Set Of Input Parameters Research Articles

Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations

The new approaches to the modelling of the Sun-solar wind-magnetosphere-radiation belts chain and their implementation at MSU's Space weather center (Space Monitoring Data Center, SMDC) are presented. The main idea is to use data from medium-term (3–5 days) forecasts of the speed of quasi-stationary solar wind streams based on data obtained from solar images in the UV wavelength range to improve the quality of forecasts of the magnetospheric factors of the space environment. An operational model for predicting the daily fluence of relativistic electrons (>2 MeV) in the Earth's outer radiation belt has been developed, with the extended set of input parameters that include the forecast of the solar wind velocity. To validate the model, the period from October 11, 2016 to February 18, 2017 was considered, during which the Sun made several revolutions and it was possible to observe the evolution of coronal holes and the development of solar sporadic phenomena. The arrival of high-speed solar wind streams from coronal holes to the Earth's magnetosphere, as well as, as impact of coronal mass ejections, provoked geomagnetic disturbances of various intensity, and caused significant variations of the electron fluxes of the Earth's outer radiation belt. SMDC website implements solar wind model based on the data of solar observations by the AIA instrument of the SDO space observatory at wavelengths of 19.3 and 21.1 nm that forecasts the daily fluence of relativistic electrons (E > 2 MeV) in geostationary orbit. It is shown that the use of the results of medium-term forecasting of the solar wind speed as an input parameter in the daily electron fluence forecast model can significantly improve its quality with a forecast horizon of up to 4 days.

Dimensional optimization of a two-body Wave energy converter using response surface methodology

This paper proposes a dimensional optimization procedure for a laboratory scale two-body point absorber wave energy converter (WEC) using the design of experiment (DoE) methodology. Response surface methodology (RSM) is utilized to estimate a second order polynomial function correlating the average absorbed power, as the objective function, to five geometric parameters. Optimum values of parameters correspond to the peak of the surface fitted to the absorbed powers calculated for different sets of input parameters selected by the Box-Behnken design (BBD). The sensitivity of the objective function with respect to each parameter is investigated. The WEC is assumed to operate under regular waves in a specified range of frequency, 0.5–1 Hz. The amplitude and complex-conjugate controls are applied to keep the power take-off (PTO) system in optimum conditions. ANSYS-AQWA is used to calculate hydrodynamic parameters of the WEC required to solve the equations governing the absorbed power.

MCRE-based finite element model updating: Cast3M implementation

In structural engineering, the search for computational models capable of making realistic predictions capturing varied physical phenomena while maintaining simplicity and reliability has gained wide attention of the scientific community. Some outcomes of this effort are the finite element model updating techniques, which consists of using inverse analysis to find realistic values for the set of input parameters. The Modified Constitutive Relation Error (MCRE) has proved to be efficient for model updating in many engineering applications. The central idea of this technique is to formulate parameter identification as a well-posed optimization problem. In general, for each new MCRE application, new formulations need to be deduced and implemented. Despite the specifics of each case, there are standard features that can be abstracted and placed in a single framework. In the present work, we abstract and materialize those features into a new computational framework using Cast3M (an open-source high-level toolbox for finite elements computation). Using typical examples from structural engineering, we test the framework’s efficiency and highlight its limitations. The results show that the proposed open-source code allows the identification of physical parameters for finite element models, such as stiffness, as long as physical and geometric linearity conditions remain valid. In addition, elastic inter-element interfaces can be included without significant changes. The benefits of the present study are twofold: (i) source codes are made available and can be used as learning support, in particular for newcomers, and (ii), it can be used as a starting point for new developments in the field of MCRE-based approaches.

Copula-based scenario generation for urban traffic models

One the most attractive features of urban traffic network models is the possibility of running hypothetical scenarios to evaluate the impact of strategic and tactical decisions. In order to provide statistically meaningful results, the simulation runs should be able to capture the complex multivariate distributions characterizing the involved variables, and a key factor is the correct modeling of possible statistical dependence among the generated inputs used to define the desired scenarios. Here we introduce a data-driven method for scenario generation based on the statistical concept of copula models, through which the marginals of single input parameters can be chosen freely without altering the joint multivariate dependence structure of the inputs. This approach is particularly suited to running what-if scenarios, in which the marginal distributions of the inputs are changed, while retaining the general joint dependence scheme. The method exploits only a finite set of measures from the network and copes with arbitrary sets of input parameters without requiring any assumption on the kind of traffic model or the shape of the involved multivariate distributions. Simulation tests involving different scenarios show that the proposed method is able to capture complex multivariate distributions of the simulation outcomes and yield reliable inferences in what-if analyses, significantly better than in the case the joint dependence is ignored.

Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic

This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.

Simple semiclassical model of pressure-broadened infrared/microwave linewidths in the temperature range 200–3000 K

Abstract There is a need for line-broadening parameters for molecules found in exoplanetary atmospheres for a variety of broadeners and a range of temperatures. The use of an easily handled semiclassical theoretical expression is suggested for the calculation of pressure-broadened linewidths for (vib)rotational transitions over a large temperature range (200–3000 K) starting from a minimal set of input parameters: kinetic molecular properties and the character of the leading term in the intermolecular interaction potential. Applications to NO and OH colliding with rare-gas atoms and non-polar molecules demonstrate good consistency with available measurements over the full temperature range indicated. The procedure therefore can be expected to provide realistic estimates for line broadening of ‘exotic’ molecules and molecular ions present in hot planetary atmospheres.

Best Combinations of Inputs for ANN-Based Solar Radiation Forecasting in Morocco

In this paper, for forecasting, with the high possible accuracy, the solar radiation intensity, an approach for identifying the optimum set of input data from large sets of input parameters is elaborated, assessed, and proposed. This approach belongs to techniques of Input Variable Selection (IVS) and was established using artificial neural networks (ANNs) as an effective means for numerically approximating the computed objective function: Monthly Average Global Solar Radiation (MAGSR). Based on the approach suggested, we could determine the best combinations of inputs that can reveal great possible correlation and approximation with the output considered parameter. Recorded data from 35 stations of different climatic zones were used for both training and testing purposes. Several new linear formulas between the MAGSR and other climatological and meteorological parameters were developed and evaluated. Based on these relationships, we can forecast other climatological and meteorological parameters like temperature, wind speed, and humidity. The statistical analysis was done, and the best performance of the proposed approach has been well checked and duly validated.

An Investigation of Performance and Emissions of Diesel Engine Using Heterogeneous Catalyst Jatropha Biodiesel: A Sustainable Model Using Taguchi and Response Surface Methodology

Abstract Excessive consumption of fossil fuels has exacerbated global warming and led to an increase in air pollution levels in the environment. The increasing oil demand prompted recent research to explore the future application of alternative, eco-friendly fuels for diesel engines. Jatropha biodiesel has been produced from JCO, using heterogeneous catalyst (CaO) through transesterification process. In this study, the performance and emission characteristics of an engine powered by a Jatropha biodiesel blends have been investigated. The application of response surface methodology (RSM) coupled with Taguchi method for optimization of engine input parameters is promising approach to derive the most accurate optimized models for output responses. Input parameters such as biodiesel blend, load, CR, and FIP were selected, experiments were designed as per L18 orthogonal array in Taguchi, and CCFCD L20 design matrix for RSM methodology. Injection timing is an essential engine characteristic, which has a considerable effect on the ordering emissions. If injection is done early, the starting air temperature and pressure are lower, which means the ignition delay will rise. The ignition delay may begin at any time after the injection begins, resulting in somewhat increased temperature and pressure initially but which then rapidly declines as the ignition delay progresses. The optimal setting of engine input parameters is recorded at 270 bar fuel injection pressure, compression ratio of 18, 7.61 kg load, and 25% blend of Jatropha biodiesel with diesel for optimum BTHE, BMEP, BSFC, Pmax, CO, and NOx emissions. Experimental results are compared with optimum output responses and deviations are found within the accepted range of errors.

