Real-time Load Research Articles

Efficacy of parallel microchannel configurations towards hot-spot thermal management of 8-core microprocessors

The electronic industry’s shift towards multicore processor technology which leads to an increase the power densities of the chip. In multicore processors the hotpot location arises depending on the computational load which leads to the generating the non-uniform heat flux. The uneven cooling of a multicore processor will affect the reliability and life span of the chip. In this study, employed parallel microchannel cooling systems (PMCHS) with different flow configurations by numerical simulations. The objective of the present work is to investigate the thermos-hydrodynamic characteristics of a PMCHS under a non-uniform heat load, the heat load is considered from an actively running 8-core processor. Here, considered that three types different flow configurations (U, I and Z) to determine the flow maldistribution, in additions the thermal performance of each flow configuration was analysed at non-uniform heat conditions. The size and shape of the PMCHS is equal to the octa-core processor which has been mimicked, and real-time heat load data of the processor has been retrieved. The present study exhibits that non-uniform thermal load creates additional non-uniform temperature distribution along with flow maldistribution in the PMCHS. Each flow configuration has a different flow maldistribution pattern, whereas the sometimes intended flow maldistribution helps to give better uniform cooling on the chip.

High risk of virologic failure among HIV-infected children and adolescents routinely followed-up in Littoral region of Cameroon.

Virological response to antiretroviral therapy (ART) remains a challenge for HIV-infected children and adolescents due to non-optimization of pediatric ART for resource-limited settings. In this study, we aimed to investigate factors associated with virologic failure (VF) in HIV-infected-children and adolescents on ART in Cameroon. A prospective patient-based cohort study was conducted among HIV-infected children (0-9 years) and adolescents (10-19 years) followed-up between November 2018 and October 2019 in 38 healthcare centers located in the Littoral region of Cameroon. The 1st viral load (VL) was assessed after 6 months of ART initiation and the 2nd VL between 3 and 6 six months later in patients with VL ≥1000 copies/ml in accordance with the national algorithm using Abbott Real-Time HIV-1 Viral Load Assay. Multivariate analyses were performed to identify the determinants of higher risk of VF. Of 1,029 HIV-infected children and adolescents (393 children and 636 adolescents), 801 (77.8%) cumulatively presented with VL <1000 copies/mL within 12 months on ART. Adolescents were more likely to have VF than children (24.5% vs 18.3%, OR: 1.39; 95%CI: 1.00-1.93; p = 0.047). Patients followed-up in decentralized care units were significantly more likely to have VF compared to those attending the accredited treatment centers (26.1% vs 16.6%, OR: 1.88, 95%CI: 1.37-2.58; p<0.001). Our findings show a high rate of VL suppression (VLS, 77.8%) among HIV-infected children and adolescents, albeit lower than the established target of 90%. Being adolescent and patients followed in the decentralized care units are high risk factors for VF, thereby necessitating routine therapeutic education of patients and guardians in resource limited countries to improve VLS.

Real-time load dispatch in hydropower plant based on D3QN-PER

The conventional in-plant load dispatch approach has limited decision-making capacity and basic objectives. Small and medium-sized hydropower plants are challenged with the issues of meeting the water dispatch requirements and ensuring the decision-making accuracy and efficiency at the same time as the intensity of peak and frequency modulation of new power systems increases. To match the unit operation strategy and the water dispatch requirements of the power plant, an in-plant load dispatch model that takes into account the needs of both power generation dispatch and water dispatch was proposed in this paper. This was done to address the challenge of meeting the water dispatch requirements. A reinforcement learning approach was used to increase the computation efficiency of optimal load dispatch to address the challenge of ensuring decision-making accuracy and efficiency for real-time load dispatch schemes. According to the application findings at the Shenxigou Hydropower Plant (SXG), (1) this model was built to complete the work of peak and frequency modulation of the power grid while also taking into account the water dispatch requirements of small and medium-sized hydropower plants. With the minimum water usage as the water dispatch objective, this model was able to reduce the calculated water usage for power generation by 1.74 × 106m3 in comparison to the load dispatch table method. The calculated cumulative water level deviation of the model was reduced by 65.14 m when the water dispatch target was water level control. (2) The correctness and effectiveness of the decision-making process could be taken into account by this model. This model could meet the requirements for real-time dispatch decision-making after enhancing load discrete accuracy and adding roll-up computation of forecast information, and its calculation times were 9.46% and 15.10% of those of the load dispatch table technique, respectively.

