Engineers Guidelines Research Articles

Enhancing Indoor Air Quality and Regulatory Compliance: An In-Depth Comparative Study on Ventilation Strategies and Their Impact on SARS-CoV-2 Transmission Risk

Enclosed-space ventilation plays a pivotal role in enhancing indoor air quality (IAQ). Various regulations and guidelines determine minimum fresh air requirements, varying by the application, country, etc. The present study employs real-time indoor CO2 measurements to calculate the actual air change rate (ACH) and models the building’s compliance with Chartered Institution of Building Services Engineers (CIBSE) guidelines to assess its impact on IAQ. The contaminant considered is the SARS-CoV-2 virus with multiple infectors, and the study calculates the virus’s transmission risk under different scenarios. Fresh air is assumed to be provided by mechanical or hybrid ventilation. The results reveal that the current building conditions are unacceptable, showing a significant gap from the recommended guideline rates. Meeting CIBSE recommendations ensures indoor air safety in terms of CO2 concentration. However, in extreme conditions with five infectors, findings show a 14% reduction in mechanical ventilation efficacy. Among the hybrid ventilation scenarios, combining CIBSE rates with natural ventilation, with a 30% door and window opening from the class outset, proves most effective in reducing the virus’s transmission risk, with a 58–70% reduction from the building’s current condition. Furthermore, implementing supplementary measures like air cleaners resulted in an additional 28–50% reduction in transmission risk.

Estimation of spudcan penetration in variable sand deposits with the Arbitrary Lagrangian Eulerian Finite Element Method

Offshore jack-up rigs are most commonly founded on large-diameter conical “spudcan” foundations, which are frequently designed using traditional analytical methods for shallow footings. This paper presents the design of a spudcan installed off the coast of Tunisia. The maximum penetration depth of the footing under the available preload is predicted by a combination of analytical techniques, 2-dimensional axisymmetric modelling and 3-dimensional Finite Element Methods (FEM) using large strain arbitrary Lagrangian-Eulerian (ALE) techniques. Spudcan penetration based on FEM simulation of CPT soil profiles forms the basis of a comparison with results from the Society of Naval Architects and Marine Engineers (SNAME) guidelines. Particular attention is given to model calibration using the limited site investigation data available. Results are presented for the effect of penetrating footings on the behaviour of neighbouring footings, showing good agreement with conventional prediction methods.

Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability

This study assesses American Society of Heating, Refrigerating and Air-Conditioning Engineers Guideline 36 (G36) with a typical medium office building. Specifically, this study employed a Modelica model of a variable air volume (VAV) system that serves this building, which includes components for representing indoor virus transmission and filtration. It then implemented the G36 control sequences for both water-side and air-side equipment in Python. After that, this study conducted the assessment by co-simulating G36 and the Modelica model using the Building Operations Testing Framework. Unlike existing works, this work has three unique features: (1) It considers the interactions between control sequences for water-side and air-side equipment of the studied VAV system. (2) It assesses the performance of G36 from the perspective of IAQ. (3) It examines the short-term behaviors of the studied building under G36 to understand the control stability. This assessment confirms significant energy savings from G36, largely because of the interaction between supply air temperature and hot water controls. It also reveals a trade-off between the ability to slow the spread of virus and the energy performance via demand controlled ventilation. Lastly, it emphasizes the necessity of tuning local feedback control when implementing G36.

Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms

The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R2) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R2: 0.84) and LNG consumption (CvRMSE: 12.52% and R2: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

Evaluation of polyethylene pipe–sandy soil interaction subjected to strike-slip faulting

Pipelines are simulated in the form of a continuous beam resting on a collection of linear springs. The relationships put forth by the American Society of Civil Engineers (ASCE) guidelines are commonly used to derive the specifications of the spring elements. Some recent studies have suggested that these relationships are accompanied by a certain degree of inaccuracy. In this study, two full-scale tests were carried out on polyethylene pipes (with diameters of 120.5 and 214 mm) buried in sandy soil. The displacement of the pipe along its length was recorded throughout the entire test. Then, using the Abaqus finite-element software package and an optimisation algorithm developed in the Matlab software, a modelling approach was adopted so that the properties of the equivalent linear springs simulating the soil could be determined. In this way, the displacements obtained from the experiments would have the highest level of congruence with the values derived from the numerical simulations. Using this approach, the initial stiffness and the maximum force resulting from the pipe–soil interaction were computed and compared with the values given by the ASCE and American Lifelines Alliance (ALA) guidelines. The results showed that for a polyethylene pipe at the condition of strike-slip faulting, these values were much smaller than the values put forth by ASCE and ALA.

