This research examined a heat transfer model to predict the activation time of a concealed sprinkler. Concealed sprinklers consist of two stages of activation. They include the release of cover plates from a recess housing and the breakage of the glass bulbs or melting of the solder links. The research analysis is divided into two sections. The first section includes the prediction of cover plate activation time (stage one) and the second section includes the prediction of glass bulb activation time (stage two). Each prediction result is compared with the experimental data conducted by Annable (2006) and Yu (2007). A lumped heat capacity method is introduced to predict the activation time of the cover plate. This method has been used for predicting the activation time of a standard pendent exposed sprinkler. It is reasonable to apply this method by assuming they are flush with the ceiling. The analysis results are compared based on the percentage of predicted and measured uncertainties. A recommendation is provided for which method is appropriate to apply to predicting the cover plate activation time. The proposed of using FDS5 simulations is to simulate the heat transfer to the sensing element (glass bulb only) within the recessed housing. The constructed simulation models comprises of ceiling within a compartment. The simulations of various sprinkler heads are performed to investigate any parameters that can potentially affect the activation time of the sprinklers. To simulate the glass bulb, combined thermal properties including glass and glycerine are modified to account for the differences in mass. Prior to stage two analysis, the FDS5 simulation was tested to predict the activation time of a standard pendent exposed sprinkler. The results showed positive progress to carry onto the next analysis. In stage two analysis, the simulations are constructed with and without the presence of vent holes within the recess housing. The combined activation time for concealed sprinklers show lack of solid predictions compared to the experimental data especially Yu experimental data.

In the last three decades the global population has been growing at an essentially constant rate, at around 1.5 per cent per year, to about 6.026 billion in 2000 when it was estimated that 47% of that population live in an urban environment. Further, a United Nations’ projection indicates that 60% of the total global population may be living in an urban settlement by the year 2025. This increasing urbanisation brings with it increased employment, that delivers affluence, which then continues the cycle of migration and movement to these growing metropolitan areas in both developed and developing countries. As cities increase in population and expand their urban area, there is a consequential expansion of urban transportation and accompanying service infrastructure. People travel daily, irrespective of their vast differences in culture, economic conditions and means of transportation. This daily mobility is sought for its own sake as well as to bridge the spatial distance that separates their homes from the work place, to accomplish their household’s domestic needs and to undertake social journeys, such as visiting friends and taking holidays. As the world’s urban population undertakes its daily mobility by a variety of transportation modes, an individual’s mobility behaviour and mode-choice is governed by a complex matrix of physical and human, social and management indicators, measures and/or drivers. A literature review describes the current understanding of this complex matrix and concludes by identifying and defining a set of fundamental underlying measures that drive private motorised, public transport and non-motorised (walking and bicycling) mobility at national, city and household levels. As practical instruments, transportation models play an important role in providing decision-makers with analytical tools to help them understand their city’s transportation and the different future scenarios it may face. While not necessarily producing foolproof information or predictions, models are still the best methods available to test the likely implications of alternative transportation policy decisions in a rapidly changing urban environment. Urban transport models are generally based on the notion that traffic can be modelled in aggregate measures through statistical data and predictive modelling techniques. In this research, dimensional analysis is used to derive sketch-plan models for private motorised, public transport and non-motorised mobility for any urban environment based on four-decades of detailed land-use and travel pattern data from a large international sample of cities. These models are developed on the basis of a set of fundamental underlying measures that are deemed to drive private motorised, public transport and non-motorised (walking and bicycling) mobility at the city level. Importantly, the models also embody three key attributes. They are: • easy to use, minimising user requirements and data inputs • policy-sensitive, capable of assessing a sufficient range of policy options • reliable and robust over time, so that the results can be consistently believed. The capacity of the sketch-plan models to predict personal mobility in an urban environment is statistically validated against an independent land-use and travel pattern data set for 83 cities located on five continents. Despite their simplicity and maintaining a consistent functional form over a time-series of four-decades and across all geographic and cultural regions, the private motorised mobility model can consistently explain up to 92% of the variance in private motorised urban mobility. The results for the public transport mobility model are less reliable and consistent, in particular when developing cities are part of the model. Results for developed or wealthier cities are much better. Reasons for these results and their inadequacies are discussed. The non-motorised modes mobility model is the least successful part of the modelling work. This can be attributed to a combination of inadequate data and, very likely, the more micro-level determinants of usage of these modes. The private motorised urban mobility sketch-plan model equation developed in this thesis is able to predict present and future trends of automobile use in individual cities to a high degree of statistical reliability. The model equation offers urban transport planners a focused direction on the fundamental measures that have the potential to control and deliver automobile restraint policies and strategies. A series of case studies shows that this model has wide applications in understanding past trends in private motorised mobility and in developing urban environmental strategy and policy through its ability to calculate and assess current and future motor vehicle emissions inventories in cities. The thesis makes suggestions for future work in this area of metropolitan level transport modelling, in particular, how to improve the public and non-motorised transport models so that total urban transport mobility can be better understood and modelled.

