Margin Determination in the Design and Development of a Thermal Control System

Thunnissen, Daniel P.; Tsuyuki, Glenn T.

doi:10.4271/2004-01-2416

Cited by 16 publications

(16 citation statements)

References 11 publications

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“…This analysis is different from similar analyses, e.g. [22] and [23], in that much of the uncertainty is in the actual operation (mission sequence) of the final subsystem and not in the design. However, these uncertainties in the operation of …”

Section: Classification and Probabilistic Modeling Of Variablesmentioning

confidence: 91%

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

Thunnissen¹,

Swenka

2007

Journal of Aerospace Computing, Information, and Communication

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A method for quantifying uncertainty in conceptual-level design via a computationallyefficient probabilistic method is described. As an example application, the investigated method is applied to estimating the propellant mass required by a spacecraft to perform attitude control. The variables of the design are first classified and assigned appropriate probability density functions. To characterize the attitude control system a slightly-modified version of Subset Simulation, an efficient simulation technique originally developed for reliability analysis of civil engineering structures, is used. The proposal distribution aspect of Subset Simulation is modified vis-à-vis the original technique to account for the general characteristics of the variables involved in conceptual-level design. The results of Subset Simulation are compared with traditional Monte Carlo simulation. The investigated method allows uncertainty in the propellant required to be quantified based on the risk tolerance of the decision maker. For the attitude control example presented, Subset Simulation successfully replicated Monte Carlo simulation results yet required significantly less computational effort, in particular for risk-averse decision makers. Nomenclaturelognormal random variable with parameters μ and σ m number of Subset Simulation levels N number of Subset Simulation samples N c number of Markov chains developed at each simulation level; N c = p 0 N

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Section: Classification and Probabilistic Modeling Of Variablesmentioning

confidence: 91%

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

Thunnissen¹,

Swenka

2007

Journal of Aerospace Computing, Information, and Communication

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“…[13][14][15] Some of the famous missions are the Defense Satellite Program, the space shuttle orbiter, Mars Pathfinder, Mars Exploration Rover and Mars Science Laboratory. [16][17][18][19][20] For spacecraft which employs pumped fluid loops' thermal control system, most of the spacecraft waste heat is transferred to radiator by means of forced liquid convection, therefore, heat conduction and heat convection become the main heat transfer approaches between components and radiators. The heat transfer behavior between components and radiators can be simulated in normal ground pressure environment directly, because the radiation could be controlled to be less than 10% of the heat transfer.…”

Section: Introductionmentioning

confidence: 99%

An equivalent ground thermal test method for single-phase fluid loop space radiator

Xianwen

Wang

Zhang

et al. 2015

Chinese Journal of Aeronautics

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Thermal vacuum test is widely used for the ground validation of spacecraft thermal control system. However, the conduction and convection can be simulated in normal ground pressure environment completely. By the employment of pumped fluid loops' thermal control technology on spacecraft, conduction and convection become the main heat transfer behavior between radiator and inside cabin. As long as the heat transfer behavior between radiator and outer space can be equivalently simulated in normal pressure, the thermal vacuum test can be substituted by the normal ground pressure thermal test. In this paper, an equivalent normal pressure thermal test method for the spacecraft single-phase fluid loop radiator is proposed. The heat radiation between radiator and outer space has been equivalently simulated by combination of a group of refrigerators and thermal electrical cooler (TEC) array. By adjusting the heat rejection of each device, the relationship between heat flux and surface temperature of the radiator can be maintained. To verify this method, a validating system has been built up and the experiments have been carried out. The results indicate that the proposed equivalent ground thermal test method can simulate the heat rejection performance of radiator correctly and the temperature error between in-orbit theory value and experiment result of the radiator is less than 0.5°C, except for the equipment startup period. This provides a potential method for the thermal test of space systems especially for extra-large spacecraft which employs single-phase fluid loop radiator as thermal control approach. ª 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

