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Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation. [ABSTRACT FROM AUTHOR]

As computer-aided design (CAD) tools have become an essential aspect of modern mechanical engineering design, the demand for CAD experts has increased significantly. The development from novice, to proficient, to expert user is of particular interest to the industrial and academic design communities. Yet little is known about the development of modeling choices, strategies, and patterns that characterize expert CAD skills; much of the past work that reports user action data is based on student or novice data. We compared the CAD modeling process across nine proficient and ten expert designers as they were tested to complete the same design task. Under identical conditions-the same time constraints in the same CAD platform and with the same task-the expert users were able to complete a larger proportion of the task with higher dimensional accuracy. While the experts were able to dissect and retrieve geometries from manufacturing drawings more efficiently than proficient users, they were also able to plan a modeling strategy that required less effort and revisions. With our experimental findings, we identify the demand for procedural knowledge-building for young engineers, with the ultimate goal of more effectively developing experts in engineering design with CAD. [ABSTRACT FROM AUTHOR]

As the most prestigious award in the field of machine design, the ASME Machine Design Award relishes its prestige and prominence. In the past 65 years since its inception in 1958, 60 eminent scholars have received this honor. This paper reviews the history of this prestigious award, including a summary of the contributions to the field of all 60 recipients, and acts as an archival document. Reviewing these awards in the past 65 years by following the tracks of its recipients elucidates developments in the field and significant contributions to the advancement of knowledge in machine design. In particular, novel contributions and remarkable achievements in the fields of mechanical engineering and machine design and development are recorded. This should be of interest to those interested in the study and in the historical development of mechanical engineering, particularly in machine design. [ABSTRACT FROM AUTHOR]

The area-based three-dimensional optical inspection of workpiece geometries is the basis for quality control, maintenance tasks, and many other typical applications in mechanical engineering and automation such as adaptive manufacturing. In the context of a cyber-physical approach for semi-autonomous post-processing of additively manufactured parts, this method provides the basis for an iterative manufacturing approach. Commercially available systems for optical inspections often rely on camera-based methods, which are, however, susceptible to reflections. This article describes an approach for developing an optical scanstation that uses blue laser line scanners in combination with a Cartesian three-axis motion system and a turntable. The focus of the work is on the development of a method for the fast extrinsic calibration of the entire scanstation. [ABSTRACT FROM AUTHOR]

Human beings are physically and cognitively variable, leading to a wide array of potential system use cases. To design safe and effective systems for highly heterogeneous populations, engineers must cater to this variability to minimize the chance of error and system failure. This can be a challenge because of the increasing costs associated with providing additional product variety. Most guidance for navigating these trade-offs is intended for late-stage design, when significant resources have been expended, thus risking expensive redesign or exclusion of users when new human concerns become apparent. Despite the critical need to evaluate accommodation-cost trade-offs in early stages of design, there is currently a lack of structured guidance. In this work, an approach to function modeling is proposed that allows the simultaneous consideration of human and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost. The model was demonstrated in a design case study. About 16 senior mechanical engineering students were recruited and tasked with performing the allocation task manually. The results were compared to the output of the optimization model. Results indicated that participants were unable to produce concepts with the same accommodation-cost efficiency as the optimization model. Further, the optimization model successfully produced a wide range of potential product concepts, demonstrating its utility as a decision-aid. [ABSTRACT FROM AUTHOR]

Since 2009, the mechanical engineering (ME) scholarship-science technology engineering and mathematics (S-STEM) Program at the University of Maryland Baltimore County (UMBC) has provided financial support and program activities to ME undergraduate students aiming at improving their retention and graduation rates. The objective of this study is to identify program activities that were most effective to help students for improvements. Current ME S-STEM scholars were asked to complete a survey that measures their scientific efficacy, engineering identity, expectations, integration, and sense of belonging, as well as how program activities impact their attitudes and perceptions. Analyses of 36 collected surveys showed that scholars reported high levels of engineering identity, expectations, and sense of belonging. However, further improvements were needed to help students in achieving scientific efficacy and academic integration into the program. Results demonstrated that pro-active mentoring was the most effective method contributing to positive attitudes and perceptions. The implemented S-STEM research-related activities and internship were viewed favorably by the scholars in helping them establish their scientific efficacy and engineering identity, and understand their expectations and goals. Community building activities were considered helpful for them to integrate into campus life and improve their sense of belonging to the campus and program. Scholars identified mentoring, research related activities, internships, and social interaction with faculty and their peers as important factors for their retention and graduation. Although the sample size was small in the study, we believe that the cost-effective activities identified could be adopted by other institutions to further improve students' retention and graduation rates in engineering programs. [ABSTRACT FROM AUTHOR]

