**2.4 Vitamin D (25(OH)D)**

28 Chronic Kidney Disease

ion levels is sensed by the parathyroid calcium-sensing receptors (CaSRs) and subsequently regulates the synthesis and secretion of parathyroid hormone (PTH) (Felsenfeld et al., 2007). PTH acts on the bone to increase the efflux of calcium and phosphate, and acts on the kidney to reduce urinary calcium excretion, inhibit phosphate reabsorption, and stimulate the production of 1,25-dihydroxyvitamin D (1,25(OH)2D). PTH is cleaved to an 84-amino-acid protein in the parathyroid gland, where it is stored with fragments in secretory granules for release. When it is released, the circulating 1–84-amino-acid protein has a half-life of 2–4 min. The hormone is cleaved both within the parathyroid gland and after secretion into the Nterminal, C-terminal, and middle region fragments of PTH, which are metabolized in the liver and in the kidneys. Enhanced PTH synthesis/secretion occurs in response to hypocalcemia, hyperphosphatemia, and/or a decrease in serum 1,25-dihydroxyvitamin D (1,25(OH)2D), whereas high serum levels of calcium or calcitriol—and, as recently shown, of Fibroblast growth factor 23 (FGF-23)—suppress PTH synthesis/secretion. The extracellular concentration of ionized calcium is the most important determinant of the minute-to-minute secretion of

In patients with CKD, this normal oscillation is somehow altered. Over the past few decades there has been a progress in development of sensitive assays in order to measure PTH. Initial measurements of PTH using C-terminal assays were inaccurate in patients with CKD because of the impaired renal excretion of C-terminal fragments (and thus retention) and the measurement of these probably inactive fragments. The development of the N-terminal assay was initially thought to be more accurate but it also detected inactive metabolites. The development of a second generation of PTH assays, the two-site immunoradiometric assay—commonly called an 'intact PTH' assay—improved the detection of full-length (active) PTH molecules. In this assay, a captured antibody binds within the amino terminus and a second antibody binds within the carboxy terminus. Unfortunately, recent data indicate that this 'intact' PTH assay also detects accumulated large C-terminal fragments, commonly referred to as '7–84' fragments; these are a mixture of four PTH fragments that include, and are similar in size to, 7–84 PTH (Gao and D'Amour 2005). In parathyroidectomized rats, the injection of a truly whole 1- to 84-amino-acid PTH was able to induce bone resorption, whereas the 7- to 84-amino-acid fragment was antagonistic, explaining why patients with CKD may have high levels of 'intact' PTH but relative hypoparathyroidism at the bone-tissue level (Slatopolsky et al., 2000; Malluche et al., 2003; Huan et al., 2006). Thus, the major difficulty in accurately measuring PTH with this assay is the presence of circulating fragments, particularly in the presence of CKD. Unfortunately, the different assays measure different types and amounts of these circulating fragments, leading to inconsistent results. More recently, a third generation of assays has become available that truly detect only the 1- to 84-amino-acid, full-length molecule: 'whole' or 'bioactive' PTH assays. There are differences in PTH results when samples are measured in plasma, serum, or citrate, and depending on whether the samples are on ice, or are allowed

PTH and vitamin D have been shown to influence cardiac and vascular growth and function experimentally in human subjects with normal renal function. Because of increased prevalence of hyperparathyroidism and altered vitamin D status in CKD, these alterations have been considered to contribute to the increased prevalence of cardiovascular disease

and hypertension seen in this patient population (Slinin Y et al., 2005).

PTH, which is normally oscillatory.

to sit at room temperature.

The parent compounds of vitamin D—D3 (cholecalciferol) or D2 (ergocalciferol)—are highly lipophilic. They are difficult to quantify in the serum or plasma. They also have a short halflife in circulation of about 24 h. These parent compounds are metabolized in the liver to 25(OH)D3 (calcidiol) or 25(OH)D2 (ercalcidiol). Collectively, they are called 25(OH)D or 25 hydroxyvitamin D. The measurement of serum 25(OH)D is regarded as the best measure of vitamin D status, because of its long half-life of approximately 3 weeks. In addition, it is an assessment of the multiple sources of vitamin D, including both nutritional intake and skin synthesis of vitamin D. There is a seasonal variation in calcidiol levels because of an increased production of cholecalciferol by the action of sunlight on skin during summer months. The gold standard of calcidiol measurement is high performance liquid chromatography (HPLC), but this is not widely available clinically. This is because HPLC is time consuming, requires expertise and special instrumentation, and is expensive. In early 1985, Hollis and Napoli developed the first radioimmunoassay (RIA) for total 25(OH)D, which was co-specific for 25(OH)D2 and 25(OH)D3. The values correlated with those obtained from HPLC analysis, and DiaSorin RIA became the first test to be approved by the Food and Drug Administration for use in clinical settings (Hollis and Napoli, 1985). Another method now carried out is liquid chromatography- tandem mass spectrometry (LC-MS/MS). Similar to HPLC, the LC-MS/MS method also has the ability to quantify 25(OH)D2 and 25(OH)D3 separately, which distinguishes it from RIA and enzyme-linked immunosorbent assay technologies. This method is very accurate and has been shown to correlate well with DiaSorin RIA (Saenger et al. 2006; Tsugawa et al., 2005). It has been suggested recently that the assays for 25(OH)D are not well standardized, and the definition of deficiency is not yet well validated. At best, clinicians should ensure that patients use the same laboratory for measurements of these levels, if carried out. The most appropriate vitamin D assays presently available seem to be those that measure both 25(OH)D2 and 25(OH)D3. Presently, approximately 20–50% of the general population has low vitamin D levels, irrespective of CKD status. However, the benefits from replacing vitamin D have not been documented in patients with CKD, particularly if they are taking calcitriol or a vitamin D analog.

### **2.5 Vitamin D (1,25(OH)2D)**

1,25(OH)2D is used to describe both hydroxylated D2 (ercalcitriol) and D3 (calcitriol) compounds, both of which have a short half-life of 4–6 h. Furthermore, in patients with earlier stages of CKD and in the general population, mild-to-moderate vitamin D deficiency, or partly treated vitamin D deficiency, is frequently associated with increased levels of 1,25(OH)2D. Thus, even accurate levels can be misleading. The serum levels of 1,25(OH)2D are uniformly low in late stages of CKD–MBD, at least in patients not treated with vitamin D derivatives (Andress et al., 2006). It has not been recommend a routine measurement of 1,25(OH)2D levels, as the assays are not well standardized, the half-life is short, and there are no data indicating that the measurement is helpful in guiding therapy or predicting outcomes (KDIGO).

#### **2.6 Alkaline phosphatases**

Alkaline phosphatases (ALP) are enzymes that remove phosphate from proteins and nucleotides, functioning optimally at alkaline pH. Measurement of the level of total ALP (t-

The New Kidney and Bone Disease:

**3.1.1 Bone turnover**

**3.1.2 Bone mineralization**

**3.1.3 Bone volume**

**3.2 Bone markers**

patophysiology:

1978).

Chronic Kidney Disease – Mineral and Bone Disorder (CKD–MBD) 31

whether treatments are effective. The traditional types of renal osteodystrophy have been defined on the basis of turnover and mineralization as follows: mild, slight increase in turnover and normal mineralization; osteitis fibrosa, increased turnover and normal mineralization; osteomalacia, decreased turnover and abnormal mineralization; adynamic, decreased turnover and acellularity; mixed, increased turnover with abnormal mineralization. It has been suggested recently that by performing bone biopsies in patients with CKD the most important parameters which should be determined are bone turnover,

In CKD patients a spectrum of bone formation rates varies from abnormally low to very high. Other measurements that help to define a low or high turnover (such as eroded surfaces, number of osteoclasts, fibrosis, or woven bone) tend to be associated with the bone-formation rate as measured by tetracycline labeling. This is the most definite dynamic measurement, hence it was chosen to represent bone turnover. It should be noted that an improvement of a bone biopsy cannot be determined on the basis of a simple change in the bone-formation rate, because the restoration of normal bone may require either an increase or a decrease in bone turnover, depending on the starting point (Melsen and Moselkilde,

It is a parameter which reflects the amount of unmineralized osteoid. Mineralization is measured by the osteoid maturation time or by mineralization lag time, both of which depend heavily on the osteoid width as well as on the distance between tetracycline labels. The classic disease with an abnormality of mineralization is osteomalacia, in which the bone-formation rate is low and the osteoid volume is high. Some patients have a modest increase in osteoid, which is a result of high bone formation rates. They do not have osteomalacia because the mineralization lag time remains normal. The overall

Bone volume contributes to bone fragility and is separate from the other parameters. The bone volume is the end result of changes in bone-formation and resorption rates: if the overall bone formation rate is higher than the overall bone resorption rate, the bone is in positive balance and the bone volume will increase. If mineralization remains constant, an increase in bone volume would also result in an increase in BMD and should be detectable by dual-energy X-ray absorptiometry (DXA). Although both cortical and cancellous bone volumes decrease in typical idiopathic osteoporosis, these compartments are frequently different in patients with CKD. In dialysis patients with high PTH levels, the cortical bone

mineralization, however, is not normal because unmineralized osteoid is increased.

volume is decreased but the cancellous volume is increased. (Lindergard et al., 1985).