Estimation of Load on Control Lines of Ram Air Parachute Designed for Precise Delivery Using 9-Dof Model

Controlled Aerial Delivery System (CADS) consists of Ram Air Parachute (RAP) Airborne Guidance Unit (AGU) and useful payload. AGU pulls the control lines attached to the control surfaces of RAP and provides required maneuverability to the system. Estimation of load on control lines is essential for design and development of Airborne Guidance Unit (AGU). This paper presents the mathematical methods of the estimation of load on control lines the results of which are validated by experiments. The mathematical estimate of the load on control lines was carried out by developing a model similar to the aircraft control surface hinge moment estimation. The mathematical model was incorporated in 9DOF model to arrive at real time load estimation on control lines during inflation and subsequent steady flight condition. Experimental results are obtained during flight trials by using load cells on control lines and current sensor on power line of actuators. The mathematical results are in good agreement with the experimental results. The load on control lines are within 6 to 8% of the suspended load.

Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models

Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.

Near Real-Time Load Forecasting of Power System Using Fuzzy Time Series, Artificial Neural Networks, and Wavelet Transform Models

Due to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand.

Experimental study of dynamic response of passive flapping hydrofoil in regular wave

The hydrofoil harnesses wave energy and converts it into thrust. In this paper, we present the results of the first experimental study investigating the dynamic behavior of a fully passive foil with spring-loaded pitch and heave in regular waves. Our study shows that the real-time load signal is multi-harmonic with strong superposition, directly proving the robust energy harvesting performance due to the restoring springs. By interpreting the hydrofoil's pose and path from an image sequence captured underwater, we conclude the dynamic evolution of the fully passive hydrofoil interacting with regular waves. The hydrofoil's dynamics exhibit asymmetric surge, pitch, and heave in a motion cycle. Furthermore, we employ a pixel capturing algorithm with self-correction utility to quantify the hydrofoil's forward displacement from the image sequence of the moving carriage. These findings provide valuable insight into the performance and potential of hydrofoils for marine propulsion.

Artificial Intelligence and Machine Learning for Real-time Energy Demand Response and Load Management

Within this compendium, an exhaustive examination is undertaken to scrutinize the intricate amalgamation of artificial intelligence (AI) and machine learning (ML) techniques within the paradigm of real-time energy demand response and load management. Placing paramount importance on the pervasive significance of AI and ML, this research expounds upon their profound capabilities to adroitly harmonize the delicate interplay between supply and demand, meticulously calibrate the multifarious dimensions of grid stability, and optimize the boundless potential inherent in renewable energy resources. An in-depth analysis ensues, encompassing the deployment of AI algorithms, poised at the vanguard of demand response optimization, and the judicious utilization of ML techniques, flawlessly calibrated to deliver unerring accuracy across varying temporal scales in the realm of load forecasting. Furthermore, the seamless integration of AI into the very fabric of intelligent appliances and Internet of Things (IoT)-enabled systems unfolds, illuminating the path towards energy consumption optimization, ascertaining an intricate tapestry of interconnected devices, and engendering an ecosystem of intelligent load management. Notably, this comprehensive exposition delves into the far-reaching implications for optimal load management and resource allocation, magnifying the transformative potential that AI-driven algorithms hold in precisely balancing energy utilization and deftly managing the intricate interdependencies that permeate load distribution. Through meticulous elucidation, this illuminating manuscript emboldens the reader with insights into the progressive advancements and myriad benefits that the tandem of AI and ML confers upon the dynamic energy sector, charting an unyielding course towards unprecedented resilience and sustainable utilization of our cherished renewable energy resources.

Method for non-invasive assessment of the effect of biologically active substances on the rate of the pH level restoration in the muscle after maximum load using 1H MRS

