Simple Energy Research Articles

Building Occupant Energy Labels (OEL): Capturing the Human Factors in Buildings for Energy Efficiency

Occupancy is one of the primary contributors to the energy performance gap, defined as the difference between actual and predicted energy usage, in buildings. This paper limits its scope to residential buildings, where occupant-centric consumption often goes unaccounted for in standard energy metrics. This paper starts from the hypothesis that a simple occupant energy efficiency label is needed to capture the essence of occupant behaviour. Such a label would help researchers and practitioners study a wide range of behavioural patterns and may better frame occupant interventions, potentially contributing more than expected to the field. Focusing on the residential sector, this research recognises that the complexity of occupant behaviour and its links to different scientific calculations requires that researchers deal with several intricate factors in their building performance assessments. Moreover, complexity arising from changing attitudes and behaviours—based on building typology, social environment, seasonal effects, and personal comfort levels—further complicates the challenge. Starting with these problems, this paper proposes a framework for an occupant energy labelling (OEL) model to overcome these issues. The contribution of the paper is twofold. Firstly, the literature is reviewed in depth to reveal current research related to occupant behaviour for labelling of humans based on their energy consumption. Secondly, a case study with energy simulations is implemented in the UK, using the CREST tool, to demonstrate the feasibility and potential of OEL. The results show that labelling occupants may help societies reduce building energy consumption by combining insights from energy statistics, surveys, and bills gathered with less effort, and can assist decision-makers in determining the best match between buildings and occupants. While the focus of this study is on residential buildings, future research is recommended to explore the applicability of OEL in office environments, where occupant behaviour and energy dynamics may differ significantly.

Phase-resolved Hubble Space Telescope WFC3 Spectroscopy of the Weakly Irradiated Brown Dwarf GD 1400 and Energy Redistribution–Irradiation Trends in Six White Dwarf–Brown Dwarf Binaries

Abstract Irradiated brown dwarfs offer a unique opportunity to bridge the gap between stellar and planetary atmospheres. We present high-quality Hubble Space Telescope/Wide Field Camera 3/G141 phase-resolved spectra of the white dwarf–brown dwarf binary GD 1400, covering more than one full rotation of the brown dwarf. Accounting for brightness variations caused by ZZ Ceti pulsations, we revealed weak (∼1%) phase-curve amplitude modulations originating from the brown dwarf. Subband light-curve exploration in various bands showed no significant wavelength dependence on amplitude or phase shift. Extracted day- and nightside spectra indicated chemically similar hemispheres, with slightly higher dayside temperatures, suggesting efficient heat redistribution or the dominance of radiative escape over atmospheric circulation. A simple radiative and energy redistribution model reproduced the observed temperatures well. Cloud-inclusive models fit the day and night spectra better than cloudless models, indicating global cloud coverage. We also begin qualitatively exploring atmospheric trends across six irradiated brown dwarfs, from the now complete “Dancing with the Dwarfs” white dwarf–brown dwarf sample. The trend we find in the dayside/nightside temperature and irradiation levels is consistent with efficient heat redistribution for irradiation levels less than ∼109 erg s−1 cm−2 and decreasing efficiency above that level.

A time-varying index for agricultural suitability across Europe from 1500–2000

Throughout the last centuries, European climate changed substantially, which affected the potential to plant and grow crops. These changes happened not just over time but also had a spatial dimension. Yet, despite large climatic fluctuations, quantitative historical studies typically rely on static measures for agricultural suitability due to the non-availability of time-varying indices. Relying on recent advances in paleoclimatology, we bridge this gap by constructing a spatio-temporal measure for agricultural suitability across Europe for a period of 500 years. Our gridded index has a 0.5° resolution and is available at a yearly level. It relies on a simple surface energy and water balance model, focusing only on so-called exogenous geographic and climatic features. Our index captures not just long-term trends, such as the Little Ice Age, but also short-term climatic shocks. It will empower researchers to explore the interplay between climatic fluctuations and Europe’s agricultural landscape, analyze human responses at a local and regional scale, and foster a deeper understanding of the region’s historical dynamics.

