According to FM Global proprietary data, power-gen gas turbine losses have consistently represented a dominant share of the overall equipment-based loss value over the past decade. Effective assessment of loss exposure or risk related to gas turbines has become and will continue to be a critical but challenging task for property insurers and their clients. Such systematic gas turbine risk assessment is a necessary step to develop strategies for turbine risk mitigation and loss prevention. This paper presents a study of outage data from the Generating Availability Data System (GADS) by the North American Electric Reliability Corporation (NERC). The risk of forced outages in turbines was evaluated in terms of outage days and number of outages per unit-year. In order to understand the drivers of the forced outages, the influence of variables including turbine age, capacity, type, loading characteristic, and event cause codes were analyzed by grouping the outage events based on the chosen values (or ranges of values) of these variables. A list of major findings related to the effect of these variables on the risk of forced outage is discussed.

Steam turbine mechanical breakdowns dominate equipment losses in the Power-Gen and Forest Product industries. As steam turbines are likely custom-built, variations in design, operation and maintenance practices across different industries could result in different levels of significance of the loss drivers. The present study focuses on comparing the turbine loss drivers and effective condition monitoring for loss mitigation in both industries. Steam turbine loss events from the two industries during a recent 10-year period were first reviewed and classified into typical turbine loss scenarios. Contributions of each loss scenario to the total loss count and value were summarized and compared across the two industries. Subsequently, applicable turbine condition monitoring methods were identified for each loss scenario, and evaluated with expert domain knowledge and available loss data. These monitoring methods were finally prioritized according to their functional effectiveness in turbine loss reduction.

An analysis is performed for a typical residential living space with intermittent energy use in a hot summer and cold winter zone. Analysis performed in this study include a dynamic, three dimensional heat transfer model that examine the intermittent heating and cooling loads in a typical living space in Shanghai. Different wall structures (non-insulation structure, exterior insulation structure, or interior insulation structure) can have different influences on the energy efficiency in buildings. Results conclude that the interior insulation structure provides the largest reduction of energy consumption in buildings when compared to the other wall structures in a hot summer and cold winter zone. For the interior insulation structure in this study, the typical thermal insulation thickness is 0.03 m.

Losses of gas turbines and steam turbines in the power generation industry are often due to mechanical breakdowns associated with flow-path component damage, especially on rotating blades. Advanced condition monitoring technologies, such as Noncontact Blade Vibration Monitoring (NBVM), have been shown to effectively detect abnormal behaviors of blades during turbine operation, and thus help to mitigate the turbine loss. Following a brief review of the technology, this paper focuses on results of analyses performed using FM Global’s turbine loss data. Loss events were categorized according to different blade damage mechanisms, turbine types and damage locations; and analyzed in order to identify the realizable benefits through noncontact blade monitoring. Based on the results of the analysis, field applications of the technology for improvement of long-term turbine condition monitoring are discussed.

Industry has been implementing condition monitoring for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FP) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

In-situ condition monitoring (CM) is a crucial element in protection and predictive maintenance of large rotating Power-Gen equipment such as gas turbines or steam turbines. In this work, selected gas turbine loss events occurring during a recent ten-year period at FM Global clients’ power generation plants were evaluated. For each loss event, a loss scenario or a chain of failures was outlined after investigating the available loss record. These loss events were then categorized based on the nature of the associated loss scenario. The study subsequently focused on the variables that could be monitored in real time to detect the abnormal turbine operating conditions, such as vibration characteristics, temperature, pressure, quality of working fluids and material degradations. These groups of condition monitoring variables were then matched with detectable failures in each loss event and prioritized based on their effectiveness for failure detection and prevention. The detectable loss events and the associated loss value were used in this evaluation process. The study finally concluded with a summary of findings and path-forward actions.

This paper studies the heave of Spar platform hull considering the moonpool water motion. The moonpool of Spar platform is a central opening throughout the cylindrical hull and the guide plate is installed at the bottom of it. If the risers in the moonpool are supported by the tensioner at the top, then the mass of the water in the moonpool is comparable to the platform hull mass and the effect of moonpool water on the platform motion is significant. Gupta et al. (2008) studied the effect of moonpool water on the heave of the Spar platform. In their work the added mass and wave loading are calculated by the Morison approach. The Spar platform is large scale structure. In this paper, the wave loading of platform hull is calculated by potential flow theory and the coupled motion of hull heave and moonpool water is studied. It is found that the hull heave motion will be decreased when the moonpool water motion is considered. The hull heave RAO appears two peaks for the large opening area of guide plate, and the higher heave is exhibited when the wave period is near the natural period of moonpool water vertical vibration.