Purpose of the study: To determine the possibility of a non-invasive evaluation of the biologically active substances (BAS) effect on the rate of a pH level restoration in a muscle after a maximum load using 1H magnetic resonance spectroscopy (MRS).Materials and methods. Creatine monohydrate and beta-alanine were taken as tested biologically active substances, used according to the manufacturer's recommendations. At the first stage, calibration curves of a pH dependence on the magnitude of chemical shifts were plotted during assigning 1H spectra of model carnosine dipeptide solutions for non-invasive determination of intramuscular pH. Further experiments were carried out on laboratory animals (mice) using a 9 T NMR spectrometer Bruker Advance III WB 400MHz WB (Bruker, Germany). In experiments on volunteers the functional test pwc170 was used for assessing the ergogenic effects of biologically active substances on rectus quadriceps femoris. The test allows to achieve the level of myocytes cytoplasm acidification with lactate, and the effectiveness of functional biologically active substances on endurance, and also the function of aerobic systems by the muscle pH rate of recovery. Detection was performed using a high-field magnetic resonance imaging scanner (Philips Healthcare, Achieva 3.0T, North Braband, The Netherlands) and two SENSE Flex-L surface ring radiofrequency coils.Results. The effect of oral intake of creatine and beta-alanine on the restoration of rectus quadriceps femoris muscle pH after an acidification of the myocytes cytoplasm with lactate was evaluated using the 1H MRS method. Reproducible results with optimal signal-to-noise ratios and width of carnosine spectral peaks were achieved in volunteers using individual protocols and 1H MRS at 3T in the quadriceps femoris. Animal experiments have highlighted the need to develop and use more accurate techniques for voxel extraction and fat suppression during in vivo 1H spectroscopy to reliably capture the chemical shifts of carnosine peaks.Conclusion. The data obtained using 1H MRS on volunteers allow us to conclude that the developed method makes it possible to non-invasively assess the effect of biologically active substances on the rate of restoration of pH level in a muscle after a critical load in real time in vivo.

Complex-Geometry 3D Computational Fluid Dynamics with Automatic Load Balancing

We present an open-source code, Xyst, intended for the simulation of complex-geometry 3D compressible flows. The software implementation facilitates the effective use of the largest distributed-memory machines, combining data-, and task-parallelism on top of the Charm++ runtime system. Charm++’s execution model is asynchronous by default, allowing arbitrary overlap of computation and communication. Built-in automatic load balancing enables redistribution of arbitrarily heterogeneous computational load based on real-time CPU load measurement at negligible cost. The runtime system also features automatic checkpointing, fault tolerance, resilience against hardware failure, and supports power- and energy-aware computation. We verify and validate the numerical method and demonstrate the benefits of automatic load balancing for irregular workloads.

Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM

A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor the status and operation of various devices in the power system in real-time and optimise the dispatch of the power system through intelligent algorithms to achieve efficient operation of the power system. However, due to its complexity and uncertainty, how to effectively perform real-time prediction is an important challenge. This paper proposes a smart grid real-time prediction model based on the attention mechanism of convolutional neural network (CNN) combined with bi-directional long and short-term memory BiLSTM.The model has stronger spatiotemporal feature extraction capability, more accurate prediction capability and better adaptability than ARMA and decision trees. The traditional prediction models ARMA and decision tree can often only use simple statistical methods for prediction, which cannot meet the requirements of high accuracy and efficiency of real-time load prediction, so the CNN-BiLSTM model based on Bayesian optimisation has the following advantages and is more suitable for smart grid real-time load prediction compared with ARMA and decision tree. CNN is a hierarchical neural network structure containing several layers such as a convolutional layer, pooling layer and fully connected layer. The convolutional layer is mainly used for extracting features from data such as images, the pooling layer is used for the dimensionality reduction of features, and the fully connected layer is used for classification and recognition. The core of CNN is the convolutional operation, a locally weighted summation operation on the input data that can effectively extract features from the data. In the convolution operation, different features can be extracted by setting different convolution kernels to achieve feature extraction and classification of data. BiLSTM can capture semantic dependencies in both directions. The BiLSTM structure consists of two LSTM layers that process the input sequence in the forward and backward directions to combine the information in both directions to obtain more comprehensive contextual information. BiLSTM can access both the front and back inputs at each time step to obtain more accurate prediction results. It effectively prevents gradient explosion and gradient disappearance while better capturing longer-distance dependencies. The CNN-BiLSTM extracts features of the data and then optimises them by Bayes. By collecting real-time data from the power system, including power, load, weather and other factors, our model uses the features of CNN-BiLSTM to deeply learn real-time load data from smart grids and extract key features to achieve future load prediction. Meanwhile, the Bayesian optimisation algorithm based on the model can optimise the model’s hyperparameters, thus improving the model’s prediction performance. The model can achieve accurate prediction of a real-time power system load, provide an important reference for the dispatch and operation of the power system, and help optimise the operation efficiency and energy utilisation efficiency of the power system.

