16 Pages

Lecture 3

Course: AOE 4643, Fall 2011
School: University of Florida
Rating:
 
 
 
 
 

Word Count: 1116

Document Preview

Hydrological Basic Concepts AOM 4643 Principles and Issues in Environmental Hydrology Structure and Properties of Water Water is a held together by a covalent bond one side has a negative charge and the other a positive charge. The positive end of one H2O molecule attracts the negative end of another => called hydrogen bond Polar covalent bond (strong) H+ 104.5o O2- H+ hydrogen bond is weaker than a...

Register Now

Unformatted Document Excerpt

Coursehero >> Florida >> University of Florida >> AOE 4643

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Hydrological Basic Concepts AOM 4643 Principles and Issues in Environmental Hydrology Structure and Properties of Water Water is a held together by a covalent bond one side has a negative charge and the other a positive charge. The positive end of one H2O molecule attracts the negative end of another => called hydrogen bond Polar covalent bond (strong) H+ 104.5o O2- H+ hydrogen bond is weaker than a covalent bond but very important. Hydrogen bond determines most of water's unique properties Thermal Properties of Water boiling point and freezing point are higher than expected for its molecular weight (because of intermolecular attraction i.e. hydrogen bonds) water exists in solid, liquid & gas phases on earth. maximum density @ 4oC ice floats, caused by hydrogen bonds forming tetrahedra at low temp; important in determining earth's climate high specific heat capacity a large input of energy raises the temperature a relatively small amount; energy goes into breaking hydrogen bonds rather than raising the temperature Structural Properties of Water cohesive, sticks to itself high surface tension drops of water are spherical. capillarity results of combination of adhesion to solid surfaces i.e. glass (water molecules are attracted to oxygen atoms in glass) by hydrogen bonds and cohesion to itself through surface tension; important for circulation of blood in body and water in soil capillary forces are what allow moist sand to maintain vertical trench walls, whereas dry sand can only maintain a slope of 30o tiny menisci hold sand grains together through the hydrogen bonds Water as a Solvent universal solvent given enough time only a few natural substances will not dissolve in water. water dissolves substances by: forming hydrogen bonds with its molecules (polar molecules) surrounding individual ions of the substance (electrolytes) water alone cannot carry an electrical current due to the hydrogen bonds which do not allow hydrogen and oxygen atoms to move around freely of one another. Electrolytes in water can cause the solution to carry a charge. The higher the salt content the higher the electrical conductivity. Basic Hydrologic Concepts Hydrologic cycle describes the continuous circulation of water from land and sea to the atmosphere and back again. Concept is based on mass balance and is simply that water changes state and is transported in a closed system Hydrologic cycle is closed only globally, not on a watershed or continental scale. Hydrologic phenomena (precipitation, ET, infiltration, groundwater, overland, streamflow) are extremely complex and although quantifiable at lab scale, may never be fully predictable at the watershed scale. Thus we represent them in a simplified way by means of the systems concept. Definition A hydrologic system is defined as a structure (surface or subsurface) or volume (atmospheric) in space, surrounded by a boundary, that accepts water and other inputs (such as air or heat energy), operates (physical, chemical, biological) on them internally and produces them as outputs. We treat the hydrologic cycle as a system whose components are precipitation, evapotranspiration, interception, runoff, infiltration, etc.. We