Assessing Model Uncertainty in the Conceptual Design of a Monopropellant Propulsion System

Thunnissen, Daniel P.; Engelbrecht, Carl S.; Weiss, Jeffrey M.

doi:10.2514/6.2003-4470

Cited by 6 publications

(5 citation statements)

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“…Furthermore, each decision-maker has different expertise, understanding and opinions about the criteria weights and alternatives rankings in the decision-making process. The selection of lean tools thus becomes a MCDM problem that includes different types of uncertainty (Rostamzadeh, 2014;Singh et al, 2016) such as fuzziness, insufficient data, stochastic data and indiscernibility (Liu et al, 2012;Thunnissen, 2003).…”

Section: Selection Methods Of Lean Management Toolsmentioning

confidence: 99%

“…A fundamental concept of uncertainty is a quantitative measurement of variability in data. That is, uncertainty refers to the idea that all data have a range of expected values, as opposed to a precise point value (Epstein, 1999;Rasul et al, 2018;Thunnissen, 2003). Uncertainty is, quite simply, the lack of accurate knowledge, regardless of what is the cause of this insufficiency.…”

Section: Selection Methods Of Lean Management Toolsmentioning

confidence: 99%

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Selection methods of lean management tools: a review

Naeemah¹,

Wong²

2021

IJPPM

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PurposeThe purpose of this paper is (1) to review, analyze and assess the existing literature on lean tools selection studies published from 2005 to 2021; (2) to identify the limitations faced by previous studies; and (3) to suggest future works that are necessary to facilitate the selection of lean tools.Design/methodology/approachA systematic approach was used in order to identify, collect and select the articles. Several keywords related to the selection of lean tools were used to collect articles from different Scopus indexed journals. Next, the study systematically reviewed and analyzed the selected papers to identify the lean tools' selection method and discussed its features and limitations.FindingsAn analysis of the results showed that previous studies have adopted two types of methods for selecting lean tools. First, there are various traditional methods being used. Second, multi-criteria decision-making (MCDM) methods were commonly used in previous studies, such as the multi-objective decision-making method (MODM), single multi-attribute decision-making (MADM) methods and hybrid (MCDM). Moreover, the study revealed that the lean tools' selection methods in previous studies were based on evaluating the relationship between either lean tools and performance metrics or lean tools and waste, or both.Research limitations/implicationsIn terms of its theoretical value, the study is considered as an extension of the previous researches performed on this topic by determining and analyzing the features of the most selection methods of lean tools. Unlike previous review papers, this review had considered discussing and analyzing the characteristics and limitations of these methods. Section 2.2 of this paper reviewed some of the categories of MCDM methods as well as some of the traditional methods used in the selected previous studies. Section 2.1 of this paper explained the concept of lean management and its application benefits. Further, only three sectors were covered by the previous studies in this review paper. This study also provided recommendations for future research. Therefore, it provided researchers with a good conception of how to conduct the studies on lean tools selection. Besides, knowing the methods used in previous studies can help researchers develop new methods to select the best set of lean tools. That is, this study provided and advanced the existing knowledge base for researchers concerning lean tools selection, especially there is limited availability of review papers on this topic. Moreover, the study showed researchers the importance of the relationship between lean tools and indicators or/and performance indicators to determine the appropriate set of lean tools so that the results of future studies will be more realistic and acceptable.Practical implicationsPractically, manufacturers face a significant challenge when selecting proper lean tools. This study may enhance managers, manufacturers and company's knowledge to identify most of the methods used to choose the best set of lean tools and what are the advantages, disadvantages and limitations of these methods as well as the latest studies that have been adopted in this topic. That means this study can direct companies to prioritize the application of lean tools depending on either the manufacturing performance metrics or/and manufacturing wastes so that they avoid incorrect application of lean tools, which will add more non-value added activities to operations. Therefore companies can decrease the time and cost losses and enhancing the quality and efficiency of the performance. Correctly implementing the best set of lean tools in companies will lead in general to correctly applying lean management in corporations. Therefore, these lean tools can boost the economic aspect of companies and society through reducing waste, improving performance indicators, preserving time and cost, achieving quality, efficiency, competitiveness, boosting employee income and improving the gross domestic product. The correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. Besides, the correct lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right selection of lean tools will have positive effects on both the environment and society. The study may also encourage manufacturers and researchers to adopt studies on lean tools selection in small- and medium-sized companies because the study referred to the importance and participation of these kinds of companies in a large proportion of the economy of developing countries. Further, the study may encourage some countries that have not previously adopted this type of study, academically and industrially to conduct lean tools selection studies.Social implicationsAs mentioned previously, the correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. The proper lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right choice of lean tools will positively affect both the environment and society.Originality/valueThe study expanded the efforts of previous studies concerning lean management features. It provided an accurate review of most lean tools selection studies published from 2005 to 2021 and was not limited to the manufacturing sector. It further identified and briefly described the selection methods concerning lean tools adopted in each paper.