Heat transfer software for local materials in Cameroon (HTSLM 1.0): simplified building energy software based on local realities

We report in this work the overall design of a software application for calculating the indoor temperature in a room of a building constructed from local materials in Cameroon based on heat transfer modes. This software is intended to be simplified and at a lower cost for the user. We dubbed it LOBATIN/HTSLM 1.0 for the first release. We describe the physical modeling background based on the stationary aspect of the transfer of heat flow via conduction, convection and radiation from the outside to the inside of the supposedly uninhabited building. We present how we manage Cameroon’s meteorological data to implement them in LOBATIN/HTSLM 1.0 and we compare our results with pre-existing software such as EnergyPlus. The curves obtained for hourly indoor temperatures for the chosen days in the five climatic zones defined for the software, all show a realistic bell-shaped evolution, thus prefiguring a digital decision support tool for such admitted infrastructures to be made of materials from the Local Materials Promotion Authority of Cameroon.

Predicting burst events in a forced two-dimensional flow: a wavelet-based analysis

Predicting and perhaps mitigating against rare, extreme events in fluid flows is an important challenge. Due to the time-localised nature of these events, Fourier-based methods prove inefficient in capturing them. Instead, this paper uses wavelet-based methods to understand the underlying patterns in a forced flow over a 2-torus which has intermittent high-energy burst events interrupting an ambient low-energy ‘quiet’ flow. Two wavelet-based methods are examined to predict burst events: (i) a wavelet proper orthogonal decomposition (WPOD) based method which uncovers and utilises the key flow patterns seen in the quiet regions and the bursting episodes; and (ii) a wavelet resolvent analysis (WRA) based method that relies on the forcing structures which amplify the underlying flow patterns. These methods are compared with a straightforward energy tracking approach which acts as a benchmark. Both the wavelet-based approaches succeed in producing better predictions than a simple energy criterion, i.e. earlier prediction times and/or fewer false positives and the WRA-based technique always performs better than WPOD. However, the improvement of WRA over WPOD is not as substantial as anticipated. We conjecture that this is because the mechanism for the bursts in the flow studied is found to be largely modal, associated with the unstable eigenfunction of the Navier–Stokes operator linearised around the mean flow. The WRA approach should deliver much better improvement over the WPOD approach for generically non-modal bursting mechanisms where there is a lag between the imposed forcing and the final response pattern.

Dynamic and Immersive Framework for Drone Delivery Services in Skyway Networks

We propose a novel dynamic and immersive 3D framework designed to facilitate the setup and customization of drone scheduling algorithms for evaluating service-based drone delivery systems. This framework features a robust system architecture that supports user-defined behavior logic . It also incorporates real-time data communication protocols for relaying timely instructions to the drones. Additionally, it integrates a comprehensive drone energy consumption model that accurately simulates the physics of drone operations and accounts for both internal and external factors affecting energy usage. The framework includes a sophisticated 3D visualization component, depicting drone deliveries from source to destination through a realistic skyway network within an interactive virtual urban environment. It also enables automated data tracking , which is crucial for testing algorithms and collecting data to support data-driven decisions and optimizations. We evaluate the framework by conducting a comprehensive usability test to assess its user interface and overall user experience. Additionally, we test the framework using a drone swarm to execute delivery requests under both simple and complex energy consumption models. The results show that the framework has a user-friendly interface and effectively supports drone delivery simulations under the complex physics-based energy consumption model.

A simplified strain energy density approach for multiaxial fatigue predictions

A simplified strain energy density approach for multiaxial fatigue predictions

“Electrohydrodynamic Drying: A Comprehensive Review of Design Considerations and Quality Effects on Dried Fruits and Vegetables”

ABSTRACTElectrohydrodynamic (EHD) drying, an emerging non‐thermal technology, gains favor for its simple construction, energy efficiency, and superior dried product quality. Operating at ambient temperature, it preserves a product's original characteristics. This study delves into EHD drying's mechanism, electrode selection, and design considerations, emphasizing its impact on vitamins, color, and bioactive compounds in fruits and vegetables. A comparative analysis with traditional and innovative drying methods is included, exploring combined and EHD‐assisted techniques for improved organoleptic and nutritional qualities. While, currently it is limited to lab‐scale, scaling up EHD drying holds promise for ensuring nutritional security, with considerations on various limiting factors and future potential.

