Modeling of a Ring Rosen-Type Piezoelectric Transformer by Hamilton’s Principle

This paper deals with the analytical modeling of a ring Rosen-type piezoelectric transformer. The developed model is based on a Hamiltonian approach, enabling to obtain main parameters and performance evaluation for the first radial vibratory modes. Methodology is detailed, and final results, both the input admittance and the electric potential distribution on the surface of the secondary part, are compared with numerical and experimental ones for discussion and validation

Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

Deploying building simulation to enhance the experimental design of a full-scale empirical validation project

Empirical validation of building simulation results is a complex and time-consuming process. A well-structured and thorough experimental design is therefore a crucial step of the experimental procedure. A full-scale empirical validation study is planned to take place within IEA EBC Annex 71: “Building energy performance assessment based on in situ measurements”. The experimental data are currently being gathered in two experiments being conducted at the Fraunhofer IBP test site at Holzkirchen in Germany. This paper describes the methodology followed during the experimental design of the project. Particular focus is on how Building Performance Simulation (BPS) was used to assist the preparation of the actual experiment, to determine suitable test sequences, magnitudes of heat inputs and temperature variations. A combination of both deterministic and probabilistic simulation (using the method of Morris) is employed to replicate the actual experiment and to assess the sensitivity of the model to uncertain input parameters. A number of experimental errors are identified in the experiment, primarily concerning the magnitude of heat inputs. Moreover, the paper includes a discussion on lessons learned from the simulations and on the reliability, reproducibility and limitations of the suggested experimental design procedure

Summary report on mechanisms underpinning beneficial plant associations based on APSIM and DAISY

This deliverable reports on the work conducted within WP3, based on two existing crop growth models, APSIM and DAISY. The objective of deliverable 3.1 is to identify the key traits and mechanisms underpinning beneficial plant associations, by calibrating, validating and running APSIM and DAISY. For each model, this report presents in detail i) the data used for model calibration and validation, and the rationale for their choice; ii) the calibration and validation process; iii) the results of simulation runs and comparison with field trial data across pedoclimatic conditions; and iv) a discussion of the key aspects driving the performance of each model and the key plant traits defining the output, with particular reference to intercropped systems. In addition, the report also presents an evaluation of resource use efficiencies in support of the modelling work. On the basis of the calibration and validation results, the two models are also contrasted. APSIM and DAISY showed some promising results for the simulation of spring wheat-faba bean and spring barley-field pea systems, towards the identification of the key traits and mechanisms driving the interaction of cereals and legumes in field conditions and across different pedoclimatic regions. Further steps are discussed towards the improvement of the model capabilities, in particular pertaining intercropped systems, also exploiting some additional experimental results relative to plant nutrient use efficiency

A wake traverse technique for use in a 2 dimensional transonic flexible walled test section

Reported two dimensional validation data from the Transonic Self-Streamlining Wind Tunnel (TSWT) concerns model lift. The models tested provided data on their pressure distributions. This information was numerically integrated over the model surface to determine lift, pressure drag and pitching moment. However, the pressure drag is only a small component of the total drag at nominal angles of attack and cannot be used to assess the quality of flow simulation. An intrusive technique for obtaining information on the total drag of a model in TSWT is described. The technique adopted is the wake traverse method. The associated tunnel hardware and control and data reduction software are outlined and some experimental results are presented for discussion

