Global sensitivity analysis for the boundary control of an open channel

International audienceThe goal of this paper is to solve the global sensitivity analysis for a particular control problem. More precisely, the boundary control problem of an open-water channel is considered, where the boundary conditions are defined by the position of a downstream overflow gate and an upperstream underflow gate. The dynamics of the water depth and of the water velocity are described by the Shallow Water equations, by taking into account the bottom and friction slopes. Since some physical parameters are unknown, a stabilizing boundary control is first computed for their nominal values, and then a sensitivity analysis is performed to measure the impact of the uncertainty in the parameters on a given to-be-controlled output. The unknown physical parameters are described by some probability distribution functions. Numerical simulations are performed to measure the first-order and total sensitivity indices

Global sensitivity analysis for the boundary control of an open channel

The goal of this paper is to solve the global sensitivity analysis for a particular control problem. More precisely, the boundary control problem of an open-water channel is considered, where the boundary conditions are defined by the position of a down stream overflow gate and an upper stream underflow gate. The dynamics of the water depth and of the water velocity are described by the Shallow Water equations, taking into account the bottom and friction slopes. Since some physical parameters are unknown, a stabilizing boundary control is first computed for their nominal values, and then a sensitivity anal-ysis is performed to measure the impact of the uncertainty in the parameters on a given to-be-controlled output. The unknown physical parameters are de-scribed by some probability distribution functions. Numerical simulations are performed to measure the first-order and total sensitivity indices

Modellering av nedbrytning av organiske materiale og nitrogendynamikk i landbruksjord : miljøinnvirkninger fra planteproduksjonssystemer

