Advancements in satellite technologies are increasing the power density of electronics and payloads. When the power consumption increases within a limited volume, waste heat generation also increases and this necessitates a proper and efficient thermal management system. Mostly, micro and nanosatellites use passive thermal control methods because of the low cost, no additional power requirement, ease of implementation, and better thermal performance. Passive methods lack the ability to meet certain thermal requirements on larger and smaller satellite platforms. This work numerically studies the performance of some of the passive thermal control techniques such as thermal straps, surface coatings, multi-layer insulation (MLI), and radiators for a 6U small satellite configuration carrying a mid-wave infrared (MWIR) payload whose temperature needs to be cooled down to 100K. Infrared (IR) imagers require low temperature, and the level of cooling is entirely dependent on the infrared wavelengths. These instruments are used for various applications including Earth observations, defence, and imaging at IR wavelengths. To achieve these low temperatures on such instruments, a micro-cryocooler is considered in this study. Most of the higher heat dissipating elements in the satellite are mounted to a heat exchanger plate, which is thermally coupled to an external radiator using thermal straps and heat pipes. The effects of the radiator size, orbital inclinations, space environments, satellite attitude with respect to the sun, and surface coatings are discussed elaborately for a 6U satellite configuration.

Publication: applsci-12-02858.pdf

Source: Applied Sciences, 2022, 12(6), 2858

Author: Shanmugasundaram Selvadurai, Amal Chandran, David Valentini, and Bret Lamprecht

Year: 2022

Content Tags: mli, multi-layer insulation, surface elements, surface coating a mesh, radiator, phase change material, thermocouples, finite element, finite elements, convergence, material properties, properties, CCHP

Thermoelectric couples are solid-state devices capable of generating electrical power from a temperature gradient (known as the Seebeck effect) or converting electrical energy into a temperature gradient (known as the Peltier effect). Thermoelectric coolers, being solid state devices, have no moving parts which makes them inherently reliable and ideal for cooling components in a system sensitive to mechanical vibration. The ability to use TECs to heat as well as cool makes them suitable for applications requiring temperature stabilization of a device over a specified temperature range. Although these devices have been around for years, they are gaining popularity in the aerospace industry for providing temperature control within optical systems and for loop heat pipes.

Historically, modeling and sizing of thermoelectric coolers was left to the analyst to work off-line from the modeling task. The analyst would then need to create his own logic in SINDA for simulating the cooler. This presenation will demonstrate how thermoelectric coolers are now easily modeled using off-the-shelf simulation routines and 3D user interfaces. The analytical demonstration includes sizing of a cooler for a specific application based on area, temperature requirements and heat load through a series of parametric analyses. Cooler performance will also be characterized at the device and system level.

Publication: ModelingAndSizingTECs.pps

Source: Aerospace Thermal Control Workshop

Author: Jane Baumann

Year: 2006

Content Tags: cooler, TEC, thermoelectrics, thermoelectric, LHP, Loop Heat Pipe, optical, user logic, parametric, thermostatic, convection heat transfer, expression editor, parameterize, steady state, transient, proportional, design opimization, system-level modeling

Thermal Desktop has the capability of modeling free molecular heat transfer (FMHT), but limitations are observed when working with large models during transient operation. To overcome this limitation, a MatLab program was developed that processes the Thermal Desktop free molecular conductors. It sets up the logic and arrays for the Thermal Desktop GUI used by SINDA/FLUINT. The theory of free molecular heating is presented along with the process required to setup the conductors, arrays, logic and Fortran subroutines for FMHT modeling in Thermal Desktop.

Publication: TFAWS07-1013.pdf

Source: TFAWS

Author: Eric T. Malroy

Year: 2007

Content Tags: transient, third-party software, user-defined Fortran array, radiation analysis groups, surface elements, radiation, radiation calculations, case set manager, user-defined Fortran arrays (UDFAs), submodels, radks

SINDA/FLUINT (Ref 1-7) is the NASA-standard heat transfer and fluid flow analyzer for thermal control systems. Because of its general formulation, it is also used in other aerospace specialties such as environmental control (ECLSS) and liquid propulsion, and in terrestrial industries such as electronics packaging, refrigeration, power generation, and transportation industries.