Osteoporosis is a major bone disease characterized by low bone mass and microarchitecture deterioration, which affects primarily trabecular sites and leads to increased bone fragility. Trabecular bone mechanical properties have direct relations with bone fragility. High-resolution image based-finite element (FE) models with the detailed 3D microstructure have been widely utilized to assess the mechanical properties of trabecular bone. Voxel-based FE model can be generated by converting individual voxels of high resolution bone images into 8-node brick elements. A number of studies have compared mechanical properties predicted by the voxel model with those by mechanical testing and have demonstrated that the voxel FE model can accurately predict the Young’s modulus and yield strength of human trabecular bone (1). However, the computational expense of the voxel-based technique, in general, limits its clinical applications, especially the nonlinear analysis for whole bone strength. Thus, it is not applicable to apply this technique to clinical use with the respect of current computer capability. There is apparent need for an alternative modeling approach that is more computationally efficient while preserving the accuracy of the predictions.

Low Engine Order (LEO) excitations on a steam turbine Last Stage low-pressure (LP) Bucket (or Blade) (LSB) are largely the result of flow unsteadiness (e.g. flow circulation and reversal) due to low steam exit velocity (Vax) off the LSB at the off-design conditions. These excitations at low frequencies impose major constraints on LP bucket aeromechanical design. In this study, bucket forced response under typical LEO excitation was analytically predicted and correlated to experimental measurements. First, transient CFD analyses were performed at typical low flow, low Vax operating conditions that had been previously tested in a subscale low pressure turbine test rig. The unsteady pressure distribution on the bucket was derived from the transient CFD analyses at frequencies corresponding to the bucket’s modes of vibration. Subsequently, these computed unsteady pressure were mapped onto a LSB finite element model, and forced response analyses were performed to estimate the bucket dynamic response, i.e. the alternating stresses and strains. The analytically predicted bucket response was compared against measured data from airfoil mounted strain gages and good correlation was found between the analytical prediction and the test data. Despite uncertainty associated with various parameters such as damping and unsteady steam forcing etc., the developed methodology provides a viable approach for predicting bucket forced response and in turn High Cycle Fatigue (HCF) capability during early phases of steam turbine LSB design.

During the operation of a power plant, a steam turbine may experience operation at full speed with little or no load (FSNL). Such an operation can take place when power demand is low or during start-up. At such an operation turbine buckets, in particular last stage buckets (LSB), can experience high stimulus coming from unsteady loading due to the flow instability. In those conditions LSBs consume energy rather than produce it. In some cases stimulus can create high alternating stresses in the LSB. As such, operation at those conditions is a particular concern in bucket aeromechanical design. To properly simulate FSNL operation in a stand alone low-pressure (LP) subscale turbine test facility, an external drive motor is normally required due to the unavailability of high and intermediate-pressure sections that would drive the LP turbine at very low load. This work shows that such a simulation can be achieved in the absence of an external drive by running the LP test turbine at higher exhaust pressure and higher mass flow. In those conditions LP exit flow velocity (VAN) similar to an actual FSNL operation will be achieved. This work shows that achieving prototypical VAN is sufficient to simulate operation at FSNL. Measurement data of the test show correlations between bucket alternating stress and turbine operating parameters such as VAN and exhaust pressure. This demonstrates that bucket responses equal or greater than those which would occur in actual FSNL conditions can be tested in a lab setup, In other words, testing at a given combination of VAN and exhaust pressure provides a limiting bucket response case for an operation at the same VAN but lower exhaust pressure. Further, numerical simulations using computational fluid dynamics were performed to prove that steam flow parameters and bucket structural mechanics characteristics in a subscale test turbine are fully representative of its full-scale counterpart, even at low flow or FSNL operating conditions, where broad spectrum of steam stimuli and bucket responses are expected.

Steam turbine power plant operations during start up and during operation at high exhaust pressure have the potential to result in an extremely low steam flow through the Low Pressure (LP) turbine. This inevitably leads to windage and results in significant temperature increases in the Last Stage Buckets (LSBs). High steam temperature can also initiate potential thermo-mechanical failure of the LSBs. Temperature prediction for a wide range of operational regimes imposes a significant challenge to modern LSB design. Extensive numerical and experimental investigations on an LP section steam turbine with LSBs of different lengths at typical low flow operation conditions have been conducted with the primary focus on LSB temperature prediction. A Low Pressure Development Turbine (LPDT) test rig was used to help develop and validate Computational Fluid Dynamic (CFD) based temperature prediction methodologies, which later were applied to predict operational temperatures for multiple LP section configurations under development. This article presents some important results of LPDT test measurements as well as CFD predictions of LP turbine flow structures and temperature distributions in last stage buckets.