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Time dependent model to analyze the magnetic refrigeration performance of gadolinium near the room temperature

  • Jimei Niu EMAIL logo and Zhigang Zheng
Published/Copyright: October 30, 2024
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 43 Issue 1

Abstract

A practical time-dependent model has been constructed to forecast the effectiveness and productivity of a magnetic regenerative refrigerator, as well as to assess its cycle efficiency. The model incorporates many irreversible factors, including the cycle frequency, heat transfer efficiency, and heat leak. Furthermore, it is utilized to scrutinize a magnetic refrigerator that employs spherical Gd particles as the magnetic substance and water as the heat transfer medium. The different cycle steps of the magnetic refrigerator are examined, while the cooling capacity and temperature differential between the two heat exchangers are appraised. The results also show that the magnetic refrigerator can obtain a temperature span of 5 K under 0.8 T magnetic field after 30 cycles in a particular situation. The findings provide valuable information for the future planning and advancement of magnetic refrigeration technology at room temperature.

Keywords: magnetic refrigerator; Ericsson model; simulation; optimization

Nomenclature

B J (x)

Brillouin function

c

specific heat capacity

g

Landé factor

H

magnetic intensity (T)

J

quantum number

k

Boltzmann constant

ξ(x)

Langevin function

M

magnetization intensity (A·m−1)

N

substance amount in moles (mol)

Q

heat quantity (J·kg−1)

s

entropy (J·kg−1·K−1)

S m

magnetic entropy

S l

lattice entropy

S e

electronic entropy

t

time (s)

T

thermodynamic temperature (K)

T c

Curie temperature (K)

η

heat transfer coefficient (J)

Ψ

heat transfer coefficient

D Gd

average particle size (mm)

μ B

Bohr magneton (J·T−1)

β

parameter, 1/kT (J−1)

λ

molecular field parameter

μ

spin magnetic moment (A·m−1)

μ 0

vacuum permeability (H·m−1)

ρ

density (kg·m−3)

Subscripts or superscripts

a, b, c, d

state point of cycle

bc, da, bc, da

cyclic process

e

electronic system

l

crystal lattice system

i

cycle serial number

m

spin system

M

constant magnetization intensity

r

effective refrigeration capacity

0

low magnetic field

1

high magnetic field

H

high temperature

L

low temperature

1 Introduction

Traditional vapor compression refrigeration systems have various drawbacks, such as low efficiency and environmental concerns like exacerbating the greenhouse effect and depletion of the ozone layer. In response to these issues and the quest for alternative refrigerants, researchers are actively investigating novel refrigeration technologies. One promising solution that has emerged is the room temperature magnetic refrigerator [1,2,3,4], which offers compact size and superior efficiency. Moreover, energy efficiency and environmentally friendly features are key characteristics of magnetic refrigerators [5,6,7,8,9,10,11].

For magnetic refrigerators, it is very important to choose proper magnetic refrigerants and thermodynamic cycles in order to obtain the best thermodynamic performances. As for magnetic refrigerants, it should have large magnetocaloric effect (MCE) and small thermal hysteresis. In recent years, The magnetic materials with large MCE has been found in various intermetallic alloys [12,13,14], such as Gd5Si2Ge2, La(Fe,Si)13, MnAsSb, MnFePSi, Ni–Mn–In, MnNiSi-based high-entropy, and so on. However, they belong to the first-order magnetic phase transition material, generally with relatively poor machine properties. Although the magnetic entropy change in Gd might be less than one of above materials, it has excellent comprehensive performance, including good machine properties. The MCE materials with second-order magnetic phase transition are often used as refrigerants in the magnetic refrigerator [15].

