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Research on Quantitative Relationship between Fire Separations and Ampacity in Underground pipe gallery Electric cabin
Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)5557–5562 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Research on Quantitative Relationship between Fire Separations and Research onThe Quantitative Relationship between Fire and 15th International Symposium on District Heating andSeparations Cooling Ampacity in Underground pipe gallery Electric cabin Ampacity in Underground pipe gallery Electric cabin Assessing theLIU feasibility usingFang theb,heat demand-outdoor a, Jiaxu WANGaa, Xuefeng *, Shu LIaaof ,Guanyu Wenjing Chenbb, Shiming Dengbb a, b Jiaxu WANG , Xuefeng LIU *, Shu LI ,Guanyu Fang , Wenjing , Shiming Deng temperature function for a long-term district heatChen demand forecast South China University of Technology, School of Electric Power , Wushan RD., Tianhe District, Guangzhou, 510641, P.R. China a
ba The
Hong Kong Polytechnic University,School Department of Building Hom, Kowloon, 510641, Hong Kong, South China University of Technology, of Electric PowerServices , WushanEngineering, RD., TianheHung District, Guangzhou, P.R.999077 China a,b,c a b c c The Hong Kong Polytechnic University, aDepartment of Building Services Engineering, Hung Hom, Kowloon, Hong Kong, 999077
b
a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
In recent years, underground pipe gallery has become more and more important. Its Special working environment In recent years, underground pipe become more and more important. Special working environment makes temperature monitoring andgallery controlhas increasingly important. paper applies Its heat-transfer differential equation makes temperature monitoring and criterion control increasingly important.distribution. paper applies heat-transfer to the cable to obtain the similarity of cable temperature After calculatingdifferential the electricequation cabin toAbstract the cable to obtain fire the separations, similarity criterion of cable temperature distribution. After calculating the electric cabin model with different it is found that the temperature distribution of the same ampacity in different model with different fire separations, it is found that the temperature distribution ofampacity the same in ampacity different fire separations conforms to certain rules. The temperature distribution of different the sameinfire District heating networks commonly addressed in the literature as one of different the effective solutions for fire decreasing fire separations to rules. The5%, temperature distribution of ampacity in theformulas same separation is alsoconforms regular: are thecertain ampacity rises. temperature rises by 7%. As most a result, the fitting of the the greenhouse emissions the building sector. These systems require which are returned through the heat separation isgas also regular:from ampacity rises. temperature riseslocation byhigh 7%.investments Asthe a result, the fitting formulas of the current carrying capacity, the length of the fire5%, separation and the of highest temperature were also sales. Due to the changedthe climate building and renovation policies, heathighest demandtemperature in the future could current carrying capacity, lengthconditions of the fireand separation the location of the were alsodecrease, proposed. prolonging the investment return period. proposed.
main scopeLtd. of this to assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2018 Elsevier All paper rights isreserved. ©forecast. 2019 Elsevier The Authors. Published byresponsibility Elsevier The district ofrights Alvalade, locatedLtd. in Lisbon (Portugal),committee was used ofas the a case The district is consisted of 665 © 2018 Ltd. All reserved. Selection and peer-review under of the scientific 10th study. International Conference on Applied This is an open access article under the CC period BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) buildings that vary in both construction and typology. Three weather scenarios (low, medium,Conference high) and on three district Selection and peer-review under responsibility of the scientific committee of the 10th International Applied Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – estimate The 10th the International Conference on Applied Energy. renovation scenarios were developed (shallow, intermediate, deep). To error, obtained heat demand values were Energy (ICAE2018). comparedUnderground with resultspipe from a dynamic heat demanddimensionless model, previously andcable validated by the authors. Keywords: gallery; similarity criterion; number;developed fire separation; ampacity The results showed that only weather change is considered, the margin of errorcable could be acceptable for some applications Keywords: Underground pipewhen gallery; similarity criterion; dimensionless number; fire separation; ampacity (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Nomenclature decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Nomenclature scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Trenovation temperature coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and T∞ temperature Inlet air temperature improve the accuracy of heat demand estimations. T∞ Inlet air temperature © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +86 13580456068.