Generally, two different types of bone markers are used to determine the bone

bone mineralization, and bone volume (TMV) (Moe et al., 2009).

ALP) is a colorimetric assay that is routinely used in clinical laboratories in automated machines. The enzyme is found throughout the body in the form of isoenzymes that are unique to the tissue of origin. Highest concentrations are found in the liver and bone, but the enzyme is also present in the intestines, placenta, kidneys, and leukocytes (Iba K et al. 2004). Specific ALP isoenzymes to identify the tissue source can be determined after fractionation and heat inactivation, but these procedures are not widely available in clinical laboratories. Bone-specific ALP (b-ALP) is measured with an immunoradiometric assay. Elevated levels of t-ALP are generally due to an abnormal liver function, an increased bone activity, or bone metastases. Levels are normally higher in children with growing bones than in adults, and often are increased after fracture. In addition, t-ALP and b-ALP can be elevated in both primary and secondary HPT, osteomalacia, and in the presence of bone metastasis and Paget's disease. In patients with CKD–MBD alkaline phosphatise may be used as an adjunct test, but if values are high, then liver function tests should be checked. t-ALP could reasonably be used as a routine test to follow response to therapy. The more expensive testing for b-ALP can be used when the clinical situation is more ambiguous. Testing for t-ALP is inexpensive and therefore may be helpful for following patients' response to therapy or determining bone turnover status when the interpretation of PTH is unclear. The use of b-ALP, an indicator of bone source, may provide additional and more specific information, although it is not readily available (Iba K et al. 2004).

#### **3. CKD – MBD and bone abnormalities**

Disorders of mineral metabolism are also associated with abnormal bone structure. It has been shown that the gold standard test for bone quality is its ability to resist fracture under strain. In animal models, this resistance can be directly tested with three-point bending mechanical tests. Bone quality is impaired in CKD, as the prevalence of hip fracture is increased in dialysis patients compared with the general population in all age groups. Dialysis patients in their forties have a relative risk of hip fracture that is 80-fold higher than that of age-matched and sex-matched control subjects. Furthermore, hip fracture in dialysis patients is associated with a doubling of the mortality observed in hip fractures in nondialysis patients (Coco M and Rush H., 2000; Alem et al., 2000). It has been shown that risk factors for hip fracture in CKD patients include age, gender, duration of dialysis, and presence of peripheral vascular disease. There are also analyses that found race, gender, duration of dialysis, and low or very high PTH levels as risk factors for hip fracture. It has been reported that both hip and lumbar-spine fractures occur independent of gender and race in CKD patients. Other risk factors for abnormal bone identified in studies from the general population are also common in CKD, including smoking, sedentary lifestyle, and hypogonadism (Alem et al., 2000). These factors are likely to increase the risk of bone fragility and fractures in CKD but have not been well evaluated. Extremes of bone turnover found in patients with CKD have significant impact on fragility and are likely additive to bone abnormalities commonly found in the aging and sedentary general population (Vassalotti et al., 2008; Melamed et al., 2008).

#### **3.1 Classification of renal osteodystrophy by bone biopsy**

Bone biopsy is performed to understand the pathophysiology and course of bone disease, to relate histological findings to clinical symptoms of pain and fracture, and to determine whether treatments are effective. The traditional types of renal osteodystrophy have been defined on the basis of turnover and mineralization as follows: mild, slight increase in turnover and normal mineralization; osteitis fibrosa, increased turnover and normal mineralization; osteomalacia, decreased turnover and abnormal mineralization; adynamic, decreased turnover and acellularity; mixed, increased turnover with abnormal mineralization. It has been suggested recently that by performing bone biopsies in patients with CKD the most important parameters which should be determined are bone turnover, bone mineralization, and bone volume (TMV) (Moe et al., 2009).


**1. Introduction**

94 Genetic Programming – New Approaches and Successful Applications

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> In program inference, the evaluation of how well a candidate solution solves a certain task is usually a computationally intensive procedure. Most of the time, the evaluation involves either submitting the program to a simulation process or testing its behavior on many input arguments; both situations may turn out to be very time-consuming. Things get worse when the optimization algorithm needs to evaluate a population of programs for several iterations, which is the case of genetic programming.

> Genetic programming (GP) is well-known for being a computationally demanding technique, which is a consequence of its ambitious goal: to automatically generate computer programs—in an arbitrary language—using virtually no domain knowledge. For instance, evolving a classifier, a program that takes a set of attributes and predicts the class they belong to, may be significantly costly depending on the size of the training dataset, that is, the amount of data needed to estimate the prediction accuracy of a single candidate classifier.

> Fortunately, GP is an inherently parallel paradigm, making it possible to easily exploit any amount of available computational units, no matter whether they are just a few or many thousands. Also, it usually does not matter whether the underlying hardware architecture can process simultaneously instructions and data ("MIMD") or only data ("SIMD").1 Basically, GP exhibits three levels of parallelism: (i) *population-level* parallelism, when many populations evolve simultaneously; (ii) *program-level* parallelism, when programs are evaluated in parallel; and finally (iii) *data-level* parallelism, in which individual training points for a single program are evaluated simultaneously.

> Until recently, the only way to leverage the parallelism of GP in order to tackle complex problems was to run it on large high-performance computational installations, which are normally a privilege of a select group of researchers. Although the multi-core era has emerged and popularized the parallel machines, the architectural change that is probably going to

1 MIMD stands for *Multiple Instructions Multiple Data* whereas SIMD means *Single Instruction Multiple Data*.

©2012 Augusto et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 2 Will-be-set-by-IN-TECH 96 Genetic Programming – New Approaches and Successful Applications Parallel Genetic Programming on Graphics Processing Units 3

revolutionize the applicability of GP started about a decade ago when the GPUs began to acquire general-purpose programmability. Modern GPUs have an astonishing theoretical computational power, and are capable of behaving much like a conventional multi-core CPU processor in terms of programmability. However, there are some intrinsic limitations and patterns of workload that may cause huge negative impact on the resulting performance if not properly addressed. Hence, this paper aims at presenting and discussing efficient ways of implementing GP's evaluation phase, at the program- and data-level, so as to achieve the maximum throughput on a GPU.

rather different from the modern multi-core CPU architecture, which has large and complex cores, reserving a considerable area of the processor die for other functional units, such as control units (out-of-order execution, branch prediction, speculative execution, etc.) and cache

Parallel Genetic Programming on Graphics Processing Units 97

This design difference reflects the different purpose of those architectures. While the GPU is optimized to handle data-parallel workloads with regular memory accesses, the CPU is designed to be more generic and thus must manage with reasonable performance a larger variety of workloads, including MIMD parallelism, divergent branches and irregular memory accesses. There is also another important conceptual difference between them. Much of the extra CPU complexity is devoted to reduce the latency in executing a single task, which classifies the architecture as *latency-oriented* [14]. Conversely, instead of executing single tasks as fast as possible, GPUs are *throughput-oriented* architectures, which means that they are designed to optimize the throughput, that is, the amount of completed tasks per unit of time.