심층신경망 기반 태양광열-공기열원 히트펌프 융복합 시스템의 성능예측모델 개발

The purpose of this study was to develop the performance prediction model for integrated system combining photovoltaic-thermal and air source heat pump, based on a deep neural network (DNN) model. This paper describes the overall procedure of constructing the DNN model, to predict the performance of the integrated system such as data collection method, data set configuration, and the DNN model structure. To verify the reliability of the performance prediction model based on DNN model, the coefficient of variation root mean square error (CV(RMSE)) proposed by American Society of Heating, Refrigerating and Air-conditioning Engineers Guideline 14 was used. The CV(RMSE) between the predicted results of the DNN model, and the output variables was calculated as 5%. Thus, the reliability of the performance prediction model based on the DNN model was verified, and the performance prediction accuracy was similar to the energy simulation model.

Study on the performance of thermal energy of a classroom built with natural materials

This study highlights the indoor thermal comfort in a classroom building mostly built with locally available construction materials combined with some passive techniques in the hot semi-arid climate of Marrakech, Morocco. The thermal assessment of this so-called Baterre building was analysed against a typical building (TB), which represents the structure most widely used in Moroccan rural classrooms and is constructed with conventional materials. A TRNSYS-based dynamic model was developed and validated using onsite monitoring measurements according to American Society of Heating, Refrigerating and Air-Conditioning Engineers (Ashrae) guideline 14. Simulation results show that the Baterre structure has a noticeable positive effect on indoor thermal comfort since its indoor air temperature remains stable while that of the TB classroom has higher oscillations. The hourly calculated indoor air temperature in Baterre is greater by 3.6°C during winter and lower by 9.54°C during summer than that in the TB. Moreover, indoor thermal comfort analysis based on the Ashrae Standard 55-2013 shows that Baterre provides good indoor thermal comfort 63% of the time all year round, while the corresponding indoor thermal comfort time value in the TB is 32%. Furthermore, the annual thermal load of Baterre is lower by 53% than that of the TB.

Seasonal adaptation of VRF HVAC model calibration process to a mediterranean climate

Today, Building Energy Models (BEM) have become essential in regulatory compliance calculations, the correct assessment of it’s Air Conditioning (AC) systems is critical for the reduction of the performance gap between BEMs and reality and increase the accuracy of evaluating buildings energy performance and it’s systems efficiency. Given that multi-split Variable Refrigerant Flow (VRF) systems have grown in the market in recent years becoming a particular trending solution to achieve building indoor comfort; the present paper focus on technical issues when modelling such VRF systems inside EnergyPlus, a white-box simulation environment, especially regarding the effects weather conditions have on the behaviour of VRF systems and it’s correlation with the AC system performance curves. The study performs an empirical validation of an optimization-based calibration methodology assessing multiple levels: average interior temperature of the different building spaces and electric energy consumption from VRF outdoor unit. It is performed using fifteen minute time-step seasonal data obtained from a fully operational building located in a typical Mediterranean climate (Greece), adjusting the parameter and curve values of the VRF system using a genetic NGSA-II algorithm (Jeplus software) for both summer and winter conditions. The generated BEM captures the building’s hourly performance for summer conditions using 1717 hours to fit into international standards. Complying with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2002 for hourly energy consumption, reaching an NMBE ⩽±10% ,Cv(RMSE) ⩽30% and R2 ⩾75% while keeping indoor temperatures on every room with a RMSE ⩽1 °C. The resulting BEM proved stable during the 2077 hours of it’s summer evaluation period, fitting into the new unseen weather and building operation conditions of 2020 which can be considered a step forward in the area of calibrating white box models. While for winter conditions the study demonstrates the value of the calibration methodology while presenting the importance of weather influence on VRF systems. Using a total of 802 hours the applied technology greatly improves the results from the baseline model, reaching a partially calibrated BEM model for winter. Which reinforces the fact that regardless of how good a baseline model is, building operating conditions and weather may will always generate a design/performance gap and therefore the calibration of a BEM is unavoidable.