SINDA/FLUINT is used to design and simulate thermal/fluid systems that can be represented in networks corresponding to finite difference, finite element, and/or lumped parameter equations. In addition to conduction, convection, and radiation heat transfer, the program can model steady or unsteady single- and two-phase flow networks.

C&R’s SinapsPlus® is a complete graphical user interface (preand postprocessor) and interactive model debugging environment for SINDA/FLUINT (Ref 8, 9). SinapsPlus also supports the C language in addition to the traditional choice of Fortran for concurrently executed user logic.

This paper describes revolutionary advances in SINDA/FLUINT, the NASA-standard heat transfer and fluid flow analyzer, changing it from a traditional point-design simulator into a tool that can help shape preliminary designs, rapidly perform parametrics and sensitivity studies, and even correlate modeling uncertainties using available test data.

Innovations include the incorporation of a complete spreadsheet-like module that allows users to centralize and automate model changes, even while thermal/fluid solutions are in progress. This feature reduces training time by eliminating many archaic options, and encourages the performance of parametrics and other what-if analyses that help engineers develop an intuitive understanding of their designs and how they are modeled.

The more revolutionary enhancement, though, is the complete integration of a nonlinear programming module that enables users to perform formal design optimization tasks such as weight minimization or performance maximization. The user can select any number of design variables and may apply any number of arbitrarily complex constraints to the optimization. This capability also can be used to find the best fit to available test data, automating a laborious but important task: the correlation of modeling uncertainties such as optical properties, contact conductances, as-built insulation performance, natural convection coefficients, etc.

Finally, this paper presents an overview of related developments that, coupled with the optimization capabilities, further enhance the power of the whole package.

Publication: sfpaper.pdf

Source: IECEC

Author: Brent A. Cullimore

Year: 1998

Content Tags: design optimization, model correlation, parameterize, parametric, two-phase flow, two-phase, optical properties, submodels, registers, expression editor, user logic, concurrent engineering, concurrent design, dynamic mode, dynamic SINDA, specific heat, solver, constraint, slip flow, Phenomena, capillary systems, mixtures, working fluids, nonequilibrium, vapor compression, uncertainty, uncertainty analysis

This paper describes revolutionary advances in SINDA/FLUINT, the NASA-standard heat transfer and fluid flow analyzer, changing it from a traditional point-design simulator into a tool that can help shape preliminary designs, rapidly perform parametrics and sensitivity studies, and even correlate modeling uncertainties using available test data.

Innovations include the incorporation of a complete spreadsheet-like module that allows users to centralize and automate model changes, even while thermal/fluid solutions are in progress. This feature reduces training time by eliminating many archaic options, and encourages the performance of parametrics and other what-if analyses that help engineers develop an intuitive understanding of their designs and how they are modeled.

The more revolutionary enhancement, though, is the complete integration of a nonlinear programming module that enables users to perform formal design optimization tasks such as weight minimization or performance maximization. The user can select any number of design variables and may apply any number of arbitrarily complex constraints to the optimization. This capability also can be used to find the best fit to available test data, automating a laborious but important task: the correlation of modeling uncertainties such as optical properties, contact conductances, as-built insulation performance, natural convection coefficients, etc.

Finally, this paper presents an overview of related developments that, coupled with the optimization capabilities, further enhance the power of the whole package.

