CN109989786A - Barren rock-tailings high dense fill slurry yield stress prediction model method for building up and application - Google Patents
Barren rock-tailings high dense fill slurry yield stress prediction model method for building up and application Download PDFInfo
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- 239000002002 slurry Substances 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005065 mining Methods 0.000 claims abstract description 7
- 239000004568 cement Substances 0.000 claims description 32
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 23
- 239000000463 material Substances 0.000 claims description 15
- 239000011435 rock Substances 0.000 claims description 13
- 239000012530 fluid Substances 0.000 claims description 10
- 239000000203 mixture Substances 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 9
- 239000007787 solid Substances 0.000 claims description 8
- 239000000654 additive Substances 0.000 claims description 7
- 230000000996 additive effect Effects 0.000 claims description 7
- 238000000518 rheometry Methods 0.000 claims description 6
- 238000012856 packing Methods 0.000 claims description 5
- 238000000611 regression analysis Methods 0.000 claims description 5
- 238000010008 shearing Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000011161 development Methods 0.000 claims description 4
- 239000011440 grout Substances 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 239000002245 particle Substances 0.000 claims description 4
- 238000013142 basic testing Methods 0.000 claims description 3
- 238000006703 hydration reaction Methods 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000012098 association analyses Methods 0.000 claims description 2
- 230000000740 bleeding effect Effects 0.000 claims description 2
- 238000012512 characterization method Methods 0.000 claims description 2
- 238000007906 compression Methods 0.000 claims description 2
- 230000008602 contraction Effects 0.000 claims description 2
- 238000009826 distribution Methods 0.000 claims description 2
- 239000010419 fine particle Substances 0.000 claims description 2
- 238000010348 incorporation Methods 0.000 claims description 2
- 230000002452 interceptive effect Effects 0.000 claims description 2
- 238000002955 isolation Methods 0.000 claims description 2
- 239000011148 porous material Substances 0.000 claims description 2
- 230000003068 static effect Effects 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims description 2
- 238000002474 experimental method Methods 0.000 abstract description 11
- 238000013461 design Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000009533 lab test Methods 0.000 abstract description 3
- 238000002360 preparation method Methods 0.000 abstract description 2
- 230000007812 deficiency Effects 0.000 abstract 1
- 239000002910 solid waste Substances 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 239000000945 filler Substances 0.000 description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000011707 mineral Substances 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 239000004606 Fillers/Extenders Substances 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000018044 dehydration Effects 0.000 description 1
- 238000006297 dehydration reaction Methods 0.000 description 1
- 239000006210 lotion Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000002244 precipitate Substances 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F15/00—Methods or devices for placing filling-up materials in underground workings
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F15/00—Methods or devices for placing filling-up materials in underground workings
- E21F15/005—Methods or devices for placing filling-up materials in underground workings characterised by the kind or composition of the backfilling material
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Consolidation Of Soil By Introduction Of Solidifying Substances Into Soil (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The present invention relates to a kind of barren rock-tailings high dense fill slurry yield stress prediction model method for building up and applications, belong to Cut-and-fill Mining Technology field.The present invention establishes yield stress prediction model, the Correlative Influence Factors of yield stress are taken into account, reduce the error of prediction model, solve the deficiencies in the prior art, the number of engineering experiment and laboratory test can be reduced, the yield stress of slurry is predicted rapidly, designs for subsequent pipeline filling system and preparation provides necessary accurate parameter.
Description
Technical field
The present invention relates to a kind of barren rock-tailings high dense fill slurry yield stress prediction model method for building up and answer
With belonging to Cut-and-fill Mining Technology field.
Background technique
Gangue, barren rock, tailings and the metallurgical slag generated during mineral resource development and utilization has become China's discharge amount
Maximum trade waste accounts for 85% or so of solid waste total amount, and if these barren rocks, tailings be not processed, will be to ring
Border pollutes.Chats-tailings high dense fill mining technology is the most effective approach for solving the useless last row in mine and putting, and
Realize one of the body supports technology of green mining.Solid wastes such as barren rock (gobi sand) are added in filling slurry can make on an equal basis
Cement consumption retrofilling body intensity is significantly improved；Under equality strength, it is possible to reduce cement consumption reduces cost in turn.And
High dense fill slurry, which has, not to be isolated, does not precipitate, and dehydration in stope amount is few (or even not being dehydrated), strength of filling mass rapid development,
The advantages such as filling quality is good, and pack effectiveness is high, and filling cost is low.