컴퓨터 모델 내 오류 원인 식별을 위한 최적화 기반 모델 개선 기법 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 윤병동.컴퓨터 이용 공학 기술의 활용도가 증가함에 따라, 각 공학분야에서는 보다 정확한 예측 능력을 가진 컴퓨터 모델을 필요로 하게 되었다. 많은 연구결과를 통해, 신뢰도 높은 계산모델을 얻기 위한 공학기술들이 개발되었다. 최적화 기반 모델 향상 기술은 계산모델 예측도 향상을 위한 공학기술 중 하나로, 모델 보정, 모델 검증, 그리고 모델 개선 과정을 포함하고 있다. 모델 보정은 계산 모델 내 미지변수의 값을 역으로 추정하는 기술이다. 모델 검증은 예측 성능의 정확도를 판단한다. 계산모델 내 미지 오류 원인이 존재하면 모델 개선을 통해 미지 원인을 탐색하는 작업을 수행한다. 최적화 기반 모델향상기술 내 세가지 세부 기술들은 모델 관련 사전 정보의 양에 따라 유기적으로, 혹은 개별적으로도 수행이 가능하다. 모델 향상 기술이 계산모델 내 영향을 주는 다양한 오류원인을 고려하여 수행되고 있으나, 최적화 기반 모델 향상기술은 여전히 계산모델의 정확도를 증가시키는데 한계점을 지니고 있다. 시험 데이터 및 계산 모델 내 다양한 오류 소스들이 결합되어 있어, 최적화 기반 모델 향상 기술은 이 오류원인들을 구분하고 각 오류원인들에 대해 적합한 솔루션을 제공하기에 부적합하다. 따라서, 이러한 문제점을 해결하고자 본 박사학위논문에서는 (1) 파라미터 추정 오류 감소를 위한 시험 설계 기법, (2) 모델 보정 시 모델링 및 시험 오류의 양을 정량화 하기 위한 비율 편향도 정량화 기법 (3) 2종 오류에 강건한 통계기반 검증 척도 비교 연구를 제안하고자 한다. 첫 번째 연구에서는 파라미터 추정 오류를 최소화하기 위한 시험 설계법 개발을 목표로 한다. 여기서 결정된 시험설계안은 모델 보정 시 사용될 시험 데이터 취득을 위한 시험 설계를 뜻한다. 계산모델 내 발생하는 모델링 오류, 그리고 시험데이터 취득 시 발생하는 계측오류 등은 모델 보정에서 정확한 파라미터 값의 추정을 방해한다. 파라미터 추정 오류를 포함한 계산모델은 주어진 시험데이터를 잘 모사하는 것처럼 보이지만, 파라미터를 과도하게 편향된 값으로 추정하여 모델링 오류를 보완한 결과이다. 이 경우, 모델 검증 시 모델이 유효하다고 판단될 수 있지만 실제로는 파라미터 추정오류와 모델링 오류를 동시에 갖고 있으므로 다양한 설계조건에서 유효하지 않은 모델이다. 따라서, 본 연구에서는 파라미터 추정오류와 모델링 오류를 구분하여 정확한 모델 검증을 유도하고자 한다. 파라미터 추정오류와 모델링 오류는 그 정도를 각각 정량화 하는 것이 불가능하므로, 파라미터 추정오류를 가장 최소화 할 수 있는 시험데이터의 종류와 취득위치를 선정할 수 있는 시험설계법을 고안하였다. 이를 위해, (1) 파라미터 추정오류를 수식적으로 유도하였고, (2) 유도된 식 내에서 사용자가 제어할 수 있는 일부항을 최소화 하도록 하였다. 제안된 시험설계법은 파라미터 추정오류와 모델링 오류를 구분하고, 모델 검증 시 유효 및 불유효의 원인이 모델링오류가 될 수 있도록 한다. 두 번째 연구에서는 파라미터 추정오류를 개선하기 위해 모델 보정 시 모델링 오류에 의한 성능 저하량을 정량화 할 수 있는 비율 편향 보정 기법을 제안한다. 첫 번째 연구에서 제안한 시험설계법은 별도의 추가 시험 데이터 없이 파라미터 추정 오류와 모델링 오류를 구분해 낼 수 있는 최선의 방법론 이지만, 모델링 오류 및 시험 오류의 영향이 큰 경우 파라미터 추정오류를 획기적으로 개선하는데 한계가 있다. 오류의 영향도가 큰 모델은 추정 파라미터의 값이 엔지니어가 가진 경험, 혹은 물리 기반 정보에 위배되는 지점으로 수렴할 수 있다. 따라서, 본 연구에서는 관측데이터 외 미지 모델 변수의 물리적 정보를 활용하여 모델링 오류 및 관측오류에 의한 성능저하도의 양을 정량화 하고자 한다. 연구에서 제안된 ‘비율편향’ 은 오류에 의한 성능저하도를 성능값의 일정한 비율로 가정하여, 모델 보정 시 최적화 알고리즘 내에서 미지모델변수와 함께 최적 값이 추정되는 항이다. 비율편향 항과 미지모델 변수가 사전의 물리적 정보에 위배되지 않는 범위 내에서 추정될 수 있도록 미지모델 변수의 범위 정보를 최적화 알고리즘의 제한조건으로 활용한다. 비율편향 보정기법은 미지모델변수의 추정값이 모델링 오류에 의한 성능저하를 보완하기 위해 과도하게 편향된 값으로 최적화 되는 현상을 바로잡을 수 있다. 세 번째 연구에서는 모델 검증 시 발생할 수 있는 결정 오류를 개선하기 위해 통계적 검증 척도의 선택 기준을 제시하고자 한다. 모델 검증은 주로 통계기반 방법인 가설검증을 활용하여 모델의 유효 및 불유효를 결정한다. 가설검증은 제 1종 오류 및 제 2종 오류의 발생 가능성을 갖고 있다. 제 2종 오류는 불유효한 모델을 유효하다고 판단하는 오류로써 실제 산업분야에 치명적인 사고를 유발할 수 있다. 본 연구에서는 제 2종 오류를 가장 적게 발생 시킬 수 있는 통계적 검증 척도를 분석하기 위해 다음과 같은 조건에서의 검증 정확도 비교 연구를 수행한다. 1) 관측 및 예측 성능의 분산이 같고 평균값의 차이로 인해 예측 성능이 불유효 한 경우, 2) 관측 및 예측성능의 평균보다 분산값의 차이로 인해 예측 성능이 불유효 한 경우. 비교연구는 모델 파라미터의 분산 정도를 4가지로 세분화 하고 관측 데이터 개수에 의한 정확도 차이를 비교하고자 관측 데이터를 3개에서 30개까지 증가시켰다. 그 결과, 성능 간 평균의 차이를 잘 정량화 하는 검증척도 및 성능 간 분산의 차이를 잘 정량화 하는 검증척도를 제안할 수 있었다. 제안된 검증척도의 평균지향 및 분산지향 특성을 증명하고자, 평균지향 척도의 극한값을 유도하여 분산값의 증가 시 척도의 값이 최대값에 도달하지 않아 검증 오류가 발생할 수 있음을 확인하였다.The increased use of computer-aided engineering (CAE) in recent years requires a more accurate prediction capability in computational models. Therefore, extensive studies have considered engineering strategies to achieve highly credible computational models. Optimization-based model improvement (OBMI), which includes model calibration, validation, and refinement, is one crucial technique that has emerged to enhance the prediction ability of computational models. Model calibration is the process of estimating unknown input parameters in a computational model. Model validation presents a judgement of the accuracy of a predicted response. If it is possible for a computational model to have model form uncertainties, model refinement explores