give up the quest to know the precise spatiotemporal water flow patterns within the system and settle instead for knowing total water storage, and spatially averaged water fluxes in and out of the control volume. Example overland flow ET(t) surface runoff groundwater discharge Q(t) The basic relations of physical hydrology this for system are derived from fundamental laws of classical physics. Particularly: Conservation of mass (m = mass of water) Conservation of energy (internal energy, kinetic energy and potential energy of the fluid) Conservation of Mass The most useful principal in hydrologic analysis and is required in almost all problems. Stated mathematically: dS + Q(t ) - I (t ) dt For our watershed problem: dS = P (t ) - Q(t ) - G (t ) - ET (t ) dt If have a steady flow problem, inflows=outflows: P (t ) = Q(t ) + G (t ) + ET dS =0 dt Conservation of Energy Second fundamental physical law utilized in physical hydrology is the conservation of energy. Total energy =internal energy + kinetic energy +potential energy Total Energy E = Eu + 1/2 mV2 + mgz Energy per unit mass e = eu + 1/2 V2 + gz Internal Energy Internal energy is the sum of sensible heat and latent heat. Sensible heat is that part of the internal energy that is proportional to the substance's temperature, i.e. deu = CpdT Latent heat - Amount of heat exchange required for inducing a phase change per gram of substance without a change in temperature. Usually a function of temperature. Latent Heat Values for Water liquid water to vapor Le = latent heat of evaporation = 597.3 - 0.57 T cal/g (2.5x106 - 2370T J/kg) This is heat absorbed (by vaporized water from surroundings) to break H bonds so evaporation can take place evaporation always accompanied by transfer of heat out of water body or surroundings to vapor latent heat transfer vapor to liquid water Lc = latent heat of condensation = -597.3 + 0.57 T cal/g (-2.5x106 + 2370T J/kg) This is heat released to surroundings when H bonds formed during condensation Latent Heat Values for Water ice to liquid Lm = latent heat of melting = 79.7 cal/g ( 0.33 * 106 J/kg) This is energy required to disrupt tetrahedral molecular structure. liquid to ice Lf = latent heat of fusion = -79.7 cal/g ( 0.33 * 106 J/kg) This is energy released as tetrahedral molecular structure is formed. ice to vapor Ls = latent heat of sublimation = 677 - 0.07 T cal/g This is energy needed to a) disrupt molecular structure then b) break H bonds Latent Heat Values for Water At typical atmospheric temp. and pressure on earth, energy required to sublimate ice to vapor generally greater than that required to melt ice through evaporation. Therefore, usually water goes through liquid phase first. Temp (oC) -10 0 10 Ls = 677 - 0.07 T (cal/g) 677.7 677 676.6 Lm + Le = 79.7 + 597.3 - 0.57 T (cal/g) 682.7 677 671.3 Latent Heat Transfer 1952 J/kg K latent heat of evaporation 539 cal/g at 100 C 2.3*106 J/kg at 100 C Internal energy latent heat of fusion = 80 cal/g = 0.33*106 J/kg specific heat Cp = 1 cal/gC =4183 J/kgK ice 2106 J/kg K 0 liquid water 100 water vapor Temp C Jumps in curve latent heat transfer to water Slope in curve sensible heat transfer to water Examples of Use of Latent Heat Properties In the SW use the latent heat of evaporation for airconditioning houses water and air is run into evaporative cooler on roofs of houses -- as water evaporates absorbs heat from air. Cooled air is returned to house. Irrigation of plants to protect from freezing. when irrigation water freezes it releases heat to the environment which increases air temperature slightly and protects plant. Latent heat transfer is the dominant cause of internal energy change for water in most hydrologic applications temperatures usually only change a few degrees C so sensible heat transfer is small.
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

