This research focuses on the development of a deterministic-probabilistic framework for agricultural land use and management, specifically for both annual crops, such as wheat, barley and maize, and permanent crops, such as vineyards. The goal is to predict crop greening and peak crop development progressively through the growing season, based on accumulating information as the crop develops and matures, and to provide an accompanying uncertainty statement (credible interval) with each prediction. The integrated area underneath the phenology curve can be associated, although not explicitly in our example, with per-area crop yield. The prediction model relies on remotely sensed data, including science data products from the Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spaceborne instruments, field data from agro-meteorological stations, and statistical data from prior years. The development of the deterministic-probabilistic model focuses on northeastern Italy, a region of small agricultural plots set in a diverse physical landscape, which is typical of many areas of old-world and developing-nation agriculture. The estimation process uses the phenological cycle of the MODIS Enhanced Vegetation Index (EVI), extracted from the satellite imagery at 500 m spatial resolution. Landsat data, at 30-m spatial resolution, are fused with MODIS data, to provide fine-scale information better suited to small-field agriculture. By applying a piecewise logistic function to model the time trajectory of EVI values, crop development and peak greenness are estimated and characterized based on the main phenological stages determined from the remote imagery trained with ground station observations. The deterministic-probabilistic model is later validated with observations from reference testing stations and statistical crop and yield data obtained independently by administrative districts such as regional and national organizations. A temporal filter of the main phenological stages, here called a crop calendar, plays a critical role. A Bayesian approach to integrate stochastically the parameters related to a certain area provides a way to include the different datasets at the different dimensions and scales and to assess the probability to obtain a vegetation index within a given uncertainty. The model becomes, therefore, a typical generalized linear model problem, deterministically described by a piecewise logistic function, with the parameters describing the peak phenological curve estimated probabilistically, with their own uncertainty. / 2026-01-31T00:00:00Z

In Nordic design of boiling water reactors, a deep water pool under the reactor vessel is employed for the core melt fragmentation and the long term cooling of decay heated corium debris in case of a severe accident. To assess the effectiveness of such accident management strategy the Risk-Oriented Accident Analysis Methodology has been proposed. The present work contributes to the further development of the methodology and is focused on the issue of ex-vessel debris coolability. The height and shape of the porous debris bed are among the most important factors that determine if the debris can be cooled by natural circulation of water. The bed geometry is formed in the process of melt release, fragmentation, sedimentation and packing of the debris in the pool. Bed shape is affected by the coolant flow that induces movement of particles in the pool and after settling on top of the bed. The later one is called debris bed self-leveling phenomenon. In this study, the self-leveling was investigated experimentally and analytically. Experiments were carried out in order to collect data necessary for the development of a numerical model with an empirical closure. The self-leveling model was coupled to a model for prediction of the debris bed dryout. Such coupled code allows to calculate the time necessary to have a coolable configuration of the bed. The influence of input parameters was assessed through sensitivity analysis in order to screen out the less influential parameters. Results of the risk analysis are reported as complementary cumulative distribution functions of the conditional containment failure probability (CCFP). Sensitivity analyses identified: effective particle diameter and debris bed porosity as the parameters that provide the largest contribution to the CCFP uncertainty. It is found that the effect of the initial maximum height of the bed on the CCFP is reduced by the self-leveling. / Kokvattenreaktorer av nordisk typ har en djup vattenbassäng under reaktorkärlet som kan utnyttjas för att kyla härdsmältan och de fragmenterade härdresterna vid ett svårt reaktorhaveri. För att bedöma effektiviteten av en sådan haverihantering har man föreslagit användande av en riskorienterad metodik för haverianalysen (ROAAM, från engelska ”Risk-Oriented Accident Analysis Methodology”). Föreliggande projekt fokuserar på kylbarhet hos härdresterna utanför reaktortanken och bidrar till den pågående vidareutvecklingen av ROAAM till ROAAM+. Höjden på och formen för den porösa ansamlingen av härdrester (här också kallad partikelbädd) är bland de viktigaste faktorerna som avgör om resteffekten kan kylas bort med hjälp av naturlig cirkulation av vattnet i bassängen. Ansamlingens geometriska form skapas under hela processen från utsläpp av  härdsmältan via fragmentering och sedimentering i bassängens botten. Formen kan sedan förändras med tiden genom att partiklar rör sig och omfördelas i kylflödet. Detta fenomen kallas en självnivellerande process. I detta arbete studeras denna självnivellerande process experimentellt och analytiskt. Experimenten utfördes i en särskild experimentuppställning utformad för att att samla in data och parametrar som behövs för att simulera fenomenet och utveckla en beräkningsmodell som sluts empiriskt. Denna modell kopplades sedan till en modell för beräkning av dryout i partikelbädden. Genom denna koppling av de två beräkningsprogrammen är det är möjligt att beräkna tiden för partikelbädden att nå en kylbar konfiguration. Inverkan av variationer i modellens indata studeras med hjälp av känslighetsanalys. Härigenom identifierades de minst inflytelserika parametrarna såsom effektiv drifttid, partikeldensitet, experimentell ovisshet i de empiriska samband som används för att sluta modellen, samt omlokaliseringstid efter det att reaktorn snabbstoppats (SCRAM).  Dessa parametrar avfördes sedan från den fortsatta känslighetsanalysen. Ett artificiellt neuralt nätverk tränades för att användas i stället för den kopplade koden och möjliggöra den beräkningseffektivitet som krävs för att studera hur osäkerheter i indata förs vidare i riskanalysen. Resultaten är presenterade i form av komplementära, kumulativa fördelningsfunktioner för den betingade sannolikheten för brott på reaktorinneslutningen (CCFP, från engelska ”conditional containment failure probability”). Det visas att CCFP kan variera inom ett brett område beroende på de valda kombinationerna av frekvensfunktioner för ingångsparametrarna. Resultaten visar att effektiv partikeldiameter och hög porositet är de två parametrar som ger de största bidragen till osäkerheten i CCFP. Vi har också funnit att fenomenet självnivellering har en gynnsam inverkan på CCFP och leder till lägre utsläppsrisk. Det vore värdefullt att förfina de modeller som beskriver bildandet av den initiala partikelbädden. Detta är särskilt viktigt i de scenarier där det finns kort tid för självnivellering innan partikelbädden börjar smälta igen, dvs när man har relativt hög initial temperatur i partikelbädden och/eller hög specifik värmeeffekt. / <p>QC 20170315</p> / APRI

Fires following earthquake (FFE) have historically produced enormous post-earthquake damage and losses in terms of lives, buildings and economic costs, like the San Francisco earthquake (1906), the Kobe earthquake (1995), the Turkey earthquake (2011), the Tohoku earthquake (2011) and the Christchurch earthquakes (2011). The structural fire performance can worsen significantly because the fire acts on a structure damaged by the seismic event. On these premises, the purpose of this work is the investigation of the experimental and numerical response of structural and non-structural components of steel structures subjected to fire following earthquake (FFE) to increase the knowledge and provide a robust framework for hybrid fire testing and hybrid fire following earthquake testing. A partitioned algorithm to test a real case study with substructuring techniques was developed. The framework is developed in MATLAB and it is also based on the implementation of nonlinear finite elements to model the effects of earthquake forces and post-earthquake effects such as fire and thermal loads on structures. These elements should be able to capture geometrical and mechanical non-linearities to deal with large displacements. Two numerical validation procedures of the partitioned algorithm simulating two virtual hybrid fire testing and one virtual hybrid seismic testing were carried out. Two sets of experimental tests in two different laboratories were performed to provide valuable data for the calibration and comparison of numerical finite element case studies reproducing the conditions used in the tests. Another goal of this thesis is to develop a fire following earthquake numerical framework based on a modified version of the OpenSees software and several scripts developed in MATLAB to perform probabilistic analyses of structures subjected to FFE. A new material class, namely SteelFFEThermal, was implemented to simulate the steel behaviour subjected to FFE events.