdependent behavior of the
electronics, structural, and thermal subsystems. From a thermal standpoint, one key requirement is thermal stability,
which leads to reduced signal noise of the electronics subsystem. The fidelity of the ocean topography measurement
drives the defined thermal stability requirements over different timescales. The KaRIn instrument provides
fundamental ocean topography measurements at wavelengths shorter than 1,000 km, which corresponds to a time
window of 2.6 minutes during nominal science orbit. To account for land passes during the science orbit, the KaRIn
requirement also extends to spatial scales longer than 12,500 km to meet the error budget, which corresponds to a 31.6
minute time window [2], [3].
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The thermal subsystem is designed with four zones in order meet acute space constraints and high electronics
dissipative heat (greater than 1,000 W). As shown in Figure 2, each zone utilizes a thermal pallet with three to five
embedded constant conductance heat pipes (CCHPs) and one loop heat pipe (LHP) with variable conductance.
Figure 2: Diagram of one of the four thermal pallets for the KaRIn instrument, which utilizes a combination
of CCHPs and LHPs to transport heat from electronics boxes.
The benefits of incorporating LHPs into the thermal architecture include the ability to transport a large quantity of
heat with limited use of survival power, and simplicity in flight system integration and ground testing. One of the
challenges of LHPs is depending on its boundary conditions, high and low frequency oscillations have been reported
and can be related by heat source fluctuations, improper radiator sizing, and varying heat sink temperatures [2].
III. STOP Model Overview
The performance budget for SWOT governs that the opto-mechanical stability of the KaRIn radio frequency chain
must be maintained in order to reduce signal noise, which requires an iterative and collaborative analysis spanning
three models. The thermal, structural and optical design establish worst-case performance requirements independently.
The optical design governs distortion limits to the structural design, which then informs deformation limits in the form
of temperature gradients to the thermal design. Firstly, the basic process flow is shown in Figure 3. The modeling
tasks occur sequentially due to the required data exchange for each step. The STOP model development is conducted
using Siemens NX and is described in detail, along with verification of the results. The results of the thermal and
structural mapping are shown from the STOP analysis, along with a description of trade study results.
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Figure 3: STOP analysis process flow.
For the scope of this paper, pointing error at the observatory level is reported. There are several contributors to
instability, including environmental load fluctuations (solar flux, albedo, Earth IR), effective emissivity of multi-layer
insulation (MLI), LHP conductance to radiator, and geometric positioning of reflective components such as solar
arrays. The orbit parameters for SWOT during science mode are as follows: 77.6° orbit inclination, altitude is 905 km,
orbital period is 103 minutes.
A. Mesh Comparison The meshes for the thermal and structural models at the payload level are shown in Figure 4. Detailed views of the
thermal model mesh and CAD geometry for critical components are subsequently shown in the following figures,
such as the V-pol and H-pol feeds, metering structure, reflectarray panels, and boom tube assembly, respectively. The
fidelity of the thermal model, which has approximately 70,000 elements, is appropriate for the advanced stage of the
project. It contains sufficient detail in the optically sensitive components to represent critical thermo-structural effects
during trade studies. Thermal mapping to the structural model was accomplished in Siemens NX. Structural
deformations are mapped to the optical model to assess optical performance.
(a)
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(b)
Figure 4: (a) Thermal model mesh, and (b) structural model mesh.
CAD model Thermal model Structural model
Figure 5: Thermal math model mesh compared with CAD model design of the feeds.
CAD model Thermal model Structural model
Figure 6: Thermal math model mesh compared with CAD model design of the metering structure.
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CAD model Thermal model Structural model
Figure 7: Thermal math model mesh compared with CAD model design of the reflectarray panels.
CAD model Thermal model Structural model
Figure 8: Thermal math model mesh compared with CAD model design of the boom tube assembly.
The beta angle is defined as the angle between the solar vector and its projection onto the orbit plane. The nominal
on-orbit scenario that has been shown to be the worst case for thermal stability is for a beta angle of 55°, which
maximizes the fluctuation in lighting environment, such as maximum solar flux with longest time in eclipse, as well
as situations of shadowing or reflected incident energy from one component of the spacecraft to another.
B. Thermal Analysis Transient temperature plots for the components that require STOP analysis are shown in the following section.
Overlaid on the plots is the eclipse duration (blue band). Temperature contours are also shown, for the orbital position
at the end of the orbit.
(a) (b)
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(c) (d)
Figure 9: Transient temperature plot of the feeds: (a) V-pol +Y, (b) H-pol +Y, (c) V-pol -Y, and (d) H-pol -Y.
Blue band denotes eclipse.
