High-performance Configuration Research Articles

Interpreting summertime hourly variation of NO2 columns with implications for geostationary satellite applications

Abstract. Accurate representation of the hourly variation in the NO2-column-to-surface relationship is essential for interpreting geostationary observations of NO2 columns. Previous research indicated inconsistencies in this hourly variation. This study employs the high-performance configuration of the GEOS-Chem model (GCHP) to analyze daytime hourly NO2 total columns and surface concentrations during summer. We use measurements from globally distributed Pandora sun photometers and aircraft observations over the United States. We correct Pandora total NO2 vertical columns for (1) hourly variations in effective temperature driven by vertically resolved contributions to the total column and (2) changes in local solar time along the Pandora line of sight. These corrections increase the total NO2 columns by 5–6 × 1014 molec. cm−2 at 09:00 and 18:00 across all sites. Fine-scale simulations from GHCP (∼12 km) reduce the normalized bias (NB) against Pandora total NO2 columns from 19 % to 10 % and against aircraft measurements from 25 % to 13 % in Maryland, Texas, and Colorado. Similar reductions are observed in NO2 columns over the eastern US (17 % to 9 %), the western US (22 % to 14 %), Europe (24 % to 15 %), and Asia (29 % to 21 %) when compared to 55 km simulations. Our analysis attributes the weaker hourly variability in the total NO2 column to (1) hourly variations in column effective temperature, (2) local solar time changes along the Pandora line of sight, and (3) differences in hourly NO2 variability from different atmospheric layers, with the lowest 500 m exhibiting greater variability, while the dominant residual column above 500 m exhibits weaker variability.

Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth

Abstract. Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5–AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µg m−3, with regional values ranging from 59.8 µg m−3 in North America to more than 190 µg m−3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η, exhibiting a population-weighted mean difference in aerosol composition of 12.3 µg m−3, which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µg m−3, which reflects spatial variation in the column–surface relationship.

Efficient Computation of Radiative Heat Recovery from Porous Ceramic Monoliths for Efficient Solar Thermochemical Fuel Production

Solar thermochemical hydrogen (STCH) produced by heat-driven water-splitting is a promising route for producing green hydrogen and other zero-emission synfuels. However, the efficiency of STCH must be dramatically increased for it to make an impact on decarbonization efforts. We have previously presented a novel Reactor Train System (RTS) for significantly increasing the efficiency of STCH by employing heat recovery from the redox material and efficient gas exchange processes. In this paper we present a higher-fidelity model for the RTS that accommodates the slow heat diffusion through the STCH redox material. For this purpose, a novel method is introduced for transient modelling of radiative heat in participating media. This method, called GREENER: Generalized Radiation Exchange Factors and Net Radiation, combines the accuracy of Monte Carlo Ray Tracing with the low computational cost of the P1 or Rosseland diffusion approximations. Along with STCH, GREENER has application for modelling volumetric solar receivers, high temperature heat recovery systems like heat exchangers and regenerators, and packed bed reactors. Using the GREENER method, the RTS counterflow radiative heat exchanger is shown to achieve heat recovery effectiveness greater than 70%. The performance of non-uniform porous redox morphologies is evaluated, and high-performing configurations are identified.

Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter.

Global geophysical satellite-derived ambient fine particulate matter (PM2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (R 2 = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by -30% to -5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM2.5 from columnar AOD.

Performance study of microwave photonic links by considering the effect of phase shifters and bias conditions on dual-drive dual parallel Mach–Zehnder modulator

Abstract This paper presents the performance analysis of different schemes for generating linearized photonic signal by varying phase shifter arrangements of a dual-drive dual parallel Mach–Zehnder modulator (DD-DPMZM). The RF signals at the electrodes of sub-modulators of DD-DPMZM are fed using different phase angles to generate optically modulated signals in the output. The proposed system consists of a DD-DPMZM and phase shifters. Spurious free dynamic range (SFDR) is evaluated for different configurations of sub modulators in upper and lower arm of DD-DPMZM. SFDR of 135.6 dB Hz4/5 is obtained for quadrature and optical carrier suppression modulation in upper and lower submodulators by suppressing third order intermodulation distortion completely. It is found to be high performance configuration for dual parallel Mach–Zehnder modulator based linear microwave photonic link.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review.