Agricultural plant production has negative environmental impacts such as nitrate leaching and emissions of greenhouse gasses (N2O and CO2). Both phenomena are affected by the decomposition of soil organic matter and plant litter in soil, which is influenced by soil properties, climate, and agricultural management. Modeling is essential to improve the understanding and to predict the effects of management and its dependence on climate and soil properties. This thesis compiles three modeling studies performed with the objective to enhance our understanding of how organic matter decomposition and N dynamics in agricultural soils influence environmental impacts from plant production. Models have been applied at three levels of scale, ranging C and N mineralization of plant residues decomposed under lab conditions, field N dynamics, and a national C balance inventory for cropland. For optimal utilization of green manure and crop residue amendments, N mineralized during plant residue decomposition should be synchronized with plant N demand to minimize N losses to the environment. To achieve such synchronization, we need good prediction of net N mineralization immobilization kinetics during decomposition of relevant agricultural plant materials. This requires robust estimation of the partitioning of plant litter C and N into rapidly and slowly decomposing pools. This study presents a novel approach to partition plant C and N between two litter pools (rapidly and slowly decomposing), i.e., the simultaneous optimization of plant-specific and global parameters (against observed C and N mineralization kinetics in laboratory incubations). The study demonstrated that for a majority of the 76 plant residues, the model was able to predict C and N mineralization with reasonable precision. However, outliers were detected, which may indicate that the use of a global parameter for the C/N-ratio of the microbial biomass is not valid in all cases (i.e., specific plant materials appear to stimulate the growth of microbes with higher or lower C/N ratios than the ratio for the majority of the residues). Biochemical fractionation (SCD) and NIR-spectra of the plant residues were available and used for regression analyses to predict the optimized partitioning of C and N between the litter pools, thus searching for ways to accurately predict partitioning parameters using NIR and SCD data. Validation against a part of the dataset, which was not used for regression analyses, demonstrated that partitioning parameters obtained by regression models of NIR and SCD data were more appropriate than from chemical fractions directly. To investigate the environmental and production efficiency of organic stockless grain production, we studied the N dynamics in organic clover-grass and cereal of a stockless organic farm in Southeast Norway for an 8-10 year period using an ecosystem model (SPN). Additionally, scenario simulations of alternative crop rotations and plowing season in the present (1980-2009) and future (2071-2100) climate conditions were performed to explore the potential for improving stockless organic grain production. In an evaluation of efficiency indicators based on production and environment, only marginal improvements were possible by changing management, and this was also the case for the simulations using the future climate. This study clearly indicates that external nutrient resources are necessary to substantially improve the N use efficiency in stockless cereal systems. Consequently, such systems may be discouraged in the future and the reintegration of livestock is recommended. With the objective to estimate the soil C balance of Norwegian cropland on mineral soils, the IPCC methodologies for default (Tier 1) and Tier 2 were applied to agricultural activity data for the inventory period 1999-2009. National CO2 emissions were primarily caused by a reduction in manure available. The default Tier 1 method overestimated the decline in soil organic C, particularly for crop rotations without manure applications, compared to the Tier 2 method. National net CO2 emissions were 313 Gg CO2 yr–1 for Tier 1 and 139 Gg CO2 yr–1 for Tier 2. A reduction in livestock numbers during the inventory period appears to be an important reason for the high emissions. Thus, the emissions (as estimated) could be reduced by maintaining a high number of livestock. However, in the total greenhouse gas budget, the accompanying methane emissions from enteric fermentation would more than outweigh the reductions in CO2 emissions by high livestock numbers. Thus, care should be taken when focusing greenhouse gas mitigating strategies on C sequestration. This study highlights the need for integrated emission budgets for policy development and also for the collection of agricultural activity data regarding manure application practices such as application rates, water content, C content, and import export dynamics. This thesis delivers a contribution to the understanding of organic matter decomposition and N dynamics in agricultural soils by modeling studies performed at different scales. The findings herein support the notion that several factors beyond the biological system are crucial to reduce the adverse environmental impacts from agricultural soils, e.g., consumption patterns, market dynamics, and legislation. Also, fundamentally restructuring current production systems by reintegration of livestock and arable farming seems the best option to improve N use efficiency and sustain soil organic matter levels. To optimize the biological capacity to reduce environmental impacts, agroecosystem models that account for plant and livestock interactions are indeed needed and useful tools to characterize sustainable agricultural systems.Landbrukets planteproduksjon har negative miljømessige konsekvenser som for eksempel nitratutvasking og utslipp av klimagasser (N2O og CO2). Begge fenomener påvirkes av nedbryting av jordas organiske materiale og planterester i jord, som er påvirket av jordas egenskaper, klima og agronomisk praksis. Modellering er viktig for å bedre forståelsen og forutsi effekten av praksis og dennes avhengighet av klima og jordsmonnsegenskaper. Denne avhandlingen sammenslår tre modellstudier utført med det formål å forbedre vår forståelse av hvordan nedbrytning av organisk materiale og N dynamikk i dyrket jord påviker miljøet fra planteproduksjon. Modeller ble anvendt på tre nivåer: karbon (C) og nitrogen (N) mineralisering fra planterester nedbrutt under kontrollerte forhold i laboratorium, N dynamikk i felt, og et nasjonalt C budsjett for dyrket mark. For optimal utnyttelse av grønngjødsel og planterester tilført jorden og minimere N tap til miljøet, bør N mineralisert fra nedbrytning av planterester være synkronisert med plante N opptaket. For å oppnå en slik synkronisering, trenger vi god prediksjon av netto N mineraliserings- og immobiliseringskinetikk under nedbrytning av plantematerialer. Dette krever robust estimering av fordelingen av plante C og N i rasktog sakte-nedbrytende puljer. Denne studien presenterer en ny tilnærming til å skille plante C og N mellom to planterestpuljer, dvs. simultan optimalisering av plantespesifikke og globale parametere (mot observerte C- og N-mineralisering kinetikk i laboratorieinkubasjoner). Studien viste at for et flertall av de 76 planterestene, var modellen i stand til å forutsi C- og N-mineralisering med tilstrekkelig presisjon. Men der var outliers, hvilket kan tyde på at bruken av en global parameter for C/N-forholdet for den mikrobielle biomasse ikke er gyldig i alle tilfeller (dvs. spesifikke plantematerialer ser ut til å stimulere veksten av mikrober med høyere eller lavere C/N-forhold enn de som vokser på flertallet av planterester). Biokjemiske fraksjonering (SCD) og NIR-spektra av planterester var tilgjengelig og ble brukt for regresjonsanalyser for å forutsi den optimaliserte fraksjonen av C og N mellom plantepuljene, og dermed søke etter måter å nøyaktig forutsi fraksjonsparameterne ved å bruke NIR og SCD data. Validering mot en del av datasettet, som ikke var brukt for regresjonsanalysene viste at fraksjoneringsparameterne ved regresjonsmodeller av NIR og SCD data var mer passende enn fra kjemiske fraksjoner direkte. For å undersøke miljø- og produksjonseffektivitet av økologisk husdyrløs kornproduksjon, studerte vi N dynamikken i et vekstskifte med kløvergress og korn på en gård i Sørøst-Norge for en 8-10 års periode ved hjelp av en økosystemmodell. I tillegg, ble scenario simuleringer av alternative vekstskite og pløyesesong under nåværende (1980-2009) og fremtidige (2071-2100) klimaforhold utført for å undersøke potensialet for forbedring av husdyrløs økologisk kornproduksjon. I en evaluering av effektivitetsindikatorer basert på produksjon og miljø var bare marginale forbedringer mulige ved å endre vekstskifte og pløyesesong og dette var også tilfellet for simuleringer med det fremtidige klima. Denne studien indikerer klart at eksterne næringsstoff er nødvendig for å vesentlig forbedre produktivitet og bærekraft i husdyrløse kornsystemer. Derfor bør slike systemer kanskje frarådes i fremtiden og muligens burde husdyr reintegreres. Med det formål å estimere jord C balansen i norsk dyrket mark på mineraljord ble IPCC standard (Tier 1) og Tier 2 metodene anvendt med landbruksaktivitetsdata for beregningsperioden 1999-2009. Nasjonale CO2-utslipp var hovedsakelig forårsaket av en reduksjon i husdyrgjødseltilgjengelighet. Standard Tier 1 metode overvurdert nedgangen i jord organisk C, spesielt for vekstskifter uten husdyrgjødsel i forhold til Tier 2 metoden. Det nasjonale netto CO2-utslipp var 313 Gg CO2 år-1 for Tier 1 og 139 Gg CO2 år-1 for Tier 2. En reduksjon i husdyrtallet i løpet av beregningsperioden synes å være en viktig årsak til utslippene. Dermed kan utslippene (som estimert) reduseres ved å opprettholde et høyt antall husdyr. Men totale klimagassutslipp fra landbruket målt i CO2-ekvivalenter ville dermed øke fordi metanutslipp fra gjæring i vommen fra et større antall dyr veier tyngre enn den oppnådde reduksjonen i CO2-utslippene fra jorden. Derfor bør man være forsiktig med å fokusere klimagassformildende strategier for C lagring i jord. Denne studien understreker behovet for integrerte utslippsbudsjetter for politikkutvikling og også for innsamling av data om driftspraksis vedrørende husdyrgjødsel, så som bruksmengder, vanninnhold, C innhold, og import-eksport dynamikk. Denne avhandlingen gir et bidrag til forståelsen av nedbrytning av organisk materiale og N dynamikk i landbruksjord ved modellstudier utført på ulike skalaer. Funnene her støtter oppfatningen at flere faktorer utover det biologiske system er avgjørende for å redusere miljøulempene fra dyrket mark, for eksempel forbruksmønster, markedsdynamikk og lovgivning. Modelleringsøvelsene støtter også oppfatningen at reintegrering av husdyr og kornproduksjon kan forbedre N effektiviteten i landbruket som helhet, og bidra til å opprettholde jordas innhold av organisk materiale. For å optimalisere den biologiske evnen til å redusere miljøbelastningen, er agroøkosystemmodeller, som inkluderer plante- og husdyrinteraksjonen, et nyttig verktøy for å karakterisere bærekraftige landbrukssystemer