Because of the chosen orbit and spacecraft attitude during science mode, the +Y Feeds are facing away from the sun;
as a result, they undergo a smaller temperature difference than the -Y Feeds. Additionally, in the nominal case, the
+Y Feeds are transmitting constantly during the orbit; the V-pol Feed dissipates 7.7 W and the H-pol dissipates 5.9 W
uniformly (and the -Y Feeds are off and thus, have zero dissipation).
Figure 10: Transient temperature plot of the metering structure. Blue band denotes eclipse.
The metering structure is thermally well-isolated from the environment, and does not exhibit large temperature swings.
Typically, it observes a hot spot at the location of the transmitting feeds. Conversely, it experiences a cold spot where
the star tracker is installed and thermally coupled to the end of the box beam on the +Y side, which has a constant
view to space in the nominal orbit attitude.
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(a) (b)
Figure 11: Transient temperature plot of the reflectarray panels for (a) +Y side and (b) -Y side. Blue band
denotes eclipse.
The +Y and -Y reflectarrays exhibit a large difference in temperature and transient behavior, for several reasons.
Firstly, the +Y panels experience some transient temperature peaks due to solar energy specularly reflected from the
spacecraft. Secondly, the -Y panels are significantly lower temperature because it is not sun-lit for a substantial portion
of the orbit, and when it is, many of its panels are shadowed by the spacecraft.
(a) (b)
Figure 12: Transient temperature plot of the boom tube assembly for (a) +Y side and (b) -Y side. Blue band
denotes eclipse.
Inversely to the behavior of the reflectarrays, the -Y boom tube assembly is constantly sun-lit, while the +Y boom
tube assembly is shadowed by the solar arrays and eclipse. However, the boom tube assembly is covered by MLI and
as a result, does not experience as wide a temperature swing as the reflectarrays.
C. Thermal Mapping
Temperature distribution on the payload is derived through thermal analysis. The temperatures at each node in the
thermal model are subsequently mapped to each node in the structural model using Siemens NX. Generally, this is
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achieved via proximity: the temperature of a given thermal model node is mapped to the structural node closest in
proximity. An additional approach provides more precise selection of mapping, by explicitly selecting a group of
nodes in the thermal model that will explicitly be mapped to a group of nodes in the structural model (e.g. selecting a
specific feed in the thermal model, and selecting the same feed in the structural model). To ensure the quality of the
mapping, the temperature contours and ranges were compared between the thermal and the structural models at a few
time steps. Several Matlab scripts were also created to check the temperature of every time step.
Thermal model Structural model
Figure 13: Temperatures mapped from the thermal model, to the structural model, at the end of the orbit.
D. Structural Analysis
The design of the payload meets requirements that include meeting minimum fundamental frequencies in lateral and
axial directions, surviving maximum launch loads, as well as requirements for jitter, sinusoidal vibration, and more.
From an optical point of view, the design shall meet the optical performance requirements after being exposed to the
on-orbit thermal environments. Nastran models were constructed to perform finite element analysis (FEA) of the
payload. The commercially available FEMAP software tool was used as the pre- and post-processor. The models were
verified by NASA standard procedures, including checks on element geometry, grounding, maximum diagonal ratio,
Nastran Epsilon, unit gravity constraint loads, free-free modal, common coefficient of thermal expansion (CTE), etc.
After several iterations of design changes and finite element analysis, the STOP-ready version of the telescope FEA
model weighs 880 kg, and comprises about 280,000 nodes and 270,000 elements.
The modal analysis of the payload with proper boundary conditions prescribed at the Spacecraft-Payload interface
predicted the frequency of the fundamental lateral mode to be 25.6 Hz. The structural model was sent to the thermal
team to perform temperature mapping from the thermal model to the structural model. Thermoelastic analyses were
then performed using the structural FEM (
Figure 4b) and the temperature data mapped from the thermal model.
Displacements due to temperature change were calculated for the reflectarrays, feeds, and instruments on the Nadir
deck. Matlab scripts were created to calculate the Power Spectrum Density (PSD) of the baseline dilation, roll, and
phase of the KaRIn antennae. Pointing errors including elevation error, azimuth error, and relative azimuth errors
were also calculated. The results are shown from Figure 14-Figure 15.
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(a) (b)
(c)
Figure 14: a) Smoothed Baseline PSD, b) Smoothed Roll PSD, , and c) Smoothed Phase PSD, for beta angle =
55°.
(a) (b)
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(c)
Figure 15: Pointing error for KaRIn at observatory level, beta angle = 55°.
