In the paper the results of measurements and two dimensional mathematical simulation for polder in the depression for 1996 are presented. The mathematical model takes into account hydraulics conductivity of soils in form of tensors, water uptake by plants and elastic capacity of soils. In the last decade in Poland many hectares of field culture changed not only ownership, but also the manner in which it was farmed. It also refers to polders with compound soil profiles and complex water conditions. Often in such areas, alluvial soils and relatively high water tables occur. These conditions are preferred in grassland as opposed to arable land farming. Very often these rules are forgotten by new farmers or they stop farming or they use their land for other activities due to bad crop yield; turning it to - for example - grasses and weeds from mowed ditches and dikes store. This causes conflicts between farmers and the holder of the melioration system or the water reservoir in the vicinity. An example of such a situation is a small polder where soil water conditions are influenced by the reservoir with retained water levels between about 0 - 1.5 m above the surface of the surrounding land. It is concluded that if the beginning of the vegetation season (April) is wet, the moisture conditions are unfavourable for crop production. But if April is dry, then even if the rest of the season is wet, the moisture conditions will still be satisfactory. This conclusion was derived from presented results of simulation. It is true only if farmers’ activities are responsible and rational for such soil and water conditions. and Štúdia prezentuje výsledky pozorovaní a dvojrozmernej matematickej simulácie poldra v depresii za rok 1966. Matematický model uvažuje s hydraulickou vodivosťou pôd v tenzorovom tvare, odberom vody rastlinami a pružnou kapacitou pôd. V poslednom desaťročí sa v Poľsku zmenili nielen vlastnícke pomery na veľkej ploche poľnohospodárskych kultúr, ale aj spôsob, akým boli obrábané. Týka sa to aj poldrov so zložitými pôdnymi profilmi a vodným režimom. Často ide o plochy s aluviálnymi pôdami a pomerne vysokou hladinou vody. Takéto podmienky sú vhodnejšie pre trávnaté porasty ako pre hospodárenie na oráčinách. Noví hospodári často zabúdajú na tieto zásady, často prestávajú hospodáriť na týchto pôdach alebo ich využívajú na iný účel z dôvodu nízkych úrod. Tieto sa tak často zmenia na skládky napr. trávy a burín z vykášaných priekop a hrádzí. Toto spôsobuje konflikty medzi farmármi a správcami melioračných sústav alebo nádrží v ich blízkosti. Príkladom takéhoto stavu je malý polder, kde vodné pomery pôd ovplyvňuje vodná nádrž s pohybom hladín okolo 0–1,5 m nad povrchom okolitých pôd. Záverom sa konštatuje, že ak počiatok vegetačnej sezóny (apríl) je vlhký, celkové vlhkostné podmienky sú pre úrodu nepriaznivé. Ak je však naopak apríl suchý, potom aj ak je zvyšok vegetačnej sezóny vlhký, celkove vlhkostné podmienky budú dostatočné. Tento záver vyplynul z uvedených výsledkov simulácie. Bude to tak však iba v prípade zodpovedných a rozumných činností farmára, dotýkajúcich sa pôdnych a vlhkostných podmienok.
Most of the existing works in the literature related to greenhouse modeling treat the temperature within a greenhouse as homogeneous. However, experimental data show that there exists a temperature spatial distribution within a greenhouse, and this gradient can produce different negative effects on the crop. Thus, the modeling of this distribution will allow to study the influence of particular climate conditions on the crop and to propose new temperature control schemes that take into account the spatial distribution of the temperature. In this work, a Finite Element Differential Neural Network (FE-DNN) is proposed to model a distributed parameter system with a measurable disturbance input. The learning laws for the FE-DNN are derived by means of Lyapunov's stability analysis and a bound for the identification error is obtained. The proposed neuro identifier is then employed to model the temperature distribution of a greenhouse prototype using data measured inside the greenhouse, and showing good results.
