Friction stir welding has become a popular forging technique used in many applications. The Swedish Nuclear Fuel and Waste Management Company (SKB) evaluates this method to seal the 5 cm thick copper canisters that will contain the spent nuclear fuel. To produce repetitive, high quality welds, the process must be controlled, and today a cascade controller is used to keep the desired stir zone temperature. In this thesis, the control system is extended to also include a plunge depth controller. Two different approaches are evaluated; the first attempt is a decentralized solution where the cascaded temperature controller is kept, and the second approach uses a non-linear model predictive controller for both depth and temperature. Suitable models have been derived and used to design the controllers; a simpler model for the decentralized control and a more extensive, full model used in the non-linear model predictive controller that relates all the important process variables. The two controller designs are compared according to important performance measures, and the achieved increase in performance with the more complex non-linear model predictive controller is evaluated. The non-linear model predictive controller has not been implemented on the real process. Hence, simulations of the closed loop systems using the full model have been used to compare and evaluate the control strategies. The decentralized controller has been implemented on the real system. Two welds have been made using plunge depth control with excellent experimental results, confirming that the decentralized controller design proposed in this thesis can be successfully used. Even though the controller manages to regulate the plunge depth with satisfying performance, simulations indicate that the non-linear model predictive controller achieves even better closed loop performance. This controller manages to compensate for the cross-connections between the process variables, and the resulting closed loop system is almost decoupled. Further research will reveal which control design that will finally be used. / ''Friction stir welding'' har blivit en populär svetsmetod inom många olika tillämpningar. På Svensk Kärnbränslehantering AB (SKB) undersöks möjligheten att använda metoden för att försegla de 5 cm tjocka kopparkapslarna som kommer innehålla det använda kärnbränslet. För att kunna producera repeterbara svetsar utav hög kvalité krävs det att processen regleras. Idag löses detta med en temperaturregulator som reglerar svetszonens temperatur. I detta examensarbete utökas styrsystemet med en regulator för svetsdjupet. Två olika lösningar har utvärderats; först en decentraliserad lösning där temperatur-regulatorn behålls och sedan en lösning med en olinjär modellprediktiv reglering (MPC) som reglerar både djup och temperatur. Passande modeller har tagits fram och har använts för att designa regulatorerna; en enklare modell för den decentraliserade regulatorn och en utökad, komplett modell som används i den olinjära MPC:n och som beskriver alla viktiga variabler i processen. Viktiga prestandamått har jämförts för de båda regulatorstrukturerna och även prestandaökningen med den olinjära MPC:n har utvärderats. Då denna regulator inte har implementerats på den verkliga processen har simuleringar av den kompletta modellen använts för att jämföra och utvärdera regulatorstrukturerna. Den decentraliserade regulatorn har implementerats och testats på processen. Två svetsar har gjorts och de har givit utmärkta resultat, vilket visar att regulatorstrukturen som presenteras i rapporten fungerar bra för reglering av svetsdjupet. Trots att den implementerade regulatorn klarar av att reglera svetsdjupet med godkänt resultat, så visar simuleringar att den olinjära MPC:n ger ännu bättre reglerprestanda. Denna regulator kompenserar för korskopplingar i systemet och resulterar i ett slutet system som är nästan helt frikopplat. Ytterligare forskning kommer avgöra vilken av strategierna som kommer att användas i slutprodukten.

The spatial patterns of land surface and subsurface characteristics determine the spatial heterogeneity of hydrological processes. Soil depth is one of these characteristics and an important input parameter required by distributed hydrological models that explicitly represent spatial heterogeneity. Soil is related to topography and land cover due to the role played by topography and vegetation in affecting soil-forming processes. The research described in this dissertation addressed the development of statistical models that predict the soil depth pattern over the landscape; derivation of new topographic variables evaluated using both serial and parallel algorithms; and evaluation of the impacts of detailed soil depth representation on simulations of stream flow and soil moisture. The dissertation is comprised of three papers. In paper 1, statistical models were developed to predict soil depth pattern over the watershed based on topographic and land cover variables. Soil depth was surveyed at locations selected to represent the topographic and land cover variation at the Dry Creek Experimental Watershed, near Boise, Idaho. Explanatory variables were derived from a digital elevation model and remote sensing imagery for regression to the field data. Generalized Additive and Random Forests models were developed to predict soil depth over the watershed. The models were able to explain about 50% of the soil depth spatial variation, which is an important improvement over the soil depth extracted from the SSURGO national soil database. In paper 2, definitions of the new topographic variables derived in the effort to model soil depth, and serial and Message Passing Interface parallel implementations of the algorithms for their evaluation are presented. The parallel algorithms enhanced the processing speed of large digital elevation models as compared to the serial recursive algorithms initially developed. In paper 3, the impact of spatially explicit soil depth information on simulations of stream flow and soil moisture as compared to soil depth derived from the SSURGO soil database has been evaluated. The Distributed Hydrology Vegetation Soil Model was applied using automated parameter optimization technique with all input parameters the same except soil depth. Stream flow was less impacted by the detailed soil depth information, while simulation of soil moisture was slightly improved due to the detailed representation of soil depth.