The Open Computing Language, or simply OpenCL, is an open specification for heterogeneous computing released by the Khronos Group2 in 2008 [25]. It resembles the NVIDIA CUDA3 platform [31], but can be considered as a superset of the latter; they basically differ in the following points. OpenCL (i) is an open specification that is managed by a set of distinct representatives from industry, software development, academia and so forth; (ii) is meant to be implemented by any compute device vendor, whether they produce CPUs, GPUs, hybrid processors, or other accelerators such as digital signal processors (DSP) and field-programmable gate arrays (FPGA); and (iii) is portable across architectures, meaning that a parallel code written in OpenCL is guaranteed to correctly run on every other supported

In order to achieve code portability, OpenCL employs an abstracted device architecture that standardizes a device's processing units and memory scopes. All supported OpenCL devices must expose this minimum set of capabilities, although they may have different capacities and internal hardware implementation. Illustrated in Figure 1 is an OpenCL general device abstraction. The terms SPMD, SIMD and PC are mostly GPU-specific, though; they could be safely ignored on behalf of code portability, but understanding them is important to write

An OpenCL device has one or more *compute units* (CU), and there is at least one *processing element* (PE) per compute unit, which actually performs the computation. Such layers are meant (i) to encourage better partitioning of the problem towards fine-grained granularity and low communication, hence increasing the scalability to fully leverage a large number of CUs when available; and (ii) to potentially support more restricted compute architectures, by

3 CUDA is an acronym for *Compute Unified Device Architecture*, the NVIDIA's toolkit for GP-GPU programming. 4 It is worthy to note that OpenCL only guarantees *functional portability*, i.e., there is no guarantee that the same code will perform equally well across different architectures (performance portability), since some low-level optimizations

efficient code for this architecture, as will become clear later on.

memory [21].

device.4

*2.2.1. Hardware model*

2 http://www.khronos.org/opencl

might fit a particular architecture better than others.

**2.2. Open Computing Language – OpenCL**

The remaining of this chapter is organized as follows. The next Section, 2, will give an overview of the GPU architecture followed by a brief description of the open computing language, which is the open standard framework for heterogeneous programming, including CPUs and GPUs. Section 3 presents the development history of GP in the pursuit of getting the most out of the GPU architecture. Then, in Section 4, three fundamental parallelization strategies at the program- and data-level will be detailed and their algorithms presented in a pseudo-OpenCL form. Finally, Section 5 concludes the chapter and points out some interesting directions of future work.


**3. Incidence and mortality**

The clinical conditions such as nodular lesions, painless small ulcers, or growths present in very early preclinical invasive early stage cancers culminate the disease. Changes can be easily seen and are clinically detectable through careful visual inspection, and palpation of the oral mucosa was the hallmark of processes. Highlighting the disease prognosis that localized early head and neck cancers ˂4 cm that has not spread to the regional lymph nodes can be successfully treated with either radiotherapy or surgery resulting in 80% of 5-year survival rates [9]. Leukoplakia may be clinically categorized as homogeneous or nonhomogeneous condition, in which a white snowy plaque or patch will form on the tongue. If the lesions have a thin, flat, uniform, smooth, and white appearance, it is categorized as homogeneous, and nonhomogeneous lesions may have a white and red appearance or tiny, white, pinhead-size raised nodules on a reddish background or a proliferative, warty presence. Erythroplakia exists as a red patch with a smooth or granular surface that cannot be categorized clinically or pathologically as any other definable disease [10]. Erythroplakia has a higher chance than leukoplakia

**Figure 1.** Head and Neck Cancer Regions.

2 Prevention, Detection and Management of Oral Cancer

to anchorage occult invasive cancer and to undergo malignant transformation.

Interweaving white lines (known as *Wickham's striae*) with a reddish border or as a mix of reddish and ulcerated areas will appear on oral lichen planus. OSMF, mostly restricted to the people of Indian subcontinent origin and in certain Pacific islands such as the Mariana Islands, presents with a blanching of the oral mucosa, burning sensation, and intolerance to spicy food. As the disease progresses, hardening and weaken of the oral and pharyngeal mucosa occur, leading to reduced mouth opening and difficulty in swallowing and speaking. Smokers who smoke with the lighted end of the tobacco product inside the mouth, known as WHO conveyed that head and neck cancer occurrence and death are high in India, Papua New Guinea, and Taiwan, China, where the habit of chewing betel quid's with tobacco or without tobacco or areca nut chewing is common, as well as in France, Eastern Europe, and parts of South America such as Brazil and Uruguay, where tobacco smoking and alcohol ingestion are high. The age-standardized incidence rates for men are, on average, twice as high as those for women. WHO reported that in selected countries where some reliable cancer registries exist, India is highest and Belarus is lowest, with incidence rates changing by more than five times in men and women. The estimated age-standardized incidence rates of head and neck cancer also fluctuate among countries in different regions [6].

In South and Southeast Asia, buccal (cheek) mucosa is the most common site for head and neck cancer; the tongue is the most prominent site in all other regions [12]. Regional differences in frequency and the site of occurrence are related to the major causes, which are betel quid and tobacco chewing in South and Southeast Asia and alcohol and smoking in Western countries [13]. The mortality rates of head and neck cancer range between 1 and 15 per 100,000 persons in different regions; mortality rates exceed 10 per 100,000 in Eastern European countries, such as the Czech Republic, Hungary, and the Slovak Republic. Head and neck cancer mortality rates are influenced by head and neck cancer incidence, access to treatment, and deviations in site distribution.

The trends in incidence and mortality among men and women are closely correlated with the patterns and trends in tobacco and alcohol use. Increase in tobacco and areca nut chewing and alcohol consumption causes an elevated incidence rate which has been reported in Karachi [14] and in Taiwan [15]. Head and neck cancer incidence and mortality rates have been gradually falling over the past 2 decades because of declining smoking prevalence and alcohol ingestion in the US [16]. However, because of human papillomavirus (HPV), there is an increase in cancers at the base of the tongue, which has been observed in white men in the United States [17].

It was reported that over the past 2 decades, incidence and mortality rates for head and neck cancer have been declining steadily in most European countries. The increasing rates had been observed in some Central European countries, such as Hungary and the Slovak Republic, reflecting changes in alcohol and tobacco consumption [18]. There was a steady decrease in the mortality rate of head and neck cancer in France since reaching a peak in the early 1990s and the decline correlated with the reduction in alcohol consumption. Incidence and mortality have been stable in the Nordic countries, the Russian Federation, and the United Kingdom. Mortality rates have been steadily declining in Australia and Hong Kong SAR, China but increasing in Japan and the Republic of Korea [19].

the associated cancer burden has declined as a result [22]; currently, 1.5 and 2% of all cancer deaths in HICs and LMICs, respectively, are attributed to high salt intake, compared with 2.2 and 2.7%, respectively, in 1990 is highlighted by Lim and others. In the United Kingdom, 10% of new stomach cancer cases diagnosed in 2010 may be attributable to high salt intake [23].

Introductory Chapter: Head and Neck Cancer http://dx.doi.org/10.5772/intechopen.86272 5

Over the past era, key factor in the decline in cancer incidence reported to be the better food preservation methods and reducing the salt. The levels of red meat consumption in recent decades may explain the low colorectal cancer rates in southern Asia [21], as well as the high rates in many Western European countries were reported [24, 25]. Researchers find association for obesity and overweight to cancer is firmer than the dietary evidence has been reported, indicating that 3 and 6% of cancer deaths in LMICs and HICs, respectively, can be attributed to excess weight, as quantified by the body mass index (BMI) [26, 27]. High BMI was raised as a cause of cancer deaths more substantively in LMICs, a 54% increase over the past 30 years,