[email protected] address:author. * E-mail Corresponding Tel.: +86 13580456068. Keywords: Heat demand; Forecast; Climate change [email protected] E-mail address: 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.587
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5558 2
r
λ
θ
Θ
Point to cable core axis distance thermal conductivity heat source density cable core dimensionless temperature(θ=T/T∞) cable insulator(Θ=(T-T∞)/T∞)
1. Introduction Underground pipe gallery can effectively solve the problems of land resources lackness, unreasonable pipelines laying etc. It can efficiently use urban underground space to make the city beautiful. Therefore, the construction of an underground pipe gallery will become more common in the future. The large construction volume of underground pipe projects makes it difficult to predict operating conditions in advance and lacks technical standards. The scale model that can be verified by experiments has become an effective way to simulate the temperature distribution of the electric cabin and to study the heat transfer characteristics of the electric cabin cable[1-3]. At present, the finite element simulation software is used at domestic and foreign to calculate the cable temperature distribution[2-5]. For the experimental calculation of cable core temperature, predicted the ampacity of power cables by measuring the sheath temperature of cable insulation is Acceptable[3-5]. Several studies have also been conducted on cable temperature in different environments, [6]studied the difference in the temperature of power cables under different environments such as the cable spacing and the cable layer. Further, the effect of pitch and cable trench depth on cable temperature was also studied[7-10]. [6]. In some articles, the effects of ambient conditions such as cyclical changes in the surface temperature and the continuous operation of the cable over a period of time are taken into consideration[11-13]. In this paper, the temperature field is simplified to a one-dimensional model, and the Dimensionless number of the cable temperature in the electric cabin is proposed from the differential equation of heat transfer. By numerically efficient simulation tool, the relationship between fire separation and ampacity was studied. The law of cable temperature distribution and the fitting formula were obtained through the experiment in order to make the future more convenient to study the heat transfer of the cable. 2. Physical model of temperature field in electric cabin 2.1. Cable core temperature field model Due to the long cable span, less bending, and large shaft diameter ratio, the experiment ignored the radial heat conduction of the cable core. To make equation Dimensionless, Defining θ=T/T∞ . Therefore, one-dimensional cable core temperature field function can be obtained based on the Fourier heat conduction equation:
f ( x, rr1 / 21T )
(1)
Therefore r 21 / 21T is a Dimensionless number. 2.2. Cable insulation temperature field model Convective heat transfer is the mainly. boundary condition of insulation. Defining T Tw ; T Tw / T to make equation Dimensionless:
r r r hr f ; 1 2 ; 2 r 2 22T 2
(2)
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5559 3
It can be seen from the above formula that r1r2 / 22T and hr2 / 2 are Dimensionless number which determine the temperature field of cable core. 3. Method and procedure 3.1. model In order to verify the reliability of similar theory, this paper sets up two models: model1, model2. The parameter provided by the relevant units is shown in the table1: Table 1. Cable parameters Parameters
Value
Parameters
Value
Electric cabin cross-sectional area (m2)
9
Cable core resistivity
0.00000002
Cable diameter (m)
0.25
Thermal conductivity of air (w/(m·k))
0.0243
Voltage (kV)
220
Thermal conductivity of wall (w/(m·k))
1.74
Ampacity (A)
1900
1
Number of cable cores
1
Cable core cross-sectional area (m2)
0.0075
Thermal conductivity of solid (w/(m·k)) Thermal conductivity of insulator (w/(m ·k)) Thermal conductivity of cable core (w/(m ·k))
0.3 401
The calculation formula for the heat value per meter of a single cable core provided by the relevant unit is Q I 2 / S ,( is cable core resistivity; S is cable core cross-sectional area). So the real cable heat value per meter is 346.6w/m. Model 2 is a laboratory test bench model, which uses electric heaters to simulate heat source and lagging to simulate insulator. According to Dimensionless number r1r2 / 22T , heat value per meter of model1 is 61.96w/m and model2 is 7.8w/m. The model parameters are shown in the table2: Table 2. Model parameters Model Parameters
Value
Insulator Inner radius of model1 (m)
0.0097
Insulator Outer radius of model1 (m)
0.025
Heat value of model1 (w/m)
61.96
Insulator Inner radius of model2 (m)
0.0065
Insulator Outer radius of model2 (m)
0.015
Heat value of model2 (w/m)
7.8
2.2 Sketch of model Front view, and side view of model is shown in Figure1. 7 3
4
5
12cm
12cm
60cm 60cm
Soild
NO.1
NO.2
NO.3
NO.4
NO.5
NO.6
NO.7
NO.8
NO.9
NO.10
NO.11
NO.12
NO.13
NO.14
NO.15
NO.16
1
Soild 2
6cm
6
20cm
5 40cm
T
T
T
T
wall
Front view 500mm
8
60cm
Side view
5 5
outlet
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5560 4
Figure. 1. Front view and side view of model 1—insulation；2—cable core；3—fan；4—wind duct；5—anemometer；6—differential pressure pickup；7—computer； 8—thermocouple