인공신경망 기반 수열 히트펌프 시스템의 성능예측모델 개발

Purpose: Recently, core technologies of the 4th industrial revolution such as machine learning and artificial intelligence are being combined with building technologies to predict the energy consumption and performance of the air-conditioning system of buildings. On the other hand, the energy independence of buildings is reinforced according to government roadmap of zero energy building. Accordingly, a water source heat pump system(WSHP) as one of the renewable energy systems is actively introduced. Method: In this study, a performance prediction model for the WSHP system is developed based on artificial neural network(ANN). This paper is described the process of constructing the ANN model such as analysis of Pearson correlation coefficient and construction of each layers, and the verification method and error analysis of the development model through coefficient of variation root mean square error(Cv(RMSE)). Result: The Cv(RMSE) was calculated as 0.09% between the prediction result of the ANN model and the calculation result of the integrated simulation model. The results predicted by the artificial neural network model met the Cv(RMSE) standard that proposed by American Society of Heating, Refrigerating and Air-conditioning Engineers Guideline 14, and the prediction result was very low error rate.

Multi-step building energy model calibration process based on measured data

Building energy models are a key element in regulatory compliance calculations. These energy performance calculations often do not accurately reflect actual operating conditions. Therefore, evaluation of energy performance comparing actual energy use of a building with the outcome of dynamic simulation models can be misleading, this difference is also known as the energy performance gap. The reduction of the gap is an important task aimed to provide confidence in the use of models for evaluation of energy efficiency. This paper is focused on reducing the technical issues (e.g. poorly adjusted thermal parameters in the envelope, inefficient boiler operator or lack of adjustment in parameters of heat pumps, baseboard radiators or air handling units) which are one of the main causes of the energy performance gap. The application of a multi-step, optimization-based, calibration methodology performed in a white-box simulation environment (EnergyPlus) using three months of ten minute time-step data to adjust HVAC parameter values with a genetic algorithm software (Jeplus) is validated on a real test site. Resulting in a BEM that fits the building’s hourly performance benchmark into international standards on three key levels: indoor temperature by Thermal Zone (TZ), heat production and electric consumption from heat pumps, which comprise all the components of a building energy model. A batch of 1500 h of heating operation, obtained from the building management system, has been used to calibrate the model. The results complied with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14–2002 at hourly interval, with NMBE ⩽±10%,Cv(RMSE) ⩽30% and R2 ⩾75% and with the International Performance Measurement and Verification Protocol (EVO) for Cv(RMSE) ⩽20% and R2 ⩾75% in the three aforementioned levels, which can be considered a step forward in the area of calibrating white box models. In addition, to prove the strength and robustness of the results, the model has been checked in a long testing and independent period of 2.500 h of heating operations with the same level of compliance. The demonstrator is the library of a school located in Denmark. The HVAC system is composed of four air–water heat pumps that deliver heating to the whole compound with the backup support of a gas boiler. The library is heated with baseboard radiators system with the support of an air handling unit used for ventilation purposes.

