The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulated in terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.

The diploma thesis is focused on the average monthly flows forecast in the selected hydrometric profile. Aim of this work will be evaluation of the calculated values and the interpretation of the results in understandable form. The next step will be find an appropriate connection between randomly-generated inputs in the form of random real flow series using the standard hydrological prediction models. This models are based on the principles of artificial intelligence and probability model. The result of the work will be verification of procedures and compilation of mean monthly flow stochastic forecast in selected hydrometric profile, which would be used for a reservoirs management, respectively for water systems management.

A spillway is an important component of a dam and serves as a flood release structure. It achieves controlled discharge of water and protects the dam from overtopping. The majority of the hydropower dams were built before the 1980s, and many spillways are undersized in light of the present design flood guidelines. Another issue that arises in connection with the high design floods is the energy dissipation capacity. Many existing energy-dissipating arrangements are insufficient or construed only for a design flood standard at the time of dam construction. The increment in the flood discharges requires that the energy dissipation should be improved to obtain sufficient capacity or higher efficiency. In addition, the high-velocity flow is a major concern in the design of spillways. If the flow velocity exceeds approximately 20 m/s, the risk of cavitation may arise. In Sweden, many dams belong to this category. To address these issues, an assessment of their discharge behaviours is required. Innovative engineering solutions for better energy dissipation and cavitation mitigation are also necessary for safe operation. This thesis presents machine learning based methods for discharge estimation. Three data-driven models are developed to study the discharge behaviours of the overflow weirs. Their reliability is validated through the comparison with the experimental and empirical results. These models are capable of giving accurate predictions and show superiority over the conventional approaches. With high accuracy and adaptability, data-driven models are an effective and fast alternative for spillway discharge prediction. This research also focuses on the hydraulic design of stepped spillways, aiming to devise innovative engineering solutions to enhance energy dissipation and reduce cavitation risks. Consequently, several unconventional step layouts are conceived and their hydraulic behaviours are investigated. The modified configurations include steps with chamfers and cavity blockages, non-linear steps and inclined steps. This part attempts to gain insight into the effects of the step geometries on the spillway hydraulics via computational fluid dynamics, which provides references for engineering applications. / Ett utskov är en viktig komponent i en damm och fungerar som ett skydd mot översvämning. Det avbördar vatten på ett kontrollerat sätt och skyddar dammen från överströmning. Majoriteten av vattenkraftsdammarna byggdes före 1980-talet och många utskov är underdimensionerade i förhållande till de nuvarande riktlinjerna för utformning med avseende på dimensionerande flöden. En annan fråga som uppstår i samband med höga flöden är energiomvandlingskapaciteten. Många befintliga arrangemang för reducering omvandling av vattnets rörelseenergi är otillräckliga eller endast anpassade för det dimensionerande flöde som gällde vid tidpunkten för dammens uppförande. En avbördningsökning kräver i sin tur att energiomvandlingsförmågan förbättras för att uppnå tillräcklig kapacitet eller högre effektivitet. Dessutom är höghastighetsflödet ett stort bekymmer vid utformningen av utskov. Om flödeshastigheten överstiger t.ex. 20 m/s uppstår risk för kavitation i vattenvägar. I Sverige hör många dammar till denna kategori. För att lösa dessa problemställningar behöver en utvärdering av avbördningsanordningar göras. Innovativa tekniska lösningar som syftar till effektiv hantering av flödesenergi och kavitationsreducering, vilka utgör nödvändiga förutsättningar för säker drift av anläggningar. Denna uppsats presenterar maskininlärningsbaserade metoder för att prognostisera avbördning i dammar. Tre datadrivna modeller har utvecklats för att studera avbördningsegenskaper hos överfallsdammarna. Deras tillförlitlighet valideras genom jämförelse med experimentella och empiriska resultat. Modellerna kan ge noggrann uppskattning, som kan användas som ett tillförlitligt alternativ för bestämning av avbördning. Forskningen fokuserar också på den hydrauliska utformningen av stegade bräddavlopp (s.k. stepped spillway), i syfte att utveckla innovativa tekniska lösningar för att åstadkomma hög energiförlust och minska kavitationsrisker. Flera okonventionella stegformade geometrier föreslås och deras hydrauliska egenskaper undersöks. Denna del syftar till att, via numerisk simulering, ge en inblick i vilka effekter olika steggeometrier har på avbördningshydrauliken, vilken tillhandahåller referens för tekniska applikationer. / <p>QC 20210205</p>

The diploma thesis is focused on the long term prediction of mean monthly flows in hydrometric profile for purposes of reservoir control optimization and optimization of reservoir systems. Discharges were predicted using by artificial neural network method. Predicted flows were statistically evaluated by relevant coefficients and then compared with the measured flows for given river hydrometric profiles.