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Section: Selection Methods Of Lean Management Toolsmentioning

confidence: 99%

Section: Selection Methods Of Lean Management Toolsmentioning

confidence: 99%

Selection methods of lean management tools: a review

Naeemah¹,

Wong²

2021

IJPPM

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“…This model uncertainty distribution can then combined with the PDF resulting from the Monte Carlo simulation. 18 In the case of SINDA predicted temperatures, it was assumed that its model uncertainty could be represented by a normal distribution with a mean of 0 °C and a standard deviation of 2.5 °C. That is to say, SINDA correctly predicts 95% of temperatures within five degrees of flight values.…”

Section: Resultsmentioning

confidence: 99%

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

Thunnissen¹,

Tsuyuki²

2004

SAE Technical Paper Series

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A method for determining margins in conceptual-level design via probabilistic methods is described. The goal of this research is to develop a rigorous foundation for determining design margins in complex multidisciplinary systems. As an example application, the investigated method is applied to conceptual-level design of the Mars Exploration Rover (MER) cruise stage thermal control system. The method begins with identifying a set of tradable system-level parameters.Models that determine each of these tradable parameters are then created. The variables of the design are classified and assigned appropriate probability density functions. To characterize the resulting system, a Monte Carlo simulation is used. Probabilistic methods can then be used to represent uncertainties in the relevant models. Lastly, results of this simulation are combined with the risk tolerance of thermal engineers to guide in the determination of margin levels. The method is repeated until the thermal engineers are satisfied with the balance of system-level parameter values. For the thermal control example presented, margins for maximum component temperatures, dry mass, power required, schedule, and cost form the set of tradable system-level parameters. Use of this approach for the example presented yielded significant differences between the calculated design margins and the values assumed in the conceptual design of the MER cruise stage thermal control system.

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“…Since 1950 the literature on this issue has been and is in progress. The studied aspects in this field are: hydrazine decomposition kinetics [8][9][10][11][12], hydrazine decomposition catalysts [13][14][15][16][17], designing and manufacturing of hydrazine thrusters [18][19][20] and performance of hydrazine thrusters [21][22][23][24][25]. In all studies, the researchers have dealt with the hydrazine thruster and catalytic decomposition of hydrazine with respect to their dynamics or kinetics but not its thermodynamic analysis.…”

Section: Introductionmentioning

confidence: 99%

Archives of Thermodynamics

Pakdehi,

Shirvani,

Zolfaghari

2019

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Most satellites stationed in space use catalytic propulsion systems for attitude control and orbit adjustment. Hydrazine is consumed extensively as liquid monopropellant, in the thrusters. Catalytic reactor is the most important section in the catalytic thruster. Ammonia and nitrogen gases are produced as a result of complete catalytic decomposition of hydrazine in the reactor, causing an increase in temperature and a rise in specific impulse. Ammonia is subsequently decomposed, leading to nitrogen and hydrogen gases. Decomposition of ammonia leads to a decrease in temperature, molecular weight and specific impulse. The latter phenomenon is unavoidable. The effect of ammonia decomposition on the reactor temperature, molecular weight of gaseous products and conclusively on specific impulse was studied in this article. At adiabatic state, thermodynamic analysis revealed that the maximum and minimum temperatures were 1655 K and 773 K, respectively. The highest molecular weight was obtained at ammonia conversion of zero and the lowest when ammonia conversion was 100%. The maximum specific impulse (305.4 S) was obtained at ammonia conversion of zero and completely conversion of ammonia, the minimum specific impulse (about 213.7 s) was obtained. For specific impulse, the result of thermodynamic calculation in this work was validated by the empirical results.

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Assessing Model Uncertainty in the Conceptual Design of a Monopropellant Propulsion System

Cited by 6 publications

References 5 publications

Selection methods of lean management tools: a review

Selection methods of lean management tools: a review

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

Archives of Thermodynamics

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