unrecognized error sources of a computational model. OBMI can adopt these three processes individually or sequentially, according to the trustworthiness of the prior knowledge of the computational modeling. Although OBMI process improvements have emerged to try to consider the major sources of errors, OBMI can still suffer from a failure to improve a computational model. Since numerous error sources in an experimental and computational model are intertwined with each other, OBMI has difficulty identifying the error sources required to enable accurate prediction ability of the computational model. Thus, eventually, OBMI may fail to propose an appropriate solution. To cope with this challenge, this doctoral dissertation research addresses three essential issues: 1) Research Thrust 1 – a new experimental design approach for model calibration to reduce parameter estimation errors; 2) Research Thrust 2) – a device bias quantification method for considering model form errors with bound information; and, Research Thrust 3) – comparison of statistical validation metrics to consider type II errors in model validation. Research Thrust 1: A variety of sources of errors in observation and prediction can interrupt the model improvement process. These error sources degrade the parameter estimation accuracy of the model calibration. When a computational model turns out to be invalid because of these error sources, the OBMC process performs model refinement. However, since model validation cannot distinguish between parameter estimation errors and modeling errors, it is difficult for the existing method to efficiently refine the computational model. Thus, this study aims to develop a model improvement process that identifies the leading cause of invalidity of a prediction. In this work, an experimental design method is integrated with optimization-based model improvement to minimize the effect of estimation errors in model calibration. Through use of the proposed method, after calibration, the computational model mainly includes the effects of unrecognized modeling errors. Research Thrust 2: The experimental design method proposed in Research Thrust 1 has the advantage of being able to identify two error sources without additional observation. However, model calibration still suffers from parameter estimation errors, since experimental design is affected by model form errors. The parameters estimated by model calibration are often unreasonable for engineers in practical settings because they have expert-based prior knowledge about the model parameters. Among the variety of physical information available, bound information about model parameters is a suitable constraint in optimization-based model calibration (OBMC). Using prior information about parameter bounds, Research Thrust 2 devises proportionate bias calibration to quantify the amount of degradation of the predicted responses that is due to model form errors in a computational model. The bias term is estimated in the optimization-based model calibration (OBMC) algorithm with unknown parameters to enable OBMC to support accurate estimation of unknown parameters within a prior bound. This study proposes a new formulation of a bias term that depends on the output responses to resolve the gap in appropriate bias that arises due to the different dimensions of the predicted responses. Research Thrust 3: Statistical model validation (SMV) evaluates the accuracy of a computational model’s predictions. In SMV, hypothesis testing is used to determine the validity or invalidity of a prediction, based on the value of a statistical