Rheological parameter of the yield stress as filling slurry is the important parameter for characterizing slurry mobility and workability, it
Acquisition can for mine filling system design and prepare necessary basis be provided.The size of yield stress directly affects subsequent again
The selection of pipeline material accurately determines slurry rheological parameter to instructing engineering design and application to be of great significance.Really
The mode for determining yield stress, generally by industrial experiment or rheological experiment, measure different cement amount different quality concentration and
Slurry under different ratio, and then obtain rheological curve and determine yield stress, but all tested, heavy workload and be easy by
The interference of the factors such as manual operation, has certain limitation in production application.
Mainly having for the research of yield stress prediction both at home and abroad at present is: (1) for concrete, fresh concrete is proposed
Prediction model；(2) prediction model established using the relationship of the slump and yield stress, error are larger；(3) crude tailings are directed to
Lotion slurry establish yield stress prediction model.
In summary, barren rock-tailings high concentration ground paste yield stress model is there is not yet play-by-play.Therefore it needs deeply
Barren rock-tailings high concentration ground paste rheological behavior is studied, is a large amount of solid wastes of bargh (mainly including barren rock and tailings)
Filling application provides theory and technology support.
Summary of the invention
It is an object of that present invention to provide a kind of barren rock-tailings high dense fill slurry yield stress prediction model foundation sides
Method and application establish yield stress for there is presently no barren rock-tailings high dense fill slurry yield stress prediction model
Prediction model takes into account the influence factor of yield stress comprehensively, reduces the error of prediction model, solves the prior art not
Foot, it is possible to reduce the experiment number of laboratory test after engineering experiment predicts the yield stress of slurry, rapidly so as to subsequent pipeline
Filling system design and preparation provide necessary accurate parameter.
To achieve the above object, The technical solution adopted by the invention is as follows:
A kind of the step of method for building up of barren rock-tailings high dense fill slurry yield stress prediction model, this method, is such as
Under:
A, the state of filling slurry is divided into Newtonian liquid, structure stream, plug flow；
High concentration ground paste belongs to structure stream, should have the following conditions:
Determine that filling slurry is high concentration state first；
The criterion of the defeated filling slurry high concentration of coarse aggregate pipe are as follows: keep not stratified filling after a certain period of time standing
Concentration corresponding to slurry is high concentration, and with this condition, the particle in slurry does not have free settling there was only compression process, only
There are gap reduction and volume contraction, without coarse grained sorting settling, without grout layer, bleeding quantity is few on slurry surface, slurry
There is apparent coarse aggregate on surface, and slurry is not stratified substantially without isolation；Meet formula (1)
In formula: the volume of Vw-- water；The stacking volume of V heap -- mixture contains cement in mixture；V is real -- mixture
Closely knit volume；
B, it is matched according to barren rock-tailings that High Concentration Situation is determined for compliance with condition, the variation range and cement of mass concentration
The variation range of additive amount carries out the basic test of rheology to the mine of required mining with stowing, by change aggregate additive amount,
The additive amount and water of cement, rheological curve under the conditions of measuring respectively, measured data must not be less than 50 groups；
C, all rheological curve application H-B for measuring step B are returned to obtain rheological parameter；
D, added with there are apparent meshing phenomenas between Coarse Aggregate in barren rock/gobi sand slurry, simultaneously because in slurry
A certain number of fine graineds will form the structure with certain rigidity under static state, generate cohesive force, can resist centainly
Shear action forms shear yield stress: τ 0；
When shearing external force is less than τ0When, slurry will not flow；When shearing external force is greater than τ0When, modeling takes place in slurry
Property flowing；
Yield stress characteristic shows that in the case where meeting High Concentration Situation, yield stress is with cement according to rheology regression result
Amount, mass concentration and useless tail ratio variation show different changing rules, in order to study filling slurry rheological parameter influence because
The primary-slave relation and yield stress of element fill its influence factor-using grey correlation theory with the changing rule of influence factor
Extender paste concentration, cement additive amount and the ratio between barren rock and classified tailings, referred to as useless tail ratio, are associated sequence；
E, association analysis the result shows that slurry, from the above analysis it can be seen that yield stress contains with water in slurry
The number of amount, cement amount, aggregate, all there is close relationships for granular media gradation, indispensable；
F, characterize the reference of each influence factor is specific, in E each factor reflection be solid content, grain composition and
Two kinds of indexs of difference solid content mass fraction and volume fraction between the material characteristics such as cement content are described, phase
Volume fraction can characterize the influence of grain density simultaneously than under, and result of study is made to have more universality, and volume fraction characterizes group
At distribution characteristics of the Loose Bodies of slurry under the effect of flow field；Packing density φ reflects the intrinsic category of Loose Bodies gradation
Property；By constructing coefficient CVThe concept of gradation is extended to slurry state by granular media, indicated in unit slurry volume by/φ
The compactness of solid particle accounts for the ratio of maximum density, reflects the journey that current volume score reaches material Grading limits state
Degree, the concentrated expression feature of granular media and fluid.The surrender that the number of water and the structure of water directly affect slurry in slurry is answered
Power；And the incorporation of cement is equivalent to increase the fine particle content in material, influence net of wadding indirectly as a result, cement amount number
Effect is equally played, and there are hydration reaction, the comprehensive volumes for using cement slurry of the influence of water and cement: C between water and cementwc
To indicate.
