Computational fluid dynamic simulations reveal deficiencies in the fluid dynamics knowledge base. The Colebrook -White equation and Moody chart for fluid friction are of particular concern. A due diligence study f inds they are based on fabricat ed data and flawed reasoning. New f luid dynamics research domains emerged in the past 100 years. Research ers moved into new domains , but procedures did not exist to verify and make available validated data fr om abandoned domains. A fluid dynamics knowledge base and AI inductive learning and monitoring resources are required to support professionals, lecturers, and STEM students . Benefits from the resource s include: • Open access to a validated fluid dynamics knowledge base • Reduced risks of fluid system failures that result in deaths and injuries , environmental damage and billions of dollars in yearly losse s • Potential for saving over a trillion US dollars to reach net zero CO 2 emissions by 2050 • Engendering STEM students to understand fluid flow behaviour bette r and construct science based conceptual models • Justifying the research facilities need to determine fluid dynamic parameters to internationally agreed level s of precision. Part 1 provides evi dence to justify fluid dynamic resource s. The form s of these resources are suggested in Part 2 . Keywords: Flawed Moody chart, fluid dynamics k nowledge base, visualising fluid flow behaviour, AI a daptive learning software, energy savings, due diligence. Part 1. Need for multimedia fluid mechanics resources 1. Introduction Researchers find flawed data when validati ng computational fluid dynamic ( CFD) predictions against experimental data. What is particularly disturbing is that data used to design state -of- the-art products and processes are flawed . The c alculations affect ed are for fluid systems that consume over 35% of global electri city, equivalent to the combined annual electricity consumption of China, India and Japan (Section 5). Savings of over 5% of global electrical energy are possible using existing fluid dynamics knowledge and data. A due diligence study provide s the evidence to support fluid dynamics researcher s in their quest to replace the flawed Moody friction factor chart . The need for due diligence is a key theme of this paper. Practising due diligence leads the author to comment on the conten tious subject of data used in recent boundary layer research projects . A due diligence study 2 indicates that many researchers have used unsound experimental data. Numerous conflicting values for fluid dynamic parameters are promoted, with persistent disagreement among researchers. Parameters are used in state-of-the-art supercomputer software for weather forecasting (1) and numerous other challenging simulations . Direct numerical simulation (DNS) of semi -organised very large scale motions (VLSM) in wall-bounded turbulent flows has revealed their importance . Studying these motions provides insights into reasons for parameter disagreements . However, the capabilities of experimental facilities availa ble to researchers cannot reveal subtle variations in flow phenomena to indisputable accuracy. International collaboration led to agree ments on fluid dynamic parameters for flow metering . National flow metering lab oratories and groupings of stakeholders support flow metering paramet ers that allow the equitable trade of trillions of dollars of fluid products per year. Fluid dynamic parameters are used in millions of CFD simulations. Their value to society is probably co mparable to that of flow metering parameters. Infrastructure is requir ed to determine their value appropriate to their importance. A report Commissioned by the UN High -Level Climate Action Champions estimate s that an investment of 125 trillion US dollars i s needed to meet net zero CO 2 emissions by 2050 (2). Decarbonising industry, transport, commerce , capturing and storing greenhouse gases , and generating electricity depend s on fluid systems. Allowing for the time to develop a fluid dynamics knowledgebase and the tools to exploit the base and for fluid system aware STEM graduates to disperse into the workplace , benefits from a fluid dynamic database and the tools to exploit the database equival ent to over a trillion US dollars could potentially be achieved by 2050 . Forty years ago , in a handbook ( 3), the author collected internal flow data from research teams worldwide and combined them with data from research programme s at the British Hydromechanics Research Association (BHRA) . Researchers at BHRA validated extensive data from other laboratories. Aware there was no mechanism to spread the data globally , it was reasoned that investors seeking to recover their investment would be the most eff ective way to reach a global audience. BHRA secured v enture capital to develop one-dimensional (1D) CFD software with data from the handbook at its core . BHRA staff involved with the research joined the commercial exploitation organisation and were instrum ental in developing the software 's multi -physics and transient capabilities . Versions of the software are today integrated with 3D CFD and life cycle management software (4). The software has been adopt ed by aerospace, automobile and other companies that produce multiple versions of products . Fluid dynamic specia lists in these companies design fluid systems , and their testing of fluid systems is rigorous . This has allowed companies to gain confidence in the handbook 's data, and it has become the de facto standard for fluid system simulation in these industries. However, unnecessary energy is mainly consumed by industries that operate large one -off process and power generation plants and commercial buildings . One-offs will likely be designed by non -fluid dynamic specialist s or designers who aim to minimis e first-time costs while meeting performance g uarantees for a process for which fluid system s are auxiliar ies. With uncertainty over fluid dynamic data , the natural action is to oversize auxiliary system s. 3 Auxiliary fluid systems in process and power plant s can consume MW s of unnecessary electricity . This paper is essentially a plea for fluid dynamics to gain a discipline home in which scientific, academi c, industrial and environmental stakeholders own a fluid dynamics knowledge base and tools to exploit the base. The author has