University of Florida - AOE - 4643
Earth's Energy Balance The hydrologic cycle is fueled by energy from the sun. Planetary geometry creates areas of energy surpluses and deficits which drive all active meteorological processes. Earth and the atmosphere are the media through which the ener
University of Florida - AOE - 4643
Composition/Characterstics of the Atmosphere 80% Nitrogen, 20% Oxygen- treated as a perfect gas Lower atmosphere extends up to 50 km. Lower atmosphere most active part of atmosphere where most of the mass and energy transfer (leading to earth's weather p
University of Florida - AOE - 4643
Formation of Precipitation Requires Cooling of air to dew point temperature (requires a lifting mechanism) Condensation of water vapor onto nuclei (dust, ions) to form droplets Growth of droplets so that a) terminal velocity > updraft velocity b) suffic
University of Florida - AOE - 4643
Estimation of Areal Precipitation from point measurements Most often interested in quantifying rainfall over an entire watershed. Has to be inferred from some sort of weighted average of available point measurements P(xi)P = i P ( xi )i =1 N Several m
University of Florida - AOE - 4643
Evaporation and Transpiration Evaporation- change of water from liquid to vapor phase Potential Evaporation - climatically controlled evaporation from a surface when the supply water to the surface is unlimited Transpiration - evaporation occurring from
University of Florida - AOE - 4643
Subsurface Water unit volume of subsurface consists of soil/rock, and pores which may be filled with water and/or air total porosity= volume voids/total volume water content=volume water/total volume saturation=volume water/volume voids degree of saturat
University of Florida - CWR - 6536
Goal of Stochastic Hydrology Develop analytical tools to systematically deal with uncertainty and spatial variability in hydrologic systems Examples of variable driving parameters and processes include rainfall rates, soil properties, aquifer properties
University of Florida - CWR - 6536
Review of Probability TheoryCWR 6536 Stochastic Subsurface HydrololgyRandom Variable (r.v.) A variable (x) which takes on values at random, and may be thought of as a function of the outcomes of some random experiment. The r.v. maps sample space of exp
University of Florida - CWR - 6536
Review of Random Process TheoryCWR 6536 Stochastic Subsurface HydrologyRandom Process A random process may be thought of as a collection or ensemble of random variables which change through time, any realization of which might be observed on any trial
University of Florida - CWR - 6536
Estimation of ensemble pdfs, cdfs, and moments from limited sampling of random fieldsStochastic Subsurface Hydrology CWR 6536Estimation of ensemble moments from field data Assume that random field is constructed of the following components: If only one
University of Florida - CWR - 6536
Properties of Covariance and Variogram FunctionsCWR 6536 Stochastic Subsurface HydrologyThe Covariance Function The covariance function must be positive definite which requires that: positive definiteness guarantees that all linear combinations of the
University of Florida - CWR - 6536
CWR 6536 Stochastic Subsurface Hydrology Optimal Estimation of Hydrologic Parameters using KrigingPurpose of KrigingTo estimate regional distribution of a spatially variable parameter To estimate accuracy of regional distribution Need scattered point me
University of Florida - CWR - 6536
CWR 6536 Stochastic Subsurface Hydrology Optimal Estimation of Hydrologic Parameters using KrigingPurpose of KrigingTo estimate regional distribution of a spatially variable parameter To estimate accuracy of regional distribution Need scattered point me
University of Florida - CWR - 6536
CWR 6536 Stochastic Subsurface Hydrology Optimal Estimation of Hydrologic Parameters using KrigingTypes of KrigingSimple kriging is optimal estimation of a random field, e.g. T(x), with a known mean, m(x), and a known covariance PTT(x,x'). Ordinary krig
University of Florida - CWR - 6536
CWR 6536 Stochastic Subsurface Hydrology Optimal Estimation of Hydrologic ParametersBlock KrigingKriging systems discussed to date use point measurements to estimate point values of the random field at unmeasured locations. called point, or punctual, kr
University of Florida - CWR - 6536
Stochastic ModelingCWR 6536 Stochastic Subsurface HydrologySource of uncertainty in model predictions include Input parameters boundary conditions initial conditions model error measurement errorStochastic Models Characterize pdfs/moments of input pa
University of Florida - CWR - 6536
Monte Carlo SimulationCWR 6536 Stochastic Subsurface HydrologySteps in Monte Carlo Simulation Create input sample space with known distribution, e.g. ensemble of all possible combinations of v, D, , m values Run each realization of v, D, , m values thr