G, by D, E, F-step it is found that yield stress major influence factors CV/ φ and CwcTo characterize；
H, by the affecting parameters determined in G, by the yield stress and the correlation of slurry basic parameter in step C,
Establish yield stress and CV/ φ and and CwcRelationship；
By statistical analysis discovery yield stress with CwcExponentially form increases, CVIt is deposited between/φ function and yield stress
In power function relationship；
It to show the relationship of different factors and yield stress, while realizing the concise prediction of yield stress, proposes barren rock-
Tailings high dense fill slurry yield stress computation model, formula (2):
τ in formula0-- yield stress pa；Cwc-- grout volume；Cv-- aggregate volume concentration；φ -- aggregate accumulation is closely knit
Degree；α, b-- experimental constant；
I, in regression analysis, custom function is created, it is related to experimental data in B/C using three Cox regression analysis methods
Data substitution is returned to obtain factor alpha, b.
Further, in step F: solid content mass fraction and volume fraction are CV；
Volume fraction size indicates granular media feature and characteristic of fluid accounts for leading degree, while making in fluid stress, electrostatic
Firmly, under pore water pressure effect, there is complicated interactive relation between granular media feature and characteristic of fluid, volume fraction is solid-liquid
The comprehensive characterization for acting on equilibrium relation, reflects the comprehensive function of granular media in a fluid:
Packing density is φ, and when configuring slurry using different materials, the gradation condition reflected is also different, this
It is one of the main reason for different slurry rheology characteristics are multifarious under same amount.
Barren rock-tailings high dense fill slurry yield stress prediction model method for building up is in mineral resource development and utilization
Application.
Compared with prior art, the present invention has the advantages that:
(1) reduce a large amount of industrial experiments and laboratory test carried out to obtain rheological parameter, reduce cost and people
Power material resources, while there is the higher yield stress prediction effect of precision.
(2) in the identical situation of selection filler, without carrying out many experiments, it can calculate and meet high concentration and want
The different filling slurry parameters asked, have saved the time of an experimental period, and therefore, experimental cost also decreases, and mentions significantly
High working efficiency,
(3) model Consideration is comprehensive, and structure is simple, applies aspect, and it is accurate to provide for subsequent pipeline design
Guarantee.
Compared with by analyzing lot of experimental data, the method that yield stress is calculated in conjunction with practical experience, this model energy
Yield stress is more accurately predicted, to substantially reduce filling cost.The method of the present invention can calculate under any proportion
Yield value of stress has broken through the limitation of existing method, application easy to spread.
Specific embodiment
The present invention is described in detail below with reference to embodiment:
The present invention is described in detail below with reference to example: based on being tested by copper mine, the selection of material is barren rock and classification tailing in experiment,
The broken density gathered materials of measuring barren rock is 2815kg/m3, and heaping compactness is 0.554, and the density of classified tailings is
2897kg/m3, heaping compactness is 0.506, predicts its yield stress numerical value.It is useless using a ratio within the scope of optimal proportion
Stone tailings 6:4 is done as one kind in filling aggregate, and cement is cementing agent, designs the filling material rheological experiment side of the condition of satisfaction
Case.