Combustion is a complex field, because it simultaneously involves several disciplines such as the heat transfer, the chemistry, the turbulence, the transport of mass etc. In this work we tried to model an experiment having a combustion chamber with a geometry close to that of spark ignition engine. This modeling based on the approach of calculated probability density function (pdf). In fact, we used a transport equation for the probability density function similar to the modeling case of the physical and chemical species. Indeed, theoretical study of this pdf is very developed, but its applications remain very limited for the several reasons. This pdf method was
coupled with a simplified chemistry and introduced into the KIVAII code in order to simulate the combustion process in a rectangular combustion chamber which is very similar to that of spark ignition engine (SI). The results found by this model of combustion are compared with the experiment at different operating conditions such as the propane/air equivalence ratio, temperature and pressure. The final results and the conclusions are satisfactory. In this paper, we present only the results relating to a given operation conditions with a study of the sensitivity of the flame radius on the
equivalence ratio. and Obsahuje seznam literatury
The present paper describes a semi-analytical fracture model based on the cracked hinge approach by Ulfkjær [1]. Some extensions of the original fomrulation are introduced and also implemented (as JAVA code) to enable the use of any softening function with arbitrary shape for the cracked part of the model, which is considered as a fictitious (cohesive) crack. The application of the model to the wedge-splitting test (WST) is validated, showing the consistency of the adopted formulations with reference data. Furthermore, the capability of the model to integrate various softening curves is verified using FEM simulations. and Obsahuje seznam literatury
The growth in the field of construction of shallow underground structures has been associated with construction of new roads, collectors and other structures. This contribution deals with modeling of distribution pattern of the maximum velocity amplitude (blasting vibration field) on surface basement. This basement will be situated within small distance from source of technical seismicity that is used as a part of technological processes. The model represents seismic effect of blasting operati on in shallow tunnel. Plaxis 2D modeling system and its dynamic module based on finite element method are used for this presentation., Martin Stolárik., and Obsahuje bibliografické odkazy
Drainage of paved and unpaved roads has been implicated as a major contributor of overland flow and erosion in mountainous landscapes. Despite this, few watershed models include or have tested for the effect roads have on discharge and sediment loads. Though having a model is an important step, its proper application and attention to distinct landscape features is even more important. This study focuses on developing a module for drainage from a road and tests it on a nested watershed (Shanko Bahir) within a larger previously studied site (Debre Mawi) that receives overland flow contributions from a highly compacted layer of soil on an unpaved road surface. Shanko Bahir experiences a sub-humid monsoonal climate and was assessed for the rainy seasons of 2010, 2011, and 2012. The model chosen is the Parameter Efficient Distributed (PED) model, previously used where saturation-excess overland flow heavily influences discharge and sediment concentration variation, though infiltration-excess occasionally occurs. Since overland flow on unpaved surfaces emulates Hortonian flow, an adjustment to the PED model (the developed module) advances possible incorporation of both flow regimes. The modification resulted in similar modeling performance as previous studies in the Blue Nile Basin on a daily basis (NSE = 0.67 for discharge and 0.71 for sediment concentrations). Furthermore, the road while occupying a small proportion of the sub-watershed (11%) contributed importantly to the early discharge and sediment transport events demonstrating the effect of roads especially on sediment concentrations. Considerations for the dynamic erodibility of the road improved sediment concentration simulation further (NSE = 0.75). The results show that this PED modeling framework can be adjusted to include unpaved compacted surfaces to give reasonable results, but more work is needed to account for contributions from gullies, which can cause high influxes of sediment.
This paper presents an empirical model and a three-layer (7:11:1) artificial neural network (ANN) approach for the determination of completely mixed activated sludge reactor volume (CMASRV). CMASRV values were estimated by a new mathematical formulation and a three-layer ANN model for 1,000 different artificial scenarios given in a wide range of seven biological variables. The predicted results obtained from each stochastic approach were compared with the well-known steady state volume model based on mass balance equations. The computational analysis showed that the proposed empirical model and ANN outputs were obviously in agreement with the steady-state volume model and all the predictions proved to be satisfactory with a correlation coefficient of about 0.9989 and 1, respectively. The maximum volume deviations from the steady-state volume equation were recorded as only 7.17% and 6.89% for the proposed model and ANN outputs respectively. In addition to volume comparison, waste sludge mass flow rates (PX), food to mass ratios (F/M), hydraulic retention times (HRTs), volumetric organic loads (LV) and oxygen requirements (ORs) were also compared for each model, and significant points of proposed approaches were evaluated.
The study presents a modification to conventional finite element method under plane strain conditions to address the problem of successive excavation of linear parts of tunnels. Although the successive excavation is a three-dimensional mechanical problem, the designers often prefer 2D analysis owing to considerably simple and transparent geometric model and fast computations when compared to a 3D solution. The main idea behind the suggested method referred to as, 2D3D model, is to express the influence of excavation of a single stroke of soil not only in the particular cross section but in the entire soil body in front of and behind the examined profile. This is achieved by introducing special finite elements which have common triangular cross-section but are of infinite length in the longitudinal direction. The longitudinal approximations of the displacement field adopt the evolution of convergence measurements, while standard linear shape functions are kept in the element triangular cross-section. A profile corresponding to the city road tunnel Blanka in Prague with available convergence measurements was examined to verify the method. The results show that the method provides reasonably accurate results when compared to the convergence confinement method without the need to subjectively determine the lambda parameter. It also significantly reduces the computational time of a more versatile but complex 3D analysis., Tomáš Janda, Michal Šejnoha and Jiří Šejnoha., and Obsahuje bibliografii
In the present study, bainite fraction results of continuous cooling of high strength low alloy steels have been modeled by artificial neural networks. The artificial neural network models were constructed by 16 input parameters including chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), Nb in solution, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. The value for the output layer was the bainite fraction. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. To make a decision on the completion of the training processes, two termination states are declared: state 1 (ANN-I model) means that the training of neural network was ended when the maximum epoch of process reached (1000) while state 2 (ANN-II model) means the training ended when minimum error norm of network gained. The entire statistical evaluators of ANN-II model has higher performance than those of ANN-I. However, both of the models exhibit valuable results and the entire statistical values show that the proposed ANN-I and ANN-II models are suitably trained and can predict the bainite fraction values very close to the experimental ones.