Morphology prediction of dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50) crystal in different solvent systems using modified attachment energy model

In order to theoretically study the growth morphology of dihydroxylammonium 5,5’-bistetrazole-1,1’-dio late(TKX-50)crystal in different solvent systems,crystal–solvent models were established,and then molecular dynamics(MD)methods were adopted as a means to simulate particle motion.Modified attachment energy(MAE)model was employed to calculate the growth morphology of TKX-50.The simulation results demonstrate that COMPASS force field and RESP charge are suitable for molecular dynamics simulation of TKX-50.The morphologically dominant growth surfaces of TKX-50 in vacuum are(020),(011),(11–1),(100)and(120),respectively.In water(H_(2)O)and N,N-dimethylformamide(DMF)solvents,the(11–1)face is the largest in the habit face,the growth rate of(020)face becomes faster.With the increase of temperature,the aspect ratios of TKX-50 crystal in DMF solvent increase,and the areas of the(120)faces decrease.In ethylene glycol/H_(2)O mixed solvent system with volume ratio of 1/1,aspect ratio of TKX-50 is relatively small.In formic acid/H_(2)O mixed solvents with different volume ratios(1/4,1/3,1/2,1/1 and 2/1),aspect ratio of TKX-50 is relatively small when volume ratio is 1/2.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Energy model based sensorless estimation method for operational temperature of braking resistor onboard metro vehicles

Purpose–This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor(BR)onboard the vehicle,which overcomes the vulnerability of having conventional temperature sensor.Design/methodology/approach–In this study,the energy model based sensorless estimation method is developed.By analyzing the structure and the convection dissipation process of the BR onboard the vehicle,the energy-based operational temperature model of the BR and its cooling domain is established.By adopting Newton’s law of cooling and the law of conservation of energy,the energy and temperature dynamic of the BR can be stated.To minimize the use of all kinds of sensors(including both thermal and electrical),a novel regenerative braking power calculation method is proposed,which involves only the voltage of DC traction network and the duty cycle of the chopping circuit;both of them are available for the traction control unit(TCU)of the vehicle.By utilizing a real-time iterative calculation and updating the parameter of the energy model,the operational temperature of the BR can be obtained and monitored in a sensorless manner.Findings–In this study,a sensorless estimation/monitoring method of the operational temperature of BR is proposed.The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR,instead of adding dedicated thermal sensors.The results also validate the effectiveness of the proposal is acceptable for the engineering practical.Originality/value–The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks.The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Energy Model for UAV Communications:Experimental Validation and Model Generalization

Wireless communication involving unmanned aerial vehicles(UAVs)is expected to play an important role in future wireless networks.However,different from conventional terrestrial communication systems,UAVs typically have rather limited onboard energy on one hand,and require additional flying energy consumption on the other hand.This renders energy-efficient UAV communication with smart energy expenditure of paramount importance.In this paper,via extensive flight experiments,we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs,and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging,if not impossible.Specifically,we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight.With about 12,000 valid power-speed data points collected,we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature.In addition,in order to exclude the potential bias caused by the theoretical energy model,the obtained measurement data is also trained using a model-free deep neural network.It is found that the obtained curve from both methods can match quite well with the theoretical energy model.Next,we further extend the study to arbitrary 2-dimensional(2-D)flight,where,to our best knowledge,no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed,direction,and acceleration.To fill the gap,we first propose a heuristic energy model for these more complicated cases,and then provide experimental validation based on the measurement results for circular level flight.

Kinetic analysis and modeling of maize straw hydrochar combustion using a multi-Gaussian-distributed activation energy model

Combustion kinetics of the hydrochar was investigated using a multi-Gaussian-distributed activation energy model(DAEM)to ex-pand the knowledge on the combustion mechanisms.The results demonstrated that the kinetic parameters calculated by the multi-Gaussian-DAEM accurately represented the experimental conversion rate curves.Overall,the feedstock combustion could be divided into four stages:the decomposition of hemicellulose,cellulose,lignin,and char combustion.The hydrochar combustion could in turn be divided into three stages:the combustion of cellulose,lignin,and char.The mean activation energy ranges obtained for the cellulose,lignin,and char were 273.7-292.8,315.1-334.5,and 354.4-370 kJ/mol,respectively,with the standard deviations of 2.1-23.1,9.5-27.4,and 12.1-22.9 kJ/mol,re-spectively.The cellulose and lignin contents first increased and then decreased with increasing hydrothermal carbonization(HTC)temperature,while the mass fraction of char gradually increased.

Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Review of multi-objective optimization in long-term energy system models

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues.The conventional optimization of long-term energy system models focuses on a single economic goal.However,the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives,such as environmental,social,and energy security.Therefore,a multi-objective optimization of long-term energy system models has been developed.Herein,studies pertaining to the multi-objective optimization of long-term energy system models are summarized;the optimization objectives of long-term energy system models are classified into economic,environmental,social,and energy security aspects;and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers.Finally,the key development direction of the multi-objective optimization of energy system models is discussed.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

Building occupancy,one of the most important consequences of occupant behaviors,is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community.With the vast development of information technologies in the era of the internet-of-things,occupant sensing and data acquisition are not limited to a single node or traditional approaches.The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time.In this paper,we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks.The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media.On top of that,the typical building occupancy schedules are extracted with assumed people counting rules.The second approach utilizes the processed Global Positioning System(GPS)tracking data provided by social networking service companies such as Facebook and Google Maps.Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules.The results show that the extracted building occupancy schedules from different data sources(Twitter,Facebook,and Google Maps)share a similar trend but are slightly distinct from each other and hence may require further validation and corrections.To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media(TOSSM),data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM.The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM)