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Aplanatic solar concentrators for tubular absorbers.

A fundamentally new, to the best of our knowledge, class of linear (2D) dual-mirror aplanats tailored to tubular absorbers is developed for the types of solar concentrators used for thermal power. It is shown that prior investigation establishing this concept possesses unrecognized additional designs, as well as having missed high-performance configurations. It is shown that our line-focus solar concentrators can attain intercept factors exceeding 0.9 at concentration values as high as 55, with practical mirror contours and assemblies. Designed expressly for tubular absorbers, they represent improvements upon previous aplanatic concentrators that were tailored to flat one-sided absorbers but applied to tubular absorbers (as well as to conventional parabolic troughs).

Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles

AbstractBattery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuses on optimal controllers for charging, thermal control, and cell balancing of electric vehicles. A potential approach for practical applications is the direct optimal control method, particularly model predictive control (MPC). The objective function, prediction model types, and manipulated variables are summarized, along with the computational performance. Typical nonlinear MPC, linear MPC, explicit MPC, and hierarchical MPC are the main formulations for the optimal control of EVs. The AI‐based approach learns the optimal control law as a function from the optimal control result data. Although few studies have applied the reinforcement approach to battery systems, additional safety considerations for constraints must be considered for real applications. Cell variations, aging factors, and uncertainty considerations have been analyzed for improving the controller design. Addressing the computational issue with a reliable optimizer is critical to the implementation of an optimal controller for EVs.

ETune: Efficient configuration tuning for big-data software systems via configuration space reduction

Configuration tuning for big-data software systems is generally challenging due to the complex configuration space and costly performance evaluation. The workload changes over time in the context of big data making this problem even more difficult. To address the low efficiency issue caused by the need to retune from scratch after workload changes, we present ETune based on a combination of an Bayesian Optimization (BO) based tuner and configuration space reduction techniques to efficiently find high-performance configurations for big-data systems. For configuration space reduction, we develop two approaches: (1) shrinking the configuration space by trading off the exploitation and exploration between the impactful parameters and other parameters in the tuning process, (2) generating the promising regions of configuration space by transferring knowledge across past tuning tasks. The two configuration space reduction methods aim to reduce the huge configuration space to a compact but promising one, and searching in the reduced configuration space can further accelerate the configuration tuning. The extensive experiments show that our configuration space reduction methods considerably boost the BO-based tuner, and ETune significantly improves the tuning efficiency compared with the transfer learning based state-of-the-arts.

Design of a silicon Mach–Zehnder modulator via deep learning and evolutionary algorithms

As an essential block in optical communication systems, silicon (Si) Mach–Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a 40~text {GHz} bandwidth and a driving voltage of 6.25~text {V}, or, alternatively, 47.5~text {GHz} with a driving voltage of 8~text {V}. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs.

Performance potential and optimal configuration study for low-GWP R161-DMETEG single-stage absorption refrigeration system via pinch technology