Since 2009, the mechanical engineering (ME) scholarship-science technology engineering and mathematics (S-STEM) Program at the University of Maryland Baltimore County (UMBC) has provided financial support and program activities to ME undergraduate students aiming at improving their retention and graduation rates. The objective of this study is to identify program activities that were most effective to help students for improvements. Current ME S-STEM scholars were asked to complete a survey that measures their scientific efficacy, engineering identity, expectations, integration, and sense of belonging, as well as how program activities impact their attitudes and perceptions. Analyses of 36 collected surveys showed that scholars reported high levels of engineering identity, expectations, and sense of belonging. However, further improvements were needed to help students in achieving scientific efficacy and academic integration into the program. Results demonstrated that pro-active mentoring was the most effective method contributing to positive attitudes and perceptions. The implemented S-STEM research-related activities and internship were viewed favorably by the scholars in helping them establish their scientific efficacy and engineering identity, and understand their expectations and goals. Community building activities were considered helpful for them to integrate into campus life and improve their sense of belonging to the campus and program. Scholars identified mentoring, research related activities, internships, and social interaction with faculty and their peers as important factors for their retention and graduation. Although the sample size was small in the study, we believe that the cost-effective activities identified could be adopted by other institutions to further improve students' retention and graduation rates in engineering programs. [ABSTRACT FROM AUTHOR]

Prototyping is an important part of the design process, but researchers currently have a limited understanding of how the resources invested in a prototype or designer teams' intent for a prototype influence decision-making and perceptions of a prototype's value. Prior work has shown that significant investment of time or money in design tasks can lead to design fixation, but little work has explored how these factors might impact design teams' perceived value of prototypes with respect to the design process. Likewise, it is unclear how resource allocation and prototype intention effects the evolution of design team knowledge and subsequent design actions. To explore these relationships, an in situ study of prototyping tendencies was performed across two mechanical engineering design courses. Results suggest that effective design teams leveraged prototypes to uncover new design knowledge and limit uncertainty early in the design process. Importantly, this study did not find any significant relationships between the cost of a prototype in terms of money and time, and the perceived value of that prototype. Nor were any significant relationships found between costs and final design outcomes. Findings suggest that the true cost and value of a prototype with respect to design outcomes are challenging to explicitly quantify. [ABSTRACT FROM AUTHOR]

This paper presents a behavior-based protocol study conducted with mechanical engineering students, where the participants developed function-structure models for a novel design problem. A modeling activity video was recorded for each participant and coded using a protocol analysis that captured the modeling sequence, actions, and elements. Pauses in the modeling process were analyzed to identify patterns based on pause time and frequency, the distribution of pauses over the modeling activity, and events preceding and following the pauses. In this study, a pause is characterized as an interruption in the modeling process lasting at least 2 s. Participants were found to spend an average of 38% of the modeling time in pauses, with more of it being allotted to the middle of the modeling activity and less toward the start and end. Three pause types are defined (short, intermediate, and long pauses) based on an analysis of pause lengths, which are then used to analyze events before and after pauses. Participants added elements to the model more frequently, compared to editing and deleting elements. Longer pauses were observed before participants before elements are removed from the model, whereas editing was done more frequently after shorter pauses. Several modeling element pairs are identified that are infrequently separated by pauses, such as the "edge" and "edge text" pair, suggesting that the designer thinks about these as paired elements rather than distinct elements. Limitations of the research methods are discussed, and finally, new research questions are identified as continuing work for this research. [ABSTRACT FROM AUTHOR]