As for thermodynamic cycles, there are lots of researchers to develop it. Smaili and Chahine [16] proposed an Ericsson-like magnetic refrigeration cycle using composite materials to induce constant magnetic entropy change as a function of temperature over the whole refrigeration range. Results showed that the refrigeration efficiency has been greatly improved. Smaili and Chahine [17], along with their colleagues, conducted an analysis on the thermodynamic efficiency of an active magnetic regenerator within a magnetic refrigeration system. Their study involved the evaluation of refrigeration capacity and coefficient of performance (COP). The results revealed a significant correlation between the performance of the active magnetic refrigerator (AMR) and the adiabatic magnetization temperature change (∆T). In 2003, Zhou et al. [18] studied and optimized the performance of a regenerated air refrigerator by finite-time thermodynamics and entropy generation minimization. Kaushik and Kumar [19] investigated the influence of regenerative loss, regenerator efficiency, and heat leak on the maximum output power in the magnetic Ericsson and Stirling cycle based on the finite time thermodynamic theory. In 2006, Yu et al. [20] also studied the magnetic Ericsson cycle using a new expression of magnetocaloric parameters, which was derived from classical Langevin theory. The results showed that there exists a maximum value of effective refrigerating capacity. The COP can be improved with increasing magnetic field strength while its rate might be decreased with the increase in the field. Petersen et al. [21] introduced a mathematical model for a reciprocating active magnetic regenerator, which consisted of a regenerator composed of parallel plates separated by channels containing a heat transfer fluid, along with a hot heat exchanger and a cold heat exchanger. Their findings demonstrated that the AMR achieved a no-load temperature span of 10.9 K under a 1 T magnetic field. Zhang et al. [22] developed a 1D AMR model to evaluate the cooling efficiency of active magnetic regenerators. Their model enabled the examination of heat transfer capabilities, viscous dissipation, and cooling efficiency of parallel wire configurations with square and triangular arrays. Additionally, numerous researchers have shown keen interest in exploring magnetic refrigeration cycles to enhance performance and optimize operational parameters of magnetic refrigerators [23,24,25,26,27].

Although the theoretical model is developing rapidly, it is becoming increasingly complex. In our work, it aims to build a simple and practical refrigeration model to predict the performance and efficiency of magnetic refrigeration and optimize the parameter of magnetic Ericsson cycle for the reciprocating room temperature magnetic regenerator. Then, the time dependent model is developed using the Langevin theory, molecule field approximate theory, and finite time thermodynamic theory.

2 Magnetic regenerative refrigerator

In the magnetic refrigerator, the magnetocaloric materials will absorb or reject the heat under the magnetic field changing (ΔH) because of MCE. MCE is an effect of heating or cooling of a magnetic material entering into or out the applied magnetic field (H). The heating or cooling need to be transferred with cold end or hot end. The heat transfer fluid is driven by the pumps, and the fluid path is changed periodically by some electromagnetic valves. A typical operating cycle of the magnetic refrigeration mainly consists of four steps: adiabatic magnetization (ΔH = 0 → ΔH > 0), high iso-magnetic-field process (ΔH > 0), adiabatic demagnetization (ΔH > 0 → ΔH = 0), low iso-magnetic-field process (ΔH = 0). The cycle is repeated, and the thermal energy is taken from the cold end to the hot end.

3 Physical model

3.1 Thermodynamic properties of ferromagnetic materials

Based on the Langevin theory, the mean magnetic moment of the magnetic system, denoted as M, can be calculated using formula (1).

(1) M ( T , H ) = N g μ B J B ( x ) ,

where T is temperature, N is the number of magnetic moments per unit volume, B J (x) is Brillouin function, g is Landé factor, x is order-parameter, J is quantum number, and μ B is Bohr magneton.

For ferromagnetic materials, B J (x) can be rewritten as

(2) B J ( x ) = 2 J + 1 2 J coth 2 J + 1 2 J x − 1 2 J coth x 2 J .

One can obtain the magnetization M using the above formula. By applying the Maxwell formula and principles from thermodynamic theory, equations (3) and (4) can be derived. These equations offer a means to compute the magnetic entropy change ΔS and the heat capacity C m for ferromagnetic substances. It is worth noting that these formulas for magnetocaloric properties are applicable not only to ferromagnetic materials but also to paramagnetic systems, as per the established principles of thermodynamics.

(3) Δ S ( T , H ) = ∫ 0 H ∂ M ∂ T d H ,

(4) C m = T ∂ S ∂ T H = S 0 ′ ( T ) − μ 0 T H ∂ M ∂ T − M ( H + λ M ) + 2 λ M T ∂ M ∂ T T + μ 0 N k x ′ 2 J + 1 2 J coth ( 2 J + 1 ) x 2 J − coth x 2 J 2 J ,

(5) ∂ M ∂ T H = N g μ B J ∂ B J ( x ) ∂ T 1 − N g μ B J ∂ B J ( x ) ∂ T = λ M + H T ξ ( x ) k T N ( g μ B J ) 2 + λ ξ ( x ) ,

where T is the temperature, λ is the molecular field parameter, ξ(x) is the Langevin function, and k is the Boltzmann constant.