Step 1, required filling slurry must assure that as high concentration ground paste, while meet decision condition:
Step 2, the filling slurry for meeting condition, it is first determined following parameter value, aggregate heap compactness, aggregate body
Product concentration and mass concentration and cement additive amount；
Step 3 carries out rheological experiment basic test to the slurry combination met the requirements, rheological curve under the conditions of measuring respectively,
Measured data must not be less than 20, be shown in Table 1:
1 experimental program table of table
Step 4, it obtains rheological curve and obtains corresponding yield value of stress by the regression analysis of H-B model, be listed in table 2
Table 2: parameter list
Step 5: the data in table and accordingly fixed condition are updated in formula (2) and carry out regression fit,
Until result restrains.Parameter a=45.588, b=5.48 are obtained, then formula (8) is represented by this time
Fitting result analysis such as table 3:
Table 3: analysis result
Conspicuousness F=8107.4 > F0.995 (2,36)=6.19 of regression equation belongs to highly significant.Using above formula as material
Starch yield stress prediction model；
Step 6: barren rock-tailings 5:5,7:3 in the case where the constant situation of barren rock, tailings basic condition carries out other preferential proportions
Etc. yield stress prediction, prediction result and actual measurement comparing result under proportions be shown in Table 4:
Table 4: Comparative result table
Prediction result has been reacted above compared with actual measured results, error industry actual production in main road within 10%
Using standard.
The present invention has comprehensively considered the various influence factors of slurry to predict yield stress, and prediction technique is simple, prediction
Precision is high.This method can predict required by technical ability reaches mining technology and meet mine to filling slurry transport condition
Parameter, can direct saving filling experimental cost compared with the method directly calculated by experiment and experience.And in selection filler
In identical situation, the relation curve under a kind of proportion need to be only calculated, the filling material under different requirements can be carried out quick
Prediction.Without repeating to test, greatly improve the efficiency.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (3)
1. a kind of barren rock-tailings high dense fill slurry yield stress prediction model method for building up, the step feature of this method
It is as follows:
A, the state of filling slurry is divided into Newtonian liquid, structure stream, plug flow；
High concentration ground paste belongs to structure stream, should have the following conditions:
Determine that filling slurry is high concentration state first；
The criterion of the defeated filling slurry high concentration of coarse aggregate pipe are as follows: keep not stratified filling slurry after a certain period of time standing
Corresponding concentration is high concentration, and with this condition, the particle in slurry does not have free settling there was only compression process, only between
Gap reduces and volume contraction, and without coarse grained sorting settling, without grout layer, bleeding quantity is few on slurry surface, slurry surface
There is apparent coarse aggregate, slurry is not stratified substantially without isolation；Meet formula (1)
In formula: Vw-- the volume of water；VHeap-- the stacking volume of mixture contains cement in mixture；VIt is real-- the compacting body of mixture
Product；
B, it is matched according to barren rock-tailings that High Concentration Situation is determined for compliance with condition, variation range and the cement addition of mass concentration
The variation range of amount carries out rheology basic test to the mine of required mining with stowing, and additive amount, the cement by changing aggregate add
Dosage and water, rheological curve under the conditions of measuring respectively, measured data must not be less than 50 groups；
C, all rheological curve application H-B for measuring step B are returned to obtain rheological parameter；
D, added with there are apparent meshing phenomenas between Coarse Aggregate in barren rock/gobi sand slurry, simultaneously because certain in slurry
The fine grained of quantity will form the structure with certain rigidity under static state, generates cohesive force, can resist certain shearing
Effect forms shear yield stress: τ0；
When shearing external force is less than τ0When, slurry will not flow；When shearing external force is greater than τ0When, plasticity stream takes place in slurry
It is dynamic；
According to rheology regression result analysis shows, in the case where meeting High Concentration Situation, yield stress with cement amount, mass concentration and
The variation of useless tail ratio shows different changing rules, in order to study the primary-slave relation of filling slurry rheological parameter influence factor with
And yield stress is with the changing rule of influence factor, using grey correlation theory to its influence factor-filling slurry concentration, water
Mud additive amount and the ratio between barren rock and classified tailings, referred to as useless tail ratio, are associated sequence；