The objectives of this study were to evaluate the current status of exposure to bio-engineering research in community college (CC) students and University of Maryland Baltimore County (UMBC) students, and to estimate relationships between research activities sponsored by the Mechanical Engineering (ME) S-STEM Scholarship Program and improvement in student enrollment/diversification, retention rates, and graduation rates. The analysis drew on data from ME undergraduate academic records at UMBC from 2008 to 2019. A survey was designed to assess the research exposure of CC and UMBC students and their evaluation of the research components included in recruitment and curriculum activities. Results show that exposure to research measured by attending a research seminar was low for the participants, around 37% for CC students and 21% for ME students at UMBC. The survey results indicate the positive impact of the scholarship programs at UMBC on the research exposure and research experience. The impact is more evident in students who originally transferred from a CC. The large increase in recruited female and CC students over the past 10?years indicated that the research-related activities of the ME S-STEM program played an instrumental role in those increases. Because of the research-related activities, the ME S-STEM program achieved retention and graduation rates higher than those in the ME undergraduate program (89% versus 60% for the 6?year graduation rate), as well a higher percentage of students enrolled in graduate school (30% versus 10%). We conclude that there is still a need to implement research-related activities in the ME undergraduate program, starting with student recruitment and continuing through the academic program. Results suggest that there is a positive impact of ME S-STEM research activities on student diversification, retention rates, and percentage of our graduates who are pursuing graduate degree. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes offer unique capabilities (i.e., opportunities) yet inherent limitations (i.e., restrictions) due to the layer-by-layer fabrication of parts. Despite the newfound design freedom and increased use of AM, limited research has investigated how knowledge of the AM processes affects the creativity of students' ideas after being exposed to AM. This study investigates this gap through an experimental study with 343 participants recruited from a junior-level mechanical engineering design course. The participants were exposed to three variations in design for additive manufacturing (DfAM) education: (1) no DfAM, (2) restrictive DfAM, and (3) opportunistic and restrictive (dual) DfAM education. The effects of these three interventions were measured through differences in (1) participants' self-reported use of DfAM in a design challenge and (2) expert assessment of the creativity of the outcomes from the said design challenge. The results of the study indicated that variations in DfAM content did not result in differences in the participants' self-reported use of either opportunistic or restrictive DfAM, with all three groups reporting similar levels of emphasis. Further, participants from all three groups reported higher use of restrictive DfAM techniques, compared with opportunistic DfAM. Moreover, while variations in the content had no effect on the creativity (uniqueness and usefulness) of the participants' design outcomes, teaching both opportunistic and restrictive DfAM did result in the generation of designs with greater AM technical goodness--a novel and significant finding in our study. The results of this study highlight the need for DfAM educational interventions that encourage students to not only learn about but also integrate both opportunistic and restrictive concepts effectively into their creative design process. This would result in the generation of innovative products that leverage the design freedom enabled by AM, yet addressing the limitations inherent in the process. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes offer unique capabilities (i.e., opportunities) yet inherent limitations (i.e., restrictions) due to the layer-by-layer fabrication of parts. Despite the newfound design freedom and increased use of AM, limited research has investigated how knowledge of the AM processes affects the creativity of students' ideas after being exposed to AM. This study investigates this gap through an experimental study with 343 participants recruited from a junior-level mechanical engineering design course. The participants were exposed to three variations in design for additive manufacturing (DfAM) education: (1) no DfAM, (2) restrictive DfAM, and (3) opportunistic and restrictive (dual) DfAM education. The effects of these three interventions were measured through differences in (1) participants' self-reported use of DfAM in a design challenge and (2) expert assessment of the creativity of the outcomes from the said design challenge. The results of the study indicated that variations in DfAM content did not result in differences in the participants' self-reported use of either opportunistic or restrictive DfAM, with all three groups reporting similar levels of emphasis. Further, participants from all three groups reported higher use of restrictive DfAM techniques, compared with opportunistic DfAM. Moreover, while variations in the content had no effect on the creativity (uniqueness and usefulness) of the participants' design outcomes, teaching both opportunistic and restrictive DfAM did result in the generation of designs with greater AM technical goodness--a novel and significant finding in our study. The results of this study highlight the need for DfAM educational interventions that encourage students to not only learn about but also integrate both opportunistic and restrictive concepts effectively into their creative design process. This would result in the generation of innovative products that leverage the design freedom enabled by AM, yet addressing the limitations inherent in the process. [ABSTRACT FROM AUTHOR]

Research in additive manufacturing (AM) has increased the use of AM in many industries, resulting in a commensurate need for a workforce skilled in AM. In order to meet this need, educational institutions have undertaken different initiatives to integrate design for additive manufacturing (DfAM) into the engineering curriculum. However, limited research has explored the impact of these educational interventions in bringing about changes in the technical goodness of students' design outcomes, particularly through the integration of DfAM concepts in an engineering classroom environment. This study explores this gap using an experimental study with 193 participants recruited from a junior-level course on mechanical engineering design. The participants were split into three educational intervention groups: (1) no DfAM, (2) restrictive DfAM, and (3) restrictive and opportunistic (dual) DfAM. The effects of the educational intervention on the participants' use of DfAM were measured through changes in (1) participants' DfAM self-efficacy, (2) technical goodness of their AM design outcomes, and (3) participants' use of DfAM-related concepts when describing and evaluating their AM designs. The results showed that while all three educational interventions result in similar changes in the participants' opportunistic DfAM self-efficacy, participants who receive only restrictive DfAM inputs show the greatest increase in their restrictive DfAM self-efficacy. Further, we see that despite these differences, all three groups show a similar decrease in the technical goodness of their AM designs, after attending the lectures. A content analysis of the participants' design descriptions and evaluations revealed a simplification of their design geometries, which provides a possible explanation for the decrease in their technical goodness, despite the encouragement to utilize the design freedom of AM to improve functionality or optimize the weight of the structure. These results emphasize the need for more in-depth DfAM education to encourage the use of both opportunistic and restrictive DfAM during student design challenges. The results also highlight the possible influence of how the design problem is stated on the use of DfAM in solving it. [ABSTRACT FROM AUTHOR]