Research on the rapid calculation method of temperature rise of cable core of duct cable under emergency load

In order to facilitate engineers to manage dynamic loads in real time and improve the utilization of power cables, it is necessary to accurately calculate the temperature rise of cable cores under duct laying conditions. The transient thermal circuit model involved in this paper helps to improve the speed of dynamic response and the accuracy of transient temperature rise calculation for power cable cores under duct laying conditions. Since the impact of the environmental conditions, exploring the computational relationships of the thermal circuit model parameters helps to improve the engineering applicability of the model. The duct cable can be broken down into three parts: the cable body, the air inside the pipe and the external environment. In engineering, the thermal circuit model parameters of the cable body can be made into a table and applied by looking up the table. The thermal circuit model parameters of air in duct are related to the cable outside diameter and duct inside diameter, and can also be engineered using tables for application. The fit gives the third part of the equation with respect to the thermal resistance coefficient of the duct and soil and the burial depth. The error in applying the decentralized thermal circuit model and parametric expressions is less than 1 °C compared to the results of finite element calculations. This proves that the decomposition of the duct power cable model improves the accuracy of the transient temperature rise calculation. The relationship equation between the parameters of the thermal circuit model and the laying conditions helps to improve the engineering adaptability of the method.

Factors affecting the lateral contribution of a raft in a piled raft system

Contribution of the raft to lateral loads in a piled raft foundation system can be influenced by various factors including vertical contact pressure, number of piles, pile length, and spacing to diameter s/d ratios. Design of the piled raft system is highly dependent upon the contribution of the raft which should be thoroughly investigated. In this article, an experimental and numerical investigation was conducted in order to assess the effects of various parameters on contribution of raft to lateral loads. In experimental approach, small-scale models of the aluminium raft and galvanized iron (GI) hollow piles were used for testing under vertical and lateral loads. Vertical and lateral load cells on piles were installed for real-time load monitoring and LVDTs were used for displacement monitoring. In numerical approach, finite element software PLAXIS 3D was used. A raft was defined as a plate element, while piles were modelled as embedded beams. Based on experimental and numerical investigations, it was found that vertical contact of raft with soil has a direct relationship lateral raft contribution. Because, due to vertical load, the firm contact between the raft and the soil develops, resulting in a significant lateral contribution. Depending on the raft’s vertical contribution, the lateral contribution of the raft was found to be ranges from 14% to 55%. The numerical results were found fairly close to the experimental results. As a result, PLAXIS 3D was used to conduct a parametric analysis and It was determined that vertical pressure was directly related while the other three parameters were inversely related to the lateral raft resistance.

A novel LH2/GH2/battery multi-energy vehicle supply station using 100% local wind energy: Technical, economic and environmental perspectives

With the gradual maturity of wind power technology, China's wind power generation has grown rapidly over the recent years. However, due to the on-site inconsumable electricity, the phenomenon of large-scale “wind curtailment” occurs in some areas. In this paper, a novel hybrid hydrogen/electricity refueling station is built near a wind farm, and a part of the surplus wind power is used to charge electric trucks, and the other part of the surplus power is used to produce “green hydrogen”. According to real-time load changes, different amounts of liquid hydrogen and gas hydrogen can be properly coordinated to provide timely energy supply for hydrogen trucks. For a 400 MW wind farm in the western Inner Mongolia, China, the feasibility of the proposed system has been carried out based on the sensitivity and reliability analysis, the static and dynamic economic modeling, with an entire life cycle analysis. Compared to the conventional technology, the initial investment of the proposed scheme (700.07 M$) decreases by 13.97%, and the dynamic payback period (10.93 years) decreases by 25.87%. During the life cycle of the proposed system, the accumulative NPV reaches 184.63 M$, which increases by 3.14 times compared to the case by conventional wind technology.

Application study of ant colony algorithm for network data transmission path scheduling optimization

Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

An Intelligent Server load balancing based on Multi-criteria decision-making in SDN

In an environment of rising internet usage, it is difficult to manage network traffic while maintaining a high quality of service. In highly trafficked networks, load balancers are crucial for ensuring the quality of service. Although different approaches to load-balancing have been proposed in traditional networks, some of them require manual reconfiguration of the device to accommodate new services due to a lack of programmability. These problems can be solved through the use of software-defined networks. This research paper presents a dynamic load-balancing algorithm for software-defined networks based on server response time and content mapping. The proposed technique dispatches requests to servers based on real-time server loads. This technique comprises three different modules, such as a request classification module, a server monitoring module, and an optimized dynamic load-balancing module using content-based routing. There are a variety of robust mathematical tools to address complex problems that have multiple objectives. Multi-Criteria Decision-Making is one of them. The performance of the proposed scheme has been validated by applying the Weighted Sum Method of the multi-criteria decision-making technique. The proposed method Server load balancing based on Multi-criteria Decision Making[SDLB-MCDM] is compared with different load-balancing schemes such as round robin, random, load-balancing scheme based on server response time [LBBSRT], and An SDN-aided mechanism for web load- balancing based on server statistics [SD-WLB]. The experimental results of SDLB-MCDM show a significant improvement of 58% when weights are equal and 50% when unequal weights are assigned to various QoS parameters in comparison with the ROUND ROBIN, RANDOM, LBBSRT and SD-WLB techniques.

