{"responseHeader":{"status":0,"QTime":6,"params":{"q":"{!q.op=AND}id:\"326810\"","hl":"true","hl.simple.post":"","hl.fragsize":"5000","fq":"!embargo_tdt:[NOW TO *]","hl.fl":"ocr_t","hl.method":"unified","wt":"json","hl.simple.pre":""}},"response":{"numFound":1,"start":0,"docs":[{"conference_title_t":"GCSC (Global Change and Sustainability Center) Research Symposium","ark_t":"ark:/87278/s6613xm3","setname_s":"ir_su","subject_t":"Thermal storage; Distributed energy; Head recovery","restricted_i":0,"department_t":"Mechanical Engineering","format_medium_t":"application/pdf","creator_t":"Rahman, Aowabin","identifier_t":"sustain/id/19","date_t":"2016-02-02","mass_i":1515011812,"publisher_t":"University of Utah","description_t":"Presented at the GCSC (Global Change and Sustainability Center) Research Symposium. The goal of this research was to develop a mathematical model of a stratified thermal storage tank containing stationary fluid with hot and cold heat exchangers.The model could be used as a screening tool to allow building designers and owners to determine size and configuration of a storage tank for operation with a distributed generation system, which will enable improved energy savings and reduced greenhouse emissions.","rights_management_t":"© Aowbin Rahman, Nelson Fumo, Amanda D. Smith","title_t":"Modeling of thermal storage tank for heat recovery applications","ocr_t":"ModelingofThermalStorageTankforHeatRecovery Applications AOWABIN RAHMAN, NELSON FUMO AND AMANDA D. SMITH UNIVERSITY OF UTAH INTRODUCTION The goal of this research was to develop a mathe-matical model of a stratified thermal storage tank containing stationary fluid with hot and cold heat exchangers.The model could be used as a screen-ing tool to allow building designers and owners to determine size and configuration of a storage tank for operation with a distributed generation system, which will enable improved energy sav-ings and reduced greenhouse emissions. INTRODUCTION RESULTS 0 1 2 3 4 300 320 340 360 380 400 time (hours) Temperature (K) T(x = 0.10 m) T(x = 1.10 m) T(x = 1.90 m) Figure 1: Temperature of stored water at vertical loca-tions 0 1 2 3 300 320 340 360 380 400 time (hours) Temperature (K) Top node Bottom node Presented Model Validation Model Figure 2: Temperature profiles of stored water at top and bottom nodes - comparison with validation model 0 1 2 3 300 310 320 330 340 350 360 370 380 time (hours) Temperature (K) Q c = 15 Q c = 10 Q c = 8 Q c = 6 Q c = 4 Figure 3: Effect of cold water flow-rate on cold-water outlet temperatures 0 0.5 1 320 330 340 350 360 370 380 390 x/H Temperature (K) Q c = 4 Q c = 6 Q c = 8 Q c = 10 Q c = 15 Figure 4: Effect of cold water flow-rate on steady-state stored water temperatures REFERENCES [1] A. Rahman, A.D Smith, and N. Fumo. Simplified modeling of thermal storage tank for distributed energy heat recovery applications. In ASME 2015 Power and Energy Conversion Conference, 2015. FUTURE RESEARCH The thermal storage model can be integrated with energy modeling packages to estimate reductions in energy savings and emissions. Future work will focus on using machine learning algorithms to predict building heating loads and emissions, and subsequently investigating the benefits of in-tegrating thermal storage in a building energy system. CONTACT INFORMATION Site-Specific Energy Lab Web energysystems.mech.utah.edu Email amanda.d.smith@utah.edu CONCLUSION The simplified model with 10 nodes can predict differentials in stored energy with 98% accuracy, while reducing model complexity and computa-tional time. DESCRIPTION OF SYSTEM The thermal storage tank considered contains two heat exchangers and stationary water inside the tank as storage medium. Heat is supplied by hot water entering the heat exchanger at the top of the tank and is withdrawn by cold water in the reverse direction. Figure 5: Schematic diagram of thermal storage The tank is assumed to be completely insulated such that no heat loss through the tank wall oc-curs, and the fluids were assumed to be at a suf-ficiently high pressure such that no change in phase occurs. MATHEMATICAL MODEL Energy balance for stored water for generic node \"n\": micp dTi dt = UAh(Th;i Ti) + kAc(Ti1 Ti) x + kAc(Ti+1 Ti) x + UAc(Tc;i Ti) (1) Similarly for heat transfer fluids, the energy equations are: mh;icp;h dTh;i dt = m_ hcp;h(Th;i1 Th;i) UAh(Th;i Ti) (2) mc;icp;c dTc;i dt = m_ ccp;c(Tc;i Tc;i+1) + UAc(Ti Tc;i) (3) The heat transfer coefficients are obtained by considering turbulent flow inside the heat exchangers. 0 20 40 60 80 100 0 1 2 3 Number of nodes Relative Convergence error (%) 0 0.2 0.4 0.6 Non−dimensional computational time econv t Figure 6: Convergence error vs. Number of nodes","id":326810,"publication_type_t":"poster","parent_i":0,"type_t":"Text","thumb_s":"/70/ac/70acd11aea4e4733203a22061beae309b411abff.jpg","oldid_t":"sustain 19","metadata_cataloger_t":"Joni Clayton","format_t":"application/pdf","modified_tdt":"2016-09-30T00:00:00Z","school_or_college_t":"College of Engineering","language_t":"eng","file_s":"/bd/da/bddaddca66d34060536ad4ffddca33a3ea093738.pdf","format_extent_t":"325,045 Bytes","other_author_t":"Fumo, Nelson; Smith, Amanda D.","created_tdt":"2016-02-02T00:00:00Z","_version_":1642982458044448768}]},"highlighting":{"326810":{"ocr_t":[]}}}