PBF-LB/M process under a silane-doped argon atmosphere: Preliminary studies and development of an innovative machine concept

In laser-based powder bed fusion of metals (PBF-LB/M) the presence of oxygen is known to encourage embrittlement and impair the wetting properties of the substrate and solidified material. Conventionally, inert gases like argon are used to reduce the residual oxygen content within the processing atmosphere. However, especially in terms of reactive materials like titanium the remaining oxygen still causes critical oxidations.In this publication, a new approach for obtaining an oxygen-free processing atmosphere is presented. Thereby the argon shielding gas is doped with reactive monosilane to reduce the residual oxygen content to a range comparable to XHV (Extreme High Vacuum, thermodynamic oxygen activity <10−15). The handling of this new silane-containing atmosphere requires the development of a special manufacturing system. Therefore, this work elucidates the development of an innovative machine system in accordance with the VDI (German Association of Engineers) Guideline 2221. For determination and specification of the underlying requirements, the interaction of the gas mixture with various construction materials and Ti–6Al–4V powder material was investigated in a test chamber. It could be shown that metallic materials and smooth surfaces are favorable for the design while polymers are likely to degrade in silane-containing atmosphere. Further, the design of the gas flow in the build chamber was optimized using flow simulation. In order to avoid the deposition of reaction products in the process zone, a laminar flow should be established, which can be achieved by baffles and honeycomb structures.

Forecasting of Shear Strength of Concrete Beam Reinforced with FRP Bar

This study develops a new approach for forecasting shear Strength of concrete beam without stirrups based on the artificial neural networks (ANN). Proposed ANN considers geometric and mechanical properties of cross section and FRP bars, and shear span-depth ratio. The ANN model is constructed from a set of experimental database available in the past literature. Efficiency of the ANN model was compared with existing approaches in the literature using comprehensive database. ANN is powerful tools in solving complex problems of civil engineering. The Levenberg–Marquardt (LM) method was applied for training algorithm. These existing approach include the American Concrete Institute design guide (ACI 440.1R-06), ISIS Canadian design manual (ISIS-M03-07), the British Institution of Structural Engineers guidelines (BISE), JSCE Design Recommendation, CNR-DT 203-06 Task Group, and Kara. The results demonstrate that ANN method has good agreement in calculating the shear strength of concrete beam reinforced with FRP bar among existing equations in recent decades.

Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models

Air conditioning in buildings accounts for 60% of the total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs), which simply and accurately allow us to forecast energy consumption with limited variables. Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month’s worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period and testing period, respectively, and the mean bias error (MBE) ranges were 4.03% to 4.97% and 3.48% to 4.39% for the training period and testing period, respectively. The chiller prediction model satisfied the energy consumption forecast performance criteria presented by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) guideline 14 (the measurement of energy and demand savings), with a performance of 24.64~25.58% and 7.12~29.39% in the training period and testing period, respectively, and MBE ranges of 2.59~3.40% and 1.35~2.87% in the training period and testing period, respectively. When the training period and testing period were combined for the AHU data, the actual energy usage forecast showed a lower error rate range of 0.22% to 1.11% for the training period and 0.17% to 2.44% for the testing period. For the chiller data, the error rate range was 0.22% to 2.12% for the entire training period, but was somewhat higher at 11.67% to 15.18% for the testing period. The study found that, even if the performance criteria were met, high accuracy results were not obtained, which was due to the poor data set quality. Although the forecast model based on artificial neural network can achieve relatively high-accuracy results with sufficient amounts of data, it is believed that this will require a thorough verification of the data used, as well as improvements in the predictive model to avoid overfitting and underfitting, to achieve such good results.

Institute of Transportation Engineers Guidelines versus Actual Trip and Parking Generation for a Transit-Oriented Development in an Auto-Oriented Region

The Institute of Transportation Engineers (ITE) guidelines serve as the most widely used reference for trip and parking generation estimates for any new development in the U.S.A. However, recent empirical studies question the efficacy of ITE guidelines in forecasting trip and parking generation in transit-oriented developments (TODs). Following the methodology of studies on seven TODs across the U.S.A., this study focuses on Dallas (TX), as a more auto-oriented American city, to explore the trip and parking generation at Mockingbird TOD. It finds that, with the exception of Station Park in Salt Lake City (which is really a transit-adjacent development rather than a TOD), the Mockingbird TOD has the lowest walk mode share (13.6%), the lowest bike mode share (0.22%), the lowest bus transit mode share (1.09%), and by far the lowest rail transit mode share (5.9%) of all the other seven TODs. Similarly, the Mockingbird TOD ranks first in relation to the driving mode share, with about 80% of its daily trips generated by driving. This is almost twice as many driving trips as the average of the other six TODs, which makes Mockingbird an auto-oriented TOD located in an auto-oriented region where more than 96% of commuting trips are by automobile. Still, the total automobile trip generation rate in Mockingbird is about 12% lower than the ITE estimates. Similarly, while the parking supply in Mockingbird TOD is less than 48% of the ITE recommended supply rate, its peak parking occupancy is only about 55% of the TOD supply.