Physically inspired methods and development of data-driven predictive systems

Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well–performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real–world problems. The proposed predictive system design cycle is a purely data–driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well–established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi–supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource–hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross–validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real–world environmental problems, in which a comparative study of Density Preserving Sampling and cross–validation has also been performed confirming great potential of the proposed methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Successful Imitation of the Japanese Lean Production System by American Firms: Impact on American Economic Growth

This paper provides some quantitative evidence about the strong links between the Lean Production System (LPS) or equivalently the holistic Just-in-Time/Quality Control (JIT/QC) system and sectoral (micro) economic growth. This evidence is supported by qualitative arguments that present the LPS or the JIT/QC philosophy as a major and fundamental organizational feature of modern economies. Though the implementation of such a system originated in Japan, the USA have been in the process of catching up in the last fifteen years. Subsequently, recently published American sectoral data (for the period between 1958 and 1996) are used to provide ample quantitative evidence of the role the JIT/QC organizational philosophy played in shaping and leading the American macro and sectoral economies in the last 40 years. The implications for the theory of economic growth and economic policy are also briefly stated.Lean Production, Just -in-Time, Quality Control, organization, American, Japanese, transaction costs, sectors, regression, error correction model, stationarity, total factor productivity, labor productivity, economic growth.

Harmonic-Balance-Based parameter estimation of nonlinear structures in the presence of Multi-Harmonic response and force

Testing nonlinear structures to characterise their internal nonlinear forces is challenging. Often nonlinear structures are excited by harmonic forces and yield a multi-harmonic response. In many systems, particularly ones with strong nonlinearities, the effect of higher harmonics in the force and responses cannot be ignored. Even if the intended excitation is a single frequency sinusoidal force, the interaction of the shaker and the nonlinear structure can lead to harmonics in the applied force. The effects of these higher harmonics of the input force on nonlinear model identification in structural dynamics are often neglected. The objective of this study is to introduce an identification method, motivated by the alternating frequency/time approach using harmonic balance (AFTHB), which is able to consider both multi-harmonic forces and multi-harmonic responses of the system. The proposed AFTHB method can include all significant harmonics by selecting an appropriate time step and sampling frequency to guarantee the accuracy of the results. An analytical harmonic-balance-based (AHB) approach is also considered for comparison. However, the inclusion of all significant harmonics of the response in the analytical expansion of the nonlinear functions is often cumbersome. Furthermore, the AFTHB method can easily cope with complex nonlinearities such as Coulomb friction and with multi-degree of freedom nonlinear systems. Including the effect of higher harmonics in the identification process reduces the approximation error due to truncation and very accurate approximation of the balanced equations of each harmonic is obtained. The proposed identification method requires prior knowledge or an appropriate estimation of the type of system nonlinearities. However, the method of model selection may be used for a set of candidate models, and avoiding a dictionary of arbitrary candidate basis functions significantly reduces the computational costs. This paper highlights the important features of the AFTHB method to ensure accurate estimation using four simulated and two experimental examples. The effects of the number of harmonics considered, the modelling error, measurement noise and the frequency range on the quality of the estimated model are demonstrated