The high specific energy consumption from all existing seawater desalination methods has heightened the motivation for having more efficient and greener desalination processes to meet the future goals of sustainable seawater desalination. One of the promising thermally-driven desalination methods is the direct-contact spray evaporation and condensation (DCSEC) where the excess enthalpy between feed and equilibrium states of evaporator chambers is exploited with reasonably high flashing efficiency. Further improvements in energy efficacy of DCSEC are boosted by firstly the incorporation of micro/nano-bubbles (M/NB) where micro or nano size subcooled vapor are embedded in the sprayed liquid droplets of evaporator, thereby lowering the temperature brine in evaporator and minimizing the thermal equilibrium effect of brine. The presence of subcooled bubbles increased the available surface area for heat transfer. Secondly, the concept of an evaporator-condenser pair of DCSEC could be extended to a multi-stage arrangement where the latent heat of vapor condensing on the water droplets sprayed within the condenser is recovered. From the experiments, the effect of incorporating the (M/NB) in the DCSEC at optimum feed flow rate yields more than 34% increase in distillate production at feed temperatures greater 47oC and the cooling inlet temperature set at 35oC. The other salient improvement found from the experiments is the increase in performance ratio (PR) up to 3.3 for a 6-stage configuration. This quantum jump in the PR is attributed to the heat recovery effect by as much as 70% of the total heat input. Arising from the DCSEC design, the implicit benefits are the low capital and operational cost, i.e., low CAPEX and OPEX. The former savings is attributed zero physical interfaces such as tube-based heat exchangers or membranes, whilst the latter savings is contributed by significant lesser use of chemicals in the pre-treatment of seawater feed. Lastly, the accompanied benefit is the robustness of the DCSEC processes where it could within stand high salinity of the brine, typically as high as 200,000 ppm.

Multi-Stage Stochastic Programming with CVaR: Modeling, Algorithms and Robustness RNDr. Václav Kozmík Abstract: We formulate a multi-stage stochastic linear program with three different risk measures based on CVaR and discuss their properties, such as time consistency. The stochastic dual dynamic programming algorithm is described and its draw- backs in the risk-averse setting are demonstrated. We present a new approach to evaluating policies in multi-stage risk-averse programs, which aims to elimi- nate the biggest drawback - lack of a reasonable upper bound estimator. Our approach is based on an importance sampling scheme, which is thoroughly ana- lyzed. A general variance reduction scheme for mean-risk sampling with CVaR is provided. In order to evaluate robustness of the presented models we extend con- tamination technique to the case of large-scale programs, where a precise solution cannot be obtained. Our computational results are based on a simple multi-stage asset allocation model and confirm usefulness of the presented procedures, as well as give additional insights into the behavior of more complex models. Keywords: Multi-stage stochastic programming, stochastic dual dynamic programming, im- portance sampling, contamination, CVaR

In the telecommunications industry, one of the most time-consuming tasks is troubleshooting and the resolution of Trouble Report (TR) tickets. This task involves the understanding of textual data which can be challenging due to its domain- and company-specific features. The text contains many abbreviations, typos, tables as well as numerical information. This work tries to solve the issue of retrieving solutions for new troubleshooting reports in an automated way by using a Natural Language Processing (NLP) model, in particular Bidirectional Encoder Representations from Transformers (BERT)- based approaches. It proposes a text ranking model that, given a description of a fault, can rank the best possible solutions to that problem using answers from past TRs. The model tackles the trade-off between accuracy and latency by implementing a multi-stage BERT-based architecture with an initial retrieval stage and a re-ranker stage. Having a model that achieves a desired accuracy under a latency constraint allows it to be suited for industry applications. The experiments to evaluate the latency and the accuracy of the model have been performed on Ericsson’s troubleshooting dataset. The evaluation of the proposed model suggest that it is able to retrieve and re-rank solution for TRs with a significant improvement compared to a non-BERT model. / En av de mest tidskrävande uppgifterna inom telekommunikationsindustrin är att felsöka och hitta lösningar till felrapporter (TR). Denna uppgift kräver förståelse av textdata, som försvåras as att texten innehåller företags- och domänspecifika attribut. Texten innehåller typiskt sett många förkortningar, felskrivningar och tabeller blandat med numerisk information. Detta examensarbete ämnar att förenkla inhämtningen av lösningar av nya felsökningar på ett automatiserat sätt med hjälp av av naturlig språkbehandling (NLP), specifikt modeller baserade på dubbelriktad kodrepresentation (BERT). Examensarbetet föreslår en textrankningsmodell som, givet en felbeskrivning, kan rangordna de bästa möjliga lösningarna till felet baserat på tidigare felsökningar. Modellen hanterar avvägningen mellan noggrannhet och fördröjning genom att implementera den dubbelriktade kodrepresentationen i två faser: en initial inhämtningsfas och en omordningsfas. För industrianvändning krävs att modellen uppnår en given noggrannhet med en viss tidsbegränsning. Experimenten för att utvärdera noggrannheten och fördröjningen har utförts på Ericssons felsökningsdata. Utvärderingen visar att den föreslagna modellen kan hämta och omordna data för felsökningar med signifikanta förbättringar gentemot modeller utan dubbelriktad kodrepresentation.