The numbers in front view are the serial numbers of the cables. The cables 1, 5, 9 and 13 are defined as the external cables. The 2, 6, 10, 14 cables are inner row cables. 5. The relationship of fire separation and cable load During the construction of the electric cabin, fire separations with vents need to be built. In this paper, several simulation experiments were conducted to explore the relationship between the ampacity and the length of the fire separations. The length of fire separations in the electric cabin for the scale models in the experiment were: 12m, 18m, 24m, 30m, 36m. The corresponding is 60m, 90m, 120m, 150m, 180m. As shown in the previous table, the full load condition has a current carrying capacity of 1900A, a variable load of 90%, and 80% of the ampacity, that is, 1710A and 1520A. Due to limited space, only the calculation results at 12m, 18m, and 24m are shown. 5.1. Temperature distribution of external cables in different fire separation at full load 84 84
fire separation of 12m
84
76
72
NO.1 NO.5 NO.9 NO.13
68
80 78 76
2
4
6
8
NO.1 NO.5 NO.9 NO.13
74 72 70
0
10
12
temperature (℃)
temperature (℃)
temperature (℃)
fire separation of 24m
fire separation of 18m
82
80
14
Distance to the entrance (m)
0
2
4
6
8
10
80
76
NO.1 NO.5 NO.9 NO.13
72 12
14
16
18
20
0
Distance to the entrance (m)
5
10
15
20
25
Distance to the entrance (m)
Figure. 2. Temperature distribution of full-load cables at different fire separations
In the first 6 meters, the temperature of each cable in the electric cabin is relatively close, but the temperature difference becomes apparent after 6m. The temperature of No. 1 cable is the highest, and the temperature of No. 13 cable is the lowest. As the distance increases, the temperature of the No. 5 cable will gradually increase and exceed that of the No. 1 cable. At the same time, the temperature is close to the limit temperature of 90° C, therefore the No. 1 cable temperature is still the highest. 5.2. Temperature comparison and function fitting of No.1 cable in different fire separation under different loads It can be seen that the maximum temperature of No. 1 cable and its position is a logarithmic function when the fire prevention interval is gradually extended. After the fire prevention interval is increased, the computer simulation closely matches the calculated results of the existing length. The fitting formulas under different loads are as follows: Table 3. Functional expressions under different loads load 100%
Functional expression
x / t1
y A0e
A0
t1
y0
y0
-15.62716
15.15619
89.02146
15.64293
77.85372
90%
y A0e
x / t1
-12.60645
80%
y0
y A0e
x / t1
y0
-9.63776，
5 Prediction and verification of fitting formulas
14.2057
67.3201
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5561 5
Parameters of fitting formulas obtained in Figure 3 are constantly changing under different load. Organize the parameters into a graph: 95 90 85
parameter value
80 75 70 65 15
A t y
10
5
80
85
90
95
100
Load factor (%)
Figure. 3. Fitting formula parameters and load rate diagram
It can be seen that A1, t1, y0 are distributed linearly with the load rate, and they satisfy: ; t1 0.1425x 2.8057 ; y0 1.085x 19.4799 . Then A1, t1, and y0 at 85% load are: A0 0.29947 x 14.31984 11.13511; 14.8192; 72.7454. and A1, t1, y0 at 95% load are: -14.12981; 16.3432; 83.5951. The temperature curve of No. 1 cable under different fireproof intervals at 85% and 95% load and the function fitting curves are shown in the figure 4: 95%load
72.0
80
66
6m NO.1 12m NO.1 18m NO.1 24m NO.1 30m NO.1 36m NO.1
64 62 60 0
5
10
15
20
25
30
35
70.5
temperture (℃)
68
temperture (℃)
temperture (℃)
70
76
6m NO.1 12m NO.1 18m NO.1 24m NO.1 30m NO.1 36m NO.1
72
68
40
Distance from the entrance (m)
0
5
10
15
20
25
30
35
Model Equation
ExpDec1 y = A1*exp(-x/t1) + y0 温度
Plot
82.5
ExpDec1 y = A1*exp(-x/t1) + y0 temperture
y0
83.68371
-12.11661
A1
-14.28242
13.00518
t1
16.41633
Reduced Chi-Sqr
0.09168
Reduced Chi-Sqr
0.08146
R-Square(COD)
0.99287
R-Square(COD)
0.99548
Adj. R-Square
0.98811
Adj. R-Square
0.99247
69.0 67.5 66.0
63.0
Equation Plot
72.20588
A1 t1
Maximum temperature point Fitting function curve
64.5
40
Model
85%load
y0
temperture (℃)
84
85%load
72
80.0
77.5
75.0
Maximum temperature point Fitting function curve
72.5
5
10
15
20
25
30
35
5
10
15
20
25
30
35
Distance to the entrance (m)
Distance to the entrance (m)
Distance from the entrance (m)
95%load
Figure. 4. Maximum temperature point for 85% and 95% load cables at different fire separation and Fitting functions
From fig.5, it can be seen that A1, t1, and y0 at 85% load are: −111.661242; 14.43356; 72.45786, the A1, t1, and y0 at 95% load are −14.19997; 15.97402; 83.51166, respectively. The specific results obtained and numerical predictions can be derived from the table 5: Table 4. Calculation and verification of fitting formula parameters Parameters A1 t1 y0
Predictive value at 85% load -11.13511 14.9182 72.7454
Function fitting value at 85% load -11.61242 14.43356 72.45786
Percent difference 4.29% 3.25% 0.39%
Predictive value at 95% load -14.12981 16.3432 83.5951
Function fitting value at 95% load -14.19997 15.9740 83.51166
Percent difference 0.49% 2.31% 0.1%
The difference between the theoretical formula's fitting formula parameters and the simulation calculation is less than 5%, which is reliable. The relationship between the location of the highest temperature point of the different load cables and the length of the fire separation can be expressed by the following formula: w x /(0.1425 1900 ) 2.8057)
y (0.29947w 14.31984) e
(1.085w 19.4799)
Where y is temperature of cable core; x is measuring point to air inlet distance; w is cable ampacity. 6. Conclusions
(3)
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5562 6
When changing the cable ampacity, the cable temperature moves up and down compared to the standard operating conditions, verifying the similarity theory obtained above. In the same load conditions, the maximum temperature of the cable has a significant relationship with the fire separation, which is approximately a logarithmic curve. The relationship between the location of the highest temperature point of the different load cables and the length of the fire separation are obtained.