Publication: sf981574.pdf

Source: ICES 1998

Author: Brent A. Cullimore

Year: 1998

Content Tags: design optimization, model correlation, parameterize, parametric, two-phase flow, two-phase, optical properties, submodels, registers, expression editor, user logic, concurrent engineering, concurrent design, dynamic mode, dynamic SINDA, specific heat, solver, constraint, slip flow, Phenomena, capillary systems, mixtures, working fluids, nonequilibrium, vapor compression, uncertainty, uncertainty analysis

Optimization and Automated Data Correlation in the NASA Standard Thermal/Fluid System Analyzer

SINDA/FLUINT (Ref 1-7) is the NASA-standard heat transfer and fluid flow analyzer for thermal control systems. Because of its general formulation, it is also used in other aerospace specialties such as environmental control (ECLSS) and liquid propulsion, and in terrestrial industries such as electronics packaging, refrigeration, power generation, and transportation industries. SINDA/FLUINT is used to design and simulate thermal/fluid systems that can be represented in networks corresponding to finite difference, finite element, and/or lumped parameter equations. In addition to conduction, convection, and radiation heat transfer, the program can model steady or unsteady single- and two-phase flow networks. CRTech's SinapsPlus® is a complete graphical user interface (preand postprocessor) and interactive model debugging environment for SINDA/FLUINT (Ref 8, 9). SinapsPlus also supports the C language in addition to the traditional choice of Fortran for concurrently executed user logic. This paper describes revolutionary advances in SINDA/FLUINT, the NASA-standard heat transfer and fluid flow analyzer, changing it from a traditional point-design simulator into a tool that can help shape preliminary designs, rapidly perform parametrics and sensitivity studies, and even correlate modeling uncertainties using available test data. Innovations include the incorporation of a complete spreadsheet-like module that allows users to centralize and automate model changes, even while thermal/fluid solutions are in progress. This feature reduces training time by eliminating many archaic options, and encourages the performance of parametrics and other what-if analyses that help engineers develop an intuitive understanding of their designs and how they are modeled. The more revolutionary enhancement, though, is the complete integration of a nonlinear programming module that enables users to perform formal design optimization tasks such as weight minimization or performance maximization. The user can select any number of design variables and may apply any number of arbitrarily complex constraints to the optimization. This capability also can be used to find the best fit to available test data, automating a laborious but important task: the correlation of modeling uncertainties such as optical properties, contact conductances, as-built insulation performance, natural convection coefficients, etc. Finally, this paper presents an overview of related developments that, coupled with the optimization capabilities, further enhance the power of the whole package.

Publication: sfpaper.pdf

Source: IECEC 1998

Author: Brent A. Cullimore

Year: 1998

Content Tags:

Recent years have witnessed more improvement to the SINDA/FLUINT thermohydraulic analyzer than at any other time in its long history. These improvements have included not only expansions in analytic power, but also the additions of high-level modules that offer revolutions in thermal/ fluid engineering itself.

One such high-level module, “Reliability Engineering,” is described in this paper. Reliability Engineering means considering tolerances in design parameters, uncertainties in environments, uncertainties in application (e.g. usage scenarios), and variations in manufacturing as the stochastic phenomena that they are. Using this approach, the probability that a design will achieve its required performance (i.e., the reliability) is calculated, providing an assessment of risk or confidence in the design, and quantifying the amount of over- or under-design present.

The design to be evaluated for reliability will likely have been produced using traditional methods. Possibly, the design was generated using the Solver optimizer, another high-level module available in SINDA/FLUINT. Using design optimization, the user quantifies the goals that make one design better than another (mass, efficiency, etc.), and specifies the thresholds or requirements which render a given design viable or useless (exceeding a performance limit, etc.). SINDA/FLUINT then automatically searches for an optimal design.

Robust Design means factoring reliability into the development of the design itself: designing for a target reliability and thereby avoiding either costly over-design or dangerous under-design in the first place. Such an approach eliminates a deterministic stack-up of tolerances, worst-case scenarios, safety factors, and margins that have been the traditional approaches for treating uncertainties.