New low-GWP R161-DMETEG mixture showed its high feasibility as an alternative working pair to overcome shortcomings of traditional ones. Its internal heat recovery capacity significantly influences operating performances. To systematically explore its ideal performance potentials and heat integration principles of the traditional single-stage absorption refrigeration system (SSARS), this paper conducted the internal heat recovery optimization and derived optimal systems by establishing an approach via pinch technology as a comprehensive planning. The ideal optimal system for maximum heat recovery under different conditions was derived initially using problem tables and Q-T graphical methods. Among all DMETEG-based fluids, R161-DMETEG exhibits the highest average performance potential of approximately 0.677 and an ideal performance improvement of 108%. Subsequently, a new configuration, called MFD-SSARS, was derived using the gird method. Its actual performances and economic feasibility under different parametrical conditions and control strategies were further explored. MFD-SSARS achieves COPs between SSARS and the ideal potential, and it can flexibly adjust internal heat recovery according to varying conditions to stimulate the performance potential. MFD-SSARS maximumly improvs COPs by approximately 65.953% relative to SSARS, reaching 96.167% of its potential under basic conditions. Despite its higher total capital costs than SSARS, it showed the lowest payback period of 2.23 years, thus proving the economic feasibility. Overall, the adopted environment-friendly working pair with designed high-performance configuration of this work serves as a technical solution to overcome current limitations of traditional absorption refrigeration technologies.

Advanced fuel and burnable absorbers designed for long-cycle operation of BNPP

The present study is based on the facts that are happening for the Bushehr Nuclear Power Plant (BNPP) and coincides with the operational changes and step-by-step modifications of the BNPP in the near future. Bushehr reactor core will change from BNPP core to TVS-2M core. In other words, a new fuel, called the TVS-2M, is being used by the Russians to upgrade WWER-1000 reactors to reduce power generation costs, increase cycle length and fuel consumption. The neutronic analysis performed in this work is in line with this change. It provides a feasibility study of BA change and increase of cycle length in BNPP reactor without changing the initial structure of the reactor core. This research provides a new core configuration that can achieve the stated goals to a large extent without changing the BNPP core to the TVS-2M core. This research provides a feasibility study of Integrated burnable absorber (IBA) change by maintaining the initial neutronic conditions and a new high-performance configuration aiming for a long-cycle operation in Iran's nuclear power reactor. In Water-Water Energetic Reactors (WWER) reactors, two alternatives to CrB2Al (current burnable absorber in BNPP) are gadolinia (Gd2O3-UO2), and erbia (Er2O3-UO2). The benefits of the combinational IBA concept are expected by compensating weak points of each other. The main contribution of this research is the use of two cylindrical coaxial IBA layers with the same volume of gadolinium pins plus erbium (separated by a thin layer of zirconium alloy (Zr + 1% Nb)) and gadolinium pins. A neutronic evaluation has been conducted to investigate the variations of effective multiplication factor versus burnup, reactivity swing, power peaking distribution, moderator temperature coefficient (MTC), critical boron concentration (CBC), and cycle length. The result shows that the cycle length can be extended from 296 days in the BNPP core to 366 days in the proposed core, which is comparable to the cycle length of the TVS-2M core. The average BA concentration in the proposed core is 5%, which is lower than the average BA concentration of 5%–8% in the TVS-2M core. Regarding the gain of 73.90 days in cycle length, the revenue is estimated to be around 19,503,000 USD, making it worth the implementation considering the number of gadolinium and erbium pins in the proposed core.

Implementation and evaluation of the GEOS-Chem chemistry module version 13.1.2 within the Community Earth System Model v2.1