7. Discussion The experimental verification will be carried out in the follow-up work. The construction method of the experimental platform has been given in Figure 1, and it has been completed. The effect of localized intense turbulence at the fire-fighting interval on the air inlet and outlet and the quantified relationship will be demonstrated in future work, and the impact of radiation will also be taken into consideration. Acknowledgment This study is supported by the National Natural Science Foundation of China (Grant No. 51778234), the Natural Science Foundation of Guangdong Province (Grant No. 2015A030310303), Guangdong Provincial Science, Technology Project (Grant No. 2017A020216024) and Special Funds for Basic Scientific Research Business Fees for Central Universities (Grant No. B6150170) and Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization Reference [1] Mingxiang Wu, Xianbo Deng, Songhua Liu. Calculation of Temperature Field Distribution and Current Carrying Capacity of Cable in Typical Laying Mode[J]. Hubei Electric Power, 2012, 36(6):37-39. [2] Adefarati T, Bansal R C. Integration of renewable distributed generators into the distribution system: a review[J]. Iet Renewable Power Generation, 2016, 10(7):873-884. [3] Xiaoliang Zhuang. Distribution Cable Operation Monitoring and Current Carrying Capacity Prediction Based on Fiber Temperature Measurement Based on Fiber Temperature Measurement [D]. South China University of Technology,2015. [4] Boukrouche F, Moreau C, Pelle J, et al. Mock-up Study of the Effect of Wall distance on the Thermal Rating of Power Cables in Ventilated Tunnels[J]. IEEE Transactions on Power Delivery, 2016, PP(99):1-1. [5] Yongming Yang, Peng Cheng, Jun Chen. Research on Cable Heat Dissipation Considering Effect of Air Flow Field and Its Influence Factors and Economic Analysis [J]. Electric Power Automation Equipment, 2013, 33(1):50-54. [6] Boukrouche F, Moreau C, Pelle J, et al. Mock-up Study of the Effect of Wall distance on the Thermal Rating of Power Cables in Ventilated Tunnels[J]. IEEE Transactions on Power Delivery, 2016, PP(99):1-1. [7] Wędzik A, Siewierski T, Szypowski M. A new method for simultaneous optimizing of wind farm’s network layout and cable crosssections by MILP optimization[J]. Applied Energy, 2016, 182:525-538. [8] Cheung H, Wang S, Zhuang C, et al. A Simplified Power Consumption Model of Information Technology (IT) Equipment in Data Centers for Energy System Real-time Dynamic Simulation[J]. Applied Energy, 2018, 222. [9] Baldinelli G, Bianchi F. Windows thermal resistance: Infrared thermography aided comparative analysis among finite volumes simulations and experimental methods[J]. Applied Energy, 2014, 136(136):250-258. [10] Lee H, Sharp J, Stokes D, et al. Modeling and analysis of the effect of thermal losses on thermoelectric generator performance using effective properties[J]. Applied Energy, 2018, 211:987-996. [11] Vrana T K, Mo O. Optimal Operation Voltage for Maximal Power Transfer Capability on Very Long HVAC Cables ☆[J]. Energy Procedia, 2016, 94:399-408. [12] Wan I W M N, Royapoor M, Wang Y, et al. Office building cooling load reduction using thermal analysis method – A case study[J]. Applied Energy, 2017, 185:1574-1584. [13] Reddy B S, Chatterjee D. Analysis of High Temperature Low Sag Conductors Used for High Voltage Transmission ☆[J]. Energy Procedia, 2016, 90:179-184.