As shown in Figure 14-Figure 15, each component meets the error allocation budget with appreciable margin in the
worst case for thermal stability. The confirmation of the magnitude of thermal stability due to orbital illumination and
eclipse gives confidence that the overall performance requirements are met by a robust structural, thermal, and optical
design at the observatory level. Additionally, the STOP analysis results, and more importantly the various resulting
errors, determines if there exists a strong correlation between a specific component and the overall performance.
Assessment of environmental loads as a function of orbital location such as Earth IR and albedo, varying MLI effective
emissivity, and optical property degradation are currently ongoing. Future studies include more refined modeling
approach of the LHP reservoir and evaporator for each thermal pallet.
IV. Lessons Learned
During the STOP analysis process, there have been many lessons learned; some issues were encountered during the
temperature mapping process that may be useful to share. The issues are summarized, with subsequent
recommendations from the authors, to highlight process improvements.
Separate radiation enclosures in NX: Calculating radiation conductances (or radks) is a computationally expensive
calculation in the thermal model. This is one of the reasons that the thermal and structural models are often separate:
the structural model may be too large to simply use the same structural FEM directly for thermal analysis.
Additionally, another reason is that many components that require high fidelity in the structural model (e.g. integral
components on the load path) were simplified or not represented in the thermal model and vice versa for components
pertinent to thermal analysis (e.g. temperature-sensitive electronics, multi-layer insulation). This can be seen more
clearly in Figure 4, particularly for the antennae on the Nadir module. The authors recommend defining separate
Radiation Enclosures if permitted, to lessen the number of radks that are calculated. For instance, a separate Radiation
Enclosure is defined solely for the components internal to the Nadir module (and similarly for the KaRIn module and
Reflectarrays).
Targeted mapping: NX uses node proximity as the default mode to match nodes between the two models. Using
the nearest node method is often used for large system models, due to time constraints, though can lead to artificial
thermal gradients. However, it is recommended to utilize Targeted Mapping Zones, which entails extensive manual
mapping to limits specific thermal nodes that can be mapped to corresponding structural nodes. As such, the drawback
of this is the significant time and effort to create these relational groups that correspond between the two models,
which must be balanced with higher accuracy.
Data or software management tool: Another productivity bottleneck during analysis flow is the data exchange
between the different models and analysts. Often, the thermal, structural, and optical data has to be converted into a
suitable format for analysis for each discipline. Data manipulation is often conducted by one-use codes or by hand. A
subsequent improvement from this effort would have been to develop an automated data management tool for the
specific set of analysis tools used, to establish consistent inputs and outputs. An even more advanced solution would
be to develop an automated software management tool, which would not only improve analysis flow, but design flow
as well. STOP analysis inherently is a multi-disciplinary analysis which utilizes different software packages for each
discipline, and has model translation hurdles that necessitates streamlined communication and data transfer methods
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between contributors. Some efforts have developed integrated analysis tool packages, such as OptiOpt and IMPipeline
[4], [5].
V. Conclusion
This paper presents an overview of the integrated STOP modeling process used to predict on-orbit thermoelastic
stability for the SWOT mission at the observatory level. The STOP analysis involves running analyses using three
different models (thermal, structural, and optical) and mapping simulation results to transfer nodal data between
models, ultimately to predict thermal distortion and measurement performance. Results are shown for the bounding
on-orbit case for maximum thermal instability and PSD results. The STOP analysis results determined that the current
mission architecture design meets system requirements for on-orbit thermal stability and PSD requirements.
Additionally, lessons learned are shared for significant improvements in accurate and automated STOP analysis flow
between independent thermal, structural, and optical models that are built using industry-standard software. Specific
recommendations to NX include defining separate radiation enclosures to reduce radk runtime, and using the Targeted
Mapping Zone feature to limit nearest-node mapping errors. Broader suggestions for streamlined STOP analysis
include adoption of top-level data or software management tools that enable tighter integration between thermal,
structural, and optical models. Recognizing methods to preserve manageable computational runtime without
sacrificing accuracy is paramount to an effective STOP design and analysis campaign.
Acknowledgments
The work described in this paper was performed at the Jet Propulsion Laboratory of the California Institute of
Technology, under contract with the National Aeronautics and Space Administration. The authors would like to thank
Howard Tseng (Jet Propulsion Laboratory), and Chris Pye and Jean Frederic Ruel (MAYA Heat Transfer
Technologies) for their support in thermal analysis and mapping.
References
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[3] R. P. Somawardhana, “Surface Water Ocean Topography Ka-band Radar Interferometer Payload Thermal
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[4] B. Cullimore, T. Panczak, J. Baumann, V. Genberg, and M. Kahan, “Integrated analysis of
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