Abstract. We implement the GEOS-Chem chemistry module as a chemical mechanism in version 2 of the Community Earth System Model (CESM). Our implementation allows the state-of-the-science GEOS-Chem chemistry module to be used with identical emissions, meteorology, and climate feedbacks as the CAM-chem chemistry module within CESM. We use coupling interfaces to allow GEOS-Chem to operate almost unchanged within CESM. Aerosols are converted at each time step between the GEOS-Chem bulk representation and the size-resolved representation of CESM's Modal Aerosol Model (MAM4). Land-type information needed for dry-deposition calculations in GEOS-Chem is communicated through a coupler, allowing online land–atmosphere interactions. Wet scavenging in GEOS-Chem is replaced with the Neu and Prather scheme, and a common emissions approach is developed for both CAM-chem and GEOS-Chem in CESM. We compare how GEOS-Chem embedded in CESM (C-GC) compares to the existing CAM-chem chemistry option (C-CC) when used to simulate atmospheric chemistry in 2016, with identical meteorology and emissions. We compare the atmospheric composition and deposition tendencies between the two simulations and evaluate the residual differences between C-GC and its use as a stand-alone chemistry transport model in the GEOS-Chem High Performance configuration (S-GC). We find that stratospheric ozone agrees well between the three models, with differences of less than 10 % in the core of the ozone layer, but that ozone in the troposphere is generally lower in C-GC than in either C-CC or S-GC. This is likely due to greater tropospheric concentrations of bromine, although other factors such as water vapor may contribute to lesser or greater extents depending on the region. This difference in tropospheric ozone is not uniform, with tropospheric ozone in C-GC being 30 % lower in the Southern Hemisphere when compared with S-GC but within 10 % in the Northern Hemisphere. This suggests differences in the effects of anthropogenic emissions. Aerosol concentrations in C-GC agree with those in S-GC at low altitudes in the tropics but are over 100 % greater in the upper troposphere due to differences in the representation of convective scavenging. We also find that water vapor concentrations vary substantially between the stand-alone and CESM-implemented version of GEOS-Chem, as the simulated hydrological cycle in CESM diverges from that represented in the source NASA Modern-Era Retrospective analysis for Research and Applications (Version 2; MERRA-2) reanalysis meteorology which is used directly in the GEOS-Chem chemistry transport model (CTM). Our implementation of GEOS-Chem as a chemistry option in CESM (including full chemistry–climate feedback) is publicly available and is being considered for inclusion in the CESM main code repository. This work is a significant step in the MUlti-Scale Infrastructure for Chemistry and Aerosols (MUSICA) project, enabling two communities of atmospheric researchers (CESM and GEOS-Chem) to share expertise through a common modeling framework, thereby accelerating progress in atmospheric science.

LlamaTune

Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) in search for high performance configurations. Looking at Microsoft production services operating millions of databases, sample efficiency emerged as a crucial requirement to use tuners on diverse workloads. This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency of existing optimizers. LlamaTune employs an automated dimensionality reduction technique based on randomized projections, a biased-sampling approach to handle special values for certain knobs, and knob values bucketization, to reduce the size of the search space. LlamaTune compares favorably with the state-of-the-art optimizers across a diverse set of workloads. It identifies the best performing configurations with up to 11X fewer workload runs, and reaching up to 21% higher throughput. We also show that benefits from LlamaTune generalize across both BO-based and RL-based optimizers, as well as different DBMS versions. While the journey to perform database tuning at cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS tuning practical at scale.

Advances in Membrane Bioreactor: High Performance and Antifouling Configurations

Advances in Membrane Bioreactor: High Performance and Antifouling Configurations

Neutrosophic battery: An introduction to high-performance battery configuration

Neutrosophic battery: An introduction to high-performance battery configuration

SEAL-Embedded: A Homomorphic Encryption Library for the Internet of Things

The growth of the Internet of Things (IoT) has led to concerns over the lack of security and privacy guarantees afforded by IoT systems. Homomorphic encryption (HE) is a promising privacy-preserving solution to allow devices to securely share data with a cloud backend; however, its high memory consumption and computational overhead have limited its use on resource-constrained embedded devices. To address this problem, we present SEAL-Embedded, the first HE library targeted for embedded devices, featuring the CKKS approximate homomorphic encryption scheme. SEAL-Embedded employs several computational and algorithmic optimizations along with a detailed memory re-use scheme to achieve memory efficient, high performance CKKS encoding and encryption on embedded devices without any sacrifice of security. We additionally provide an “adapter” server module to convert data encrypted by SEAL-Embedded to be compatible with the Microsoft SEAL library for homomorphic encryption, enabling an end-to-end solution for building privacy-preserving applications. For a polynomial ring degree of 4096, using RNS primes of 30 or fewer bits, our library can be configured to use between 64–137 KB of RAM and 1–264 KB of flash data, depending on developer-selected configurations and tradeoffs. Using these parameters, we evaluate SEAL-Embedded on two different IoT platforms with high performance, memory efficient, and balanced configurations of the library for asymmetric and symmetric encryption. With 136 KB of RAM, SEAL-Embedded can perform asymmetric encryption of 2048 single-precision numbers in 77 ms on the Azure Sphere Cortex-A7 and 737 ms on the Nordic nRF52840 Cortex-M4.