In any real system or product, heat transfer and fluid flow play a limited role: there are many other aspects to a successful design than the realm of thermal/fluids that is encompassed by SINDA/FLUINT. Therefore, this paper concludes with brief descriptions of methods for performing interdisciplinary design tasks.

Publication: releng1.pdf

Source: CRTech White Paper

Author: Brent A. Cullimore

Year: 2000

Content Tags: design optimization, reliability engineering, robust design, constraints, boundary conditions, concurrent design, concurrent engineering, batteries, flow control, orifices, radiator, registers, two-phase flow, solver, model correlation, dynamic SINDA, dynamic mode, variables, Monte Carlo, material properties, third-party software, uncertainty analysis, uncertainty

Publication: NewOsummary.pdf

Source: ASME

Author: Brent A. Cullimore

Year: 2001

Content Tags: model calibration, CFD, parametric, design optimization, design synthesis, Phenomena

The major influence on the reliability of electronics is temperature, yet thermal/fluid modeling is plagued with uncertainties and unknowns. Nonetheless, if appropriate values of these unknown parameters are available for any specific electronics package, then its temperature response can be accurately predicted using modern thermal/fluid analysis tools.

Traditionally, uncertainties are dealt with by a combination of testing, safety factors or margins, and worst-case design scenarios. Analyses are performed iteratively in a repetitive “point design evaluation” mode. Computer-based design simulation tools have emphasized increasing detail and fidelity to physical phenomena, seemingly ignoring the fact that the inputs to these simulations are highly uncertain.

This paper describes both current and future methods of dealing with uncertainties in thermal engineering. It introduces advanced tools and alternative methodologies that can automate not only the quantification of reliability, but can also help synthesize designs on the basis of reliability. It advocates using rapid gains in computer speed not to increase the degree of detail in a model, but to help the engineer find a robust design by automating high-level design tasks.

Publication: IPACK2001-15516.pdf

Source: InterPack

Author: Brent A. Cullimore

Year: 2001

Content Tags: parameterize, parametric, contact conductance, design synthesis, Phenomena, robust design, design optimization, design variables, reliability engineering

Nonlinear Programming Applied to Calibrating Thermal and Fluid Models to Test Data (Semi-Therm 2002)

Publication: calibrating.pdf

Source: Semi-Therm

Author: Jane Baumann, Brent Cullimore

Year: 2002

Content Tags: model calibration, model correlation, condenser, condensers, validation, design optimization, parametric

This paper describes readily available techniques for automating the search for worst-case (e.g., “hot case”, “cold case”) design scenarios using only modest computational resources. These methods not only streamline a repetitive yet crucial task, they usually produce better results.

The problems with prior approaches are summarized, then the improvements are demonstrated via a simplified example that is analyzed using various approaches. Finally, areas for further automation are outlined, including attacking the entire design problem at a higher-level.

Publication: WorstCase-ICES.pdf

Source: ICES

Author: B. Cullimore

Year: 2003

Content Tags: parametric, model correlation, design optimization, convergence

Thankfully, the age of stand-alone fixed-input simulation tools is fading away in favor of more flexible and integrated solutions. “Concurrent engineering” once meant automating data translations between monolithic codes, but sophisticated users have demanded more native integration and more automated tools for designing, and not just evaluating point designs. Improvements in both interprocess communications technology and numerical solutions have gone a long way towards meeting those demands.

This paper describes a small slice of a larger on-going effort to satisfy current and future demands for integrated multidisciplinary tools that can be highly customized by end-users or by third parties. Specifically, the ability to integrate fully featured thermal/fluid simulations into Microsoft’s Excel™ and other software is detailed. Users are now able not only to prepare custom user interfaces, they can use these codes as portals that allow integration activities at a larger scale. Previous enabling technologies are first described, then examples and repercussions of current capabilities are presented, and finally in-progress and future technologies are listed.