ScienceDirect ScienceDirect
Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)5557–5562 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Research on Quantitative Relationship between Fire Separations and Research onThe Quantitative Relationship between Fire and 15th International Symposium on District Heating andSeparations Cooling Ampacity in Underground pipe gallery Electric cabin Ampacity in Underground pipe gallery Electric cabin Assessing theLIU feasibility usingFang theb,heat demand-outdoor a, Jiaxu WANGaa, Xuefeng *, Shu LIaaof ,Guanyu Wenjing Chenbb, Shiming Dengbb a, b Jiaxu WANG , Xuefeng LIU *, Shu LI ,Guanyu Fang , Wenjing , Shiming Deng temperature function for a long-term district heatChen demand forecast South China University of Technology, School of Electric Power , Wushan RD., Tianhe District, Guangzhou, 510641, P.R. China a
ba The
Hong Kong Polytechnic University,School Department of Building Hom, Kowloon, 510641, Hong Kong, South China University of Technology, of Electric PowerServices , WushanEngineering, RD., TianheHung District, Guangzhou, P.R.999077 China a,b,c a b c c The Hong Kong Polytechnic University, aDepartment of Building Services Engineering, Hung Hom, Kowloon, Hong Kong, 999077
b
a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
In recent years, underground pipe gallery has become more and more important. Its Special working environment In recent years, underground pipe become more and more important. Special working environment makes temperature monitoring andgallery controlhas increasingly important. paper applies Its heat-transfer differential equation makes temperature monitoring and criterion control increasingly important.distribution. paper applies heat-transfer to the cable to obtain the similarity of cable temperature After calculatingdifferential the electricequation cabin toAbstract the cable to obtain fire the separations, similarity criterion of cable temperature distribution. After calculating the electric cabin model with different it is found that the temperature distribution of the same ampacity in different model with different fire separations, it is found that the temperature distribution ofampacity the same in ampacity different fire separations conforms to certain rules. The temperature distribution of different the sameinfire District heating networks commonly addressed in the literature as one of different the effective solutions for fire decreasing fire separations to rules. The5%, temperature distribution of ampacity in theformulas same separation is alsoconforms regular: are thecertain ampacity rises. temperature rises by 7%. As most a result, the fitting of the the greenhouse emissions the building sector. These systems require which are returned through the heat separation isgas also regular:from ampacity rises. temperature riseslocation byhigh 7%.investments Asthe a result, the fitting formulas of the current carrying capacity, the length of the fire5%, separation and the of highest temperature were also sales. Due to the changedthe climate building and renovation policies, heathighest demandtemperature in the future could current carrying capacity, lengthconditions of the fireand separation the location of the were alsodecrease, proposed. prolonging the investment return period. proposed.
main scopeLtd. of this to assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2018 Elsevier All paper rights isreserved. ©forecast. 2019 Elsevier The Authors. Published byresponsibility Elsevier The district ofrights Alvalade, locatedLtd. in Lisbon (Portugal),committee was used ofas the a case The district is consisted of 665 © 2018 Ltd. All reserved. Selection and peer-review under of the scientific 10th study. International Conference on Applied This is an open access article under the CC period BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) buildings that vary in both construction and typology. Three weather scenarios (low, medium,Conference high) and on three district Selection and peer-review under responsibility of the scientific committee of the 10th International Applied Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – estimate The 10th the International Conference on Applied Energy. renovation scenarios were developed (shallow, intermediate, deep). To error, obtained heat demand values were Energy (ICAE2018). comparedUnderground with resultspipe from a dynamic heat demanddimensionless model, previously andcable validated by the authors. Keywords: gallery; similarity criterion; number;developed fire separation; ampacity The results showed that only weather change is considered, the margin of errorcable could be acceptable for some applications Keywords: Underground pipewhen gallery; similarity criterion; dimensionless number; fire separation; ampacity (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Nomenclature decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Nomenclature scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Trenovation temperature coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and T∞ temperature Inlet air temperature improve the accuracy of heat demand estimations. T∞ Inlet air temperature © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +86 13580456068.