Development of an Injection Molding Process for Long Glass Fiber-Reinforced Phenolic Resins

Glass fiber-reinforced phenolic resins are well suited to substitute aluminum die-cast materials. They meet the high thermomechanical and chemical demands that are typically found in combustion engine and electric drive train applications. An injection molding process development for further improving their mechanical properties by increasing the glass fiber length in the molded part was conducted. A novel screw mixing element was developed to improve the homogenization of the long fibers in the phenolic resin. The process operation with the mixing element is a balance between the desired mixing action, an undesired preliminary curing of the phenolic resin, and the reduction of the fiber length. The highest mixing energy input leads to a reduction of the initial fiber length L0 = 5000 μm to a weighted average fiber length of Lp = 571 μm in the molded part. This is an improvement over Lp = 285 μm for a short fiber-reinforced resin under comparable processing conditions. The mechanical characterization shows that for the long fiber-reinforced materials, the benefit of the increased homogeneity outweighs the disadvantages of the reduced fiber length. This is evident from the increase in tensile strength from σm = 21 MPa to σm = 57 MPa between the lowest and the highest mixing energy input parameter settings.

Parametric design and evaluation of TPMS-like cellular solids

Triply periodic minimal surfaces (TPMS) present a starting point for designing infinitely many novel cellular solids, creating the need for a structured approach to their design and evaluation. In this study the design space is formalised using a set of input parameters and evaluation metrics to provide a structured and reproducible approach to designing a TPMS-like cellular solids. Interactions between the design parameters and evaluation metrics are identified using a full-factorial design of experiments. From this the most impactful design parameters are identified as the TPMS equation, type of structure, and volume fraction. It was found that the gyroid-surface structure provided the most flexibility, with near-isotropic mechanical properties across a wide range of volume fractions, allowing the creation of reduced mass/stiffness designs without introducing anisotropy or variation in Poisson’s ratio. The volume fraction at which the connectivity of a structure changes, “pinch-off limits” are presented for six of the most commonly investigated TPMS-like cellular solids. Additionally, the effects of common transformations are presented including: examples of how phase-shift and/or rotation can be used to modify the features which intersect the free-boundary of a design; dimensional analysis which shows the practical effects changing unit cell size; and analysis showing the effect of rotating a cellular structure on both the mechanical properties and surface orientation. These computational studies are supported with a physical case study which demonstrates how several design parameters may be modified to change the manufacturability of a TPMS-like cellular solid.

Predicting solid–liquid interfacial characteristics during rapid solidification

A numerical model capable of predicting cellular and dendritic growth at low and high velocities based on Hunt’s approach (Hunt, 1990) is proposed to quantify the degree of non-equilibrium at the solid–liquid interface and thus to determine if an additive manufacturing process route exhibits rapid solidification. The developed model accounts for various undercoolings, as well as the variation in the partition coefficient, liquidus slope, and diffusion coefficient at high velocities. The model is used to estimate the range of cellular/dendritic primary spacing during Laser Powder Bed Fusion (LPBF) for a given set of input parameters. The magnitude of various undercooling values, along with the tip Péclet number and spacing Péclet number are then estimated in order to quantify the extent of non-equilibrium at the solid–liquid interface. Good qualitative agreement is achieved between these values and the analytical KGT model. This new model can help to understand how the solid–liquid interface varies with changes in the processing conditions and material system during LPBF, and thus to improve control of microstructure evolution.

Multi-criteria optimization of food production by an example of optimization of the process of rendering poultry fat in the presence of electroactivated liquid

To date, in the production and management of the food industry, the mathematical foundations of solving problems according to one criterion (one-criterion optimization) have been well studied. However, in various areas of engineering solutions, there are multicriteria problems in which a system or process must be optimized according to several criteria simultaneously. Since individual criteria usually conflict with each other, as a rule, no set of input parameters would achieve the optimum of all criteria simultaneously. In this regard, there is uncertainty in the choice of the optimal solution and the complexity of the choice of a specific mathematical method. The authors have made a brief review of publications devoted to the application of multicriteria optimization in food production. As a result, it turned out that in most of the reviewed publications, a preliminary examination of the problem was not carried out, and the solution method was chosen according to the principle of the greatest simplicity. At the same time, it is known that there is no universal method for solving multicriteria problems and the use of each algorithm is associated with rather strict restrictions and assumptions. In this regard, the authors dedicated this work to the presentation of a typical information technology for solving a multi-criteria production problem in the food industry using standard means of computer mathematics - the MathCad system, taking into account all methodological recommendations. As an example, the problem of multicriteria optimization of the process of melting ostrich fat was considered, the conditions of which were taken from an open source. As a result, the optimal parameters of the process were found in the presence of two and three criteria of optimality. The described technology can be used as a basis for solving other multi-criteria optimization problems of food production.