Necrophagy in cave environments: ecological pressure due to food scarcity? A case study of necrophagy by a harvestman Discocyrtanus canjinjim Carvalho & Kury, 2017 (Arachnida: Opiliones) preying on an Eidmanacris sp. (Orthoptera: Phalangopsidae) carcass

Cave environments present stable abiotic conditions, including permanent darkness, high humidity, and mild temperatures, while biotic factors reflect simplified ecological networks and energy constraints. Cave invertebrates, primarily detritivores and generalists, demonstrate specific adaptations to these conditions. Predation and necrophagy are critical behaviors shaped by the cave's scarcity of food resources. In this study, we report a rare necrophagy event involving Discocyrtanus canjinjim Carvalho & Kury, 2017 (Arachnida: Opiliones) feeding on a deceased cricket (Eidmanacris sp.) (Orthoptera: Phalangopsidae) in the Ponte de Pedra I Cave, Brazil. This limestone cave features large entrances and supports an oligotrophic system. The event occurred in an aphotic zone, where the harvestman displayed no flee behavior despite external stimuli. The presence of other predatory arachnids suggests potential intra-guild competition. Opiliones in the Cerrado and Caatinga exhibit opportunistic carnivory, consuming various prey taxa, including insects and vertebrates. Necrophagy, intra-guild predation, and cannibalism are behaviors reported in subterranean populations due to limited food availability. Climate change and anthropogenic pressures, such as deforestation and mining, threaten the stability of cave environments. Observations like this contribute to understanding the ecological dynamics within caves, highlighting the importance of preserving these fragile ecosystems.

Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

Reduced theory of symmetric and antisymmetric exchange interactions in nanowires

We investigate the behavior of minimizers of perturbed Dirichlet energies supported on a wire generated by a regular simple curve $\gamma$ and defined in the space of $\mathbb{S}^2$-valued functions. The perturbation $K$ is represented by a matrix-valued function defined on $\mathbb{S}^2$ with values in $\RR^{3 \times 3}$. Under natural regularity conditions on $K$, we show that the family of perturbed Dirichlet energies converges, in the sense of $\Gamma$-convergence, to a simplified energy functional on $\gamma$. The reduced energy unveils how part of the antisymmetric exchange interactions contribute to an anisotropic term whose specific shape depends on the curvature of $\gamma$. We also discuss the significant implications of our results for studies of ferromagnetic nanowires when Dzyaloshinskii-Moriya interaction (DMI) is present.

Prediction of the Temperature Field Development in Large Heat Storage Tanks for Integration in Network Simulation

Integrating an increasing amount of renewable energy into district heating systems requires thermal energy storages. For the planning and operation of the whole system valid and powerful storage models are essential for implementation in e. g. thermo-hydraulic network simulations and operational optimisation tools. The modelling of large tank thermal energy storages (TTES) up to 60,000 m³ can be insufficient by using a simple energy balance model as it does not provide information about the temperature profile. The detailed model called FreeTTES is capable of predicting the temperature distribution inside a large atmospheric TTES. This model simplifies fluid dynamic processes by assuming horizontal homogeneity and analyses them only in vertical direction. The analytical fluid mechanics approach is combined with numerical, measurement data-based approaches. The validation of the model is performed using DTS (distributed temperature sensing) data

The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

An acoustic nanogenerator based on porous PDMS/ZnO/PVDF-TrFE structure for self-powered intermediated-frequency sound monitoring

Abstract Sound energy is widely present in natural environment, nowadays predominantly collected using voluminous and heavy rigid devices, limiting their portability. Till now, there are few reports about using flexible materials to collect sound energy, and it is usually susceptible to environmental humidity. This study combines flexible polymer polydimethylsiloxane (PDMS), piezoelectric material polyvinylidene difluoride trifluoroethylene (PVDF-TrFE), nano zinc oxide (ZnO), and conductive carbon nanotubes (CNTs) to fabricate a porous nano ZnO/PVDF-TrFE/CNTs PDMS sound-driven nanogenerator (PZPCP SNG). It amalgamates frictional and piezoelectric effects: its porous structure enables efficient collection of vibration and frictional energy generated by sound waves; the piezoelectric effect of PVDF-TrFE and nano ZnO facilitates the conversion of acoustic mechanical energy, collectively enhancing the generator's output. Experimental optimization yields the best production conditions, achieving optimal outputs of 481.1 mV (open-circuit voltage), 209.13 nA (short-circuit current) under 400 Hz/125 dB sound stimuli, with a surface power density of 9.1 μW/m2 (volumetric power density of 2.28 mW/m3). And it can convert sound ranging from 63–3000 Hz. With hardware circuitry, up to 5 series-connected light-emitting diodes (LEDs) can be illuminated within the circuit, demonstrating a certain degree of sound recognition capability. The proposed PZPCP SNG design is simple, effective, and lightweight, enabling flexible and stable intermediated-frequency acoustic energy harvesting. Its hydrophobic structural characteristics render it adaptable to various humidity, presenting a new approach toward widespread low-power sound detection and self-powering applications.

