Publications

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FDM/FEM System-level Analysis of Heat Pipes and LHPs in Modern CAD Environments

Publication: aerospace2005heatpipes.pps

Source: Aerospace Thermal Control Workshop

Author: Brent Cullimore, Jane Baumann

Year: 2005

Content Tags: LHP, Loop Heat Pipe, radiation analysis groups, concurrent engineering, heat pipe, system-level modeling, noncondensable gas, VCHP, CCHP, wall, two-phase heat transfer, two-phase flow, condenser, condensers, evaporator, evaporators

Steady State and Transient Loop Heat Pipe Modeling

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

Reliability Engineering and Robust Design: New Methods for Thermal/Fluid Engineering

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

Optimization and Automated Data Correlation

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:

Optimization, Data Correlation, and Parametric Analysis Features in SINDA/FLUINT Version 4.0

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.

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

Thermal Analysis on Plume Heating of the Main Engine on the Crew Exploration Vehicle Service Module

The crew exploration vehicle (CEV) service module (SM) main engine plume heating is analyzed using multiple numerical tools. The chemical equilibrium compositions and applications (CEA) code is used to compute the flow field inside the engine nozzle. The plume expansion into ambient atmosphere is simulated using an axisymmetric space-time conservation element and solution element (CE/SE) Euler code, a computational fluid dynamics (CFD) software. The thermal analysis including both convection and radiation heat transfers from the hot gas inside the engine nozzle and gas radiation from the plume is performed using Thermal Desktop. Three SM configurations, Lockheed Martin (LM) designed 604, 605, and 606 configurations, are considered. Design of multilayer insulation (MLI) for the stowed solar arrays, which is subject to plume heating from the main engine, among the passive thermal control system (PTCS), are proposed and validated.

Publication: TFAWS07-1012.pdf

Source: TFAWS

Author: Xiao-Yen J. Wang and James R.Yuko

Year: 2007

Content Tags: nozzles, expansion, CFD, mli, multi-layer insulation, convection heat transfer, steady state, heat flux

Tank Sizing Analysis for Reduced Gravity Cryogenic Transfer Receiver Tank

Understanding fluid behavior in microgravity is essential to further development of cryogenic storage in space environments. The Reduced Gravity Cryogenic Transfer project is designed to investigate tank chilldown in a microgravity environment onboard a parabolic flight. This work focused on examining the feasibility of chilling down different tank sizes using liquid nitrogen within the time constraints of the flight. Thermal models of four different tank geometries were made using Thermal Desktop and SINDA/FLUINT. The tank wall was modeled as a series of solid finite elements while the fluid inside the tank was represented by twinned liquid and vapor lumps. Fluid was injected into the bottom of the tank to simulate a dip tube and vented out of the top of the tank. The tank wall temperature as well as the state of the fluid inside the tank was tracked throughout the simulation. Several different cases were run with different chilldown operations for each tank model using a combination of charge, hold, and vent cycles. The average wall temperature, propellant mass savings and thermal efficiency of each of the four tanks were compared under seven different chilldown operations. A recommendation was made for the receiver tank size based on these parameters.

Publication: TFAWS2021-CT-01.pdf

Source: TFAWS 2021

Author: Erin M. Tesny, Daniel M. Hauser, Jason W. Hartwig

Year: 2021

Content Tags: two-phase, twinned tanks, chilldown, solid finite elements, finite elements, finite element, parametric analysis, parametric, material properties