Although morphological charts are widely taught used tools in engineering design, little formal guidance is provided regarding their representation and exploration. Thus, an experiment was conducted to elucidate the influence of functional ordering on the exploration of morphological charts. Two design prompts were used, each with five different functional arrangements: (1) most-to-least important function, (2) least-to-most important function, (3) input-to-output function, (4) output-to-input function, and (5) Random. Sixty-seven junior mechanical engineering students were asked to generate integrated design concepts from prepopulated morphological charts for each design prompt. The concepts were analyzed to determine the frequency with which a given means was selected, how much of the chart was explored, the sequence of exploration, and the influence of function ordering. Results indicated a tendency to focus upon the initial columns of the chart irrespective of functional order. The most-to-least-important functional order resulted in higher chances and a uniformity of design space exploration. [ABSTRACT FROM AUTHOR]

Pistons are fundamental structural elements in any engineering practices such as mechanical, civil, aerospace, and offshore engineering. Their strength strongly depends on buckling load, and such information is a major requirement in the design process. Euler's linear buckling equation is the most common and most used model in design. It is well suited for linear elastic members without geometrical imperfections and nonlinear behavior. Several analytical and experimental investigations of typical hydraulic cylinders have been carried out through the years but most of the available standards still use a linear approach with many simplifications. Pistons are slender beams with not-uniform cross section, which need a stronger effort than the classical Euler's approach. The present paper aims to discuss limitations of current DNV standards for piston design in offshore technologies when compared to classical numerical approaches and reference results provided by the existing literature. [ABSTRACT FROM AUTHOR]

A significant gap exists between engineering students' perceptions of prototypes and prototyping abilities and professionals' perceptions and abilities. Structured prototyping frameworks have recently been developed and proposed as a means to help students close this gap, but the effects of these frameworks on students' behavior have not been assessed. The purpose of this work is to investigate if and how a structured prototyping framework affects the self-reported prototyping behaviors of engineering students. Understanding how structured prototyping frameworks affect students can provide educators with a deeper understanding of the way their students adopt and understand design methods. A mixed method study is presented. A 15-item survey and two open-ended questions were distributed to 235 students in a junior-level mechanical engineering design class in order to capture self-reported prototyping behavior. Quantitative results indicate that significant differences in engineering students' prototyping behaviors exist across time and between groups. Results from qualitative analysis indicate that students in the control group focused solely on improving technical quality, while students in both experimental groups focused on a wider range of design qualities. This study is the first to demonstrate that a structured prototyping framework can affect engineering students' self-reported prototyping behaviors during design activities. [ABSTRACT FROM AUTHOR]

A numerical approach was developed based on multidimensional computational fluid dynamics (CFD) to predict knocking combustion in a cooperative fuel research (CFR) engine. G-equation model was employed to track the turbulent flame front and a multizone model was used to capture auto-ignition in the end-gas. Furthermore, a novel methodology was developed wherein a lookup table generated from a chemical kinetic mechanism could be employed to provide laminar flame speed as an input to the Gequation model, instead of using empirical correlations. To account for fuel chemistry effects accurately and lower the computational cost, a compact 121-species primary reference fuel (PRF) skeletal mechanism was developed from a detailed gasoline surrogate mechanism using the directed relation graph (DRG) assisted sensitivity analysis (DRGASA) reduction technique. Extensive validation of the skeletal mechanism was performed against experimental data available from the literature on both homogeneous ignition delay and laminar flame speed. The skeletal mechanism was used to generate lookup tables for laminar flame speed as a function of pressure, temperature, and equivalence ratio. The numerical model incorporating the skeletal mechanism was employed to perform simulations under research octane number (RON) and motor octane number (MON) conditions for two different PRFs. Parametric tests were conducted at different compression ratios (CR) and the predicted values of critical CR, delineating the boundary between "no knock" and "knock," were found to be in good agreement with available experimental data. The virtual CFR engine model was, therefore, demonstrated to be capable of adequately capturing the sensitivity of knock propensity to fuel chemistry. [ABSTRACT FROM AUTHOR]