No description available.

In the present thesis, the objective was to find the maximum amount of payload mass that can be put into a 500 km polar orbit by a 1400 kg air-launched multi-stage rocket launched from a fighter jet platform. To fulfill the objective an algorithm incorporating several modules was developed. The modules performed calculations based on theoretical models and literature values to arrive at optimal design variables. From the design the maximum payload mass was able to be derived and it was concluded that a three-stage launch vehicle was able to deliver a 22.0 kg payload to the desired orbit. / I den här avhandlingen var syftet att hitta den maximala mängden nyttolastmassa som kan transporteras av en 1400 kg flerstegsraket uppskjuten från luften till en 500 km polär bana. För att uppfylla målet utvecklades en algoritm med flera moduler. Modulerna utförde beräkningar baserade på teoretiska modeller och litteraturvärden för att komma fram till optimala designvariabler. Från konstruktionen kunde den maximala nyttolastmassan härledas och det konstaterades att en trestegsraket kunde leverera en nyttolast på 22.0 kg till den önskade omloppsbanan.

In the present thesis, the objective was to find the maximum amount of payload mass that can be put into a 500 km polar orbit by a 1400 kg air-launched multi-stage rocket launched from a fighter jet platform. To fulfill the objective an algorithm incorporating several modules was developed. The modules performed calculations based on theoretical models and literature values to arrive at optimal design variables. From the design the maximum payload mass was able to be derived and it was concluded that a three-stage launch vehicle was able to deliver a 22.0 kg payload to the desired orbit. / I den här avhandlingen var syftet att hitta den maximala mängden nyttolastmassa som kan transporteras av en 1400 kg flerstegsraket uppskjuten från luften till en 500 km polär bana. För att uppfylla målet utvecklades en algoritm med flera moduler. Modulerna utförde beräkningar baserade på teoretiska modeller och litteraturvärden för att komma fram till optimala designvariabler. Från konstruktionen kunde den maximala nyttolastmassan härledas och det konstaterades att en trestegsraket kunde leverera en nyttolast på 22.0 kg till den önskade omloppsbanan.

No description available.

In this thesis we examine methodologies for determining free energy differences (FEDs) of phases via Monte Carlo simulation. We identify and address three generic issues that arise in FED calculations; the choice of representation, the choice of estimator, and the choice of sampling strategy. In addition we discuss how the classical framework may be extended to take into account quantum effects. Key words: Phase Mapping, Phase Switch, Lattice Switch, Simulated Tempering, Multi-stage, Weighted Histogram Analysis Method, Fast Growth, Jarzynski method, Umbrella, Multicanonical, Path Integral Monte Carlo, Path Sampling, Multihamiltonian, fluctuation theorem.

