Academic literature on the topic 'Skewness-Kurtosis relationship'

Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

This study empirically investigates the relationship between realized higher-order moments and the Fear and Greed Index as a measure of sentiments. We estimate daily realized moments using 5 min return data of the S&P 500 index from 3 January 2011 to 18 September 2020. We find that the Fear and Greed Index significantly impacts realized volatility during periods of extreme fear. Additionally, various sentiment indicators influence realized skewness and realized kurtosis. The VIX index significantly reduces realized skewness across all sentiment levels. Bearish and bullish sentiments have a significant negative relationship with negative realized skewness during periods of extreme fear and extreme greed. However, the Fear and Greed Index and bearish and bullish sentiments have a significant positive relationship with positive realized skewness. During extreme fear, the Fear and Greed Index and bearish and bullish sentiments have a significant negative relationship with realized kurtosis. These results remain consistent when considering the non-linear characteristics of the Fear and Greed Index during periods of extreme fear and extreme greed. These findings highlight the relevance of understanding sentiment in financial risk management and its significant relationship with the asymmetric and extremity characteristics of asset returns.

Purpose: This study aims to illustrate the relationship between the liquidity of a stock and the return distribution of all companies listed on the Indonesian Stock Exchange. Design/methodology/approach: The correlation between stock liquidity and return distribution, represented using skewness and kurtosis methods, was determined by the author through two-way tests using line regression (fixed cross-section regression) and machine learning (random forest regressor) for Indonesia data Stock exchange from 2014 to 2023. Findings: The results indicate a connection between stock liquidity and return distribution. When evaluating stock liquidity, the zero return days technique gives a more significant relationship, while the kurtosis of return method gives a more significant return distribution. It is impossible to determine with absolute certainty whether the correlation is positive or negative; it depends on how stock liquidity and return distribution are measured. Research limitations/implications: The correlation between stock liquidity and return distribution cannot be characterized as an absolute relationship due to the intricacy and variety of approaches used to measure it. Practical implications: The kurtosis of return has a better relationship with stock liquidity, and “zero return day” and “relative spread” are still more strongly correlated with the return distribution. Originality/value: As the first study to use machine learning and linear regression to examine the relationship between stock liquidity and return distribution, this paper expands the body of knowledge. Second, it examines which return distribution – skewness or kurtosis – has a stronger correlation with stock liquidity. Third, we examine which liquidity metrics—relative spread, Amihud liquidity, price impact ratio, and zero return days—are most related to the return distribution.

Sediments are particles derived from the dismantling of rocks from the land and pieces of shell and remains of marine organisms that contain organic matter, included carbonate sediment. The total carbonate sediment content was influenced by many factors, such as sediment grain type. This study aimed to determine the carbonate content in sediments and to determine their relationship to the sediment grain characteristic on the Cilacap coast. The sediment's carbonate content used the titration method, while the sediment grain test used a dry filter. Statistical analysis was used to determine the sediment grain characteristic (mean, sorting, skewness, and kurtosis). The results showed that sediments' total carbonate content had a range of 1.93% - 6.23%, with an average of 4.21%. Sediments are dominated by fine sand with very well sorted, very platykurtic, and very fine skewed characteristics. The relationship between sediment grain characteristics and total sediment carbonate content showed a good correlation due to the sorting factor. Other parameters such as mean size and skewness have been shown a low correlation, whereas kurtosis has a shallow relationship with carbonate content.

In the study, it was aimed to investigate the relationship between emotional intelligence and psychological resilience levels of female football players. The sample of the research consists of 276 female football players selected by convenience sampling method. As a data collection tool, the "Emotional Intelligence Trait Scale (TE1Qqu-SF) Short Form" scale and the "Short Psychological Resilience Scale" were adapted and used in the last part. The data that obtained from female football players were analyzed statistically with the SPSS package program. The statistical information of the participants was evaluated by using the descriptive survey method in the study. First of all, validity and reliability analyzes were performed by looking at the Cronbach Alpha values of the scales. Skewness and kurtosis values were evaluated statistically by Normal Distribution test analysis. After examining the skewness and kurtosis values of the study group, the hypotheses of socio-demographic variables were tested and correlation analysis was performed. As a result, while no significant difference was found in female football players according to marital status and athlete's age, it was found that there was a positive relationship between emotional intelligence and psychological resilience.