Publication: COMAPI-ICES.pdf

Source: ICES

Author: B. Cullimore, S. G. Ring, J. Baumann

Year: 2004

Content Tags: parametric, parameterize, dynamic mode, dynamic SINDA, third-party software

The NASA-standard thermohydraulic analyzer, SINDA/ FLUINT, has been used to model various aspects of loop heat pipe  (LHP) operation for more than 12 years. Indeed, this code has many features that were specifically designed for just such specialized tasks, and is unique in this respect. Furthermore, SINDA is commonly used at the vehicle (integration) level, has a large user base both inside and outside the aerospace industry, has several graphical user interfaces, preprocessors, postprocessors, has strong links to CAD and structural tools, and has built-in optimization, data correlation, parametric analysis, reliability estimation, and robust design tools.

Nonetheless, the LHP community tends to ignore these capabilities, yearning instead for “simpler” methods. However, simple methods cannot meet the challenging needs of LHP modeling such as transient start-up and noncondensible gas (NCG) effects, are often hardware-specific or proprietary, or cannot be used in a vehicle-level analysis.

There are many reasons for this hesitancy to use SINDA/ FLUINT as it was intended. First, hardware developers tend to be less versed in analytic methods than the user community they serve. Second, there are political hurdles, such as the fact that ESA contractors are required to use ESA sponsored software. Third, the state-of-the-art in LHPs is not so advanced that the analysts can be ignorant of the complex two-phase thermohydraulic and thermodynamic processes and phenomena involved, and unfortunately most thermal analysts are accustomed only to “dry” thermal control (radiation, conduction, etc.).

Fourth, the general-purpose and complete nature of SINDA/FLUINT tends to make it intimidating, especially in light of the third reason listed above. SINDA/FLUINT is not designed strictly for LHPs or even for LHP-like systems; it has been used for everything from nuclear reactor cooling to dynamic models of human hearts and tracheae. The user’s manuals and standard training classes†  rarely mention capillary phenomena because only a fraction of SINDA/FLUINT’s users are thus inclined. It is to address this fourth reason that this paper has been written, since the authors can do little to redress the first three problems.

This paper summarizes the available modeling capabilities applicable to various LHP design and simulation tasks. Knowledge of LHPs is assumed.

Publication: lhp.pdf

Source: ICES

Author: Brent Cullimore, Jane Baumann

Year: 2000

Content Tags: Loop Heat Pipe, LHP, noncondensible gas, condensers, evaporators, slip flow, phase suction, design optimization, reliability engineering, noncondensible gases, two-phase flow, two-phase, compensation chamber, network elements, nonequilibrium, wicks, capillary systems, liquid surface, interface, CAPPMP, iface, conduction

This paper describes the application of the general purpose SINDA/FLUINT thermohydraulic analyzer to the modeling of vapor compression (VC) cycles such as those commonly used in automotive climate control and building HVAC systems. The software is able to simulate transient operation of vapor compression cycles, predicting pressures, coefficients of performance, and condenser/evaporator liquid positions in a closed two-phase system with a fixed fluid charge. The program can also be used to size components, to estimate the impact of tolerances and other variations, and to help estimate uncertainties given limited test data.

SINDA/FLUINT has a user base numbering in the thousands. It has several graphical user interfaces, preprocessors, and postprocessors; has strong links to CAD and structural tools; and has built-in optimization, data correlation, parametric analysis, reliability estimation, and robust design tools.

Nonetheless, widespread application to vapor compression cycles is comparatively recent (Ref 2), and is largely due to an increased demand for transient modeling of air conditioning systems. Toward this requirement, SINDA/FLUINT’s unique abilities to analyze transient two-phase phenomena have been recognized as being critical to achieving accurate performance predictions.