Design and Deployment of a Generic Software for Managing Industrial Vision Systems

Computer vision systems have become a key element of modern companies. However, its implantation not only requires specialized hardware components but also software to control the correct performance of all these components. The purpose of this article is to present the design and deployment of a software created to manage the different computer vision systems of any company. First, using our experience in mechanization and industrialization of company processes, a general company specification will be proposed as well as the main software requirements that have to be satisfied. Then, a three-level modular design composed of the core, a configuration module, and user interfaces with functionalities capable of satisfying the defined requirements will be presented. Special attention will be given to time restrictions and components’ synchronization. In addition, the different tests that have been carried out to control the correct performance of the software will be shown. The development process will end with a generic, modular, and scalable software able to fit different industrial scenarios by simply modifying a set of input parameters. To illustrate the correct performance of the proposal, the details of its installation in four real companies with different needs will be presented. The proposed work has a practical use in industry and it also provides a thorough description of the main components involved in computer vision systems of real company environments and how to manage them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The aim of the article is to provide a software solution to manage the computer vision systems of a company. The proposed software is simple enough to be controlled by company operators with no need for great expertise on the technical aspects, but rather on the industrial workflow to be controlled. To develop such a software, it has been necessary to know about all the elements involved in the industrial scenarios controlled by computer vision systems as well as their relationships. This article presents all these elements providing a global and complete view of the problem with special attention to synchronization, which is fundamental to satisfy industrial time restriction requirements. The software is presented from a technical point of view, but also from a practical one, since different examples of its application in real cases are given. These examples illustrate the adaptability of the proposal to different scenarios and also exemplify how the different elements are modified to fit the different situations. In addition, this article provides a comprehensive overview of real industrial situations, focusing on the most important issues faced during real installations.

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

AbstractThis paper investigates the impact of the different sources of excitation variability within the stochastic kriging framework recently developed by the authors for the estimation of the distribution of engineering demand parameters (EDPs) in applications that the seismic hazard is described through stochastic ground motion models. For a given seismic event, described by seismicity characteristics such as the moment magnitude and the distance to source, one can distinguish two type of uncertainties in the excitation description: (g.i) the stochastic sequence utilized within the excitation model; (g.ii) the predictive models relating the ground motion parameters, such as significant duration, arias intensity, or frequency properties of seismic waves, to the seismicity characteristics. The original formulation of the authors treats (g.i) as aleatoric uncertainty and (g.ii) as parametric uncertainty, including the latter in the model parameters for the risk characterization, representing the metamodel input. Implementation establishes the EDP distribution approximation by utilizing a database of EDP estimates for a set of different input parameters, considering replications of these estimates for different stochastic sequences for some small part of this database. The database with replications is first leveraged to approximate the heteroscedastic behavior with respect to the aleatoric uncertainty, and the entire database is subsequently used, coupled with the previous heteroscedasticity approximation, to establish the stochastic kriging predictions for the EDP distribution. For excitation models with a large number of ground motion parameters, the original formulation leads to a significant increase of the input dimensionality for the metamodel development, requiring a larger database for facilitating accurate EDP approximations. Here, an alternative formulation is investigated, considering some (or perhaps even all) of the ground motion parameters to be part of the aleatoric uncertainty of the excitation description. It is shown that careful selection is needed for the exact ground motion parameters that can be treated as part of this aleatoric uncertainty, to accommodate an accurate overall EDP approximation. Similarities are discussed for applications that consider the description of the seismic hazard through the selection of ground motions based on intensity measures. An extensive validation of this new approach is presented considering two different stochastic ground motion models.