With the advance of the internet of things and building management system(BMS)in modern buildings,there is an opportunity of using the data to extend the use of building energy modeling(BEM)beyond the design phase.Potential applications include retrofit analysis,measurement and verification,and operations and controls.However,while BMS is collecting a vast amount of operation data,different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata,i.e.,the point tags.This results in a need to interpret and manually map any BMS data before using it for energy analysis.The mapping process is labor-intensive,error-prone,and requires comprehensive prior knowledge.Additionally,BMS metadata typically has considerable variety and limited context information,limiting the applicability of existing interpreting methods.In this paper,we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables.The framework is based on unsupervised density-based clustering(DBSCAN)and a novel fuzzy string matching algorithm“X-gram”.Therefore,it is generalizable among different buildings and naming conventions.We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques.Using two building cases from Singapore and two from the United States,we demonstrated that the framework outperformed baseline methods by 25.5%,with the measurement extraction F-measure of 87.2%and an average mapping accuracy of 91.4%.

Identifying the scope of the Lhasa Metropolitan Area based on a spatial field energy model

The cultivation and development of modern metropolitan areas with the aim of establishing new regional centers with competitive edge is a key objective for the new-type urbanization directions in China.The construction of the Lhasa Metropolitan Area is of great significance for the promotion of the South Asia Channel,the‘Belt and Road’initiative,the Bangladesh-China-India-Myanmar Economic Corridor,the Himalaya Economic Cooperation Zone,and for rapid development and long-term stability of the Qinghai-Tibet Plateau.This paper examines the scope of the Lhasa Metropolitan Area including Chengguanqu(Chengguan District),Doilungdeqen,Dagze,Lhunzhub,Damxung,Nyemo,Quxu,Maizhokunggar,Samzhubze Qu(Samzhubze District),Gyangze,Rinbung,Bainang,Nedong,Gonggar,and Zhanang using a spatial field energy model that combines nodality and accessibility indices and considers multiple indicators including traffic flow between cities.By combining factors such as the natural background,population agglomeration,the social economy,infrastructure construction,and the urban spatial structure of the Lhasa Metropolitan Area,it is proposed to build a bow-and-arrow-shaped urban system with‘one core,two centers,one axis,and two wings’along the valleys and the transportation trunk lines of the area.The study advocates the construction of a pure land industrial system comprising a green cultural and tourism-oriented plateau.

Energy-Efficient UAVs Coverage Path Planning Approach

Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

Impacts of gravitational-wave standard siren observations from Einstein Telescope and Cosmic Explorer on weighing neutrinos in interacting dark energy models

Multi-messenger gravitational wave(GW)observation for binary neutron star merger events could provide a rather useful tool to explore the evolution of the Universe.In particular,for the third-generation GW detectors,i.e.the Einstein Telescope(ET)and the Cosmic Explorer(CE),proposed to be built in Europe and the U.S.,respectively,lots of GW standard sirens with known redshifts could be obtained,which would exert great impacts on the cosmological parameter estimation.The total neutrino mass could be measured by cosmological observations,but such a measurement is model-dependent and currently only gives an upper limit.In this work,we wish to investigate whether the GW standard sirens observed by ET and CE could help improve the constraint on the neutrino mass,in particular in the interacting dark energy(IDE)models.We find that the GW standard siren observations from ET and CE can only slightly improve the constraint on the neutrino mass in the IDE models,compared to the current limit.The improvements in the IDE models are weaker than those in the standard cosmological model.Although the limit on neutrino mass can only be slightly updated,the constraints on other cosmological parameters can be significantly improved by using the GW observations.

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.Building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in EnergyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.

Simulating dispatchable grid services provided by flexible building loads:State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.

Thermogravimetric analysis of bamboo-tar under different heating rates based on distributed activation energy model

Carbon fiber is a kind of new polymer material with excellent mechanical properties and being applied widely.The process of carbon fiber prepared by bamboo tar,including extraction,condensation,spinning,oxidation and carbonation,is influenced by the pyrolysis kinetics significantly.In this paper,the thermogravimetric analysis(TGA)of bamboo tar produced in the process of pyrolysis and gasification of the bamboo which is known as Phylostachys sulphurea,was analyzed by the distributed activation energy model(DAEM)to understand the kinetic properties and parameters of bamboo tar.The thermogravimetric analysis of bamboo tar which is used as the raw material of carbon fiber was conducted under 5 different heating rates(i.e.5,10,15,30 and 50℃/min,etc.)in nitrogen atmosphere.The results show that the activation energy of bamboo tar and the exponential factor increased significantly with the increase of the heating rate,and the low heating rate is advantageous to the extraction of bamboo tar solvent and the thermal polycondensation,which can provide scientific reference for the optimization of carbon fiber technology.The thermal weight results show that the temperature range of bamboo tar being decomposed rapidly is 213℃-410℃.The ranges of the activation energy were calculated by DAEM,which have small difference in comparisons with five heating rates when the conversion rate is at 0.1-0.6 and the average value of the activation energy is 119 kJ/mol.The stability range of the activation energy is enlarged when the conversion rate is greater than 0.6 and heating rate increases.