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“…Lo cierto es que los métodos estocásticos pueden suponer un tiempo de cálculo órdenes de magnitud superior al cálculo clásico de incertidumbres y en ocasiones no aportar gran diferencia en cuanto a los resultados [17]. En otras, efectivamente pueden mostrar características del modelo no reproducibles mediante un enfoque lineal o utilizando simplemente parámetros estadísticamente independientes con distribuciones normales [18]. Uno de los trabajos pendientes de realizar es, en consecuencia, establecer un marco teórico que permita evaluar la conveniencia de utilizar uno u otro método.…”

Section: [W]unclassified

“…De hecho ni siquiera debería asignarse la misma distribución en distintas fases del proyecto. En las fases iniciales del proyecto es más habitual asignar distribuciones que llevan asociada una mayor incertidumbre, como las uniformes y las triangulares [18,43], al no estar denidos los valores de los parámetros. Por ejemplo, tener la elección abierta entre varias pinturas blancas para un radiador al inicio del diseño podría llevar asociada una distribución uniforme, frente a la distribución normal (debida a las variaciones aleatorias entre distintas muestras) una vez haya sido escogida la pintura en concreto que va a utilizarse.…”

Section: Otros Métodos Para El Cálculo De Incertidumbresunclassified

“…Por ejemplo, tener la elección abierta entre varias pinturas blancas para un radiador al inicio del diseño podría llevar asociada una distribución uniforme, frente a la distribución normal (debida a las variaciones aleatorias entre distintas muestras) una vez haya sido escogida la pintura en concreto que va a utilizarse. Este tipo de enfoque es el recomendado en general por los autores que aplican métodos estocásticos para el análisis de incertidumbres en control térmico espacial [15,16,18], aunque habitualmente sólo incluyen ejemplos de distribuciones para algunos parámetros y fases concretas del desarrollo (en general se recurre frecuentemente a la distribución uniforme).…”

Section: Otros Métodos Para El Cálculo De Incertidumbresunclassified

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Análisis de incertidumbres en sistemas de control térmico en ambientes espaciales

Juan¹,

Alejandro²

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Thermal control constitutes a key area in the design of space vehicles. The main objective of this discipline is achieving that the temperatures of all the vehicle subsystems are within their allocated temperature ranges in order to successfully accomplish their respective missions. One of the main tools that the thermal control subsystem has to accomplish its objective are the mathematical models used to calculate the thermal predictions. These predictions have, however, an associated uncertainty that comes as the result of the inherent uncertainty of the thermal parameters. This uncertainty is part of the thermal predictions and must be included in them in order to assess their reliability. There are two main approaches towards introducing this uncertainty in the thermal results: it can be done either applying some xed design (uncertainty) margins coming from statistical analyses of previous missions, or either calculating these uncertainty margins specically for each mission. This doctoral dissertation is focused on the second option, uncertainty calculation.Uncertainty calculation in spacecraft thermal control and design is generally performed using one of these two methods: Statistical Error Analysis (SEA) or Monte Carlo Simulation (MCS). These two methods present dierences both in accuracy and in time of execution. In this thesis both features are compared, and the sources of possible divergence between their results are identied. Having these sources of divergence in mind, a new methodology has been developed. In this new method, called One-dimensional Generalized SEA (OGS), the temperature uncertainty is obtained through the PDF of temperature variation ∆T , which is obtained through the convolution of the PDF of the individual contributions of the dierent thermal parameters to ∆T . In order to obtain the PDF of the individual contributions, the original PDF of the dierent parameters are transformed using surrogate models, which are generally non-linear. These surrogate models are the result of the sensitivity analysis that gives the inuence on temperature of the dierent parameters.

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Margin Determination in the Design and Development of a Thermal Control System

Cited by 16 publications

References 11 publications

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

An equivalent ground thermal test method for single-phase fluid loop space radiator

Análisis de incertidumbres en sistemas de control térmico en ambientes espaciales

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