Self-Oscillation of Liquid Crystal Elastomer Fiber-Slide System Driven by Self-Flickering Light Source.

Self-oscillation, a control approach inspired by biological systems, demonstrates an autonomous, continuous, and regular response to constant external environmental stimuli. Until now, most self-oscillation systems have relied on a static external environment that continuously supplies energy, while materials typically absorb ambient energy only intermittently. In this article, we propose an innovative self-oscillation of liquid crystal elastomer (LCE) fiber-slide system driven by a self-flickering light source, which can efficiently regulate the energy input in sync with the self-oscillating behavior under constant voltage. This system primarily consists of a photo-responsive LCE fiber, a slider that includes a conductive segment and an insulating segment, a light source, and a conductive track. Using the dynamic LCE model, we derive the governing equation for the motion of the LCE fiber-slider system. Numerical simulations show that the LCE fiber-slide system under constant voltage exhibits two distinct motion phases, namely the stationary phase and the self-oscillation phase. The self-oscillation occurs due to the photo-induced contraction of the LCE fiber when the light source is activated. We also investigate the critical conditions required to initiate self-oscillation, and examine key system parameters influencing its frequency and amplitude. Unlike the continuous energy release from the static environmental field in most self-oscillation systems, our LCE fiber-slide self-oscillation system is driven by a self-flickering light source, which dynamically adjusts the energy input under a constant voltage to synchronize with the self-oscillating behavior. Our design features advantages such as spontaneous periodic lighting, a simple structure, energy efficiency, and ease of operation. It shows significant promise for dynamic circuit systems, monitoring devices, and optical applications.

Combustion versus Gasification in Power- and Biomass-to-X Processes: An Exergetic Analysis.

Residual biomass is a promising carbon feedstock for the production of electricity-based organic chemicals and fuels since, unlike carbon dioxide captured from point sources or air, it also has a valuable energy input. Biomass can be converted into an intermediate stream suitable for Power-to-X processes mainly via combustion or gasification. Such combined processes are generally called biohybrid or Power- and Biomass-to-X processes. To investigate the potential of biomass utilization in Power- and Biomass-to-X processes and identify inherent efficiency differences between these pathways, we model the process units with simple mass and energy balances considering empirical parameters for the key process units and perform an exergetic analysis. The analysis is conducted for several molecules of interest for the chemical and transport sectors with different C:H:O ratios, i.e., methane, methanol, dimethyl ether, and dodecane. For all considered products, the Power- and Biomass-to-X processes with biomass gasification, either with pure oxygen or steam as oxidizing agents, have a significantly higher (∼15-20 percentage points) exergy efficiency. This difference is mainly due to the lower exergy loss for water electrolysis since a lower amount of hydrogen is needed and to the higher exergy efficiency of the gasification unit compared to that of the combustion unit. Therefore, gasification-based Power- and Biomass-to-X processes have clear thermodynamic advantages in the ideal case. These conclusions obtained with the simple models are confirmed by modeling a Power- and Biomass-to-Methanol process in detail, also accounting for practical factors such as side reactions, incomplete reactant conversion, and ash formation.

Performance of a CO2-based mixture cycled transcritical pumped thermal energy storage system

The transcritical CO2 cycle has the characteristics of high power density, high stability and high thermal energy transfer efficiency. It has been employed in pumped thermal energy storage system and ice slurry often serves as the cold storage material. However, ice slurry is not an optimal choice for cold storage due to its huge volume and high cost. In this study, an innovative transcritical pumped thermal energy storage system cycled with CO2-based mixtures is proposed. Temperature of the CO2-based mixture changes during the phase transition process, which means that simple two-tank cold energy storage arrangement with fluid heat materials is feasible to achieve the phase change of working fluid. R32, R161, propane, butane and pentane are screened to mix with CO2, respectively. Multi-parametric analysis of proposed system is conducted to assess its technical and economic performance. The Monte-Carlo method is employed to investigate the system uncertainty. Results display that for a 10 MW/8h scale the system cycled with CO2/R32 has the minimum levelized cost of storage of 0.1655 $/kWh, with a round trip efficiency of 65.51 %. The optimal levelized cost of storage is 0.1678 $/kWh, 0.1801 $/kWh, 0.1781 $/kWh and 0.1801 $/kWh for R161, propane, butane, and pentane, in sequence. The system investment cost decreases as the operating durations increase. The mean levelized cost of electricity and energy capacity cost are separately reduced by 29.35 % and 48.24 % as the installed capacity is extended from 10 MW to 10000 MW.