Publication: vc.pdf

Source: ICES

Author: Brent A. Cullimore

Year: 2000

Content Tags: condensers, evaporator, registers, evaporators, slip flow, orifices, valves, capillary tubes, parameterizing, parametric analysis, reliability engineering, robust design, model correlation, vapor compression, compressor, parametric, parameterize, solver, transient

Thermal analysis is typically executed with multiple tools in a series of separate steps for performing radiation analysis, generating conduction and capacitance data, and for solving temperatures. This multitude of programs often leads to many user files that become unmanageable with their multitude, and the user often looses track as to which files go with which cases. In addition to combining the output from multiple programs, current processes often involve the user inputting various hand calculations into the math model to account for MLI/Insulation and contact conductance between entities. These calculations are not only tedious to make, but users often forget to update them when the geometry is changed.

Several new features of Thermal Desktop are designed to automate some of the tedious tasks that thermal engineers now practice. To start with, Thermal Desktop is a single program that does radiation analysis, generates conduction/capacitance data and automates the building of a SINDA/FLUINT model to solve for temperatures. Some of these new features of Thermal Desktop are Radiation Analysis Groups, Property Aliases, MLI/Insulation Objects, Contact Conductance Objects, Model Browser, and the Case Set Manager.

This paper describes the application and benefits of Thermal Desktop along with other unique features used to automate the thermal analysis process.

Publication: tDesktop99.pdf

Source: ICES

Author: Mark J. Welch, Tim Panczak

Year: 1999

Content Tags: radiation analysis groups, property, alias, multi-layer insulation, mli, insulation, contact conductance, model browser, case set manager

Thermal analysis is typically performed using a point design approach, where a single model is analyzed one analysis case at a time. Changes to the system design are analyzed by updating the thermal radiation and conduction models by hand, which can become a bottleneck when attempting to adopt a concurrent engineering approach. This paper presents the parametric modeling features that have been added to Thermal DesktopTM to support concurrent engineering. The thermal model may now be characterized by a set of design variables that are easily modified to reflect system level design changes. Geometric features, optical and material properties, and orbital elements may all be specified using user-defined variables and expressions. Furthermore, these variables may be automatically modified by Thermal Desktop’s optimization capabilities in order to satisfy user-defined design goals, or for correlating thermal models to test data. By sharing the set of design variables among analysis models spanning multiple disciplines, further integrated analysis and design may be accomplished. The framework into which Thermal Desktop is embedded in order to support an integrated Thermal/Structural/Optical design, analysis, and optimization system is also presented.

Publication: 00ICES-266.pdf

Source: ICES

Author: Timothy D. Panczak, Brent A. Cullimore

Year: 2000

Content Tags: concurrent engineering, parametric, parameterize, register, registers, dynamic mode, dynamic SINDA, symbol manager, expression editor, expressions, design optimization, orbital heating, model correlation, solver, optical properties, heat pipes, symbol, variables, case set manager, properties, structural

Historically, thermal/fluid modeling began as a means of validating and sometimes correcting passively cooled designs that had been proposed by nonspecialists in heat transfer and fluid flow. As dissipation fluxes have risen, and as air cooling reaches the limits of its usefulness, involvement of thermal engineers is required earlier in the design process. Thermal engineers are now commonly responsible for sizing and selecting active cooling components such as fans and heat sinks, and increasingly single and two-phase coolant loops.

Meanwhile, heat transfer and fluid flow design analysis software has matured, growing both in ease of use and in phenomenological modeling prowess. Unfortunately, most software retains a focus on point-design simulations and needs to do a better job of helping thermal engineers not only evaluate designs, but also investigate alternatives and even automate the search for optimal designs.

This paper shows how readily available nonlinear programming (NLP) techniques can be successfully applied to automating design synthesis activities, allowing the thermal engineer to approach the problem from a higher level of automation. This paper briefly introduces NLP concepts, and then demonstrates their application both to a simplified fin (extended surface) as well as a more realistic case: a finned heat sink.

Publication: Optimizing.pdf

Source: ITherm

Author: Brent A. Cullimore

Year: 2002

Content Tags: design optimization, parametric, design synthesis, design variables, variables, sink temperature