aimed to bring together sufficient information in this paper to provide a resource from which Science Institutions and others can justify to governments and research funders that a fluid dynamics discipline home would substantially benefit society . This does, however, mean this paper is extensive . Technical detail is mainly confined to the Appendices. Providing evidence to justify a discipline home has taken the author into STEM education areas characterised by an almost overwhelming surfeit of publications to convince academia to adop t research -based teaching approaches . As pointed out in the literature , papers can be unread by their target audience. The e ducational r eference s in this paper are examples that the author considers insightful in creating a case for developing a fluid dynamics knowledge base. Figures in the text are limited to those required in a due diligence study to illustrate the fabricat ion of experimental results . The f igures related to due diligence on other flawed experiments can be found in the relevant reference. Fluids in motion are essential to many areas of science, the built and the natural environment, and are fundamental to life. Scientific disciplines and industrial sectors have assumed responsibility for aspects of fluid dynamics of importance to them. This has led to the fragmentation of the fluid dynamics knowledge base. Areas that are not seen as academically challenging have stagnated. These academically neglecte d areas contribute significantly to excess energy use and CO 2 emissions . Turbulent boundary layers are an active area of research with hundreds of academic research groups. J ustifi cation for funding mainly relies on the potential for energy savings. It can be shown that advances in understanding turbulent boundary layer s have many benefits. Contrary to persistent claims, benefits do not include substantially reducing energy consumption to meet net zero greenhouse gas emissions by 2050 (Section 10). Information is provided where investment is required to reduce greenhouse gas emissions . 1.1 Historical development s in fluid system simulation Engineers in the 19th century built piping systems to supply clean wa ter and remove sewage. These systems were human history's most important health event and made modern cities possible. Without a significant fluid dynamics science base, engineers devised nonhomogeneous equations, monographs and tables to conduct f luid system calculations. As oil and gas, hea ting and ventilation, and other indu stries developed, they devised friction equations and calculation methods specific to their industry . These non -scientific prediction methods are included in fluid mechanics textbooks, design manuals and computer program s. Their range of ap plicab ility is not well documented, and some are being applied outside their range . 4 In the nineteen -thirties, fluid dynami cists began to build a firm science base for internal flow system simulation , but their work was interrupted by World War 2. After the war , internal flow systems were not a research priority. Piping and duct systems grew in size to the extent that component pressure losses depended on Reynolds numbers. System complexity increased , and so -called component "minor losses " became the major losses in numerous systems. Over -prediction of pressure losses resulted and continues to result in the oversizing of fluid machines (Section 4). The Reynolds number dependency of component loss coefficients is not ackno wledged in student textbooks, design manuals and co mputer programs. Between 1950 and 1980 , research groups greatly extended parts of the database, but their contributions are not widely known or included in design manuals and textb ooks. A recent study o n the importance of fluid dynamics to the UK economy ( 5) found that "Fluid dynamics generate £13.9 billion worth of output from over 2,200 firms and employs 45,000 people. The total UK turnover of firms engaged in fluid dynamics exce eds £200 billion and together they employ over 500,000 people, illustrating how fluid dynamics activity is often embedded in larger organisations. " Globally companies ' turnover in embedded fluid dynamics activities is trillions of dollars per year. Much of this embedded fluid dynamics activity is carried out by scientists and engineers who are not fluid dynamics specialists. This highlights the importance of effective fluid dynamics teaching in STEM curricula (Part 2 ). Every year events in fluid systems ca use pressure containments to fail . Failures result in deaths, injuries, environmental damage, and billions of dollars of yearly losses (Appendix A ). Fluid system accidents usually involve causes that investigators find organisation boards and management sh ould have addressed. However, had scientists and engineers better understood fluid flow behaviour in the years preceding the design of a failed fluid system , then events leading to accidents may have been avoided. Empowered with solid backgrou nd knowledge in fluid dynamics and the ability to conceptualise relevant phenomena, individuals can act to reduce or eliminate conditions that lead to breaches of fluid system containments. Many fluid system accidents evolved over a significant timescale. If operators and their supervisors had better understood fluid flow behaviour, they could have avoided or mitigated the consequences of unfolding events in the time available. Fluid system designers, operators and managers need to see in their mind 's eye f low behaviour within fluid systems, including the effects of control and monitoring systems under steady -state and upset conditions. Fluid dynamic researchers rely on advanced computer visualisation of physical and computational experiments to understand c omplex flow behaviour. They use visual presentations to communicate with colleagues and other researchers and to prepare scholarly papers. However, in their lecturer 's role, they generally do not emphasise visualisation in the mind 's eye. They generally teach fluid mechanics as a n equation -based subject (Part 2 ). The 2020 Annual Review of Fluid Mechanics includes a paper , "Machine Learning for Fluid Mechanics " (6), with the following observation "Indeed, physics -based engineering of fluid systems is a highwater mark of human achievement. However, there are serious challenges associated with