This study makes energy and exergy analysis of a sample organic Rankine cycle (ORC) with a heat exchanger which produces energy via a geothermal source with a temperature of 140 °C. R600a is preferred as refrigerant to be used in the cycle. The changes in exergy destructions (of irreversibility) and exergy efficiencies in each cycle element are calculated in the analyses made based on the effectiveness of heat exchanger used in cycle and evaporator temperature changing between 60 and 120°C for fixed pinch point temperature differences in evaporator and condenser. Parameters showing system performance are assessed via second law approach. Effectiveness of heat exchanger and temperature of evaporator are taken into consideration within the scope of this study, and energy and exergy efficiencies of cycle are enhanced maximum 6.87% and 6.21% respectively. Similarly, exergy efficiencies of evaporator, heat exchanger, and condenser are increased 4%, 82%, and 1.57%, respectively, depending on the effectiveness of heat exchanger and temperature of evaporator. [ABSTRACT FROM AUTHOR]

Ten years prior to this paper, innovative mechanical products were analyzed and found to embody multiple innovation characteristics--an average of two more than competing products in the marketplace. At the time, it was not known whether these products would be successful over time and whether the number or type of innovation characteristics would be related with success. In this work, products from the previous study were categorized into well- and under-adopted products. Also, each product was categorized according to the type of firm that launched it: a new venture or an established firm. The innovative products enjoyed a success rate of 77% on average. The success was not dependent on the number or type of innovation characteristics embodied by the product. However, products developed in new ventures embody, on average, one more innovation characteristic and enjoy a slightly higher success rate than those launched by established firms. [ABSTRACT FROM AUTHOR]

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available. [ABSTRACT FROM AUTHOR]

The damage mechanism of fracturing fluids has always been the hot research topic in the development of low-permeability reservoir with hydraulic fracturing. At present, the research in this area is conducted mostly by the conventional core fluid flow test designed with industrial standards, less in the experiment operated from a microperspective. Against the reservoir cores with different permeability, and based on the results of SEM, mercury injection experiment, and core fluid flow test, this paper uses the technology of nuclear magnetic resonance (NMR) to systematically analyze the degree and rule of water-sensitivity, water-block, and solid-phase adsorption damage resulted from hydroxypropyl guar gum (HPG) and carboxymethyl guar gum (CMG) fracturing fluids, and proposes a comprehensive test method for evaluating the fracturing fluids damage to the reservoir. The test results show that fracturing fluid infiltrating into the core causes the increase of bound water, mobile water retention, and solid-phase macromolecule substance absorption inside the core in varying degrees, decreasing the reservoir permeability. The extent of reservoir water-sensitivity damage is positively correlated with the increment of bound water, and the extent of water-block damage is positively correlated with mobile water retention volume. The adsorption and retention of solid-phase macromolecule substance causes largest loss of core permeability, averaging about 20%, and it is main damage factor of fracturing fluids, the water-sensitivity damage causes 11% of core permeability loss, and the water-block damage causes 7% of loss. As the reservoir permeability doubles, the comprehensive damage resulted from guar gum fracturing fluid decreases by 14%. The comprehensive damage of CMG fracturing fluid to reservoir is 6.6% lower than that of HPG fracturing fluid, and the lower the reservoir permeability, the larger the gap between damage of CMG and HPG fracturing fluids. With the technology of NMR, the objective and accurate evaluation of various damages to reservoir resulted from fracturing fluids is realized, and the corresponding relation between damage mechanism and damage extent is established, which provides reference for research on improvement of fracturing fluid properties and reservoir protection measures. [ABSTRACT FROM AUTHOR]

In this paper, experimental and numerical techniques have been utilized to quantify heavy oil properties in CO2 huff-n-puff processes under reservoir conditions. Experimentally, fluid properties together with viscosity reduction of heavy oil and interfacial properties between CO2 and heavy oil have been quantified, while five cycles of CO2 huff-n-puff processes have been conducted to determine oil recovery together with component variation of produced and residual oils. Theoretically, numerical simulation has been conducted to analyze the underlying recovery mechanisms associated with the CO2 huffn-puff processes. CO2 huff-n-puff processes are only effective in the first two cycles under the existing experimental conditions, while the effective sweep range is limited near the wellbore region, resulting in poor oil recovery in the subsequent cycles. As for produced oil, its viscosity, density, resin and asphaltene contents, and molecular weight of asphaltene are reduced, whereas, for the residual oil, they are increased. The asphaltene component in the residual oil shows weak stability compared to that of the produced oil, while the ultimate oil recovery after the fifth CO2 cycle of huff-n-huff processes is measured to be 31.56%. [ABSTRACT FROM AUTHOR]