Technical Advances in Aviation Electrification: Enhancing Strategic R&D Investment Analysis through Simulation Decomposition

Computational decision-making in “real world” environmental and sustainability contexts frequently requires the need to contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has frequently been employed to integrate the uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can be used to support the processing, analyzing, and communicating of the influence of multi-variable uncertainties on the decision-making process. In this paper, the novel Simulation Decomposition (SimDec) analytical technique is used to quantitatively examine carbon emission impacts resulting from a transformation of the aviation industry toward a state of greater airline electrification. SimDec is used to decompose a Monte Carlo model of the flying range of all-electric aircraft based upon improvements to batteries and motor efficiencies. Since SimDec can be run concurrently with any Monte Carlo model with only negligible additional overhead, it can easily be extended into the analysis of any environmental application that employs simulation. This generalizability in conjunction with its straightforward visualizations of complex stochastic uncertainties makes the practical contributions of SimDec very powerful in environmental decision-making.

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On the number of Monte Carlo runs in comparative probabilistic LCA

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-019-01698-4 ◽

2019 ◽

Vol 25 (2) ◽

pp. 394-402 ◽

Cited By ~ 5

Author(s):

Reinout Heijungs

Keyword(s):

Probability Theory ◽

Monte Carlo ◽

Probability Distributions ◽

Standard Theory ◽

Numerical Illustration ◽

Analytical Technique ◽

Estimated Parameters ◽

Alternative Systems ◽

Input Parameters ◽

Statistical Hypotheses

Abstract Introduction The Monte Carlo technique is widely used and recommended for including uncertainties LCA. Typically, 1000 or 10,000 runs are done, but a clear argument for that number is not available, and with the growing size of LCA databases, an excessively high number of runs may be a time-consuming thing. We therefore investigate if a large number of runs are useful, or if it might be unnecessary or even harmful. Probability theory We review the standard theory or probability distributions for describing stochastic variables, including the combination of different stochastic variables into a calculation. We also review the standard theory of inferential statistics for estimating a probability distribution, given a sample of values. For estimating the distribution of a function of probability distributions, two major techniques are available, analytical, applying probability theory and numerical, using Monte Carlo simulation. Because the analytical technique is often unavailable, the obvious way-out is Monte Carlo. However, we demonstrate and illustrate that it leads to overly precise conclusions on the values of estimated parameters, and to incorrect hypothesis tests. Numerical illustration We demonstrate the effect for two simple cases: one system in a stand-alone analysis and a comparative analysis of two alternative systems. Both cases illustrate that statistical hypotheses that should not be rejected in fact are rejected in a highly convincing way, thus pointing out a fundamental flaw. Discussion and conclusions Apart form the obvious recommendation to use larger samples for estimating input distributions, we suggest to restrict the number of Monte Carlo runs to a number not greater than the sample sizes used for the input parameters. As a final note, when the input parameters are not estimated using samples, but through a procedure, such as the popular pedigree approach, the Monte Carlo approach should not be used at all.

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Investigating the reliability of a hedonic travel cost model: a Monte Carlo approach

Canadian Journal of Forest Research ◽

10.1139/x94-048 ◽

1994 ◽

Vol 24 (2) ◽

pp. 358-363 ◽

Cited By ~ 3

Author(s):

Michael S. Common ◽

Daniel W. McKenney

Keyword(s):

Decision Making ◽

Sensitivity Analysis ◽

Monte Carlo ◽

Cost Model ◽

Monte Carlo Model ◽

Consumer Surplus ◽

Travel Cost ◽

Management Decision ◽

Travel Cost Model ◽

Management Decision Making

The reliability of nonmarket welfare estimates has been examined by analysts in a variety of contexts. Much of the focus of previous work has been on individual, rather than aggregate values. This paper examines the reliability of aggregate consumer surplus estimates via a Monte Carlo model. The basic elements of a hedonic travel cost model are represented in a forest management decision-making context. One result is that what would appear as minor errors in visitor estimates between sites has a significant impact on aggregate consumer surplus estimates. The results serve to emphasize that sensitivity analysis is critical when using nonmarket welfare estimates for decision making.

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A COMBINED P1 AND MONTE CARLO MODEL FOR MULTIDIMENSIONAL RADIATIVE TRANSFER PROBLEMS IN SCATTERING MEDIA

Computational Thermal Sciences An International Journal ◽

10.1615/computthermalscien.v2.i6.60 ◽

2010 ◽

Vol 2 (6) ◽

pp. 549-560 ◽

Cited By ~ 10

Author(s):

Wojciech Lipinski ◽

Leonid A. Dombrovsky

Keyword(s):

Monte Carlo ◽

Radiative Transfer ◽

Monte Carlo Model ◽

Scattering Media ◽

Transfer Problems

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Stochastic Order and Generalized Weighted Mean Invariance

Entropy ◽

10.3390/e23060662 ◽

2021 ◽

Vol 23 (6) ◽

pp. 662

Author(s):

Mateu Sbert ◽

Jordi Poch ◽

Shuning Chen ◽

Víctor Elvira

Keyword(s):

Monte Carlo ◽

Probability Distributions ◽

Stochastic Order ◽

Arithmetic Mean ◽

Stochastic Orders ◽

Arithmetic Means ◽

Increasing Convex Order ◽

Monte Carlo Techniques ◽

Multiple Importance Sampling ◽

Invariance Properties

In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov–Nagumo mean, generalizes the classical mean and appears in many disciplines, from information theory to physics, from economics to traffic flow. Stochastic orders are defined on weights (or equivalently, discrete probability distributions). They were introduced to study risk in economics and decision theory, and recently have found utility in Monte Carlo techniques and in image processing. We show in this paper that, if two distributions of weights are ordered under first stochastic order, then for any monotonic series of numbers their weighted quasi-arithmetic means share the same order. This means for instance that arithmetic and harmonic mean for two different distributions of weights always have to be aligned if the weights are stochastically ordered, this is, either both means increase or both decrease. We explore the invariance properties when convex (concave) functions define both the quasi-arithmetic mean and the series of numbers, we show its relationship with increasing concave order and increasing convex order, and we observe the important role played by a new defined mirror property of stochastic orders. We also give some applications to entropy and cross-entropy and present an example of multiple importance sampling Monte Carlo technique that illustrates the usefulness and transversality of our approach. Invariance theorems are useful when a system is represented by a set of quasi-arithmetic means and we want to change the distribution of weights so that all means evolve in the same direction.

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