3.2 Model of time dependent magnetic Ericsson refrigeration cycle

The Ericsson cycle includes four steps, as shown in Figure 1(a), but the conventional models used to analyze the Ericsson cycle do not cover the effect of running time. However, any process needs time to run. In order to improve the Ericsson cycle model, the time axis was added in this work. As shown in Figure 1(b), the time-dependent Ericsson refrigeration cycle is more in line with the actual situation.

Figure 1 The model of magnetic Ericsson refrigeration cycle: (a) independent of time and (b) depends on time.
Figure 1

The model of magnetic Ericsson refrigeration cycle: (a) independent of time and (b) depends on time.

According to the magnetic refrigeration cycle, the temperature range in four steps, referring to Figure 1(b), can be expressed as follows [21,28]:

  1. Magnetizing process (a(i)–b(i)): the magnetic field increases from H 0 to H 1, the time consumed is t ab. For the first time cycle (i = 0), the hot end temperature T H(0) and the cold temperature T L(0) are set as T H(0) = T L(0).

    (6) Q ab ( i ) = T H ( i ) ( S a ( i ) − S b ( i ) ) = T H ( i ) ( S m ( T H ( i ) , H 0 ) + S l ( T H ( i ) ) + S e ( T H ( i ) ) − S m ( T H ( i ) , H 1 ) − S l ( T H ( i ) ) − S e ( T H ( i ) ) ) = T H ( i ) Δ S ab ( i ) ( T H ( i ) , H 1 ) ,

    (7) Δ T 1 ( i ) = Q H ( i ) C p ( T H ( i ) , H 1 ) ,

    where C p is the specific heat capacity of magnetic materials, S m is the magnetic entropy, S l is the lattice entropy, and S e is the electronic entropy.

  2. High iso-magnetic-field process (b(i)–c(i)): during the time t bc, the magnetic field is kept as H 1. At the temperature close to room temperature, the lattice and electronic contribution to the total heat capacity cannot be ignored, and need to be taken into account.

(8) Q bc ( i ) = ∫ T L ( i ) T H ( i ) C H ( H 1 , T ) d T = ∫ T L ( i ) T H ( i ) C m ( T H ( i ) , H 1 ) + C l ( T H ( i ) ) + C e ( T H ( i ) ) d T ,

(9) Δ T 2 ( i ) = Δ Q bc ( i ) Δ S ab ( i ) .

  1. Demagnetizing process (c(i)–d(i)): the magnetic field decreases from H 1 to H 0, the time consumed is t cd.

(10) Q cd( i ) = T L ( i ) Δ S cd ( i ) ( T L ( i ) , H 1 ) ,

(11) Δ T 3 ( i ) = Q cd ( i ) C p ( T L ( i ) , H 1 ) .

  1. Low iso-magnetic-field stage (d–a): during the time t da, the magnetic field is kept at H 0.

(12) Q da ( i ) = ∫ T L ( i ) T H ( i ) C H ( H 1 , T ) d T = ∫ T L ( i ) T H ( i ) C m ( T H ( i ) , H 1 ) + C l ( T H ( i ) ) + C e ( T H ( i ) ) d T ,

(13) Δ T 4 ( i ) = Δ Q da ( i ) Δ S da .

From the above analysis, one can obtain the hot end and cold end temperatures T H(i) and T L(i) of every step by equations (14) and (15), respectively.

(14) T H = ∑ i = 1 n T H ( i ) + 2 ψ 1 π η H e t 1 3 − 1 e t 1 3 + 1 × Δ T 1 ( i ) + e t 2 3 − 1 e t 2 3 + 1 × Δ T 2 ( i ) ,

(15) T L = ∑ 1 n T L ( i ) + 2 ψ 2 π η L e t 3 3 − 1 e t 3 3 + 1 × Δ T 3 ( i ) + e t 4 3 − 1 e t 4 3 + 1 × Δ T 4 ( i ) ,

(16) Q c = ∑ i = 1 n Q c ( i ) + 2 ψ 2 π η L e t 3 3 − 1 e t 3 3 + 1 × Q cd ( i ) + e t 4 3 − 1 e t 4 3 + 1 × Q da ( i ) − e t 2 3 − 1 e t 2 3 + 1 × Q bc ( i ) ,

(17) P = ∑ i = 1 n ( P ( i ) + Q ab ( i ) − Q cd ( i ) + Q bc ( i ) − Q da ( i ) ) ,

(18) COP = Q c P ,

where η H and η L are the heat exchange coefficients between the magnetoccaloric material and fluid in hot end and cold end, respectively. Ψ 1 and Ψ 2 are the heat transfer coefficient from fluid to environment at hot end and cold end, respectively, and t 1, t 2, t 3, t 4 are the consuming time of each cycle step. Q c is the net cooling quantity, P is the power input, and COP is the coefficient of performance. The related parameter values have been chosen to be η H = η L = 0.6, Ψ 1 = Ψ 2 = 0.8, t 1 = t 2 = t 3 = t 4 = 2 s. If we want to change the refrigeration condition, these parameters can be tuned. This work focuses on the different cycle periodicity effect on the magnetic refrigeration performance.