Performance assessment method for roof-integrated TSSCs

This paper proposes a performance assessment method for roof-integrated two-stage solar concentrators (TSSCs) as thermo-photovoltaic systems using a parametric modeling approach. The productivity of TSSCs does not only depend on system design, but also on building geometries ensuring good solar gains. Further, the integration process of TSSCs with buildings is complex and needs to address concerns from multiple domains. The proposed approach intends to be a rigorous yet adaptable method to inform decision making where multiple stakeholders from building and energy domains are involved, and promote the use of TSSCs as sustainable, building-integrated energy production technology. Our method contributes to engineering the TSSC designs in relation to the building design. The method allows generating various design alternatives by controlling a set of input parameters for buildings (i.e., roof type and slope, and building orientation) and TSCCs (i.e., design type, geometric ratio, and separation distance between mirrors). The method applies a parametric modeling approach that enables the generation of 20,736 design options of building and TSSCs in a collaborative manner, by featuring various combinations of the design inputs. We validated the method in an illustrative case study of a single-family house (California) for a number of performance indicators related to building and TSSCs e.g., the covered roof area, the annual average load match index (av.LMI), and the number of solar cells. We further perform a sensitivity analysis to find the relative importance of design parameters and discuss our results in terms of maximizing av.LMI and minimizing the number of cells and covered roof area. The illustrative application represents that the method enables designers to adopt a performance-based design approach to simultaneously design both; building, and TSSCs that can be integrated with the building, and searches for the most appropriate solutions i.e., building ensuring good solar potential, and TSSC designs offering high av.LMI, covering less building’s (roof) area, and utilizing less solar cells. Hence, the method serves as a foundation for future employing generative design approaches for building-integrated TSSCs in the early design stages to support informed decision making processes with a focus on solar integration, and energy planning and transition for self-sufficient and sustainable buildings.

Evaluation of Evaporative Degradation of Arc Torch Cathodes in Hydrocarbon-Containing Plasmas for Spraying, Thermal Protection Testing and Related Technologies

Design of non-transferred DC electric arc plasma torches (EAPTs) operated with plasma gases containing alkane hydrocarbons, as a promising type of heaters for a number of technologies (thermal spraying, surface hardening, testing of thermal protection systems, etc.), requires taking into account the evaporation rate of surface cathode material (as one of the channels of its ablative degradation). For this procedure, as the first stage, thermodynamic methods can be used to simulate the composition and properties of reactive C–H–O–N–Ar–Me-systems with variable set of such input parameters as the ratio of components of plasma-forming mixture, its pressure and temperature. We theoretically estimated the evaporative degradation of the material for three variants of EAPT cathode with alkane-containing plasmas (“hot” thermochemical zirconium and thermionic tungsten, and “cold” copper) in equilibrium and quasi-equilibrium modes of “plasma gas + surface cathode material”-mixture, with use of generalized thermodynamic properties of the materials. The calculation for conditions with pressure, which is characteristic for EAPT discharge chamber, showed that when varying the initial composition of the plasma-forming mixture (from oxidizers (air or combustion products of alkanes) to reducing gases based on the products of combined partial oxidation and pyrolysis of alkanes), the effect of a difference in the cathode evaporation rate EAI was observed in systems based on (air + alkane)-mixtures near the melting point of surface cathode substances, in a comparison with the case of EAPTs with more conventional gases (commercial N2, air) and, importantly, for two variants of the analyzed cathodes (with the exception of copper). In addition, the electrode erosion value was compared for simulated zirconium cathode (in terms of erosion evaporative component) when operating on the combustion products of alkanes from “air + CH4”-mixture, and for some known EAPTs with similar cathodes in other gases. Using the case of earlier tested DC plasma torch with rod Zr-cathode (with microheterogeneous surface) as an example, it was found that our calculation indicates non-monotonic dynamics of EAI value and fractions of Zr-containing vapors as a result of the change of the fuel-air equivalence ratio f of initial reactive mixture. This effect is inconsistent with measured cathode composition, which shows a probability of nonequilibrium character of thermal and diffusion processes in near-electrode plasma and surface layer (~1 mm) of the electrode, at least in the modes with arc current in the torch near 300 A. Besides this, it should be noted that obtained modeling data on the behavior of zirconium compounds (ZrO2, ZrC) in C–H–O–N–Ar–Zr-system can be used not only for improvement of the torch cathodes, but also for design of new Zr-containing thermal protection systems to predict preliminary their ablation rate in a flow of products of combustion (including incomplete one) of engine-, rocket- and other fuels. Similarly, the results on the copper compounds behavior near the metal evaporation temperature can be useful for optimizing the process of plasma spraying of copper alloy coatings.