A buccal mucosa carcinoma is a violent form of head and neck cancer associated with the high rate of locoregional reappearance and poor existence [29]. It is the most common cancer in men and the third most common cancer in women in India [30]. National Cancer Registry Program (India) has stated approximately 13,500 cases of buccal mucosa carcinoma from various Indian cancer registries [31]. Indian Council of Medical Research guideline on buccal mucosa carcinoma management reported that there is the absence of national and international data specifically on buccal mucosa cancer, and very few randomized studies from India on various aspects of buccal mucosa cancer are available [32]. Tumor thickness and other prognostic factors were institutes to be not correct predictors of relapse and most of the studies were not adequately powered to draw a definite conclusion. Patients with head and neck cancer observed by computed tomography, magnetic resonance imaging (MRI), ultrasound-guided fine needle aspiration biopsy, and positron emission tomography (PET) have significant false positive and false negative results, invariably not capable of detecting nodal metastasis [33, 34]. Genetic alterations recognized to date have not been used clinically in the assessment of surgical margins, and no study has developed a gene signature that can accurately predict which patients with buccal cancer are at a higher risk of disease recurrence. Traditional surgical procedures may miss involved lymph nodes due to a limitation in histopathology examinations [35]. Lymph-node metastasis can be predicted by gene expression profiles of primary oral cavity squamous cell carcinomas [36]. Further, markers of prognosis will be validated by qRT-PCR technique. Gene expression profiles of primary tumor and their matched normal mucosa and comparing with different tumor stage and lymph-node status and to identify clinically significant prognostic markers

Increase in the incidence of buccal mucosa carcinoma was observed from the hospital registry over the last 15 years [3]. The incidence and cumulative risk of buccal mucosa cancer in

**Figure 2** describes the mechanism of salivary involvement with diet and cancer.

compared with a 26% increase in HICs over the same period [28].

**5. Buccal mucosa cancer—our perspective**

is warranted.

## **4. Diet and head and neck cancer**

The current report highlighted that about 15% of all cancer deaths are related to unhealthy diets, including high intake of red meat, processed meat, and sodium, as well as low intake of fruits and vegetables [20]. Most research is still not consistent about specific nutritional items yet for the cause of cancer geographically [21]. The most current comprehensive review of diet and cancer risks confirms an elevated risk related to red and processed meat but finds less evidence supporting the benefit of the consumption of fruits and vegetables in reducing risk [22]. When well-established risk factors are included in the estimation of cancer, the variations in the strength of the evidence in different studies over time makes the measurement of dietary intake problematic in epidemiological studies. Nearly 5% of cancer deaths worldwide (387,000) would be attributable to dietary risk factors [20]. Ingestion of red meat and processed meat increases the risk of colorectal cancer by an estimate of 43% [21]. Ingestion of red meat and processed meat is generally rising in low- and middle-income countries (LMICs), but is stable in high-income countries (HICs). Another major dietary risk factor is high salt intake, which increases the risk of stomach cancer. Globally, salt intake has declined, and

**Figure 2.** The mechanism of salivary involvement with diet and cancer.

the associated cancer burden has declined as a result [22]; currently, 1.5 and 2% of all cancer deaths in HICs and LMICs, respectively, are attributed to high salt intake, compared with 2.2 and 2.7%, respectively, in 1990 is highlighted by Lim and others. In the United Kingdom, 10% of new stomach cancer cases diagnosed in 2010 may be attributable to high salt intake [23].

**Figure 2** describes the mechanism of salivary involvement with diet and cancer.

Over the past era, key factor in the decline in cancer incidence reported to be the better food preservation methods and reducing the salt. The levels of red meat consumption in recent decades may explain the low colorectal cancer rates in southern Asia [21], as well as the high rates in many Western European countries were reported [24, 25]. Researchers find association for obesity and overweight to cancer is firmer than the dietary evidence has been reported, indicating that 3 and 6% of cancer deaths in LMICs and HICs, respectively, can be attributed to excess weight, as quantified by the body mass index (BMI) [26, 27]. High BMI was raised as a cause of cancer deaths more substantively in LMICs, a 54% increase over the past 30 years, compared with a 26% increase in HICs over the same period [28].


**5. Results and discussions**

All simulations were performed for 20 s of real flow time and the first 5 s were neglected to reduce the start-up effect. Thus, the results reported were averaged over the last 15 s of real flow time. Bubble properties were calculated from the volume fraction contours produced by the CFD software, Fig. 3.

These volume fraction contours were then analysed by the in-house code. The first step in analysing bubble properties is to discriminate the bubble from the rest of the bed. This was done by setting a solid volume fraction cut-off point to produce discriminated volume

Numerical Simulation of Dense

2 mm 4 mm 5 mm 10 mm

0 0.01 0.02 0.03 0.04 0.05 0.06

> 2 mm 4 mm 5 mm 10 mm

0.0 0.2 0.4 0.6 0.8 1.0 1.2

**Mean bubble rise velocity [m/s]**

**Mean bubble diameter [m]**

Gas-Solid Multiphase Flows Using Eulerian-Eulerian Two-Fluid Model 247

**Mean bubble diameter [m]**

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

> 2 mm 4 mm 5 mm 10 mm

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

**Mean bubble rise velocity [m/s]**

2 mm 4 mm 5 mm 10 mm

Fig. 4. Comparison of mean bubble diameter for different grid sizes.

0 0.1 0.2 0.3 0.4 0.5

NT

**Bed height [m]**

NT

Fig. 5. Comparison of mean bubble rise velocity for different grid sizes.

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

**5.2 Two-dimensional versus three-dimensional simulation**

2D NT 2 96000 70 4 24000 18 5 15300 12 10 3840 5 S6 2 101838 75 4 27694 27 5 18469 16 10 5190 11 3D NT 4 120000 159 5 61440 75 S6 4 145705 219 5 72924 105 Table 2. Computational time requirements for different solution domains, bed geometries

Generally, all practical gas-solid flows are three-dimensional (3D) in nature and numerical simulations in 3D domain should be performed to validate and study these 3D flows.

[mm] Number of cells Simulation time per second

0 0.1 0.2 0.3 0.4 0.5

**Bed Height [m]**

0 0.1 0.2 0.3 0.4 0.5

S6

**Bed height [m]**

S6

of real flow time [h]

Domain Geometry Grid size

and grid sizes.

fraction contours from the CFD code (ANSYS FLUENT 12.1) used. In the results reported in this book, a bubble was assumed to be the area with solid volume fraction of less than or equal to 0.2. There is no uniform definition of bubble boundary in literature with threshold values ranging from 0.15 to 0.30 for the solid volume fraction with 0.2 mostly used. Some sensitivity analyses performed showed that there is no significant difference in rise velocity and bubble shape with the different bubble boundaries used while the mean bubble diameter slightly varied with the threshold values (Asegehegn et al., 2011a). In this work, 50 frames per seconds were sampled to be consistent with the experimental analysis. Therefore, for the total 15 seconds of real flow time 750 frames are analysed for the statistical analysis of bubble properties. Though this was much less than the number of frames in the experimental results, considering the computational effort needed it was found to be sufficient for the statistical analysis of bubble properties.

Fig. 3. Instantaneous volume fraction contour plots.

#### **5.1 Influence of grid size**

It is common practice to perform grid sensitivity analyses in order to ensure grid independent solution during numerical simulations. However, it is usually difficult to achieve grid independent solution using the granular kinetic theory as the granular theory closure equations do not give grid independent solutions with reasonable grid sizes. However, in this work a grid sensitivity analysis was performed at least to ensure the mean bubble properties are not significantly affected by the grid size used. For this purpose simulations were performed for both beds with grid sizes ranging from 2 mm to 10 mm. The results are plotted in Fig. 4 and 5 for the mean bubble diameter and mean bubble rise velocity respectively. With the exceptions of a grid size of 10 mm other sizes (5 mm, 4 mm, 2 mm) provided comparable results hence the grid size of 5 mm was selected taking into account the accuracy and computational time required.