equation -based analysis of fluids, including high dimensionality and 5 nonlinearity, which defy closed -form sol utions and limit real -time optimisation and control efforts. At the beginning of a new millennium, with increasingly powerful tools in machine learning and data -driven optimisation, we are again learning how to learn from experience. " An essential part of experience -based learning is resolving conflicts between contradictory ideas and da ta. The field of fluid dynamics is full of contradictory ideas and data that practitioners claim support particular turbulence models. Research funding has been directed toward understanding turbulence for over a hundred years. There has been little funding or academic merit in c onducting due diligence on past experimental studies, though these studies support current research activities. This situation is changing because o f the need to validate CFD studies. Direct numerical simulation (DNS) of some mean flow variables, such as pipe friction, is now possible (Section 5.2). Recent DNS studies of how friction factors vary between smooth and fluid dyna mically rough flow conditions ( 7) have confirmed a review of the knowledge regarding wall-bounded turbulent flo ws (8) that the Moody friction factor diagram is erroneous . The Revie w (8), by six international turbulent flow researchers, recommended that the Moody diagram "be phased out ". The recommendation in The Review (8) that the Moody diagram should be phased out was not based on a due diligence study of the data sources used to generate the diagram. It was based on evidence accumulated over tens of years that the diagram does not represent reality. A due diligence study of the experimental work beh ind the diagram finds in Appendix B: • Experimental work was seriously flawed • Fabricated data • Erroneous observations and conclusions. 2 The need to practice due diligence . The Moody friction diagram (9)is the most f amous and useful fluid mechanics diagram . Globally, millions of engineers and scientists use it. The most famous fluid dynamics model is Prandtl 's boundary layer model. Models a re vital, but a model 's limitations are discovered as technologies advance . A more accurate model is sought, but the limitations of the existing models are not necessarily communicated to those who need to consider the model 's limitations. In the case of Prandtl 's boundary layer model, 80 years after one limitation was demonstrated, this limitation is yet to be included in fluid mechanics textbooks. An early example of boundary layers for which Prandtl 's boundary layer model should be reformed and the Col ebrook -White equation on which the Moody diagram is based are linked through C M White of Imperial College and the Royal Society. It is appropriate to discuss this link to show that learning fluid mechanics through experience needs to be within a due diligence environment. A paper by White, "The equilibrium of grains on the bed of a stream ", was published in the Proce edings of the Society in 1940 (10). It described remarkable ingenuity in measuring shear stresses of water flow over a bed of granular materi al. White acknowledges the limitations of his experiments, but his approach 's meticulousness meant that his observations 6 were valid. From particle movements and measurements with turbulent flow, he observed that: • Areas of a loose flatbed of granular materi al are set in motion well before the mean shear stress is sufficient to move a bed generally • Local fluctuations cause the shear stress at a bed ’s surface to vary and reach a maximum of about twice the mean. White attributed what he observed to turbulence outside the viscous layer penetrating to the bed. These fluctuations are now associated with turbulent burst and sweep events that eject low-energy fluid from the wall and bring high -energy fluid to the wall (8) and the passage of very large scale motions ( VLSM) above a bed (11). The importance of burst and sweep events on phenomena such as flow-accelerated heat transfer, corrosion and thermally induced fatigue cracking is not included in textbooks. In contrast to White 's exemplary experimental work, Colebrook and White 's "Experiments with fluid friction in roughened pipes ", published in th e Society Proceedings in 1937 ( 12), contains experimental errors, unwarranted assumption s, and fabricated data (Appendix B). Colebrook used the paper 's results in "Turbulent flow in pipes with particular reference to the transition region between the smooth and rough pipe laws ", published in 1939 by the Institute of Civil Engineers (1 3). This paper contains the Colebrook -White equation, in which Colebr ook claimed better -represented friction factors in the region between smooth pipe flow and fully turbulent flow than those measure d experimentally by Nikuradse (1 4) (Appendix B.2). When correctly interpreted, the measurements in the Colebrook -White paper support Nikuradse 's work. From the writing style of the 1939 paper, it can be concluded that Colebrook was responsible for the 1937 paper in Proceedings of the Society. Colebrook misled White, G I Taylor , who communicated the pa per to the Society, Hu nter Rou se (15), Moody ( 9), who justified the Moody diagram based on Rouse 's judgement of the validity of the Colebrook White equat ion, and numerous researchers over the past 80 years. His deceit has caused incalculable damage scientifically and delayed the understanding of events in the transition region between fluid dynamically smooth and rough wall flow. Due process can correct the research record but not the numerous thesis and papers that have relied to some extent on Colebrook 's work. Researchers hav e added to misconceptions about the transition region by making incorrect assumptions about Niku radse 's pipe roughness (Appendix B .2.) The author is one of many who ha ve recommended using the Moody diagram for engineering calculations (3). To reduce compu ting time , the author recommend using an explicit friction facto r equation (1 6) instead of the implicit Colebrook -White equation . As this paper shows , the author , 50 years ago , failed to carry out due diligence on Colebrook’s work. Had the author done so , it is likely, many researchers would not have used Colebrook’s work. The author 's fail ure may at least cause some future researchers to c onduc t due diligence on the