With the increasing repetition of the refrigeration cycle, the refrigeration temperature span increases.

4 Numerical results and discussion

In this work, Gd is selected as the magnetocaloric material. The related parameters chosen are shown in Table 1 [21,29]. The calculation process is shown in Figure 2.

Table 1

Parameters of Gd used in the calculation

T c (K) 293K γ (J·K−1·mol−1) 0.0109
g 2 C p (J·kg−1·K−1) 235
J 7/2 ρ (kg·m−3) 7,900
θ D (K) 173 D Gd (mm) 3
Figure 2 The flowchart of the MATLAB program.
Figure 2

The flowchart of the MATLAB program.

From Figure 3, it can be seen that ∆C changed abruptly in the vicinity of 293 K, which made the magnetic entropy change in gadolinium to reach the maximum at Curie temperature. The results are in good agreement with the experiments [30].

Figure 3 The C p of Gd as a function of magnetic field and temperature.
Figure 3

The C p of Gd as a function of magnetic field and temperature.

Figure 4. shows the hot end and cold end temperatures as the function of the cycle time, when the field and cycle periodicity is 0.8 T and 8 s, respectively. After 250 s cycles, the temperature of cold end is 291 K, which results in a ∆T of 5 K (temperature span between the cold end and the hot end, ΔT = T H − T L). Figure 5 further demonstrates the gradual approach toward the cyclic steady-state. This can be observed when the temperature difference (∆T) undergoes a significant 5 K change within the initial 250 s, followed by a much slower decrease of only 0.2 K between 250 and 500 s.

Figure 4 The hot end and cold end temperatures as the function of time in 0.8 T.
Figure 4

The hot end and cold end temperatures as the function of time in 0.8 T.

Figure 5 The cold end temperature vs field intensity.
Figure 5

The cold end temperature vs field intensity.

Figure 5 shows that the cold end temperature decreases with the increasing time in the different magnetic fields (from 0.5 to 3.5 T). It indicated that the time consumed from start to the cyclic steady-state reduces with increasing field from 0.5 to 3.5 T. On the other hand, the cold end temperature reaches the minimum (287 K) at H = 3.5 T after 200 s. Thus, it is implied that the large magnetic field can enable the MCE materials to generate large temperature span ∆T within each cycle.

Figure 6 shows the cold end temperature as a function of time with different cycle periodicity at H = 0.8 T when heat transfer coefficient η keeps constant. It is evident that the duration required to reach the cyclic steady-state diminishes as the cycle periodicity decreases from 60 to 2 s. The gradual approach to the steady-state suggests that operating the magnetic refrigerator requires a significant amount of time, leading to a decrease in refrigeration efficiency.

Figure 6 The cold end temperature vs time at H = 0.8 T.
Figure 6

The cold end temperature vs time at H = 0.8 T.

Figure 7 shows the COP as a function of time with different cycle periodicity at H = 0.8 T when heat transfer coefficient η keeps constant. It is revealed that COP decreases with the increase in time, but increases with the increase in cycle periodicity from 2 to 60 s. For this phenomenon, the reason is that the heat exchange times increase with the increasing cycle periodicity from 2 to 60 s. The long time of thermal exchange shows that the more thermal generated by magnetic work in high magnetic field discharges to cold end although there is some heat to be lost. To obtain a large temperature span at the cold end, the coefficient of performance (COP) must always be at its optimal value even in different cycle periods. So, we random select a section from the refrigeration process, which has the characteristic by function of COP, temperature drop of cold end, and different cycle periodicity, as shown in Figure 8. It is a section at 100 s.

Figure 7 The COP vs time with different cycle periodicity at H = 0.8 T.
Figure 7

The COP vs time with different cycle periodicity at H = 0.8 T.

Figure 8 The COP, temperature drop of cold end vs different cycle periodicity.
Figure 8

The COP, temperature drop of cold end vs different cycle periodicity.