In Table 2 the different computational times needed per one second of real flow time is presented for the different grid sizes as well as 2D and 3D simulations. All simulations were performed on a Quad-Core Intel Xeon processor (3 GHz each) workstation and a time step of 2.5×10-5 s was used. It can be clearly seen that the simulation time significantly increased as the grid size reduced. The difference between the NT and S6 can be attributed to the increase in number of cells in S6 as a result of the refinement near the tube surfaces.

fraction contours from the CFD code (ANSYS FLUENT 12.1) used. In the results reported in this book, a bubble was assumed to be the area with solid volume fraction of less than or equal to 0.2. There is no uniform definition of bubble boundary in literature with threshold values ranging from 0.15 to 0.30 for the solid volume fraction with 0.2 mostly used. Some sensitivity analyses performed showed that there is no significant difference in rise velocity and bubble shape with the different bubble boundaries used while the mean bubble diameter slightly varied with the threshold values (Asegehegn et al., 2011a). In this work, 50 frames per seconds were sampled to be consistent with the experimental analysis. Therefore, for the total 15 seconds of real flow time 750 frames are analysed for the statistical analysis of bubble properties. Though this was much less than the number of frames in the experimental results, considering the computational effort needed it was found to be

It is common practice to perform grid sensitivity analyses in order to ensure grid independent solution during numerical simulations. However, it is usually difficult to achieve grid independent solution using the granular kinetic theory as the granular theory closure equations do not give grid independent solutions with reasonable grid sizes. However, in this work a grid sensitivity analysis was performed at least to ensure the mean bubble properties are not significantly affected by the grid size used. For this purpose simulations were performed for both beds with grid sizes ranging from 2 mm to 10 mm. The results are plotted in Fig. 4 and 5 for the mean bubble diameter and mean bubble rise velocity respectively. With the exceptions of a grid size of 10 mm other sizes (5 mm, 4 mm, 2 mm) provided comparable results hence the grid size of 5 mm was selected taking into

In Table 2 the different computational times needed per one second of real flow time is presented for the different grid sizes as well as 2D and 3D simulations. All simulations were performed on a Quad-Core Intel Xeon processor (3 GHz each) workstation and a time step of 2.5×10-5 s was used. It can be clearly seen that the simulation time significantly increased as the grid size reduced. The difference between the NT and S6 can be attributed to the

increase in number of cells in S6 as a result of the refinement near the tube surfaces.

sufficient for the statistical analysis of bubble properties.

Fig. 3. Instantaneous volume fraction contour plots.

account the accuracy and computational time required.

**5.1 Influence of grid size**

Fig. 4. Comparison of mean bubble diameter for different grid sizes.

Fig. 5. Comparison of mean bubble rise velocity for different grid sizes.

Table 2. Computational time requirements for different solution domains, bed geometries and grid sizes.

#### **5.2 Two-dimensional versus three-dimensional simulation**

Generally, all practical gas-solid flows are three-dimensional (3D) in nature and numerical simulations in 3D domain should be performed to validate and study these 3D flows.

Numerical Simulation of Dense

u=2.0Umf.

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

> 0.0 0.2 0.4 0.6 0.8 1.0 1.2

**Mean bubble rise velocity [m/s]**

**Mean bubble diameter [m]**

m, u=2.0Umf.

parametric studies, 2D simulations remain indispensable.

NT

Experiment 3D 2D

Experiment 3D 2D

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

NT

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

Gas-Solid Multiphase Flows Using Eulerian-Eulerian Two-Fluid Model 249

was believed that neglecting the front and back walls in the 2D simulations resulted in higher bubble rise velocities. In the experiment as well as 3D simulations, as a result of small bed thickness, the down flowing particles along the front and back walls increased the drag experienced by the bubbles, thus slowed the bubbles. On the other hand these walls were neglected in the 2D simulations and bubbles could move freely in the bed without experiencing the drag of down flowing particles as well as the friction of walls. Others also reported similar results, e.g. Krishna et al. (2000) and Li et al. (2010). Krishna et al. (2000) studied the influence of walls on bubble rise velocity for both gas-liquid and gas-solid systems and they reported that the rise velocity of single gas bubbles was significantly reduced as the ratio of bubble to bed width increased. Though 2D simulations have certain limitations and are physically different form the 3D flow exit in practical applications, they can provide quiet satisfactory results compared to experimental observations. It is clear that 3D simulations are more realistic and should be preferred, but with current, even in the near future, computer capacity these are far from reaching. Comparing the computational time needed for 2D and 3D simulations as shown in Table 2 above, it was found that 3D simulations are 5 to 9 times more expensive than their 2D equivalents. As an example in order to simulate 20 s of real flow time in the 2D grid with a mesh size of 5 mm using the no tube geometry 240 h (approx. 10 days) of simulation time were necessary while this was increased to 1500 h (approx. 62 days) in the case of 3D simulations. Therefore, as also deduced by Xie et al. (2008) and Cammarata et al. (2003), especially for conducting extensive

Fig. 6. Comparison of mean bubble diameter between 2D and 3D simulations, dp = 347 m,

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

**Mean bubble diameter [m]**

Experiment 3D 2D

Experiment 3D 2D

0 0.1 0.2 0.3 0.4 0.5

S6

**Bed height [m]**

S6

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

Fig. 7. Comparison of mean bubble rise velocity between 2D and 3D simulations, dp = 347

0.0 0.2 0.4 0.6 0.8 1.0 1.2

**Mean bubble rise velocity [m/s]**

However, even with improved computational facilities, 3D simulations are still computationally prohibitive even for engineering-scale fluidized beds. Therefore, the majority of simulation studies encountered in the literature are limited to two-dimensional (2D) coordinate system to simulate 3D fluidized beds. However, there are no studies proving that 2D computations are sufficient for validation and parametric study of fluidized beds. 2D simulations are most likely to be successful in cases where the flow is presumed to be 2D, that is in cases where the variations in space and time in a given direction of the physical space are negligible compared to the variations encountered in the other directions. This is typically in the case of pseudo-2D beds, which have small depth compared to the height and width. In such cases, the particle motion can be effectively suppressed in the depth direction thus resembling 2D motion of the particles in the axial and radial directions only. Many researchers have applied 2D Cartesian simulations to model such rectangular pseudo-2D beds and found reasonable agreement between model prediction and experimental results. However, such comparisons leave an important difference between the pseudo-2D experimental beds and 2D numerical models. The front and back walls, which are neglected in the numerical model, could have considerable influence on the hydrodynamics of the fluidized beds. So far, only Li et al. (2010) have performed both 2D and 3D simulations of a pseudo-2D and compared the results with experimental measurements. They found considerable difference in the bubble rise velocity predicted by the 2D and 3D simulations while the bubble diameter predicted by both domains is generally comparable. Cammarata et al. (2003) performed simulations using 2D and 3D domains of a rectangular bed and the results of bubble diameter were compared with correlation from the literature. These authors found considerable difference of the bubble diameter predicted by the simulations. On the other hand, Peirano et al. (2001) analysed and compared simulation results of the power spectra of pressure fluctuations, bed height, and probability density function of particle volume fraction with experimental measurements. They observed a significant difference between 2D and 3D simulations and concluded that 2D simulations should be used with caution and only for sensitivity analysis. Though all the above authors concluded that 3D simulations should be preferably performed except maybe in cases where the flow is by nature 2D, they also indicate that 2D simulations could be used to conduct sensitivity analyses.

In this work, simulations were performed for two different particle sizes and two bed geometries with and without immersed horizontal tubes using 2D and 3D domains and the results were compared to experimental data obtained from pseudo-2D bed. The mean bubble diameter and rise velocity are shown in Fig. 6 and 7 respectively for the particle with mean diameter of 347 m. Similar results were obtained for the other particle size as well but the results are not presented here to reduce redundancy. As shown in Fig. 6 the mean bubble diameter predicted by both 2D and 3D simulations are generally in very good agreement with the experimental though the 2D simulations predicted slightly smaller bubbles than the 3D. This was consistent with the results of Li et al. (2010) while Cammarata et al. (2003) reported otherwise. The results obtained from 2D simulations showed greater divergence from the experimental data with increasing bed height. Though not shown here, this was more pronounced with increasing superficial velocity. A significant difference between 2D and 3D simulations was observed in mean bubble rise velocity, Fig. 7. 2D simulations predicted much higher rise velocity than 3D simulations and significantly deviate from the experiment measurements. This was largely attributed to the wall effect. It