In the traditional human capital framework, education is often considered as an investment, rather than consumption, while consumption is not necessarily precluded. Whether education is an investment is empirically unclear and relatively under-explored. We shed light on this issue by estimating the risk–return trade-off in the context of education. If education is indeed an investment, risk could play an important role in individual educational decisions just as with risky assets. As portfolio theory predicts, there could be a trade-off between returns to education and risks concerning those returns: higher risks are generally associated with higher returns. We contribute to the literature by proposing various measures of risk based on the entire distribution of returns to education recovered by our nonparametric models. Our results confirm a trade-off between returns and variance. We also found statistically significant impacts for the higher moments: skewness and kurtosis. Interestingly, we found the relationship between mean returns and variance to be linear, and the relationship between expected returns and higher-moments (skewness and kurtosis) is non-linear.

A positive perception situation positively increases the person’s worthiness, self-confidence and perspective towards time. The current study determined the relationship between the positive perception and social well-being levels of students during the COVID-19 quarantine period. The study group consisted of 236 students, 110 females and 126 males (mean age=21.30±1.99). Positive perception scale and social well-being scale were used. Skewness-kurtosis normality distribution test was used to determine whether the measurements are suitable for normal distribution. Skewness-kurtosis technique showed normal distribution in all dimensions according to the technique. In the analysis of the study data, Pearson product moment correlation analysis with descriptive statistics and multivariate analysis of variance or MANOVA analyzes scores were performed to determine the relationship between positive perception and social well-being. As a result, it is understood that students have over mid-level of positive perception and mid-level social well-being levels. According to the results of the correlation analysis, it was concluded that there was a positive and meaningful relationship between the social well-being and sub-dimensions of the positive perception scale.

Abstract The paper presents a new idea of approach to the analysis of surface morphology of cereal grains. In this paper, the surfaces of maize were analyzed using an optical 3D measurement system to determine the surface morphology and parameters of surface topography. It was established how changes in the moisture influence on the surface characteristics. Comprehensive parametric analysis was conducted for maize grains with different moisture contents. The objective of this study was to comprehensively characterize the surface morphology of maize in contact area using standardized 3D roughness parameters as well as other characteristics such as structure direction and isotropy. This is the first study that has presented, elucidated, and discussed the relationships between some morphological parameters of cereal grains. This research contributes to better understanding of the mixing process. The main findings are: the values of roughness parameters kurtosis and skewness are characterized by higher values for maize grains with lower moisture, compared with maize grains with higher moisture content; there is a close relationship between the maize grain moisture content and the surface roughness parameters of kurtosis and skewness; maize grains have an anisotropic structure.