University of Florida - CWR - 6536
Stochastic Modeling Approximate Analytical SolutionsCWR 6536 Stochastic Subsurface HydrologyStochastic model predictions can be obtained in several ways: Exact analytical solutions Monte Carlo techniques Approximate analytical solutions Approximate num
University of Florida - CWR - 6536
Stochastic Analysis of Groundwater Flow ProcessesCWR 6536 Stochastic Subsurface HydrologyMethods for deriving moments for groundwater flow processes Exact analytic solutions possible only if analytical solution to governing equation available. Not very
University of Florida - CWR - 6536
Approximate Analytical/Numerical Solutions to the Groundwater Flow ProblemCWR 6536 Stochastic Subsurface Hydrology3-D Saturated Groundwater Flow K K K 0= + + x x y y z z K(x,y,z) random hydraulic conductivity field (x,y,z) random hydraulic head fiel
University of Florida - CWR - 6536
Approximate Analytical Solutions to the Groundwater Flow ProblemCWR 6536 Stochastic Subsurface Hydrology3-D Steady Saturated Groundwater Flow K K K 0= + + x x y y z z K(x,y,z) random hydraulic conductivity field (x,y,z) random hydraulic head field wa
University of Florida - CWR - 6536
Approximate Analytical Solutions to the Groundwater Flow ProblemCWR 6536 Stochastic Subsurface HydrologySystem of Approximate Moment Eqns to order 20 = 20 ( x) + F ( x) 0 ( x) 0 = x '2 Pf1 ( x, x' ) + x ' F ( x' ) x ' Pf1 ( x, x' ) + x ' Pff ( x, x' )
University of Florida - CWR - 6536
Stochastic Analysis of Subsurface TransportCWR 6536 Stochastic Subsurface HydrologySubsurface Solute Transportc c c + vi = Dij t xi xi xj Assumes constant porosity, non-decaying, nonsorbing, dilute solute Dij molecular diffusion and hydrodynamic disp
University of Florida - EEL - 4744
EEL 4744C Dr. Gugel Last Name_ First Name _ Fall 2011 Exam #2 UFID#_ Open book/open notes, 90-minute exam. No electronic devices are required or permitted. All work and solutions are to be written on the exam where appropriate.Point System (for instructo
University of Florida - EEL - 4744
University of FloridaDepartment of Electrical & Computer Engineering Page 1/15EEL 4744-Spring 2011 31 March 2011Dr. Eric M. Schwartz15-Apr-11 1:29 PMExam 2Last Name, ,First NameInstructions: Turn off cell phones, beepers and other noise making de
University of Florida - EEL - 4744
27-Mar-12-1:22 PMExam 2 InfoEEL 4744EEL4744 Second Exam 120 minutes Questions require understanding Questions deal with "real stuff" Relevant topics include: Anything from Exam 1 possible Labs 1-6; HW 1-4 Class notes through lecture 15 Keypad, LCD Pa
University of Florida - EEL - 4744
University of FloridaElectrical & Computer EngineeringEEL 4744Dr. Eric M Schwartz1-Mar-12Page 1/1Homework 4Revision 0Instructions Note: Late HW is not accepted! HW is due at the beginning of class. Put your "last name, first name" and the HW numbe
University of Florida - EEL - 4744
University of Florida Electrical & Computer Engineering Dept. Page 1/5EEL 4744 Spring 2012Revision 2Dr. Eric M. Schwartz Brandon Cerge & Eric Jeffers, TAs16-Mar-122Lab 5: Interrupts, Serial Communication, External MemoryOBJECTIVESIn this lab you wi
University of Florida - EEL - 4744
University of Florida Electrical and Computer Engineering Dept. Page 1/4EEL 4744 Spring 2012Revision 3Dr. Eric M. Schwartz Michael Carroll, TA 27 March 2012Lab 6: LCD and A/D: Digital VoltmeterOBJECTIVESIn this lab you will learn how to control an L
University of Florida - EEL - 4744
Crystalfontz America, IncorporatedCHARACTER LCD MODULE SPECIFICATIONSCrystalfontz Model Number Hardware Version Data Sheet Version Product PagesCFAH1602Z-YYH-ET Revision A Revision 1.0, November 2008 www.crystalfontz.com/product/CFAH1602Z-YYH-ET.htmlC
University of Florida - EEL - 4930
Introduction to Reconfigurable Computing1Reconfigurable Logic: Best of Both WorldsReconfigurable LogicMicroprocessor Advantages ASIC Disadvantages High flexibility Low development cost and fast time-to-market Low part cost May not meet design cons
University of Florida - EEL - 4930
Generic RC ArchitectureHost CPU sLocal Interconnect(s)MemNode ArchitectureNIC Node InterconnectRC DevicesCoupling in Reconfigurable SystemsEx: xtremeDataEx: Nallatech, GiDELDifferent levels of coupling in a reconfigurable system. (Reconfigurable
University of Florida - EEL - 4930
Reconfigurable Supercomputing with Scalable Systolic Arrays and InStream Control for Wavefront Genomics ProcessingSAAHPC'10C. Pascoe (speaker), A. Lawande H. Lam, A. GeorgeNSF Center for High-Performance Reconfigurable Computing (CHREC), University of
University of Florida - EEL - 4930
INTRODUCTIONImage processing is one of the main applications to tap the maximum parallelism out of FPGA as compared to a microprocessor. Thus in order to test the speedup that can be achieved out of an FPGA as compared to microprocessor, we plan to imple