E, association analysis the result shows that, yield stress and the content of water, the number of cement amount, aggregate, granular media gradation in slurry
All there is close relationships, indispensable；
F, each influence factor is quoted specifically to characterize, each factor reflection is solid content, grain composition and cement in E
Difference between content material characteristic；Two kinds of indexs of solid content mass fraction and volume fraction are described, in contrast
Volume fraction can characterize the influence of grain density simultaneously, and result of study is made to have more universality, volume fraction (CV) characterize composition
Distribution characteristics of the Loose Bodies of slurry in the case where flow field acts on；Packing density φ reflects the intrinsic category of Loose Bodies gradation
Property；
G, pass through building coefficient CVThe concept of gradation is extended to slurry state by granular media, indicated in unit slurry volume by/φ
The compactness of interior solid particle accounts for the ratio of maximum density, reflects current volume score and reaches material Grading limits state
Degree, the concentrated expression feature of granular media and fluid；The number of cement amount similarly plays effect, and exists between water and cement
Comprehensive volume (the C for using cement slurry of the influence of hydration reaction, water and cementwc) indicate；
H, by D, E, F, G step it is found that yield stress major influence factors CV/ φ and CwcTo characterize；
I, by the affecting parameters determined in F, G, H, by the yield stress and the correlation of slurry basic parameter in step C,
Establish yield stress and CV/ φ and and CwcRelationship；
By statistical analysis discovery yield stress with CwcExponentially form increases, CVThere are powers between/φ function and yield stress
Functional relation；
It to show the relationship of different factors and yield stress, while realizing the concise prediction of yield stress, proposes barren rock-tailings
High dense fill slurry yield stress computation model, formula (2):
τ in formula0-- yield stress pa；Cwc-- grout volume；Cv-- aggregate volume concentration；φ -- aggregate packing density；α,
B-- experimental constant；
J, in regression analysis, custom function is created, using three Cox regression analysis methods to experimental data related data in B/C
Substitution is returned to obtain factor alpha, b.
2. barren rock according to claim 1-tailings high dense fill slurry yield stress prediction model method for building up,
Feature is as follows:
In step F: solid content mass fraction and volume fraction are CV；
Volume fraction size indicates that granular media feature and characteristic of fluid account for leading degree, at the same fluid stress, electrostatic force,
Under pore water pressure effect, there is complicated interactive relation between granular media feature and characteristic of fluid, volume fraction is solid-liquid effect
The comprehensive characterization of equilibrium relation reflects the comprehensive function of granular media in a fluid.
Packing densityThe build-in attribute of Loose Bodies gradation is reflected when configuring slurry using different materials, institute
The main reason for gradation condition of reflection is also different, this is also under same amount, and different slurry rheology characteristics are multifarious it
One；
Yield stress is mainly had tightly by the domination of slurry gradation structure and net of wadding structure, net of wadding structure and the content of the water in slurry
Close connection, the moisture in slurry are film water, the closing water in structure hole and a certain amount of Free water composition；
The number of water and the structure of water directly affect the yield stress of slurry in slurry；And the incorporation of cement equivalent to increase
Fine particle content in material, influence net of wadding indirectly as a result, cement amount number similarly play effect, and between water and cement
There are hydration reaction, the comprehensive volume for using cement slurry of the influence of water and cement: CwcTo indicate.
3. barren rock of any of claims 1 or 2-tailings high dense fill slurry yield stress prediction model method for building up is in mine
Produce the application in resources development and utilization.
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|---|---|---|---|---|
| CN110333165A (en) * | 2019-07-26 | 2019-10-15 | 北京科技大学 | A kind of filling material sedimentation function determination method based on rheology |
| CN117637080A (en) * | 2024-01-26 | 2024-03-01 | 昆明理工大学 | Yield stress prediction method based on filling slurry differential analysis |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333165A (en) * | 2019-07-26 | 2019-10-15 | 北京科技大学 | A kind of filling material sedimentation function determination method based on rheology |
| CN110333165B (en) * | 2019-07-26 | 2021-02-12 | 北京科技大学 | Filler precipitation performance determination method based on rheology |
| CN117637080A (en) * | 2024-01-26 | 2024-03-01 | 昆明理工大学 | Yield stress prediction method based on filling slurry differential analysis |
| CN117637080B (en) * | 2024-01-26 | 2024-04-09 | 昆明理工大学 | Yield stress prediction method based on filling slurry differential analysis |
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| CN109989786B (en) | 2021-01-26 |
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