validation metric that quantifies the difference between the predicted and observed results. Errors in hypothesis testing decisions are troublesome when evaluating the accuracy of a computational model, since an invalid model might be used in practical engineering design activities and incorrect results in these settings may lead to safety issues. This research compares various statistical validation metrics to highlight those that show fewer errors in hypothesis testing. The resulting work provides a statistical validation metric that is sensitive to a discrepancy in the mean or variance of the two distributions from the predictions and observations. Statistical validation metrics examined in this study include Kullback-Leibler divergence, area metric with U-pooling, Bayes factor, likelihood, probability of separation, and the probability residual.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 8 1.3 Dissertation Layout 13 Chapter 2 Literature Review: Optimization-based and Bayesian-based Model Improvement 14 2.1 Optimization-based Model Improvement (OBMI) 14 2.1.1 Model Calibration 15 2.1.2 Model Validation 18 2.1.3 Model Refinement 22 2.2 Bayesian-based Model Improvement with Bias Correction 25 2.3 Summary and Discussion 28 Chapter 3 Experimental Design for Identifying Error Sources Between Parameter Estimation Errors and Model Form Errors 31 3.1 Coupled Error Sources in Model Calibration 33 3.2 Optimization-based Model Improvement with Experimental Design 37 3.2.1 Derivation of Parameter Estimation Errors in Model Calibration 37 3.2.2 Identification of Error Sources by Employing Experimental Design 39 3.3 Case Studies 42 3.3.1 Analytical Case Study: Cantilever Beam Model 43 3.3.2 Engineering Case Study: Automotive Wheel Rim FEM Model 52 3.4 Summary and Discussion 64 Chapter 4 Proportionate Bias Calibration with Bound Information to Consider Unrecognized Model Form Errors 66 4.1 Limitations of Experimental Design for OBMI with the Effect of Model Form Errors 68 4.2 Proportionate Bias Calibration with Bound Information of Model Parameters 71 4.2.1 The Formulation of Proportionate Bias 71 4.2.2 Proportionate Bias Calibration with Bound Information of Unknown Model Parameters 74 4.3 Case Studies 76 4.3.1 Analytical Case Study: Cantilever Beam Model 77 4.3.2 Engineering Case Study 1: Automotive Wheel Rim FEM Model 82 4.3.3 Engineering Case Study 2: Automotive Steering Column Assembly FEM model 86 4.4 Summary and Discussion 92 Chapter 5 Comparison of Statistical Validation Metrics to Reduce Type II Errors in Model Validation 94 5.1 Brief Review of Statistical Validation Metrics 97 5.1.1 Area metric 100 5.1.2 Likelihood 100 5.1.3 Kullback-Leibler Divergence (KLD) 101 5.1.4 Bayes Factor 101 5.1.5 Probability of Separation (PoS) 102 5.1.6 Probability Residual (PR) 102 5.2 A comparison study of statistical validation metrics 103 5.2.1 Problem definition 103 5.2.2 Results of statistical model validation accuracy 108 5.3 Discussion and Demonstration 116 5.3.1 Discussion about the low accuracy of the area metric in a variance change 116 5.3.2 Discussion about the low accuracy of the Probability of Separation (PoS) in a variance change 121 5.4 Case Study 124 5.5 Summary and Discussion 132 Chapter 6 Conclusion 134 6.1 Contributions and Significance 134 6.2 Suggestions for Future Research 137 Appendix A Analytical Derivation of Probability of Separation (PoS) with Normal and Lognormal Distribution 140 A.1 Analytical Derivation of PoS Metric with a Normal Distribution 141 A.2 Analytical Derivation of PoS Metric with a Lognormal Distribution 143 References 147 국문 초록 159박