No description available.

A scale consisting of eight suggestions worded with specific sensory predicates was administered to a large undergraduate introductory psychology class. Following the presentation of the suggestions, Self-Scoring Forms were filled out to assess the subjects' response to auditory (A), visual (V), and kinesthetic (K) suggestions. prior to the conclusion of the session, subjects were asked to write a brief essay describing their experience of the suggestion portion of the session. Subject essays were content analyzed for the use of predicates (including, but not only A, V, and K). Frequency of usage of A, V, and K predicates were compared with responses to A, V, and K suggestions to determine the amount of consistency between preference for the use of a specific category of sensory predicates and responsiveness suggestions worded in similar language. No significant correlations between the use of specific sensory predicates and response to specific sensory suggestions were found.

The use of statistics has been common practice within the petroleum industry for over a decade. With such a mature subject that includes specialized software and numerous articles, the challenge of this project was to introduce a duplicable method to perform deterministic regression while confirming the mathematical and actual validation of the resulting model. A five-step procedure was introduced using Statistical Analysis Software (SAS) for necessary computations to obtain a model that describes an event by analyzing the environmental variables. Since SAS may not be readily available, the code to perform the five-step methodology in R has been provided. The deterministic five-step procedure methodology may be applied to new fields with a limited amount of data. As an example case, 17 wells drilled in north central Texas were used to illustrate how to apply the methodology to obtain a deterministic model. The objective was to predict the number of days required to drill a well using environmental conditions and technical variables. Ideally, the predicted number of days would be within +/- 10% of the observed time of the drilled wells. The database created contained 58 observations from 17 wells with the descriptive variables, technical limit (referred to as estimated days), depth, bottomhole temperature (BHT), inclination (inc), mud weight (MW), fracture pressure (FP), pore pressure (PP), and the average, maximum, and minimum difference between fracture pressure minus mud weight and mud weight minus pore pressure. Step 1 created a database. Step 2 performed initial statistical regression on the original dataset. Step 3 ensured that the models were valid by performing univariate analysis. Step 4 history matched the models-response to actual observed data. Step 5 repeated the procedure until the best model had been found. Four main regression techniques were used: stepwise regression, forward selection, backward elimination, and least squares regression. Using these four regression techniques and best engineering judgment, a model was found that improved time prediction accuracy, but did not constantly result in values that were +/- 10% of the observed times. The five-step methodology to determine a model using deterministic statistics has applications in many different areas within the petroleum field. Unlike examples found in literature, emphasis has been given to the validation of the model by analysis of the model error. By focusing on the five-step procedure, the methodology may be applied within different software programs, allowing for greater usage. These two key parameters allow companies to obtain their time prediction models without the need to outsource the work and test the certainty of any chosen model.