Assessment of Exposure to Electric Vehicle Inductive Power Transfer Systems: Experimental Measurements and Numerical Dosimetry

High-power inductive power transfer (IPT) systems for charging light and heavy electric vehicles pose safety concerns if they are installed in uncontrolled environments. Within the framework of the European Project EMPIR-16ENG08 MICEV, a wide experimental and numerical study was conducted to assess the exposure of the general public to IPT stray magnetic fields for two different exposure scenarios: (1) for an IPT model system derived from the SAE J2954 standard operating at 85 kHz for a light electric vehicle coupled with the model of a realistic car-body model; and (2) for an IPT model system with a maximum rated power of 50 kW at 27.8 kHz for a real minibus that was reproduced with some simplifications in two different 3D finite element method (FEM) simulation tools (Opera 3D and CST software). An ad hoc measurement survey was carried out at the minibus charging station to validate the simulations of the real bus station for both aligned and misaligned IPT coils. Based on this preliminary study, a safety factor was chosen to ensure a conservative dosimetric analysis with respect to the model approximations. As highlighted in this study, the vehicle-body serves as an efficient screen to reduce the magnetic field by at least three orders of magnitude close to the coils. By applying FEM, computed spatial distribution to the Sim4Life software, the exposure of three Virtual Population human anatomical phantoms (one adult, one child, and a newborn) was assessed. The three phantoms were placed in different postures and locations for both exposure scenarios. The basic restriction limits, established by the current guidelines, were never exceeded within the vehicles; however, the basic restrictions were exceeded when an adult crouched outside the minibus, i.e., near the coils, or when a newborn was placed in the same location. Borderline values were observed in the light car. In the case of the bus, limits coming from the Institute of Electrical and Electronics Engineers (IEEE) guidelines are never exceeded, while basic restrictions coming from the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines are exceeded up to 12% for an adult and up to 38% for a newborn. This paper presents novel dosimetric data generated in an IPT system for heavy vehicles and confirms some of the literature data on light vehicles.

The new V-Model of VDI 2206 and its validation

Abstract Since 2016, a new version of the VDI (German Association of Engineers) Guideline 2206 has been developed by the Technical Committee VDI GMA 4.10 “Interdisciplinary Product Creation”. This article presents the revision results of the VDI Guideline 2206:2004 “Design methodology for mechatronic systems”. The core content of the guideline is an updated and enhanced V-Model for Mechatronic and Cyber-Physical Systems. The inherent concern logic of the V-Model represents the logical sequence of tasks. Its key advantage lies in staying independent from the chosen form of project organization. This way, the V-Model can be applied in classically managed projects as well as in agile projects. In addition, the article describes how the revision was performed and which potentials were tapped. Based on the identified potentials, the new V-Model is derived, explained and illustrated. New contents such as the introduction of checkpoints and the integration of requirements engineering are explained in detail. Furthermore, the pursued scientific procedure and the results of the International Validation Workshop with 25 experts from science and industry are proposed.