University of Florida - EEL - 4930
ASMexample.vhdLIBRARY ieee ; USE ieee.std_logic_1164.all ; ENTITY ASMexample IS PORT ( Clock, Resetn, InBit, BufFull state CountEN, RegLD, OutFlag END ASMexample ; : IN STD_LOGIC ; : BUFFER STD_LOGIC_VECTOR (1 DOWNTO 0); : OUT STD_LOGIC ) ;ARCHITECTURE
University of Florida - EEL - 4930
Reconfigurable Computing: A Survey of Systems and SoftwareKATHERINE COMPTONNorthwestern UniversityAND SCOTT HAUCKUniversity of WashingtonDue to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a
University of Florida - EEL - 4930
DIMEtalk 3.1 Reference GuideNT108-0305 - Issue 7Contacting Nallatech: Support: WWW: Go to www.nallatech.com and click `support'. Email: support@nallatech.com Phone/Fax Europe and Asia-Pacific: Phone: +44 (0)1236 789500 WWW: www.nallatech.com North Ameri
University of Florida - EEL - 4930
DIMEtalk 3.1 User GuideNT107-0305 - Issue 3Contacting Nallatech: Support: WWW: Go to www.nallatech.com and click `support'. Email: support@nallatech.com Phone/Fax Europe and Asia-Pacific: Phone: +44 (0)1236 789500 WWW: www.nallatech.com North America Ph
University of Florida - EEL - 4930
University of Florida - EEL - 4930
Lab 2(a): VHDL Finite State Machine and DatapathEEL 4930/5934 Spring 2012 Objective:The objective of Lab 2(a) is to design and simulate a circuit in VHDL that calculates Fibonacci numbers.Fibonacci Calculator Introduction:For this part of the lab, you
University of Florida - EEL - 4930
Lab 2(b): Introduction to DIMEtalk and Nallatech PlatformEEL 4930/5934 Spring 2012 Objective: In this part of Lab 2, you will be learning the basics of the Nallatech board and the DIMETalk design environment.2(b) Part 1 - Installation/Tutorial1. Follow
University of Florida - EEL - 4930
Lab 3: Fibonacci Calculator using Nallatech PlatformEEL 4930/5934 Spring 2012Objectives: In this lab, you will be implementing a Fibonacci calculator (similar to the one you did in a previous lab) on the Nallatech board and interface it with a software
University of Florida - EEL - 4930
Lab 4: Simple Pipelined DatapathEEL 4930/5934 Spring 2012Introduction:In this lab, you will be implementing a circuit with a pipelined datapath. The circuit will utilize one blockRAM (BRAM) to continually feed four 8-bit inputs into the datapath every
University of Florida - EEL - 4930
Lab 5: Clock Domain CrossingEEL 4930/5934 Spring 2012Introduction:In this lab, you will learn how to properly communicate across clock domains. If not handled correctly, signals that cross clock domains can become metastable, which if propagated throug
University of Florida - EEL - 4930
EEL 4930/5934 Reconfigurable Computing Midterm Exam Spring Semester 201018 pts.Name _1. Systolic Architecture (a) Given the following algorithm in pseudo-code, draw a datapath that is fully-pipelined and with the maximum loop-unrolling. for (i=1; i < 1
University of Florida - EEL - 4930
University of Florida - EEL - 4930
EEL 4930/5934 Reconfigurable Computing Midterm Exam Spring Semester 2011 1. Scalable Systolic Array paper The figure on the right is reproduced 14 pts. from the Scalable Systolic Array paper. It shows a scoring matrix for the Needleman-Wunsch algorithm in
University of Florida - EEL - 4930
University of Florida - EEL - 4930
University of Florida - EEL - 4930
Numerical Representation and AnalysisFor RC App DesignsNumerical RepresentationsOrder of preference1.Integer Fixedpoint FP Floatingpoint (singleprecision) FP Floatingpoint (doubleprecision)2.3.4.Integer representationAdvantages: o.Very efficie
University of Florida - EEL - 4930
EEL 4930/5935: Reconfigurable Computing Term ProjectProject team: 2 students per team; exceptions must be approved by me Due dates: Project proposal: Thursday 3/15/2012 on eLearning Project progress review: Week of 4/2 4/6; PowerPoint presentation on the
University of Florida - EEL - 4930
Project requirements and grading PowerPoint presentation and Demonstration (30 mins) Report for each team (up to 10 pages plus appendices, references, etc.) Grading: 25% of final course grade I expect each member of the group to contribute equally to th
University of Florida - EEL - 4930
Project Grading Project title:Team members:Grading factors: Complexity of the projectDesign: Amount of wide parallelism? Amount of pipeline parallelism? Elegant? Innovative?Demonstration, does it work? Simulation?On delta?Speedup Computation only Da
University of Florida - EEL - 4930
Reconfigurable Supercomputing with Scalable Systolic Arrays and In-Stream Control for Wavefront Genomics ProcessingC. Pascoe, A. Lawande, H. Lam, A. GeorgeNSF Center for High-Performance Reconfigurable Computing (CHREC) University of FloridaY. Sun, W.
Pittsburgh - PSY - 160
Pittsburgh - PSY - 160
Pittsburgh - PSY - 160
Pittsburgh - PSY - 160