[email protected] address:author. * E-mail Corresponding Tel.: +86 13580456068. Keywords: Heat demand; Forecast; Climate change [email protected] E-mail address: 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.587
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
5558 2
r
λ
θ
Θ
Point to cable core axis distance thermal conductivity heat source density cable core dimensionless temperature(θ=T/T∞) cable insulator(Θ=(T-T∞)/T∞)
1. Introduction Underground pipe gallery can effectively solve the problems of land resources lackness, unreasonable pipelines laying etc. It can efficiently use urban underground space to make the city beautiful. Therefore, the construction of an underground pipe gallery will become more common in the future. The large construction volume of underground pipe projects makes it difficult to predict operating conditions in advance and lacks technical standards. The scale model that can be verified by experiments has become an effective way to simulate the temperature distribution of the electric cabin and to study the heat transfer characteristics of the electric cabin cable[1-3]. At present, the finite element simulation software is used at domestic and foreign to calculate the cable temperature distribution[2-5]. For the experimental calculation of cable core temperature, predicted the ampacity of power cables by measuring the sheath temperature of cable insulation is Acceptable[3-5]. Several studies have also been conducted on cable temperature in different environments, [6]studied the difference in the temperature of power cables under different environments such as the cable spacing and the cable layer. Further, the effect of pitch and cable trench depth on cable temperature was also studied[7-10]. [6]. In some articles, the effects of ambient conditions such as cyclical changes in the surface temperature and the continuous operation of the cable over a period of time are taken into consideration[11-13]. In this paper, the temperature field is simplified to a one-dimensional model, and the Dimensionless number of the cable temperature in the electric cabin is proposed from the differential equation of heat transfer. By numerically efficient simulation tool, the relationship between fire separation and ampacity was studied. The law of cable temperature distribution and the fitting formula were obtained through the experiment in order to make the future more convenient to study the heat transfer of the cable. 2. Physical model of temperature field in electric cabin 2.1. Cable core temperature field model Due to the long cable span, less bending, and large shaft diameter ratio, the experiment ignored the radial heat conduction of the cable core. To make equation Dimensionless, Defining θ=T/T∞ . Therefore, one-dimensional cable core temperature field function can be obtained based on the Fourier heat conduction equation:
f ( x, rr1 / 21T )
(1)
Therefore r 21 / 21T is a Dimensionless number. 2.2. Cable insulation temperature field model Convective heat transfer is the mainly. boundary condition of insulation. Defining T Tw ; T Tw / T to make equation Dimensionless:
r r r hr f ; 1 2 ; 2 r 2 22T 2
(2)
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
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It can be seen from the above formula that r1r2 / 22T and hr2 / 2 are Dimensionless number which determine the temperature field of cable core. 3. Method and procedure 3.1. model In order to verify the reliability of similar theory, this paper sets up two models: model1, model2. The parameter provided by the relevant units is shown in the table1: Table 1. Cable parameters Parameters
Value
Parameters
Value
Electric cabin cross-sectional area (m2)
9
Cable core resistivity
0.00000002
Cable diameter (m)
0.25
Thermal conductivity of air (w/(m·k))
0.0243
Voltage (kV)
220
Thermal conductivity of wall (w/(m·k))
1.74
Ampacity (A)
1900
1
Number of cable cores
1
Cable core cross-sectional area (m2)
0.0075
Thermal conductivity of solid (w/(m·k)) Thermal conductivity of insulator (w/(m ·k)) Thermal conductivity of cable core (w/(m ·k))
0.3 401
The calculation formula for the heat value per meter of a single cable core provided by the relevant unit is Q I 2 / S ,( is cable core resistivity; S is cable core cross-sectional area). So the real cable heat value per meter is 346.6w/m. Model 2 is a laboratory test bench model, which uses electric heaters to simulate heat source and lagging to simulate insulator. According to Dimensionless number r1r2 / 22T , heat value per meter of model1 is 61.96w/m and model2 is 7.8w/m. The model parameters are shown in the table2: Table 2. Model parameters Model Parameters
Value
Insulator Inner radius of model1 (m)
0.0097
Insulator Outer radius of model1 (m)
0.025
Heat value of model1 (w/m)
61.96
Insulator Inner radius of model2 (m)
0.0065
Insulator Outer radius of model2 (m)
0.015
Heat value of model2 (w/m)
7.8
2.2 Sketch of model Front view, and side view of model is shown in Figure1. 7 3
4
5
12cm
12cm
60cm 60cm
Soild
NO.1
NO.2
NO.3
NO.4
NO.5
NO.6
NO.7
NO.8
NO.9
NO.10
NO.11
NO.12
NO.13
NO.14
NO.15
NO.16
1
Soild 2
6cm
6
20cm
5 40cm
T
T
T
T
wall
Front view 500mm
8
60cm
Side view
5 5
outlet
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
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Figure. 1. Front view and side view of model 1—insulation；2—cable core；3—fan；4—wind duct；5—anemometer；6—differential pressure pickup；7—computer； 8—thermocouple