However, even with improved computational facilities, 3D simulations are still computationally prohibitive even for engineering-scale fluidized beds. Therefore, the majority of simulation studies encountered in the literature are limited to two-dimensional (2D) coordinate system to simulate 3D fluidized beds. However, there are no studies proving that 2D computations are sufficient for validation and parametric study of fluidized beds. 2D simulations are most likely to be successful in cases where the flow is presumed to be 2D, that is in cases where the variations in space and time in a given direction of the physical space are negligible compared to the variations encountered in the other directions. This is typically in the case of pseudo-2D beds, which have small depth compared to the height and width. In such cases, the particle motion can be effectively suppressed in the depth direction thus resembling 2D motion of the particles in the axial and radial directions only. Many researchers have applied 2D Cartesian simulations to model such rectangular pseudo-2D beds and found reasonable agreement between model prediction and experimental results. However, such comparisons leave an important difference between the pseudo-2D experimental beds and 2D numerical models. The front and back walls, which are neglected in the numerical model, could have considerable influence on the hydrodynamics of the fluidized beds. So far, only Li et al. (2010) have performed both 2D and 3D simulations of a pseudo-2D and compared the results with experimental measurements. They found considerable difference in the bubble rise velocity predicted by the 2D and 3D simulations while the bubble diameter predicted by both domains is generally comparable. Cammarata et al. (2003) performed simulations using 2D and 3D domains of a rectangular bed and the results of bubble diameter were compared with correlation from the literature. These authors found considerable difference of the bubble diameter predicted by the simulations. On the other hand, Peirano et al. (2001) analysed and compared simulation results of the power spectra of pressure fluctuations, bed height, and probability density function of particle volume fraction with experimental measurements. They observed a significant difference between 2D and 3D simulations and concluded that 2D simulations should be used with caution and only for sensitivity analysis. Though all the above authors concluded that 3D simulations should be preferably performed except maybe in cases where the flow is by nature 2D, they also indicate that 2D simulations could be used

In this work, simulations were performed for two different particle sizes and two bed geometries with and without immersed horizontal tubes using 2D and 3D domains and the results were compared to experimental data obtained from pseudo-2D bed. The mean bubble diameter and rise velocity are shown in Fig. 6 and 7 respectively for the particle with mean diameter of 347 m. Similar results were obtained for the other particle size as well but the results are not presented here to reduce redundancy. As shown in Fig. 6 the mean bubble diameter predicted by both 2D and 3D simulations are generally in very good agreement with the experimental though the 2D simulations predicted slightly smaller bubbles than the 3D. This was consistent with the results of Li et al. (2010) while Cammarata et al. (2003) reported otherwise. The results obtained from 2D simulations showed greater divergence from the experimental data with increasing bed height. Though not shown here, this was more pronounced with increasing superficial velocity. A significant difference between 2D and 3D simulations was observed in mean bubble rise velocity, Fig. 7. 2D simulations predicted much higher rise velocity than 3D simulations and significantly deviate from the experiment measurements. This was largely attributed to the wall effect. It

to conduct sensitivity analyses.

was believed that neglecting the front and back walls in the 2D simulations resulted in higher bubble rise velocities. In the experiment as well as 3D simulations, as a result of small bed thickness, the down flowing particles along the front and back walls increased the drag experienced by the bubbles, thus slowed the bubbles. On the other hand these walls were neglected in the 2D simulations and bubbles could move freely in the bed without experiencing the drag of down flowing particles as well as the friction of walls. Others also reported similar results, e.g. Krishna et al. (2000) and Li et al. (2010). Krishna et al. (2000) studied the influence of walls on bubble rise velocity for both gas-liquid and gas-solid systems and they reported that the rise velocity of single gas bubbles was significantly reduced as the ratio of bubble to bed width increased. Though 2D simulations have certain limitations and are physically different form the 3D flow exit in practical applications, they can provide quiet satisfactory results compared to experimental observations. It is clear that 3D simulations are more realistic and should be preferred, but with current, even in the near future, computer capacity these are far from reaching. Comparing the computational time needed for 2D and 3D simulations as shown in Table 2 above, it was found that 3D simulations are 5 to 9 times more expensive than their 2D equivalents. As an example in order to simulate 20 s of real flow time in the 2D grid with a mesh size of 5 mm using the no tube geometry 240 h (approx. 10 days) of simulation time were necessary while this was increased to 1500 h (approx. 62 days) in the case of 3D simulations. Therefore, as also deduced by Xie et al. (2008) and Cammarata et al. (2003), especially for conducting extensive parametric studies, 2D simulations remain indispensable.

Fig. 6. Comparison of mean bubble diameter between 2D and 3D simulations, dp = 347 m, u=2.0Umf.

Fig. 7. Comparison of mean bubble rise velocity between 2D and 3D simulations, dp = 347 m, u=2.0Umf.

Numerical Simulation of Dense

observed from the experiment.

Experiment\_NT Simulation\_NT Experiment\_S6 Simulation\_S6

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

0 0.1 0.2 0.3 0.4 0.5

dp = 246 m Umf = 0.0876 m/s

**Bed height [m]**

NT

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

0.00

0.02

0.04

0.06

**Mean bubble diameter [m]**

0.08

0.10

0.12

**Mean bubble diameter [m]**

mm and Umf = 0.0876 m/s.

Gas-Solid Multiphase Flows Using Eulerian-Eulerian Two-Fluid Model 251

sizes. For beds without immersed tubes the simulation and experimental results were in very good agreement until a certain height near the eruption zone. After this height the simulation predicted no growth of bubbles while a continuous growth of bubbles was

Fig. 9. Comparison of mean bubble diameter between simulation results and experimental data for beds with and without immersed tubes and two different particle sizes, u=2Umf.

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

**Mean bubble diameter [m]**

Experiment\_NT Simulation\_NT Experiment\_S6 Simulation\_S6

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

0 0.1 0.2 0.3 0.4 0.5

dp = 347 m Umf = 0.144 m/s

**Bed height [m]**

S6

0 0.1 0.2 0.3 0.4 0.5

**Bed height [m]**

Fig. 10. Comparison of mean bubble diameter between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

**Mean bubble diameter [m]**

In the case of beds with horizontal tubes good qualitative agreement was achieved between the simulation and experimental results for both particle sizes. However, the simulation slightly underpredicted bubble diameter in the tube bank region. This was partially attributed to the wall effect that was neglected in the numerical simulation while the experiments were performed using pseudo-2D beds. From both simulation and experiment it can be concluded that the presence of tubes resulted in higher bubble splitting and coalescence which eventually reduced the mean bubble size. Small bubbles formed at the distributor rose and grew by coalescence until they reached the first row of tubes. Upon

#### **5.3 Influence of immersed horizontal tubes 5.3.1 Bubble aspect ratio**

Aspect ratio is an important characteristics of a bubble since it strongly influence the bubble's hydrodynamics. Fig. 8 shows the mean bubble aspect ratio for the two bed geometries (NT and S6) and different superficial velocities. Similar results were obtained for the second particle size and the graphs are not shown here to reduce redundancy. In general, the simulation showed good agreement with the experimental data for all geometries. For the bed without internal tubes both the simulation and experimental results predicted an increase in aspect ratio with bed height. This indicated the flattening and vertical stretching of bubbles with increasing bubble size. Generally, for beds without internal obstacles, the bubble shape was found to be nearly circular when the bubble was smaller and flattened, distorted and elongated when the bubble was bigger. Similar results were reported by Hatano et al. (1986) from experimental observations.

In the case of beds with internal horizontal tubes bubble aspect ratio was found to depend more strongly on tube geometry rather than bubble size or bed height. Bubbles were seen to elongate vertically when they moved between the tubes in a row and retained their original shape and became nearly circular when they were away from the tube rows. This resulted in oscillation of the aspect ratio in the tube bank region as shown in Fig. 8. The elongation of bubbles in the vertical direction was mainly seen as a result of the reduction in flow area and bubbles squeezed and deformed to fit the space between the tubes. As a bubble squeezed it stretched and elongated vertically as the area/volume of the bubble should at least remain the same provided that no splitting has occurred during the process. Qualitatively the simulation predicted a similar trend for fluctuations in the aspect ratio in the tube bank region, however, it predicted bubbles that were relatively circular as opposed to the corresponding experimental data. This could be associated with the no-slip boundary condition imposed on the walls for the gas phase.

Fig. 8. Comparison of mean bubble aspect ratio between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246 mm and Umf=0.0876 m/s.