Methodology of advanced data validation and reconciliation application in industrial thermal processes

Data validation and reconciliation is a technique that uses process information and mathematical methods in order to correct measurements in thermal industrial processes. On the need to measurements data validation and reconciliation (DVR) in order to use them for further calculations is indicated in the Association of German Engineers guidelines – VDI 2048. Moreover, the necessity of removing measurement errors, in the international standard for the integration of enterprise and control systems ISA-95 is recommended. The paper presents in a comprehensive manner methodology of advanced DVR application in computer systems supporting supervising of the industrial thermal processes. The necessity of preliminary measurement data validation has been indicated. The problems of detection and identification of measurements with a gross error were presented. A method that enables the detection of steady and unsteady states in thermal processes has been shown. The statistical tests for control the assumed accuracy of measurements after advanced DVR calculations have been displayed. A comprehensive analysis of the usefulness of these statistical tests used in the detection and identification of gross measurement errors in the advanced DVR method was carried out. The presented computational example illustrating the use of statistical methods for the detection and identification of gross measurement errors clearly shows that the use of these methods does not solve the problem in the case of advanced DVR tasks with the non-linear form of conditional equations. In this case, the additional methods presented in the article must be used. An overall methodology of applying the generalized advanced DVR method in distributed control systems of thermal processes in the industry was developed. Based on that, a block diagram of the worked-out methodology of the application mentioned DVR method in the industrial thermal processes has been elaborated. The developed overall methodology and aggregated block diagram enable an integrated approach to the generalized advanced DVR method in industrial thermal processes.

Outline of Japan Society of Civil Engineers (JSCE) Guideline on Design and Application Methods of Galvanized Steel Reinforcement for Concrete Structures

Outline of Japan Society of Civil Engineers (JSCE) Guideline on Design and Application Methods of Galvanized Steel Reinforcement for Concrete Structures

Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks

In the past, remarkable behavior evaluations were carried out on concrete beams reinforced with FRP bars in the longitudinal direction without shear reinforcement. The aim of this study is to develop an artificial neural network (ANN) approach for predicting shear resistance of concrete beams. Proposed method considers geometric and mechanical properties of cross section and FRP bars, and shear span-depth ratio. Capability of the proposed method was compared with existing approaches in the literature using comprehensive database. The existing approaches include the American Concrete Institute design guide (ACI 440.1R-06), ISIS Canadian design manual (ISIS-M03-07), the British Institution of Structural Engineers guidelines (BISE), JSCE Design Recommendation, CNR-DT 203-06 Task Group, and Kara. The findings show that proposed method has excellent agreement with the experimental database.

P249 Impacts on automated HLA typing method from environmental factors

Aim Many environmental factors could affect the machinery used for automation. A 39 day study evaluated the consistency of HLA typing results in a laboratory setting with HVAC (Heating, Ventilation and Air Conditioning) installed. Methods Calibrated iSensix temperature and humidity sensors were used. The environmental data were obtained from iSensix web software (2012). All the Pre-PCR and post-PCR HLA SSO and NGS typing tests were conducted with robotic automation methods using Beckman Biomek, Eppendorf epMotion and One Lambda LABXpress. One donor sample serving as in-house control for SSO and NGS HLA typing was used for typing consistency analysis. A total of 29 SSO runs were performed and 17 NGS runs during the study period. Four previously typed proficiency samples were also retested for HLA typing consistency analysis. Results Within the 39 day study period, the average relative humidity (RH) inside the pre-PCR room was 35.04%, ranging from 19.68 to 51.16%. Pre-PCR room average temperature was 24.2 o C, ranging from 16.8 to 25.2 o C. The post PCR room average RH was 32.24%, ranging from 12.34 to 50.02%. Pre-PCR room average temperature was 24.2 o C, ranging from 23.8 to 25.2 o C. SSO typing results from In-house control sample are 99.95% (186/187 tests) concordant. One sample was analyzed with outdated IMGT template. NGS typing results from 17 runs were 100% concordant. Typing results from the previous proficiency samples were also in 100% agreement. Conclusions There are wide range of temperature and humidity requirements for optimal robotic machinery to operate. Most US buildings with HVAC will follow American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) guidelines. The ASHRAE guidelines recommend environmental condition is 68–74 o F (20.0–23.3 o C) in the winter and 72–80 o F (22.2–26.6 o C) in the summer with a RH of 30–60%. Our typing results concordance rate showed robotic automated HLA typing methods can withstand a wide range of environmental variations at our lab geographic location and elevation. Laboratory building with HVAC installed and operated following ASHRAE guidelines should have no issues obtaining a reliable HLA typing results using robotic automated typing methods.