The numbers in front view are the serial numbers of the cables. The cables 1, 5, 9 and 13 are defined as the external cables. The 2, 6, 10, 14 cables are inner row cables. 5. The relationship of fire separation and cable load During the construction of the electric cabin, fire separations with vents need to be built. In this paper, several simulation experiments were conducted to explore the relationship between the ampacity and the length of the fire separations. The length of fire separations in the electric cabin for the scale models in the experiment were: 12m, 18m, 24m, 30m, 36m. The corresponding is 60m, 90m, 120m, 150m, 180m. As shown in the previous table, the full load condition has a current carrying capacity of 1900A, a variable load of 90%, and 80% of the ampacity, that is, 1710A and 1520A. Due to limited space, only the calculation results at 12m, 18m, and 24m are shown. 5.1. Temperature distribution of external cables in different fire separation at full load 84 84
fire separation of 12m
84
76
72
NO.1 NO.5 NO.9 NO.13
68
80 78 76
2
4
6
8
NO.1 NO.5 NO.9 NO.13
74 72 70
0
10
12
temperature (℃)
temperature (℃)
temperature (℃)
fire separation of 24m
fire separation of 18m
82
80
14
Distance to the entrance (m)
0
2
4
6
8
10
80
76
NO.1 NO.5 NO.9 NO.13
72 12
14
16
18
20
0
Distance to the entrance (m)
5
10
15
20
25
Distance to the entrance (m)
Figure. 2. Temperature distribution of full-load cables at different fire separations
In the first 6 meters, the temperature of each cable in the electric cabin is relatively close, but the temperature difference becomes apparent after 6m. The temperature of No. 1 cable is the highest, and the temperature of No. 13 cable is the lowest. As the distance increases, the temperature of the No. 5 cable will gradually increase and exceed that of the No. 1 cable. At the same time, the temperature is close to the limit temperature of 90° C, therefore the No. 1 cable temperature is still the highest. 5.2. Temperature comparison and function fitting of No.1 cable in different fire separation under different loads It can be seen that the maximum temperature of No. 1 cable and its position is a logarithmic function when the fire prevention interval is gradually extended. After the fire prevention interval is increased, the computer simulation closely matches the calculated results of the existing length. The fitting formulas under different loads are as follows: Table 3. Functional expressions under different loads load 100%
Functional expression
x / t1
y A0e
A0
t1
y0
y0
-15.62716
15.15619
89.02146
15.64293
77.85372
90%
y A0e
x / t1
-12.60645
80%
y0
y A0e
x / t1
y0
-9.63776，
5 Prediction and verification of fitting formulas
14.2057
67.3201
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
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Parameters of fitting formulas obtained in Figure 3 are constantly changing under different load. Organize the parameters into a graph: 95 90 85
parameter value
80 75 70 65 15
A t y
10
5
80
85
90
95
100
Load factor (%)
Figure. 3. Fitting formula parameters and load rate diagram
It can be seen that A1, t1, y0 are distributed linearly with the load rate, and they satisfy: ; t1 0.1425x 2.8057 ; y0 1.085x 19.4799 . Then A1, t1, and y0 at 85% load are: A0 0.29947 x 14.31984 11.13511; 14.8192; 72.7454. and A1, t1, y0 at 95% load are: -14.12981; 16.3432; 83.5951. The temperature curve of No. 1 cable under different fireproof intervals at 85% and 95% load and the function fitting curves are shown in the figure 4: 95%load
72.0
80
66
6m NO.1 12m NO.1 18m NO.1 24m NO.1 30m NO.1 36m NO.1
64 62 60 0
5
10
15
20
25
30
35
70.5
temperture (℃)
68
temperture (℃)
temperture (℃)
70
76
6m NO.1 12m NO.1 18m NO.1 24m NO.1 30m NO.1 36m NO.1
72
68
40
Distance from the entrance (m)
0
5
10
15
20
25
30
35
Model Equation
ExpDec1 y = A1*exp(-x/t1) + y0 温度
Plot
82.5
ExpDec1 y = A1*exp(-x/t1) + y0 temperture
y0
83.68371
-12.11661
A1
-14.28242
13.00518
t1
16.41633
Reduced Chi-Sqr
0.09168
Reduced Chi-Sqr
0.08146
R-Square(COD)
0.99287
R-Square(COD)
0.99548
Adj. R-Square
0.98811
Adj. R-Square
0.99247
69.0 67.5 66.0
63.0
Equation Plot
72.20588
A1 t1
Maximum temperature point Fitting function curve
64.5
40
Model
85%load
y0
temperture (℃)
84
85%load
72
80.0
77.5
75.0
Maximum temperature point Fitting function curve
72.5
5
10
15
20
25
30
35
5
10
15
20
25
30
35
Distance to the entrance (m)
Distance to the entrance (m)
Distance from the entrance (m)
95%load
Figure. 4. Maximum temperature point for 85% and 95% load cables at different fire separation and Fitting functions
From fig.5, it can be seen that A1, t1, and y0 at 85% load are: −111.661242; 14.43356; 72.45786, the A1, t1, and y0 at 95% load are −14.19997; 15.97402; 83.51166, respectively. The specific results obtained and numerical predictions can be derived from the table 5: Table 4. Calculation and verification of fitting formula parameters Parameters A1 t1 y0
Predictive value at 85% load -11.13511 14.9182 72.7454
Function fitting value at 85% load -11.61242 14.43356 72.45786
Percent difference 4.29% 3.25% 0.39%
Predictive value at 95% load -14.12981 16.3432 83.5951
Function fitting value at 95% load -14.19997 15.9740 83.51166
Percent difference 0.49% 2.31% 0.1%
The difference between the theoretical formula's fitting formula parameters and the simulation calculation is less than 5%, which is reliable. The relationship between the location of the highest temperature point of the different load cables and the length of the fire separation can be expressed by the following formula: w x /(0.1425 1900 ) 2.8057)
y (0.29947w 14.31984) e
(1.085w 19.4799)
Where y is temperature of cable core; x is measuring point to air inlet distance; w is cable ampacity. 6. Conclusions
(3)
Jiaxu Wang et al. / Energy Procedia 158 (2019) 5557–5562 Author name / Energy Procedia 00 (2018) 000–000
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When changing the cable ampacity, the cable temperature moves up and down compared to the standard operating conditions, verifying the similarity theory obtained above. In the same load conditions, the maximum temperature of the cable has a significant relationship with the fire separation, which is approximately a logarithmic curve. The relationship between the location of the highest temperature point of the different load cables and the length of the fire separation are obtained.