IoT-Based Real-Time Monitoring System for a Smart Energy House

Monitoring Systems and the Internet of Things have become increasingly important, among others, in Renewable Energy applications. A combination of measuring sensors and actuators controlled and processed by an intelligent central system is necessary to reduce energy consumption automatically. This paper develops a modern concept for a Smart Energy House. The functionality and the hardware implementation are explained in detail based on a concrete simulation of the self-refilling water tank. The system comprises various separate located IoT modules integrated with a central host using TCP/IP network infrastructure, as well as communication technologies and protocols, such as WiFi and MQTT. Those sensors, also called clients, are wireless devices designed to measure different environmental conditions, such as temperature and humidity, in real-time. Changes in the current water level and battery charging progress are also being monitored. The host consists of three parts: storage service, web-based monitoring platform, and program logic for decision-making methods. Most of the processes, such as electrical control, data collection, information query, and analyzing functions, were implemented using Python libraries and self-written algorithms. An essential part of an intelligent home monitoring and automation system is secured remote accessibility and maintenance in any emergency. Therefore, the system supports both local and remote access. Data visualization and alarming routines are implemented within the web-based free software “Grafana” combined with the time-series database “InfluxDB”. Finally, the results demonstrated in this article show that the system has excellent application prospects due to its stability, low cost, high performance, user-friendly and customary configuration. The concept has been improved and prepared for the Smart Energy House test object located in Berlin.

Does open innovation always work? The role of complementary assets

The aim of this research is twofold: first, to identify Open Innovation (OI) configurations, and second, to analyze which configurations are associated with a high, and which with a low, OI performance. Using a configurational approach (involving complementarity and equifinality), we propose that firms use different OI configurations, by combining three openness practices (high number of alliances, partner diversity, and external R&D) and four complementary organizational assets (human capital, alliance coordination capabilities, inter-organizational learning capabilities, and patenting orientation), and that such configurations affect OI performance. We conducted a survey study on the Spanish biotech industry. A sample of 75 companies was analyzed using fuzzy-set qualitative comparative analysis (fsQCA). The results revealed seven high-performing configurations and six low-performing configurations. Configurations combining one or more OI practices and complementary organizational assets achieved a high OI performance, while open configurations lacking the necessary organizational assets were not successful. Closed configurations also performed poorly. The theoretical and managerial implications are discussed.

Individual Cognitive Capabilities, Strategy Tools, and their Combined Impact on Strategic Options

Dynamic capabilities are vital for a firm to survive and prosper in changing economic contexts. Prior research has substantially advanced our understanding of dynamic capabilities, but little is yet known about their microfoundations. In particular, the cognitive underpinnings of individual capabilities have received little attention, yet they ultimately contribute to performance differences on the organizational level. The present study addresses this gap by exploring the interplay among various individual cognitive capabilities and strategy tools, and it examines their impact on the generation of high-quality strategic options. Taking into account that cognitive capabilities form complex combinations of skills that need to be considered holistically, we adopt a configurational perspective. In specific terms, we conduct an experimental study and we rely on fuzzy-set methodology to analyze how various individual cognitive capabilities, in the form of analytic skill, strategic intelligence, and level of creativity combine with scenario analysis or SWOT analysis to affect the generation of high-value and high-novelty strategic options. Our results provide compelling evidence for the existence of specific high-performing configurations, whilst emphasizing that excellence in generating high-quality strategic options is highly dependent on the specific set of individual mental skills and strategy tools applied.