Numerical estimation of the influence of pre-induced micro-cracks in the stress concentration zone on the SIFs of a lower suspension arm

Structural steel is ductile in nature, this is the reason it is used in most of the Sectors in the manufacturing industry. Despite its structural strength, it faces compelling and challenging failures due to unstable, fatigue, dynamic and shock loads. &#x0D; This research study evaluates the structural response on one of these loading conditions using the finite element method. The design of a lower suspension arm of an automobile is modelled in Solidworks 2020 and is solved for static elastic conditions in Ansys 2021 R1. A set of pre-induced fractures are then integrated into the computational model in the Stress concentration zones in different parts of the body and solved independently. A total of five micro-cracks are induced with each crack consisting of six contours. For the numerical simulation of lower suspension arm, real-time loading conditions must be attained to resemble real-world loading scenario. Hence, 4 modes of solving were chosen which would depict the real-world failure scenario where the suspension lower arm can attain maximum loads. The maximum load values are estimated in each mode and is integrated into the model with predefined boundary conditions for the computational approach. &#x0D; A detailed numerical comparative conclusion is drawn regarding the SIFs of every mode and the crack that pertains maximum crack propagation rate.

Experimental Study on the Effect of Off-Sized Rollers on the Dynamic Load Distribution in a Cylindrical Roller Bearing

Abstract The differences in the roller diameters within a roller bearing are unavoidable in practice, which is known as the off-sized effect. The off-sized effect results in the dynamic variation of the load distribution in bearings and further the abnormal vibration and premature failure of bearings. This paper experimentally investigates the effect of off-sized rollers on the dynamic load distribution in a cylindrical roller bearing. Different configurations of off-sized rollers are given considering the oversized and undersized roller conditions, and the number and position of off-sized rollers. The real-time roller-raceway contact loads distributed in the bearing are measured under these test cases. The results show that a single off-sized roller leads to the loading change directly on itself and further the load rebalances among the adjacent rollers. Under the conditions of multiple off-sized rollers, a stack effect of the competition rule of roller loading dominates the variation range of the dynamic load distribution. As results of the stack effect, among all roller configurations, the configuration of multiple off-sized rollers adjacent to each other leads to the minimum range of the dynamic load distribution, while the configuration of two off-sized rollers separated by a regular roller leads to the maximum one.

Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation

Despite significant advances in distributed renewable energy systems (DRES), the technology still faces several substantial challenges that prevent the large-scale adoption of these systems into a country’s energy sector. The intermittency of renewables, uncertainties associated with real-time multi-horizon weather and load forecasts, and lack of comprehensive control systems are among the main technical and regulatory challenges for the real-world adoption of DRES. This paper outlines the current state of knowledge in the real-world operation of DRES and also describes pathways and methodologies that enable and facilitate the uptake of DRES in a country’s energy sector.

How Hard Is It Really? Assessing Game-Task Difficulty Through Real-Time Measures of Performance and Cognitive Load

Background Serious and entertainment game designers strive to create engaging, immersive, and often, challenging games. This task involves modifying game mechanics or environments to create experiences with differing levels of challenge to meet player skill. The balance between different game mechanics or environments, and the differing levels of challenge they pose, is typically understood through iterative testing. Balance and challenge becomes increasingly important in serious games and simulation training as these games commonly need to be engaging and impart learning content. Overburdening players’ cognitive capacity with either too much gameplay challenge or learning content may reduce the educational effectiveness of the game. Aim. In this research, we develop a game-based driving simulation with different gameplay tasks to explore the impact of different types of challenges and game aesthetics on real-time cognitive load and task performance, which may inform serious game design. We also test the validity of a game-embedded real-time cognitive load measuring method. Method A total of 31 participants undertook the driving simulation experiment under three different aesthetic conditions using a within-subject experimental design. Cognitive load was measured using three different methods, and performance was measured via in-game metrics. Additionally, demographic and engagement surveys were also completed. Results Player performance and cognitive load respond differently to different types of challenge, and an appropriate level of game challenge can lower cognitive load. The embedded cognitive load measure was validated as an effective method for evaluating real-time cognitive load during gameplay. Conclusion The results demonstrate the validity of a dual measure approach for future adaptive serious games and simulation training environments combining performance and cognitive load. An easy to implement, and robust, in-game measure for cognitive load has been validated in real-world conditions. From these results, a system for dynamic difficulty adjustment is proposed tailored towards serious games and simulation.