7. Discussion The experimental verification will be carried out in the follow-up work. The construction method of the experimental platform has been given in Figure 1, and it has been completed. The effect of localized intense turbulence at the fire-fighting interval on the air inlet and outlet and the quantified relationship will be demonstrated in future work, and the impact of radiation will also be taken into consideration. Acknowledgment This study is supported by the National Natural Science Foundation of China (Grant No. 51778234), the Natural Science Foundation of Guangdong Province (Grant No. 2015A030310303), Guangdong Provincial Science, Technology Project (Grant No. 2017A020216024) and Special Funds for Basic Scientific Research Business Fees for Central Universities (Grant No. B6150170) and Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization Reference [1] Mingxiang Wu, Xianbo Deng, Songhua Liu. Calculation of Temperature Field Distribution and Current Carrying Capacity of Cable in Typical Laying Mode[J]. Hubei Electric Power, 2012, 36(6):37-39. [2] Adefarati T, Bansal R C. Integration of renewable distributed generators into the distribution system: a review[J]. Iet Renewable Power Generation, 2016, 10(7):873-884. [3] Xiaoliang Zhuang. Distribution Cable Operation Monitoring and Current Carrying Capacity Prediction Based on Fiber Temperature Measurement Based on Fiber Temperature Measurement [D]. South China University of Technology,2015. [4] Boukrouche F, Moreau C, Pelle J, et al. Mock-up Study of the Effect of Wall distance on the Thermal Rating of Power Cables in Ventilated Tunnels[J]. IEEE Transactions on Power Delivery, 2016, PP(99):1-1. [5] Yongming Yang, Peng Cheng, Jun Chen. Research on Cable Heat Dissipation Considering Effect of Air Flow Field and Its Influence Factors and Economic Analysis [J]. Electric Power Automation Equipment, 2013, 33(1):50-54. [6] Boukrouche F, Moreau C, Pelle J, et al. Mock-up Study of the Effect of Wall distance on the Thermal Rating of Power Cables in Ventilated Tunnels[J]. IEEE Transactions on Power Delivery, 2016, PP(99):1-1. [7] Wędzik A, Siewierski T, Szypowski M. A new method for simultaneous optimizing of wind farm’s network layout and cable crosssections by MILP optimization[J]. Applied Energy, 2016, 182:525-538. [8] Cheung H, Wang S, Zhuang C, et al. A Simplified Power Consumption Model of Information Technology (IT) Equipment in Data Centers for Energy System Real-time Dynamic Simulation[J]. Applied Energy, 2018, 222. [9] Baldinelli G, Bianchi F. Windows thermal resistance: Infrared thermography aided comparative analysis among finite volumes simulations and experimental methods[J]. Applied Energy, 2014, 136(136):250-258. [10] Lee H, Sharp J, Stokes D, et al. Modeling and analysis of the effect of thermal losses on thermoelectric generator performance using effective properties[J]. Applied Energy, 2018, 211:987-996. [11] Vrana T K, Mo O. Optimal Operation Voltage for Maximal Power Transfer Capability on Very Long HVAC Cables ☆[J]. Energy Procedia, 2016, 94:399-408. [12] Wan I W M N, Royapoor M, Wang Y, et al. Office building cooling load reduction using thermal analysis method – A case study[J]. Applied Energy, 2017, 185:1574-1584. [13] Reddy B S, Chatterjee D. Analysis of High Temperature Low Sag Conductors Used for High Voltage Transmission ☆[J]. Energy Procedia, 2016, 90:179-184.