From Figure 8, one can see that temperature span of cold end decreases with the increasing cycle periodicity. But this does not mean that the shorter cycle periodicity is better for the refrigeration performance. We have to notice that the magnetic refrigeration performance is a competitive result of multiple factors. When the cycle periodicity is about 20 s, the competitive performance of magnetic refrigerator reaches the peak. In our experiment, the cycle periodicity of 16 s is chosen because we found that further reducing the cycle periodicity cannot improve the refrigeration speed. So, it can be seen that simulation results agree with the experiment results. More investigation will proceed in the future.

In order to evaluate the time-dependent Ericsson model, we compared it with reported data, as listed in Table 2. Baglivo et al. [27] reported that Langevin’s statistical mechanical theory accurately explains how a refrigeration machine operates based on a magnetic Ericsson cycle. The Ericsson cycles are most effective in illustrating the actual experimental findings, despite the fact that the theoretical data do not align perfectly with the experimental results. This is in agreement with our results. As listed in Table 2, the temperature span and COP are varied for different theory models. This might be resulted from setting different parameters in the model. From Table 2, the weakest magnetic field and frequency in our model lead the temperature span to the lowest value, but the temperature span from our time-dependent Ericsson model is basically same with our experimental result (3.9 K).

Table 2

Results of time-dependent Ericsson model and some reported theory model

Model Magnetic field Frequency Temperature span Ref.
AMR 1 T 0.5 Hz 11.3 K [31]
AMR 1.4 0.5–2 Hz 10 K [22]
AMR 1 T 1 Hz 20 K [32]
AMRR 2 T — 12 K [33]
Ericsson — — COPmax = 7.2 [34]
Ericsson — — COP = 0.8–1.48 [35]
Ericsson 0.8 T 0.125 Hz 5 K This work

5 Conclusion

A practical time-dependent model for magnetic regenerative refrigerator was proposed in this work.

  • The role of model. It can be used to predict the performance and efficiency of magnetic refrigerator with various transfer fluids.

  • The simulation results. The increasing frequency can increase the refrigeration capacity of the magnetic refrigerator, although partly reducing the system efficiency. The magnetic refrigerator yielded the temperature span of 5 K under 0.8 T magnetic field after 30 cycles.

  • The future development. The model is more universal and easier to use and can provide some new instruction to optimally design the magnetic refrigerators.

Acknowledgements

This work was supported by Characteristic innovation projects of Guangdong Province (No. 2020KTSCX203).

  1. Funding information: This work was supported by Characteristic innovation projects of Guangdong Province (No. 2020KTSCX203).

  2. Author contributions: Jimei Niu designed and organized the research work and edited the manuscript; and Zhigang Zheng conducted the experiments and carried out the calculation and data analysis.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: All authors confirm that all data used in this article can be published in the Journal “High Temperature Materials and Processes.”

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Received: 2024-04-22
Revised: 2024-08-15
Accepted: 2024-09-20
Published Online: 2024-10-30