Analyzing the Total Attractive Force and Hydrogen Storage on Two-Dimensional MoP2 at Different Temperatures Using a First-Principles Molecular Dynamics Approach.

We performed first-principle molecular dynamics (FPMD) calculations to test the total attraction force on a physisorbed molecule at a given temperature and ambient pressure and applied it to the hydrogen storage on the 2D material MoP2. We considered a pristine material and one with 12.5% of Mo vacancies. By optimization, we calculated a gravimetric capacity for pristine MoP2 of 5.72%, with an adsorption energy of -0.13 eV/molecule. We found 6.02% and -0.14 eV/molecule for the defective surface. Next, we applied our approach to determine if the molecular hydrogen physisorption obtained by simple energy optimization exists for a given temperature and ambient pressure. We used this approach to determine the number of molecules adsorbed on the surface at a given temperature. Thus, we conducted a FPMD calculation at temperature T1, using optimization as the initial system configuration. Subsequently, we performed a second FPMD calculation at a temperature T2 (with T2 << T1), using the steady configuration of the first FPMD calculation as the initial configuration. We identified as adsorbed molecules at temperature T1, only those forced back toward the surface at temperature T2 due to kinetic energy loss at the lower temperature. The defective surface gave the best gravimetric capacity, ranging from 5.27% at 300 K to 6.02% at 77 K. The latter met the requirement from the US-DOE, indicating the potential practical application of our research in hydrogen storage.

The Importance of Energy Management in Public University Campuses

Energy consumption has gained great attention in recent years, particularly due to the changes in the geopolitics situation and the difficulty in ensuring security in energy sources supply. Several national and international governments set new and more stringent targets to reduce fossil fuel consumption, also proposing specific short-term and mid-term actions. For instance, the Italian Government emanates the Italian National Plan to Reduce Natural Gas consumption in 2022, particularly focused on residential, commercial and public buildings. University of L’Aquila takes inspiration from this planning, to revise its energy management and promote technical actions and a communication campaign aimed at reducing energy consumption. The short-term actions are related to the HVAC plants management of its building: a) shortening the heating days and reducing the daily hours HVAC switching on; b) slight reduction of the indoor comfort temperature; c) reduction of lighting time and intensity. Moreover, virtuous behavior has been encouraged with a communication campaign addressed to employees and students. Results obtained in only one year are very exciting: the natural gas consumption has been reduced more than 20% in academic year 2022/2023, with respect to average values of previous years, with an estimated GHG emissions avoided close to 140 tCO2 for the specific faculty considered. These results are very positive in the GreenMetric perspective, and they boost the importance also of a quite simple but effective energy management strategy and diffusion of awareness on energy and environmental issues.

Smoldering Ignition and Transition to Flaming Combustion of Pine Needle Fuel Beds: Effects of Bulk Density and Heat Supply

The smoldering of pine needle fuel beds (PNBs) has been a common subject of research because of its importance in initiating the rekindling of forest floor fires. Experimental studies of the coupling effects of the bulk density and external heat supply on smoldering in PNBs have been scarce up to now. In this study, laboratory smoldering experiments were conducted to study the coupling effects of bulk density (30–55 kg m−3) and heat supply (ignition-off temperature Toff = 190 °C and 230 °C). Different ignition modes were observed under the same conditions, including non- ignition (NI), flaming ignition (FI), and the smoldering-to-flaming (StF) transition. The results in this study showed that the bulk density had distinct effects on different ignition modes: the increase in the bulk density facilitated the StF transition but impeded the FI. The coupling effects between the bulk density and heat supply became more intricate, especially at lower bulk densities and at a reduced heat supply. Additionally, a simple energy balance equation was established to explain the coupling effects of bulk density and heat supply on ignition behavior. The critical mass loss rate (MLR) for the StF transition ranged from 0.01 g s−1 to 0.03 g s−1, while the critical MLR for FI was 0.035 g s−1. The modified combustion efficiency (MCE) index for the StF transition decreased from approximately 79.6% to 70.1% as the density increased from 30 kg m−3 to 55 kg m−3. In contrast, the MCE for FI was approximately 90% across all the bulk densities. The StF transition delay time increased from 50 s at 30 kg m−3 to 1296 s at 55 kg m−3 when Toff = 230 °C. Further reduction in heat supply led to an increase in the delay time for the StF transition by diminishing the intensity of smoldering combustion. This work advances the fundamental understanding of how heat supply and bulk density impact smoldering ignition modes, ultimately aiding in the development of wildfire prevention strategies.