#### **5.3.2 Bubble size**

Fig. 9 and 10 present the simulation results and comparisons with experimental data of mean bubble diameter for the two bed geometries (NT and S6) and two different particle

Aspect ratio is an important characteristics of a bubble since it strongly influence the bubble's hydrodynamics. Fig. 8 shows the mean bubble aspect ratio for the two bed geometries (NT and S6) and different superficial velocities. Similar results were obtained for the second particle size and the graphs are not shown here to reduce redundancy. In general, the simulation showed good agreement with the experimental data for all geometries. For the bed without internal tubes both the simulation and experimental results predicted an increase in aspect ratio with bed height. This indicated the flattening and vertical stretching of bubbles with increasing bubble size. Generally, for beds without internal obstacles, the bubble shape was found to be nearly circular when the bubble was smaller and flattened, distorted and elongated when the bubble was bigger. Similar results

In the case of beds with internal horizontal tubes bubble aspect ratio was found to depend more strongly on tube geometry rather than bubble size or bed height. Bubbles were seen to elongate vertically when they moved between the tubes in a row and retained their original shape and became nearly circular when they were away from the tube rows. This resulted in oscillation of the aspect ratio in the tube bank region as shown in Fig. 8. The elongation of bubbles in the vertical direction was mainly seen as a result of the reduction in flow area and bubbles squeezed and deformed to fit the space between the tubes. As a bubble squeezed it stretched and elongated vertically as the area/volume of the bubble should at least remain the same provided that no splitting has occurred during the process. Qualitatively the simulation predicted a similar trend for fluctuations in the aspect ratio in the tube bank region, however, it predicted bubbles that were relatively circular as opposed to the corresponding experimental data. This could be associated with the no-slip boundary

Fig. 8. Comparison of mean bubble aspect ratio between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

**Mean bubble aspect ratio [-]**

0 0.1 0.2 0.3 0.4 0.5 0.6

S6

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

**Bed height [m]**

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

Fig. 9 and 10 present the simulation results and comparisons with experimental data of mean bubble diameter for the two bed geometries (NT and S6) and two different particle

were reported by Hatano et al. (1986) from experimental observations.

**5.3 Influence of immersed horizontal tubes**

condition imposed on the walls for the gas phase.

NT

0 0.1 0.2 0.3 0.4 0.5 0.6

**Bed height [m]**

mm and Umf=0.0876 m/s.

**5.3.2 Bubble size**

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

**Mean bubble aspect ratio [-]**

**5.3.1 Bubble aspect ratio**

sizes. For beds without immersed tubes the simulation and experimental results were in very good agreement until a certain height near the eruption zone. After this height the simulation predicted no growth of bubbles while a continuous growth of bubbles was observed from the experiment.

Fig. 9. Comparison of mean bubble diameter between simulation results and experimental data for beds with and without immersed tubes and two different particle sizes, u=2Umf.

Fig. 10. Comparison of mean bubble diameter between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246 mm and Umf = 0.0876 m/s.

In the case of beds with horizontal tubes good qualitative agreement was achieved between the simulation and experimental results for both particle sizes. However, the simulation slightly underpredicted bubble diameter in the tube bank region. This was partially attributed to the wall effect that was neglected in the numerical simulation while the experiments were performed using pseudo-2D beds. From both simulation and experiment it can be concluded that the presence of tubes resulted in higher bubble splitting and coalescence which eventually reduced the mean bubble size. Small bubbles formed at the distributor rose and grew by coalescence until they reached the first row of tubes. Upon

Numerical Simulation of Dense

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

and Umf = 0.0876 m/s.

conclusions can be drawn:

**7. Acknowledgment**

of Economics and Technology.

losing much of the information.

0 0.1 0.2 0.3 0.4 0.5

NT

**Bed height [m]**

**6. Conclusion**

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

**Mean bubble rise velocity [m/s]**

data.

Gas-Solid Multiphase Flows Using Eulerian-Eulerian Two-Fluid Model 253

Experiment\_2Umf Experiment\_3Umf Experiment\_4Umf Simulation\_2Umf Simulation\_3Umf Simulation\_4Umf

Fig. 12. Comparison of mean bubble rise between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246 mm

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

**Mean bubble rise velocity [m/s]**

0 0.1 0.2 0.3 0.4 0.5

S6

**Bed height [m]**

Numerical simulations using the Eulerian-Eulerian TFM were performed for pseudo-2D gas-solid fluidized beds with and without immersed horizontal tubes. The simulation results of bubble characteristics were compared and validated with experimental data obtained by a digital image analysis technique. From the results of this work the following

The two-fluid model is capable of predicting the main bubble characteristics such as bubble aspect ratio, diameter and rise velocity with and without immersed tubes. The calculated bubble properties were in general in good agreement with the experimental

3D simulations gave better results than 2D simulations compared with experiments. However, they are computationally expensive and 2D simulations can be successfully used for parametric study of gas-solid fluidized beds of engineering scale without

The major difference between 2D and 3D simulations was observed in predicting the bubble rise velocity where 2D simulations overpredicted the bubble rise velocity compared to

As a result of higher bubble splitting and coalescence as well as additional drag, the bubble diameter and rise velocity were lower for beds with horizontal tubes than beds without tubes. Moreover, bubble diameter and rise velocity were found to strongly depend on the tube bank geometry rather than the superficial velocity in the case of beds with dense immersed tubes while these bubble properties strongly vary with superficial

The authors gratefully acknowledge the funding of this research project by the "Entrepreneurial Regions"-Initiative established by the German Federal Ministry of Education and Research and Research and COORETEC-Initiative from the Federal Ministry

the corresponding 3D simulations and experimental data.

velocity in the case of beds without immersed tubes.

reaching the first row of tubes they split then grew by coalescence until they reached the second row of tubes. This continued until the last row of tubes after which bubbles rapidly grew without restriction until they finally reached the top of the bed and erupted. As shown in Fig. 10 both the simulation and experiment showed that the growth of bubbles in the tube bank region was mainly dictated by tube bank geometry rather than superficial velocity or bed height. In the tube free region, below and above the tube bank, bubble growth resembled a similar trend as in the case of the bed without immersed tubes.

#### **5.3.3 Bubble rise velocity**

Fig. 11 and 12 illustrate comparisons between simulation and experimental results for bubble rise velocities for the two bed geometries and particle sizes. For beds both with and without immersed tubes the simulation overpredicted rise velocity as compared to the experimental results and it was more pronounced at the upper part of the beds. This was largely associated with the wall effect as discussed above.

Fig. 11. Comparison of mean bubble rise velocity between simulation results and experimental data for beds with and without immersed tubes and two different particle sizes, u=2Umf.

In the tube bank region the rise velocity was highly influenced by the presence of tubes. The reduction in bubble size due to frequent splitting in the tube bank region caused a decrease in bubble rise velocity compared to beds without internal obstacles. Both the experiment and the simulation showed higher rise velocity at the upper part of the tubes and lower rise velocity at the lower part of the tubes. The higher rise velocity seen at the upper part of the tube rows can be explained mainly due to the elongation of bubbles. As a result of elongation of a bubble and stretching the centroid of the bubble moved further in distance than it would if it were circular. This caused the centroid of the bubbles to move further in the vertical direction than they usually do. It was observed that bubbles with higher aspect ratios had higher rise velocities than those with lower aspect ratios. Hatano et al. (1986) also reported similar results for beds without internal obstacles. The reason for the lower mean rise velocity at the bottom of the tubes was due to the semi-stagnant bubbles that occurred at this location as a result of small bubble formation and splitting of bubbles into large and small daughters (Asegehegn et al. 2011a).

Fig. 12. Comparison of mean bubble rise between simulation results and experimental data for beds with and without immersed tubes and different superficial velocities, dp= 246 mm and Umf = 0.0876 m/s.


**3. Resistance factor and residual resistance factor**

Resistance factor (RF) provides information on the effective viscosity of the polymer solution during flow in porous media relative to water before polymer flooding [38]; therefore, RF indicates the effectiveness of the polymer system as a mobility control agent during enhanced oil recovery (EOR) [6, 19, 29, 40, 41]. While, the residual resistance factor (RRF) measures the increased pressure drop across the porous media due to polymer retention (mechanical entrapment and polymer adsorption) [5, 6, 19, 29, 37–44].