Comprendre et prédire la dynamique de la biodiversité sous contraintes du changement global représente un défi scientifique majeur. Cependant, la réponse de la biodiversité au changement global est intrinsèquement complexe. En effet, les facteurs de changement en jeu n'affectent pas seulement la diversité des espèces et leur abondance, mais modifient également les interactions biotiques entre espèces, ce qui impacte l‘assemblage des communautés et leurs dynamiques. Dans ce contexte, l'étude de la diversité des traits fonctionnels pourrait permettre des avancées significatives car les traits reflètent la manière dont les espèces répondent et influencent leur environnement. Pour rendre l'approche fonctionnelle opérationnelle à l'étude de la complexité des systèmes écologiques, nous avons développé une démarche analytique novatrice qui repose sur l'étude des formes de distribution de traits. La forme des distributions de traits peut être caractérisée par une la relation entre la skewness et la kurtosis : la SKR. Plus précisément, nous avons développé deux indicateurs clés (Chapitre 1), dérivés de la SKR : le TADeve qui caractérise l'équitabilité fonctionnelle, et le TADstab qui caractérise la stabilité des distributions de traits.En nous appuyant sur les prairies permanentes comme modèle d'étude, nous avons mis en avant la pertinence de l'étude de l'équitabilité (TADeve) et de la stabilité (TADstab) des distributions de traits afin de dissocier l'influence de processus déterministes (p.ex. filtre de l'habitat, différentiation de niche) de la stochasticité inhérente aux systèmes écologiques (Chapitre 1). La mobilisation d'un jeu de données de suivi de prairies permanentes gérées de manière contrastée sur le long terme (17 ans), nous a permis de montrer que la dynamique des distributions de traits n'était pas aléatoire mais dépendante des pratiques de gestion (Chapitre 2). Les prairies gérées de manière intensive (haut niveau de fertilisation) sont associées à des distributions de traits instables et inéquitables. Ces résultats sont cohérents avec un scénario théorique du « filtre de l'habitat » et l'effet d'une compétition intense limitant la diversité. Dans le cas des prairies gérées de manière extensive (absence de fertilisation), les distributions de traits sont remarquablement équitables et stables. Ces résultats sont cohérents avec un scénario théorique de la « différenciation de niche » prédisant une coexistence stable d'espèces fonctionnellement contrastées. Par ailleurs, nous avons montré que l'arrêt de la fertilisation entraîne, en quelques années, une convergence vers des distributions de traits équitables et stables, favorisant ainsi le recrutement à long terme d'une flore prairiale riche et diversifiée. Nous montrons également que l'équitabilité et la stabilité des distributions de traits s'expliqueraient par l'émergence d'une complémentarité entre espèces dominantes et subordonnées permettant une stabilisation sur le long terme de l'assemblage fonctionnel et de toute la communauté végétale (Chapitre 3). Enfin, une étude comparative des patrons de diversité fonctionnelle, entre des communautés prairiales gérées et des communautés végétales naturelles, nous a permis de montrer que ces communautés partagent des organisations fonctionnelles similaires. L'observation de patrons communs dans le temps et dans l'espace suggère l'existence de règles générales déterminant à la fois l'assemblage, la diversité et la dynamique des communautés prairiales.En conclusion, l'approche SKR semble adaptée à l'étude de systèmes complexes dynamiques, tels que les systèmes écologiques en contexte de changement global. A l'ère de l'anthropocène, l'identification de règles générales d'assemblage basées sur les traits permettrait de concevoir des modes de gestion adaptés à la préservation et à la restauration de la biodiversité, ainsi qu'au maintien de la multifonctionnalité des écosystèmes
Understanding and predicting the dynamics of biodiversity under global change is a major scientific challenge. However, biodiversity responses to global change are inherently complex. Drivers of change not only affect species diversity and abundance but also alter biotic interactions between species, which may impact community assembly and dynamics. In this context, studying the diversity of functional traits within communities could lead to significant advances, as traits reflect how species respond to and influence their environment. To make the trait-based approach operational for the study of complex ecological systems, we developed an innovative analytical framework based on the study of the shapes of trait distributions. The shapes of trait distributions can be characterised by an inequality between the skewness and the kurtosis, the Skewness-Kurtosis Relationship (SKR). Using this inequality, we developed two key indicators (Chapter 1): the TADeve, which characterises the evenness of trait distributions, and the TADstab, which characterises the stability of trait distributions.Using permanent grasslands as a study model, we highlighted the relevance of studying the evenness (TADeve) and stability (TADstab) of trait distributions in order to disentangle the influence of deterministic processes (e.g. habitat filtering, niche differentiation), while accounting for the inherent stochasticity of ecological systems (Chapter 1). Using a long-term dataset of managed permanent grasslands (17-years), we demonstrated that the temporal variability of trait distributions was not random, but depended on management practices (Chapter 2). Intensively managed grasslands (high levels of fertilisation) are associated with unstable and uneven trait distributions. These results are consistent with predictions of the “habitat filtering” theory and the occurrence of intense competition between plant species that limit local diversity. Conversely, extensively managed grasslands (no fertilisation) were linked to remarkably even and stable trait distributions over time. Furthermore, we also showed that the cessation of fertilisation in extensively-managed grasslands led to a rapid convergence towards even and stable trait distributions, which promoted the long-term recruitment and persistence of a rich and diverse grassland flora. These findings are consistent with a theoretical scenario of niche differentiation, which predicts a stable coexistence among functionally contrasting species. Finally, we found that the high evenness and stability of trait distributions, in extensively managed grasslands, are explained by a functional complementarity between dominant and subordinate species, facilitating the long-term stabilisation of the functional assemblage and of the entire plant community (Chapter 3). Using observational data from various ecological context, we showed that semi-natural and natural plant communities shared a similar functional organisation. The observation of common functional patterns over space and time suggests the existence of general rules governing the assembly, diversity, and dynamics of plant communities.In conclusion, the SKR approach appears to be a suitable tool to study complex dynamic systems, such as ecological systems in the context of global change. In the Anthropocene era, identifying general assembly rules based on functional traits could enable the design of management methods adapted to the conservation and restoration of biodiversity, as well as the maintenance of ecosystem multifunctionality

