Performance Evaluation of a Model Predictive Control for Thermal Energy Storage Integrated Heat Pump Systems through a Co-Simulation
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This presentation cover results of four years of bifacial technology performance and degradation study, presented at the bifiPV 2024.
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The SCALE code system has been widely used for nuclear criticality safety, reactor physics, radiation shielding, source term generation, and inventory analyses by researchers, industry, and regulatory bodies. Although limited support for shared- and distributed-memory parallel processing was introduced via C++ threading, OpenMP, and MPI, a hybrid parallel programming model with both distributed- and shared-memory parallelism has not been fully supported in the SCALE code system.
A Controller Performance Evaluation (CPE) methodology for multi-input/multi-output digital control systems was developed and tested on an aeroelastic wind-tunnel model. Modern signal processing methods were used to implement control laws and to acquire time domain data of the whole system (controller and plant) from which appropriate transfer matrices of the control system could be generated. Matrix computational procedures were used to calculate singular values of return-difference matrices at the plant input and output points to evaluate the performance of the control system. The CPE procedures effectively identified potentially destabilizing controllers and confirmed the satisfactory performance of stabilizing ones.
Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.
The development of a controller performance evaluation (CPE) methodology for multiinput/multioutput digital control systems is described. The equations used to obtain the open-loop plant, controller transfer matrices, and return-difference matrices are given. Results of applying the CPE methodology to evaluate MIMO digital flutter suppression systems being tested on an active flexible wing wind-tunnel model are presented to demonstrate the CPE capability.
Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.
The purpose of QASE RT is to enable system analysts and software engineers to evaluate performance and reliability implications of design alternatives. The program resulted from two Small Business Innovation Research (SBIR) projects. After receiving a description of the system architecture and workload from the user, QASE RT translates the system description into simulation models and executes them. Simulation provides detailed performance evaluation. The results of the evaluations are service and response times, offered load and device utilizations and functional availability.
A courtyard is a traditional and popular construction feature found in China’s urban buildings. This case study evaluates the performance of the traditional courtyard design of the Jiangnan Museum, located in Jiangsu Province. In the evaluation, the spatial layout of courtyards is adjusted, the aspect ratio is changed, and an ecological buffer space is created. To model and evaluate the performance of the courtyard design, this study applied the Computational fluid dynamics (CFD) software, Parabolic Hyperbolic Or Elliptic Numerical Integration Code Series (PHOENICS), for wind environment simulation, and the EnergyPlus-based software, DesignBuilder, for energy simulation. Results show that a good combination of courtyard layout and aspect ratio can improve the use of natural ventilation by increasing free cooling during hot summers and reducing cold wind in winters. The results also show that ecological buffer areas of a courtyard can reduce cooling loads in summer by approximately 19.6% and heating loads in winter by approximately 22.3%. The study provides insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate during both winter and summer.
Abstract not provided.
This paper presents a methodology to incorporate and analyze the impact of semiconductor device part-to-part variation on power converter performance. By integrating extensive static and dynamic device characterization data with an automated compact model generation process that reflects manufacturing variability, device models with inherent variability features are utilized in converter simulations for a comprehensive assessment of performance impacts. The traditional converter performance evaluation process typically yields fixed efficiency values, often dismissing the inherent part-to-part variability caused by the manufacturing process of semiconductor devices. To address this limitation, a large population of devices was characterized to capture variations in static parameters-such as transfer, output, and capacitance characteristics-as well as dynamic behaviors, including switching losses. This data-driven approach enables the development of individual compact models, which were then integrated into converter simulations to evaluate efficiency ranges rather than single point estimated values. The converter simulation results show that part-to-part component variation can lead to significant efficiency deviations, exceeding several percentage points in high-power conversion applications. By offering a more accurate representation of converter behavior under real-world manufacturing conditions, this methodology enables designers to anticipate performance variability, improving the robustness of power converter designs.
Evaluation of interferometrically measured mirror metrology data and characterization of a telescope wavefront can be powerful tools in understanding of image characteristics of an x-ray optical system. In the development of soft x-ray telescope for the International X-Ray Observatory (IXO), we have developed new approaches to support the telescope development process. Interferometrically measuring the optical components over all relevant spatial frequencies can be used to evaluate and predict the performance of an x-ray telescope. Typically, the mirrors are measured using a mount that minimizes the mount and gravity induced errors. In the assembly and mounting process the shape of the mirror segments can dramatically change. We have developed wavefront sensing techniques suitable for the x-ray optical components to aid us in the characterization and evaluation of these changes. Hartmann sensing of a telescope and its components is a simple method that can be used to evaluate low order mirror surface errors and alignment errors. Phase retrieval techniques can also be used to assess and estimate the low order axial errors of the primary and secondary mirror segments. In this paper we describe the mathematical foundation of our Hartmann and phase retrieval sensing techniques. We show how these techniques can be used in the evaluation and performance prediction process of x-ray telescopes.
Evaluation based on standard TOPSAR product with 10m pixel spacing.
Real-time, reactive telerobotic mission control operations require personnel to actively operate and manage remotely controlled vehicles or robots in real time. Due to the physical separation of the vehicle from the operator, such operations present many additional factors that can influence fatigue (i.e., degraded mental performance) and workload (i.e., mental and physical cost of task requirements), making it difficult to assess how long an individual can safely and accurately conduct operations. The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission involves remotely controlling a lunar vehicle from an Earth-based mission control station. METHODS: In order to determine how long personnel could successfully maintain alertness and performance while operating a rover, we studied seven trained operators (i.e., 5 drivers, 2 real-time scientists) in a simulated mission control environment. We further assessed workload in order to determine whether perceived workload levels would change relative to time on task and by time of day. Operators completed two five-hour simulations in a randomized order, beginning at noon and at midnight. Performance was evaluated every 30 minutes using the psychomotor vigilance task (PVT), Karolinska Sleepiness Scale (KSS), and NASA Task Load Index (NASA-TLX). RESULTS: On average, participants rated themselves as sleepier (5.06 ± 2.28) on the night drives compared to the day drives (3.12 ± 1.44; P < .0001). Workload was rated higher during the day drives (37.93 ± 20.09) compared to night (32.09 ± 21.74; P < .01). Lastly, participants had no change in average reaction time between the two drives. CONCLUSIONS: From the analysis, we observed performance began to degrade after approximately three hours of driving. Our findings suggest that rotating drivers at least every three hours would be prudent to allow for breaks, and to minimize performance degradation, particularly during night shifts.
The rapid proliferation of heterogeneous programming languages and multi-vendor hardware has underscored the critical need to evaluate the performance portability of scientific applications. In this work, we present the systematic porting and optimization of a massively parallel fluid-structure interaction code across multiple heterogeneous programming frameworks for deployment on leadership-class supercomputers from major vendors. Our analysis focuses on at-scale performance for simulations involving hundreds of millions of deformable cells, executed on a combination of CPUs and GPUs spanning thousands of nodes on exascale machines. We benchmark the performance of each implementation, highlighting the trade-offs inherent in adopting diverse programming models. Key insights regarding the portability of CUDA on multi-vendor platforms, the superior multi-core CPU performance from SYCL, and architectural considerations on performance optimization are distilled from our experience, offering guidance to other users of high performance computing based on our findings.