Finite element analysis and experimental validation of reinforced concrete single-mat slabs subjected to blast loads

Title from PDF of title page, viewed on June 5, 2013Thesis advisor: Ganesh ThiagarajanVitaIncludes bibliographic references (pages 152-154)Thesis (M.S.)--School of Computing and Engineering. University of Missouri---Kansas City, 2013The work done in this research is to study the response of reinforced concrete slabs subjected to blast loading as they can be used as protective structures around the main structure. An experimental investigation has been performed in a separate study involving blast-testing of 12 reinforced concrete slabs in a shock tube (Blast Load Simulator). The data from this experimental investigation was made available for performing advanced finite element analysis done in this research to study the behavior of these slabs towards blast loading. A non-linear transient dynamic finite element analysis program LS-DYNA® is used for this study. The finite element models of these 12 slab panels are developed in LS-DYNA® and blast pressures equivalent to those generated in the experiment are applied to them. These slabs include two material combinations based on their strength namely, the high-strength concrete reinforced with high-strength steel slabs and normal-strength concrete reinforced with normalstrength steel slabs. The primary objective is to study the response of material combinations to blast loading by using two different concrete material models available in LS-DYNA namely, Winfrith Concrete Model and Concrete Damage Model Release 3 and comparing it with the experimental results. Validation of these models with experimental data will provide anumerical analysis procedure which will be less expensive and safer than performing blast testing. On performing this study, finite element analysis and experimental validation of reinforced concrete single-mat slabs subjected to blast loading it is concluded that the Winfrith Concrete Model predicts a better response in terms of deflection and crack propagation for both normal and high strength concrete. Concrete Damage Model Release 3 needs additional parameters to be defined based on concrete laboratory testing data for it to predict a better response in the normal-strength and high-strength category. These additional parameters have been developed and recommended in this study.Introduction -- Literature review -- Objective and scope -- Experimental investigation -- Numerical modeling in LS-DYNA® -- Numerical analysis results and comparison with experiments -- Discussion of results -- Conclusions and future work -- Appendix A. Pressure and impulse data for 12 slabs -- Appendix B. Pressure and impulse plots for 12 RC slabs -- Appendix C. summary tables -- Appendix D. LS-DYNA inpu