A Modelagem de Equações Estruturais (SEM, do inglês Structural Equation Modeling) é uma metodologia multivariada que permite estudar relações de causa/efeito e correlação entre um conjunto de variáveis (podendo ser elas observadas ou latentes), simultaneamente. A técnica vem se difundindo cada vez mais nos últimos anos, em diferentes áreas do conhecimento. Uma de suas principais aplicações é na conrmação de modelos teóricos propostos pelo pesquisador (Análise Fatorial Conrmatória). Existem diversas medidas sugeridas pela literatura que servem para avaliar o quão bom está o ajuste de um modelo de SEM. Entretanto, é escassa a quantidade de trabalhos na literatura que listem relações entre os valores de diferentes medidas com possíveis problemas na amostra e na especicação do modelo, isto é, informações a respeito de que possíveis problemas desta natureza impactam quais medidas (e quais não), e de que maneira. Tal informação é importante porque permite entender os motivos pelos quais um modelo pode estar sendo considerado mal-ajustado. O objetivo deste trabalho é investigar como diferentes perturbações na amostragem, especicação e estimação de um modelo de SEM podem impactar as medidas de qualidade de ajuste; e, além disso, entender se o tamanho da amostra influencia esta resposta. Simultaneamente, também se avalia como tais perturbações afetam as estimativas, dado que há casos de perturbações em que os parâmetros continuam sendo bem ajustados, mesmo com algumas medidas indicando um mau ajuste; ao mesmo tempo, há ocasiões em que se indica um bom ajuste, enquanto que os parâmetros são estimados de forma distorcida. Tais investigações serão realizadas a partir de simulações de exemplos de amostras de diferentes tamanhos para cada tipo de perturbação. Então, diferentes especicações de modelos de SEM serão aplicados a estas amostras, e seus parâmetros serão estimados por dois métodos diferentes: Mínimos Quadrados Generalizados e Máxima Verossimilhança. Conhecendo tais resultados, um pesquisador que queira aplicar a técnica de SEM poderá se precaver e, dentre as medidas de qualidade de ajuste disponíveis, optar pelas que mais se adequem às características de seu estudo.
The Structural Equation Modeling (SEM) is a multivariate methodology that allows the study of cause-and-efect relationships and correlation of a set of variables (that may be observed or latent ones), simultaneously. The technique has become more diuse in the last years, in different fields of knowledge. One of its main applications is on the confirmation of theoretical models proposed by the researcher (Confirmatory Factorial Analysis). There are several measures suggested by literature to measure the goodness of t of a SEM model. However, there is a scarce number of texts that list relationships between the values of different of those measures with possible problems that may occur on the sample or the specication of the SEM model, like information concerning what problems of this nature impact which measures (and which not), and how does the impact occur. This information is important because it allows the understanding of the reasons why a model could be considered bad fitted. The objective of this work is to investigate how different disturbances of the sample, the model specification and the estimation of a SEM model are able to impact the measures of goodness of fit; additionally, to understand if the sample size has influence over this impact. It will also be investigated if those disturbances affect the estimates of the parameters, given the fact that there are disturbances for which occurrence some of the measures indicate badness of fit but the parameters are not affected; at the same time, that are occasions on which the measures indicate a good fit and there are disturbances on the estimates of the parameters. Those investigations will be made simulating examples of different size samples for which type of disturbance. Then, SEM models with different specifications will be fitted to each sample, and their parameters will be estimated by two dierent methods: Generalized Least Squares and Maximum Likelihood. Given those answers, a researcher that wants to apply the SEM methodology to his work will be able to be more careful and, among the available measures of goodness of fit, to chose those that are more adequate to the characteristics of his study.