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/htmp-2024-0054
Keywords for this article
magnetic refrigerator; Ericsson model; simulation; optimization
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. De-chlorination of poly(vinyl) chloride using Fe2O3 and the improvement of chlorine fixing ratio in FeCl2 by SiO2 addition
  3. Reductive behavior of nickel and iron metallization in magnesian siliceous nickel laterite ores under the action of sulfur-bearing natural gas
  4. Study on properties of CaF2–CaO–Al2O3–MgO–B2O3 electroslag remelting slag for rack plate steel
  5. The origin of {113}<361> grains and their impact on secondary recrystallization in producing ultra-thin grain-oriented electrical steel
  6. Channel parameter optimization of one-strand slab induction heating tundish with double channels
  7. Effect of rare-earth Ce on the texture of non-oriented silicon steels
  8. Performance optimization of PERC solar cells based on laser ablation forming local contact on the rear
  9. Effect of ladle-lining materials on inclusion evolution in Al-killed steel during LF refining
  10. Analysis of metallurgical defects in enamel steel castings
  11. Effect of cooling rate and Nb synergistic strengthening on microstructure and mechanical properties of high-strength rebar
  12. Effect of grain size on fatigue strength of 304 stainless steel
  13. Analysis and control of surface cracks in a B-bearing continuous casting blooms
  14. Application of laser surface detection technology in blast furnace gas flow control and optimization
  15. Preparation of MoO3 powder by hydrothermal method
  16. The comparative study of Ti-bearing oxides introduced by different methods
  17. Application of MgO/ZrO2 coating on 309 stainless steel to increase resistance to corrosion at high temperatures and oxidation by an electrochemical method
  18. Effect of applying a full oxygen blast furnace on carbon emissions based on a carbon metabolism calculation model
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  20. Thermo-mechanical effects and microstructural evolution-coupled numerical simulation on the hot forming processes of superalloy turbine disk
  21. Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms
  22. Effect of calcium treatment on inclusions in 38CrMoAl high aluminum steel
  23. Effect of isothermal transformation temperature on the microstructure, precipitation behavior, and mechanical properties of anti-seismic rebar
  24. Evolution of residual stress and microstructure of 2205 duplex stainless steel welded joints during different post-weld heat treatment
  25. Effect of heating process on the corrosion resistance of zinc iron alloy coatings
  26. BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
  27. Innovative approaches to enhancing crack repair: Performance optimization of biopolymer-infused CXT
  28. Structural and electrochromic property control of WO3 films through fine-tuning of film-forming parameters
  29. Influence of non-linear thermal radiation on the dynamics of homogeneous and heterogeneous chemical reactions between the cone and the disk
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  32. Performance evaluation of fly ash-lime-gypsum-quarry dust (FALGQ) bricks for sustainable construction
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  35. Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features
  36. Effect of tempering temperature on the microstructure and mechanical properties of Q125 shale gas casing steel
  37. Review Articles
  38. A review of emerging trends in Laves phase research: Bibliometric analysis and visualization
  39. Effect of bottom stirring on bath mixing and transfer behavior during scrap melting in BOF steelmaking: A review
  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
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  44. Topical Issue on Focus of Hot Deformation of Metaland High Entropy Alloys - Part II
  45. Synthesis of aluminium (Al) and alumina (Al2O3)-based graded material by gravity casting
  46. Experimental investigation into machining performance of magnesium alloy AZ91D under dry, minimum quantity lubrication, and nano minimum quantity lubrication environments
  47. Numerical simulation of temperature distribution and residual stress in TIG welding of stainless-steel single-pass flange butt joint using finite element analysis
  48. Special Issue on A Deep Dive into Machining and Welding Advancements - Part I
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  50. Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach
  51. Experimental and numerical analysis of temperature distributions in SA 387 pressure vessel steel during submerged arc welding
  52. Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate
  53. Multi-response optimization of friction stir welding using fuzzy-grey system
  54. Mechanical and micro-structural studies of pulsed and constant current TIG weldments of super duplex stainless steels and Austenitic stainless steels
  55. Stretch-forming characteristics of austenitic material stainless steel 304 at hot working temperatures
  56. Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
  57. Study of phase equilibrium of refractory high-entropy alloys using the atomic size difference concept for turbine blade applications
  58. A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network
  59. A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method
  60. Special Issue on Recent Developments in 3D Printed Carbon Materials - Part II
  61. Innovations for sustainable chemical manufacturing and waste minimization through green production practices
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  63. Characterization of Co–Ni–TiO2 coatings prepared by combined sol-enhanced and pulse current electrodeposition methods
  64. Hot deformation behaviors and microstructure characteristics of Cr–Mo–Ni–V steel with a banded structure
  65. Effects of normalizing and tempering temperature on the bainite microstructure and properties of low alloy fire-resistant steel bars
  66. Dynamic evolution of residual stress upon manufacturing Al-based diesel engine diaphragm
  67. Study on impact resistance of steel fiber reinforced concrete after exposure to fire
  68. Bonding behaviour between steel fibre and concrete matrix after experiencing elevated temperature at various loading rates
  69. Diffusion law of sulfate ions in coral aggregate seawater concrete in the marine environment
  70. Microstructure evolution and grain refinement mechanism of 316LN steel
  71. Investigation of the interface and physical properties of a Kovar alloy/Cu composite wire processed by multi-pass drawing
  72. The investigation of peritectic solidification of high nitrogen stainless steels by in-situ observation
  73. Microstructure and mechanical properties of submerged arc welded medium-thickness Q690qE high-strength steel plate joints
  74. Experimental study on the effect of the riveting process on the bending resistance of beams composed of galvanized Q235 steel
  75. Density functional theory study of Mg–Ho intermetallic phases
  76. Investigation of electrical properties and PTCR effect in double-donor doping BaTiO3 lead-free ceramics
  77. Special Issue on Thermal Management and Heat Transfer
  78. On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach
  79. Time dependent model to analyze the magnetic refrigeration performance of gadolinium near the room temperature
  80. Heat transfer characteristics in a non-Newtonian (Williamson) hybrid nanofluid with Hall and convective boundary effects
  81. Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
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