Towards patient selection for cranial proton beam therapy – Assessment of current patient-individual treatment decision strategies

Proton beam therapy shows dosimetric advantages in terms of sparing healthy tissue compared to conventional photon radiotherapy. Those patients who are supposed to experience the greatest reduction in side effects should preferably be treated with proton beam therapy. One option for this patient selection is the model-based approach. Its feasibility in patients with intracranial tumours is investigated in this thesis. First, normal tissue complication probability models for early and late side effects were developed and validated in external cohorts based on data of patients treated with proton beam therapy. Acute erythema as well as acute and late alopecia were associated with high-dose parameters of the skin. Late mild hearing loss was related to the mean dose of the ipsilateral cochlea. Second, neurocognitive function as a relevant side effect for brain tumour patients was investigated in detail using subjective and objective measures. It remained largely stable during recurrence-free follow-up until two years after proton beam therapy. Finally, potential toxicity differences were evaluated based on an individual proton and photon treatment plan comparison as well as on models predicting various side effects. Although proton beam therapy was able to achieve a high relative reduction of dose exposure in contralateral organs at risk, the associated reduction of side effect probabilities was less pronounced. Using a model-based selection procedure, the majority of the examined patients would have been eligible for proton beam therapy, mainly due to the predictions of a model on neurocognitive function.:1. Introduction 2. Theoretical background 2.1 Treatment strategies for tumours in the brain and skull base 2.1.1 Gliomas 2.1.2 Meningiomas 2.1.3 Pituitary adenomas 2.1.4 Tumours of the skull base 2.1.5 Role of proton beam therapy 2.2 Radiotherapy with photons and protons 2.2.1 Biological effect of radiation 2.2.2 Basic physical principles of radiotherapy 2.2.3 Field formation in radiotherapy 2.2.4 Target definition and delineation of organs at risk 2.2.5 Treatment plan assessment 2.3 Patient outcome 2.3.1 Scoring of side effects 2.3.2 Patient-reported outcome measures – Quality of life 2.3.3 Measures of neurocognitive function 2.4 Normal tissue complication probability models 2.4.1 Types of NTCP models 2.4.2 Endpoint definition and parameter fitting 2.4.3 Assessment of model performance 2.4.4 Model validation 2.5 Model-based approach for patient selection for proton beam therapy 2.5.1 Limits of randomised controlled trials 2.5.2 Principles of the model-based approach 3. Investigated patient cohorts 4. Modelling of side effects following cranial proton beam therapy 4.1 Experimental design for modelling early and late side effects 4.2 Modelling of early side effects 4.2.1 Results 4.2.2 Discussion 4.3 Modelling of late side effects 4.3.1 Results 4.3.2 Discussion 4.4 Interobserver variability of alopecia and erythema assessment 4.4.1 Patient cohort and experimental design 4.4.2 Results 4.4.3 Discussion 4.5 Summary 5. Assessing the neurocognitive function following cranial proton beam therapy 5.1 Patient cohort and experimental design 5.2 Results 5.2.1 Performance at baseline 5.2.2 Correlation between subjective and objective measures 5.2.3 Time-dependent score analyses 5.3 Discussion and conclusion 5.4 Summary 6. Treatment plan and NTCP comparison for patients with intracranial tumours 6.1 Motivation 6.2 Treatment plan comparison of cranial proton and photon radiotherapy 6.2.1 Patient cohort and experimental design 6.2.2 Results 6.2.3 Discussion 6.3 Application of NTCP models 6.3.1 Patient cohort and experimental design 6.3.2 Results 6.3.3 Discussion 6.4 Summary 7. Conclusion and further perspectives 8. Zusammenfassung 9. Summar

On evaluating the performance of problem structuring methods:an attempt at formulating a conceptual model

In the past decade there has been a discussion on the need for and degree of empirical evidence for the effectiveness of problem structuring methods (PSMs). Some authors propose that PSMs are used in unique situations which are difficult to study, both from a methodological and a practical perspective. In another view experimental validation is necessary and, if not obtained, PSMs remain substantially invalidated and thus ‘suspect’ with regard to their claims of effectiveness. Both views agree on one point: the necessity of being clear about the important factors in the context in which a method is used, the method’s aims and its essential elements through which these aims are achieved. A clear formulation of central variables is the core of a theoretical validation, without which empirical testing of effects is impossible. Since the process of PSMs is sometimes referred to as ‘more art than science’, increased clarity on the PSM process also supports the transfer of methods. In this article we consider goals important to most PSMs, such as consensus and commitment. We then focus on outcomes of group model building, and expectations on how context and group modeling process contributes to outcomes. Next we discuss the similarity of these central variables and relations to two sets of theories in social psychology: the theory of planned behavior and dual process theories of persuasion. On the basis of these theories we construct a preliminary conceptual model on group model building effectiveness and address its practical applicability for research on PSM