The recent advent of high-frequency data has given rise to the notion of realized skewness and realized kurtosis. Unlike sample skewness and sample kurtosis which is normally computed from long samples of low-frequency return series (daily, weekly, monthly return series, and so on), realized skewness and realized kurtosis is computed from high-frequency return series (1-second, 1-minute, 5-minute return series, and so on). The relevance of high-frequency return data has been extensively documented in the extant financial literature. Researchers have shown that with high-frequency return data, realized variance converges to the sample variance and is an efficient estimator of the quadratic variation. However, realized skewness and realized kurtosis do not converge to the sample skewness and sample kurtosis values. This is because the second realized moments depend on both the diffusion and jump components of the observed price, whereas, the third and fourth realized moments depend exclusively on just the jump component. This implies that information embedded in realized skewness and realized kurtosis is different from that of sample skewness and sample kurtosis. This thesis contributes to the body of literature by adopting, deducing, and following various theoretical methodologies, simulation techniques and empirical procedures to offer a new perspective, which cautions researchers to be observant of the optimal sampling frequency for their country of investigation when using high-frequency return series. Primarily, researchers should also be aware of the effects of sampling-interval and holding-intervals on the estimated realized skewness and realized kurtosis and its implication to high-frequency finance. Researchers need to be critical of the type of volume used for information flow, and its relationship with realized skewness and realized kurtosis. Finally, it is important to note the significance of high-order moment pricing models in capturing the cross-section of asset returns under various market conditions (upturn and downturn markets) and sample-periods (pre-crisis, crisis, and post-crisis period). The second chapter of this thesis investigates the optimal sampling frequency for computing realized variance for the DJI30 index and its component stocks, and also whether the obtained sampling frequency could be extended to the Australian framework. To the best of my knowledge, this study is the first to investigate the preferred sampling frequency with a focus on the Australian stocks, and not naively extending the 5-minutes rule of thumb from the US framework. Using 1-second (high-frequency) raw prices downloaded from Thomson Reuters Tick History/Securities Industry Research Centre of Asia-Pacific (TRTH/SIRCA) database from 2010 to 2015, this study computes daily RVs and find that the standard 5-minute interval for the US market holds, a ‘10-’ to ‘30-minute’ sampling frequency is the preferred interval for the Australian framework. The third chapter of this thesis investigates theoretically and empirically, how realized skewness and realized kurtosis are affected by holding-interval and sampling-interval. In particular, before any computations of realized skewness and realized kurtosis are carried out, one often predetermines the holding-interval and sampling-interval and thus implicitly influencing the actual magnitudes of the computed values of the realized skewness and realized kurtosis (i.e. they have been found to be interval-variant). To-date, little theoretical or empirical studies have been undertaken in the high-frequency finance literature to properly investigate and understand the effects of these two types of intervalings on the behaviour of the ensuring measures of realized skewness and realized kurtosis. This chapter fills this gap by theoretically and empirically analyzing as to why and how realized skewness and realized kurtosis of market returns are influenced by the selected holding-interval and sampling-interval. Using simulated and price index data from the G7 countries, this study then proceeds to illustrate via count-base signature plots, the theoretical and empirical relationships between the realized skewness and realized kurtosis and the sampling-intervals and holding-intervals. The fourth chapter of this thesis investigates empirically volume-higher order moment relationship by employing various proxies of information flow. The relationship between volume and realized volatility has been extensively documented in the extant financial literature. However, minimal attention has been accorded to volume-realized skewness and volume-realized kurtosis relationships. The insight in this chapter is that these additional higher-order realized moments hold volume-dependent relationships that have been neglected. The empirical analysis employs 142 Australian stocks from 2003 to 2017 downloaded at 15-minute sampling-intervals from the TRTH/SIRCA database and compute their weekly and monthly realized high-order moments. It is found that the volume proxy influences the signage of the ensuring volume-higher-order realized moment regression coefficients. This study then attempts to explain the empirical findings via three common volume-related hypotheses cited in the extant volume literature and conclude that the DOH (Difference of Opinion) hypothesis implicitly encompasses or nests both the SIAH (Sequential Information Arrival) and MDH (Mixture of Distribution) hypotheses. These two subtle but significant findings have yet to be reported in extant volume or trading-related studies. The fifth chapter follows a set of methodologies documented in the extant literature for investigating the higher-order co-moment risk-return relationship for the Australian stock market. Using 142 stocks from 2003 to 2017 downloaded from TRTH/SIRCA database. For this study, monthly realized return and monthly higher-order co-moment estimates are computed from 15-minute series. The high-frequency return data will ensure robust estimates for the empirical analysis. The empirical results show that the average return for standard beta and kappa risks are asymmetric and diametrically opposite in upmarket and downmarket periods, while gamma risks yield significant gains to the investor regardless of the market condition (the results are consistent for the three methodologies considered: (i) the single sorting of excess return on risk measures, (ii) double sorting of excess returns on risk measures, and (iii) the Fama-MacBeth cross-sectional regression). Additional results from the Fama-MacBeth cross-sectional regression shows that gamma and kappa risk factors remain priced, even in the presence of continuous beta and jump beta. It is found that not only the normalized covariance risk factor is important in asset pricing but also normalized co-skewness and co-kurtosis risk factors are also priced separately. This study further splits the full-sample data into sub-periods and observes that the level of significance of the risk premium varies across the sub-periods. The results contribute to the debate on whether systematic realized higher-order co-moments can explain the cross-sectional Australian stock returns. To conclude, this thesis brings to light some research questions and answers related to realized skewness and realized kurtosis that are yet to be considered in the existing high-frequency finance literature and hence contributes to the body of knowledge in field of finance.