**Figure 2(a)** and **(b)** plots RF as a function of volume of fluid injected expressed as a fraction of pore volume (PV) normalized for permeability and porosity for tests—Baseline # 1.1 and SAP-AP1 # 1.2 and Baseline # 2.1 and SAP-AP1 # 2.2 respectively.

**Figure 2** demonstrates that the RF curves of the AP1 polymer and the SAP-AP1 network have similar flow behavior. After the injection of 0.013 PV, the RF values increase continuously with increasing volume of fluid injected until a maximum RF value was reached at about 0.02 PV. Then, the RF values decreased with increased throughput in the sand packs until the RF values leveled off. The RF value plateaued out at an average value of 5.6 for the SAP-AP1 system, while for the baseline AP1 polymer, the RF value plateaued out at an average value of 2.0. Therefore, the SAP-AP1 system offered on average 3.6 times higher effective viscosity within the porous media relative to the effective viscosity achieved by polymer AP1.

The RF-curves eventually reached plateau values for both systems: the SAP-AP1 and the baseline. This may occur due to the dynamic disassembling and reassembling of the non-covalent

**Figure 2.** RF versus volume of fluid injected: (a) Baseline # 1.1 and SAP-AP1 # 1.2 and (b) Baseline # 2.1 and SAP-AP1 # 2.2.

were monitored. Material balance was applied to determine oil and water saturations, as well as the percentage of oil recovered from each injection stage. More details of the experimental

**] Porosity [%] Permeability [mD]**

The results obtained from the displacement tests were analyzed by plotting the resistance factor (RF), the residual resistance factor (RRF), percentage of cumulative oil recovery, ratio of remaining oil saturation over initial oil saturation (*S*ro/*S*oi), and water oil ratio (WOR) as a function of volume of fluid injected. The volume of fluid injected was expressed as a fraction of pore volume normalized by porosity and permeability using the capillary bundle parameter [36–39] to compare the displacement tests at the same porosity and permeability

Resistance factor (RF) provides information on the effective viscosity of the polymer solution during flow in porous media relative to water before polymer flooding [38]; therefore, RF indicates the effectiveness of the polymer system as a mobility control agent during enhanced oil recovery (EOR) [6, 19, 29, 40, 41]. While, the residual resistance factor (RRF) measures the increased pressure drop across the porous media due to polymer retention (mechanical

**Figure 2(a)** and **(b)** plots RF as a function of volume of fluid injected expressed as a fraction of pore volume (PV) normalized for permeability and porosity for tests—Baseline # 1.1 and

**Figure 2** demonstrates that the RF curves of the AP1 polymer and the SAP-AP1 network have similar flow behavior. After the injection of 0.013 PV, the RF values increase continuously with increasing volume of fluid injected until a maximum RF value was reached at about 0.02 PV. Then, the RF values decreased with increased throughput in the sand packs until the RF values leveled off. The RF value plateaued out at an average value of 5.6 for the SAP-AP1 system, while for the baseline AP1 polymer, the RF value plateaued out at an average value of 2.0. Therefore, the SAP-AP1 system offered on average 3.6 times higher effective viscosity

within the porous media relative to the effective viscosity achieved by polymer AP1.

The RF-curves eventually reached plateau values for both systems: the SAP-AP1 and the baseline. This may occur due to the dynamic disassembling and reassembling of the non-covalent

procedure employed during the sand-pack displacement tests are provided in [35].

Baseline # 1.1 173 24 3085 SAP AP1 # 1.2 177 25 2812 Baseline # 2.1 169 24 2746 SAP AP1 # 2.2 185 26 1758

**3. Resistance factor and residual resistance factor**

**Displacement test # Pore volume [cm3**

**Table 1.** Sand-pack properties.

230 Cyclodextrin - A Versatile Ingredient

entrapment and polymer adsorption) [5, 6, 19, 29, 37–44].

SAP-AP1 # 1.2 and Baseline # 2.1 and SAP-AP1 # 2.2 respectively.

reference.

intra- and intermolecular interactions (i.e., decomplexation and complexation of host-guest interactions, breaking and reforming of hydrophobic interactions, and hydrogen bonds, among others) under the influence of the shear forces imposed during the flow process. Eventually, equilibrium of the shear forces and the flow resistance forces of the network structures are reached, and the RF curves leveled off. The stabilization of the RF curves also suggests that propagation of the SAP-AP1 systems and the baseline polymers took place along the sand-pack systems. These observations agree with previous research on flow behavior of associating polymers through porous media [28, 38, 44]. Overall, the optimum SAP-AP1 formulation consistently provides higher resistance factors and consequently a better mobility control than the baseline polymer AP1.

The performance of both polymers AP1 and SAP-AP1 in terms of the residual resistance factor, RRF, is presented in **Figure 3(a)** and **3(b)**, which plots RRF values as a function of volume of brine injected for tests Baseline # 1.1 and SAP-AP1 # 1.2 and Baseline # 2.1 and SAP-AP1 # 2.2, respectively.

**Figure 3** indicates that the RRF curves for both systems decrease continuously as the volume of brine injected increases that eventually stabilize. The average end RRF value for the

**Figure 3.** RRF versus volume of fluid injected: (a) Baseline # 1.1 and SAP-AP1 # 1.2 and (b) Baseline # 2.1 and SAP-AP1 # 2.2.

AP1 was 0.5, while the average end RRF value for the SAP-AP1 system was 0.02, suggesting insignificant pore plugging and/or permeability reduction due to polymer retention, which is expected in unconsolidated and/or high permeability porous media [37, 42].


*Edited by Pavol Svorc*

The autonomic nervous system is one of the most important involuntary control mechanisms that primarily controls and modulates the functions of the visceral organs. The book discusses some of the specificities of the autonomic nervous system in terms of dendritic development in the sympathetic compartment, as well as a detailed description of noradrenergic groups and their key role in the modulation of all antinociceptive and autonomic responses elicited by painful or threatening situations. In the book, only those cases are mentioned that are closely related to disorders or changes of function of the autonomic nervous system. This book can evoke interest in many researchers who want to use the information for the advancement of their research towards a better understanding of the autonomic regulatory mechanisms.

ISBN 978-1-78984-192-3

Autonomic Nervous System

Published in London, UK © 2018 IntechOpen © Svisio / iStock


*3.3. Identification of Defects, Deficiencies, or Operating Errors*

Most often, the study of random vibrations is dedicated to or requires the inclusion of the identification of sources of errors, defects, deficiencies, premature wear, etc. in the operating regime. Measuring the random vibrations of structures and improving or even optimising them based on the measurements has become one of the most commonly used methods in the study of phenomena affected by vibrations [54–58].

The research carried out in the MCLS complex cultivator case highlighted work deficiencies, especially the problem of low-quality control of the working depth. It was shown in [41] that using only two working depth control wheels (located in the front of the MCLS) is insufficient. A pair of wheels would also be necessary at the rear. In addition, the suspension-traction system (connection to the tractor) should also be checked to not raise the structure at the front during work. The identification of this problem was made by analysing the distribution of the intensity of the measured force, which indicated appreciably higher forces on the row of organs at the back (ch1, ch17, ch18, see Figure 6) in relation to those measured on the first row of organs behind the tractor (ch4, ch23, and ch24, see Figure 5). The main reason and validation of the suspicion were found by analysing the images in [42]. This deficiency partially generates the non-uniformity of the working depth, treated in Section 2.1.4.

Another deficiency valid only in the case where the MCLS operates in exploitation mode in any of its work variants is unsatisfactory soil fragmentation. According to the analysis in the field and on the photos, it is necessary to couple some shredding elements (discs, rollers, etc.) to the back of the structure (the granulometric analysis of the processed soil was not performed). In the research regime of the pure effects of the working body, the installation of additional shredding structures is prohibited because it alters the desired effects.

Another category of possible deficiencies, which can be avoided partially at the design stage and through systematic experimentation in working mode, are resonances and interferences, also called knocks. Both phenomena are defined in the literature, for example [59]. A search for possible resonances in the supports of the working organs is presented in Section 2.1.4 when calculating the frequency spectra for the twelve supports of the working bodies. The literature includes the results of some studies related to the problems of vibrations in agricultural machines [25,26,34,60–67].