The objective of this research is to evaluate the influence of the factors identified by the Johnson, Kendall and Robert’s theory that affect the strength of the detachment force necessary to remove a particle of contaminant from a surface, and the roughness of the surface in which the contaminant is present, on predicting the efficiency of removal of loose contamination. Two methods were used to reach this objective: the first method consisted of quantifying the contamination by weight and the second method of quantifying the contamination by counting alpha and gamma particles. As a result, it was determined that for particles of 5 μm, the interaction between contaminant-wipe and contaminant-surface were significant. However, for particles between 37-149 μm, the contaminant-surface interaction was the only significant interaction affecting the amount of contamination removed. The results obtained were already used at a contaminated site, confirming the prediction of contamination removed

Seeding rate in hard red spring wheat (HRSW) (Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can aid growers and eliminate the need for costly seeding rate research. Research was conducted to determine the OSR of newer HRSW cultivars (released in 2013 or later) in diverse environments. Nine cultivars with diverse genetic and phenotypic characteristics were evaluated at four seeding rates in 11 environments throughout the northern Great Plains region in 2017-2018. Results from ANOVA indicated environment and cultivar were more important than seeding rate in determining grain yield. Though there was no environment x seeding rate interaction (P=0.37), OSR varied among cultivar within each environment. Cultivar x environment interactions were further explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. Data from seeding rate trials conducted in ND and MN from 2013-2015 were also used. A novel method for characterizing cultivar for tillering capacity was developed and proposed as a source for information on tillering to be used in statistical modelling. A 10-fold repeated cross-validation of the seeding rate data was analyzed by 10 statistical learning algorithms to determine a model for predicting OSR of newer cultivars. Models were similar in prediction accuracy (P=0.10). The decision tree model was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE=1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength, tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha-1. Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars.

A model was developed to predict the effects of soil salinity on crop growth. The model makes three major assumptions: 1) Relative yield for a growing season is directly related to the ratio of actual and potential transpiration. 2) Water uptake by plants is in response to the water potential gradient between the plant roots and the surrounding soil. 3) The effects of the soil salinity on crop growth is solely due to the affect of osmotic potential in decreasing the water potential. Minor assumptions also included are concerned with the plant growth cover, plant root growth, and the separation of E and T from ET. The model's ability to predict crop growth under various irrigation amounts and frequency of application, irrigation water quality, and initial soil salinity was compared with field measured results. The model predicted reductions of crop yield as irrigation rates were decreased which agreed closely to field measurements where salinity was held constant. Increasing salinity of the irrigation water from the normal (EC=0.5 mmhos/cm) to the rate corresponding to the value estimated for the lower Colorado River in 2000 A.D. (EC=2.0 mmhos/cm), was predicted to have a very slight effect on yield for one years use. This agreed with the field measurements. It was found that the model under predicted the effects of high initial soil salinity (simulating many years of salt buildup) on yield. These results indicated that high initial soil salinity for corn had more effect on crop growth than just the osmotic effect for the field situation studied.

A model is developed to predict the effects of soil salinity on crop growth. As an outgrowth and extension of the modelling efforts of Nimah and Hanks (1973a) and Gupta (1972), this model makes these principal assumptions in order to arrive at a quantitative relationship: 1) relative yield for a growing season is directly related to the ratio of actual and potential transpiration. 2) Water uptake by plants is in response to the water potential gradient between the plant at the soil surface and the soil surrounding the plant roots. 3) the effect of salinity on crop growth is solely due to the effect of osmotic potential in decreasing the water potential gradient. In addition, minor assumptions are made regarding plant cover growth, plant root growth, and separation of E and T from ET. The model was tested to assess its accuracy and was then used to make calculations regarding the relationships of plant growth, irrigation amount and water quality, initial soil salinity, and crop type. Due to the presence of a water table at two meters in the simulations, deep rooted crops showed the best growth under most conditions. Decreases in irrigation and increases in soil salinity were detrimental to crop growth. Irrigation water quality was not effective in decreasing crop growth in one season but was shown to be a factor in long term calculations. Simulations of ten-year management schemes are shown in order to demonstrate long term effects. Finally, a method is presented to evaluate different irrigation systems and calculations are made which compare a flood irrigation system and a sprinkler system.