Abstract Direct energy deposition (DED) has been widely used for additive manufacturing of metallic components toward a variety of applications. Surface characteristics of DED-fabricated components play key roles in determining the property and performance. Besides the average surface roughness which has been extensively investigated in literature, surface skewness and kurtosis are critical for surface integrity, particularly its durability due to stress concentration points. In this work, surface skewness and kurtosis of DED-fabricated 316L stainless steel as affected by processing parameters are investigated. In particular, the surface quality is measured using a microscopic structured light scanning (SLS) system, which is a relatively fast, low-cost, high-efficiency dimensional inspection metrology as compared to other methods. The results demonstrated the correlations between the printing parameters (laser power and scanning speed) and the surface topography of DED printed parts. It is found that the skewness and kurtosis of the surface are more sensitive to the change in scanning speed within a relatively low laser power range. Skewness is positively correlated with the scanning speed, while kurtosis shows a negative correlation with the scanning speed. Given a high scanning speed, Kurtosis and Skewness are more sensitive to the changes of scanning speed. Understanding the relationship between DED processing parameters and areal surface characteristics provides guidance and insights for process optimization and post-processing design towards additive manufacturing of high-performance metallic components.

Experimental investigation on non-uniform surface roughness affecting to flow resistance has been carried out. Experiment was performed with the concentric cylinder device. In this experiment, we evaluated non-uniform surface roughness of various surface by the optical method and measured friction coefficient in turbulent flow over each surface roughness. As an evaluation of non-uniform surface roughness, we measured surface roughness profiles of each surface by a laser displacement sensor. Based on this surface roughness profiles, we calculated some roughness parameters such as the root mean square roughness. One important result indicates the relationship between friction coefficient and roughness Reynolds number. The friction coefficient increases logarithmically with increasing roughness Reynolds number. Moreover, to discuss the effect of non-uniform roughness in detail, the Probability Density Function (PDF) of the roughness height and the spectrum of the surface roughness profiles were analyzed. As a result, the frictional drag over the rough surface can be mostly evaluated by the roughness Reynolds number which is defined by the root mean square roughness when the probability density distribution of the surface roughness profile has Gaussian distribution. However, if the probability density distribution does not have Gaussian distribution, kurtosis and skewness of surface roughness profile are also important parameter for the evaluation of the surface roughness.

<div class="section abstract"><div class="htmlview paragraph">The energy consumption of a vehicle is typically determined either by testing or in simulation. While both approaches are valid, they only work for a specific drive cycle, they are time intensive, and they do not directly result in a closed-form relationship between key parameters and consumption. This paper presents an alternative approach that determines the consumption based on a simple analytical model of the vehicle and statistical parameters of the drive cycle, specifically the moments of the velocity. This results in a closed-form solution that can be used for analysis or synthesis.</div><div class="htmlview paragraph">The drive cycle is quantified via its moments, specifically the average speed, the standard deviation of the speed as well as the higher order moments skewness, and the kurtosis. A mixed quadratic term is added to account for acceleration or aggressiveness, but it is noticeably distinct from the conventional metric of positive kinetic energy (PKE). The vehicle is quantified using a polynomial model of the traction force and of the primary energy consumption of the powertrain. This model form fits both conventional and electrified powertrains, including all the component efficiencies.</div><div class="htmlview paragraph">Through a statistical analysis of the model, the primary energy consumption can be related to both the model parameters and the statistical properties of the drive cycle. This result can be useful for the analysis of a drive cycle, for the analysis of a powertrain, for economy optimization, and for control purposes. An example of a Nissan LEAF powertrain is presented over different cycles.</div></div>