Optimizing the performance of solar panel cooling apparatus by application of response surface methodology

The high temperature of the solar photovoltaic module reduced the power output and efficiency of the solar panel. Water cooling is one of the best options for reducing the temperature of solar panels. In this research paper, an experimental setup of solar panel with water cooling arrangements has been developed and optimized. The measured experimental data and data generated by model equations have been optimized by the Response Surface Methodology (RSM) tool in the Design of Experiment. The optimized responses are the efficiency of solar panels, module temperature (MT) of solar panel, and exergetic efficiency. The main input parameters solar flux, water inlet velocity, and atmospheric temperature varied from 600 W/m2 to 1000 W/m2, 0.5 to 0.9 m/s, and 25°C to 45°C, respectively. The best set of input parameters after optimization in RSM is 705 W/m2 solar flux, 0.7263 m/s water inlet velocity, and 32.87°C atmospheric temperature on which the values of responses MT, exergetic efficiency and solar panel efficiency are 48.98 °C, 19.18 %, and 18.88 % respectively. The experimental setup of solar panel cooling has been run on these input parameters setting and responses are validated with the predicted value of the RSM and the previous research. The calculated percentage error in MT is 2.41%, solar panel efficiency is 1.62%, and exergetic efficiency is 3.82%.

Efficient surrogate model based probabilistic analysis of helical soil nailed wall under seismic conditions

This study demonstrates an efficient and precise surrogate model to probabilistically analyze the helical soil nailed walls (HSNWs) under seismic conditions. The modified pseudo-dynamic (MPD) approach is employed which produces realistic results in comparison to its former counterparts. The unit weight (γ), internal friction angle of soil (ϕ), and shear wave velocity (Vs ) are chosen as random variables. The influence of the normalized frequency, damping ratio, and size of the helix on the reliability of HSNW is presented in detail. The plots reveal that the proposed surrogate model is an accurate method with the value of coefficient of determination (R2 ) as 0.99 for the present study. In addition to it, it is 99 times more efficient when compared to the direct Monte-Carlo Simulation (MCS) (50000 iterations). For the same input parameters, the minimum number of helical nails required for a safe reinforced wall are less than the grouted and the driven ones, given their larger pull-out capacity. Moreover, the required number of helical soil nails (HSNs) computed from probabilistic analysis are 42.85% more than the deterministic one for a given set of input parameters which lay a strong base for performing the probabilistic analysis for a precise measure of stability.

Application of RSM to Optimize Solar Pump LCOE and Power Output

In this research paper, the input parameters of a solar pump have been optimized with the help of the Response Surface Methodology tool for improving the overall performance of the solar pump. In this study, an experimental setup of a solar pump has been installed in FET MJP Rohilkhand University Bareilly at location 28.36°N, 79.43°E. Based on experimental findings, the all-output parameters have been optimized on the basis of two input parameters, namely tilt angle and surface azimuth angle. The optimized solar pump input parameter setting on which solar pump performance was maximum 18.2291° TA and 6.2568 SA at which levelized cost of electricity 0.0401 $/kWh, power out by solar panels 3650.5879 W, solar flux 874.97 W/m2, the exegetic efficiency 14.64%, the overall efficiency 11.3460%, and the panel efficiency 16.27%.

Development of high‐fidelity design‐driven wind blade manufacturing process models to investigate labor predictions in wind blade manufacture

AbstractWind turbines have shown significant advancements in efficiency and power output in the last 20 years. However, during the same time period, parallel advances in the manufacturing of wind blades have not happened due to the reluctance of wind blade manufacturers to make significant capital investments into unproven automation technologies. This reluctance is due in part to the lack of access to a robust techno‐economic model that can give feedback on if and how these investments will impact the overall cost to make a blade. In the current research, existing costing methods are reviewed and a new techno‐economic model for estimating the number of man‐hours required to manufacture a wind turbine blade is proposed. A set of standardized input parameters for the proposed model is developed from time studies completed at three wind blade manufacturing facilities. The proposed model uses 77 discrete processing steps. The credibility of the proposed model is validated by its ability the predict the required man‐hours for the manufacture of previously produced blades by the blade manufacturers. Additionally, validation is done against models of publicly available designs. The proposed model delivers more accurate and realistic estimates over prior models reflecting the application of current production techniques and detailed design information.