Wir betrachten ein Kraftwerkssystem mit thermischen Blöcken und Pumpspeicherwerken und entwickeln dafür ein Modell für den kostenoptimalen Wochenbetrieb. Auf Grund der Ungewißheit des Bedarfs an elektrischer Energie ist das mathematische Modell ein mehrstufiges stochastisches Problem. Dieses Modell beinhaltet viele gemischt-ganzzahlige stochastische Entscheidungsvariablen. Die Variablen einzelner Einheiten sind aber nur durch wenige Nebenbedingungen miteinander verbunden, welches die Zerlegung in stochastische Teilprobleme erleichtert. Diese stochastischen Teilprobleme besitzen deterministische Analoga, deren Lösungsverfahren entsprechend erweitert werden können. In dieser Arbeit werden ein Abstiegsverfahren für stochastische Speicherprobleme und eine Erweiterung der dynamischen Programmierung auf stochastische Probleme betrachtet. Die Lösung des dualen Problems führt zu Schattenpreisen, die bestimmte Einsatzentscheidungen bevorteilen. Die Heuristik zur Suche von primalen zulässigen Punkten wertet eine Folge von zugeordneten Economic-Dispatch-Problemen aus. Die Kombination der Einschränkung auf dual bevorzugte Fahrweisen (Lagrangian reduction) mit der Auswertung einer Folge von Economic-Dispatch-Problemen (Facettensuche) führt zu einem effizienten Verfahren. Die numerischen Ergebnisse an Hand realistischer Daten eines deutschen Versorgungsunternehmens rechtfertigen diesen Zugang. / We consider a power generation system comprising thermal units and pumped hydro storage plants, and introduce a model for its weekly cost-optimal operation. Due to the uncertainty of the load, the mathematical model represents a dynamic (multi-stage) stochastic program. The model involves a large number of mixed-integer (stochastic) decisions but its constraints are loosely coupled across operating power units. The coupling structure is used to design a stochastic Lagrangian relaxation method, which leads to a decomposition into stochastic single unit subproblems. The stochastic subproblems have deterministic counterparts, which makes it easy to develop algorithms for the stochastic problems. In this paper, a descent method for stochastic storage problems and an extension of dynamic programming towards stochastic programs are developed. The solution of the dual problem provides multipliers leading to preferred schedules (binary primal variables). The crossover heuristics evaluates the economic dispatch problems corresponding to a sequence of such preferred schedules. The combination of the restriction on dual preferred schedules (Lagrangian reduction) with the evaluation of a sequence (facet search) leads to an efficient method. The numerical results on realistic data of a German utility justify this approach.

abstract: Passive cooling techniques, specifically passive downdraft cooling (PDC), have proven to be a solution that can address issues associated with air conditioning (AC). Globally, over 100 buildings have integrated PDC in its different forms, most of which use direct evaporative cooling. Even though all surveyed buildings were energy efficient and cost-effective and most surveyed buildings were thermally comfortable, application of PDC remains limited. This study aims to advance performance of the single stage passive downdraft evaporative cooling tower (PDECT), and expand its applicability beyond the hot dry conditions where it is typically used, by designing and testing a multi-stage passive and hybrid downdraft cooling tower (PHDCT). Experimental evaluation on half-scale prototypes of these towers was conducted in Tempe, Arizona, during the hot dry and hot humid days of Summer, 2017. Ambient air dry-bulb temperatures ranged between 73.0°F with 82.9 percent coincident relative humidity, and 123.4°F with 7.8 percent coincident relative humidity. Cooling systems in both towers were operated simultaneously to evaluate performance under identical conditions. Results indicated that the hybrid tower outperformed the single stage tower under all ambient conditions and that towers site water consumption was at least 2 times lower than source water required by electric powered AC. Under hot dry conditions, the single stage tower produced average temperature drops of 35°F (5°F higher than what was reported in the literature), average air velocities of 200 fpm, and average cooling capacities of 4 tons. Furthermore, the hybrid tower produced average temperature drops of 45°F (50°F in certain operation modes), average air velocities of 160 fpm, and average cooling capacities exceeding 4 tons. Under hot humid conditions, temperature drops from the single stage tower were limited to the ambient air wet-bulb temperatures whereas drops continued beyond the wet-bulb in the hybrid tower, resulting in 60 percent decline in the former’s cooling capacity while maintaining the capacity of the latter. The outcomes from this study will act as an incentive for designers to consider incorporating PDC into their designs as a viable replacement/supplement to AC; thus, reducing the impact of the built environment on the natural environment. / Dissertation/Thesis / Doctoral Dissertation Architecture 2017