With the advances in the membrane technology, there is an ongoing quest to determine the best optimal configuration for an adopted treatment as well as it’s polishing to achieve cumulative sustainability for the treatment process. Henceforth, this thesis report is an evaluation to devise a membrane filtration process for investigating the possibility of treating pre-sedimented municipal wastewater with ceramic ultrafiltration, optimizing the membrane as a pre-treatment for reverse osmosis as an overall strategy for recovering wastewater. Methods and various technological trends pertaining to membrane filtration of municipal wastewater were researched and documented, Interestingly the five most influential factors governing the membrane performance are identified: 1) Back pulsing Frequency 2) VRF 3) Run Time 4) Cross-Flow Rate 5) Trans Membrane Pressure (TMP). To get a thorough and holistic overview of parametric influence design of experiment (DOE) is devised to find the influence of above-given factors on outcoming responses as COD Reduction (%), Membrane Flux and Turbidity reduction (%). 16+3 DOE factorial tests are executed at Hammarby Sjöstadsverk, Joint Research Facility of IVL Swedish Environmental Research Institute & KTH Royal Institute of Technology on pilot plant WASLA incorporating an ATECH GmBh 20kDa, Type 7/6 Ultrafiltration membrane module where Factorial experiments resulted in a maximum value of flux of 274 LMH, 88.75% reduction of COD and 99.94% reduction of Turbidity. Moreover, response values obtained from the Results of factorial experiments are fed in MODDE, generating a model using PLS Regression, The model summary presented predictivity and reproducibility trends w.r.t responses used. Furthermore, COD resulted in the worst fit followed by Turbidity, and the best fit was observed for Membrane Flux where model fit represented the ability to predict the respective parameter. Optimization tool is utilised to simulate a case scenario where the Membrane flux response is maximized to a high value of 300 LMH and correspondingly 211.885 LMH value is recorded, Furthermore factor influence is identified to be TMP> VRF> Cross Flow >BP Frequency >Runtime. Overall COD reductions are found out to be heavily influenced by the varying incoming feed therefore it is hard to analyze their interactions and predict their subsequent reduction behavior. Back pulsing overall was found out to be another non-influential factor colluding with results throughout the experimental duration with very little or no effect on the permeate water quality. / Med framstegen inom membrantekniken finns det en kontinuerlig strävan att fastställa bästa optimala konfigurationen för en antagen behandling samt att den är polerad för att uppnå kumulativ hållbarhet för behandlingsprocessen. Framgent är denna avhandlingsrapport en utvärdering för att utforma en membranfiltreringsprocess för att undersöka möjligheten att behandla försedimenterat kommunalt avloppsvatten med keramisk ultrafiltrering, optimera membranet som en förbehandling för omvänd osmos som en övergripande strategi för att återvinna avloppsvatten. Metoder och olika teknologiska trender avseende membranfiltrering av kommunalt avloppsvatten undersöktes och dokumenterades. Intressant identifieras fem mest inflytelserika faktorer som styr membranprestanda: 1) Ryggpulserande frekvens 2) VRF 3) Körtid 4) Korsflödeshastighet 5) Trans Membrantryck (TMP). För att få en grundlig och holistisk överblick över parametrisk inflytande experimentet är utformat för att hitta påverkan av ovan givna faktorer på utgående svar som COD- Reduktion (%), Membranflöde och Turbiditetsreduktion (%). 16 + 3 DOE-faktortest utfördes vid Hammarby Sjöstadsverk, Joint Research Facility för IVL Swedish Environmental Research Institute & KTH Royal Institute of Technology på pilotanläggningen WASLA med en ATECH GmBh 20kDa, typ 7/6 Ultrafiltreringsmembranmodul där faktoriella experiment resulterade i en maximalt flödesvärde på 274 LMH, 88,75% reduktion av COD och 99,94% reduktion av turbiditet. Moreover, response values obtained from the Results of factorial experiments are fed in MODDE, generating a model using PLS Regression, The model summary presented predictivity and reproducibility trends w.r.t responses used. Furthermore, COD resulted in the worst fit followed by Turbidity, and the best fit was observed for Membrane Flux where model fit represented the ability to predict the respective parameter. Optimization tool is utilised to simulate a case scenario where the Membrane flux response is maximized to a high value of 300 LMH and correspondingly 211.885 LMH value is recorded, Furthermore factor influence is identified to be TMP> VRF> Cross Flow >BP Frequency >Runtime. Overall COD reductions are found out to be heavily influenced by the varying incoming feed therefore it is hard to analyze their interactions and predict their subsequent reduction behavior. Back pulsing overall was found out to be another non-influential factor colluding with results throughout the experimental duration with very little or no effect on the permeate water quality.