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Optimizing pollutant exposure, energy consumption, and thermal comfort in a house via deep reinforcement learning control
No full textEfficient indoor environment management is challenging due to the complex interdependent nature of pollutant levels, energy use, and thermal comfort. While data-driven and physics-based algorithms have been explored to optimize these conflicting objectives, their real-world integration is constrained by modeling, computational, and learning challenges. Reinforcement learning (RL) has gained attention as a potential substitute in such scenarios. This work developed a reward-based deep RL agent for optimizing pollutant exposure, energy consumption, and thermal comfort by controlling ventilation rates and set temperature of the heating, ventilation, and air conditioning (HVAC) unit. Two RL agents were developed: Agent 1 controlled only the ventilation rate, and Agent 2 controlled both the ventilation rate and the HVAC set temperature. A reward function with weighted combinations of energy (W1), pollutant exposure (W2), and thermal discomfort (W3) enabled specific control strategies with and without an HVAC filter. RL agents’ reliability under varying pollutant emission scenarios was evaluated against a physics-based dynamic optimization (DynOpt) strategy. Agent 1 achieved ∼26 %–∼133 % higher normalized exposure reduction (NER) than DynOpt at a W1/W2 ratio of 1/3. On the other hand, Agent 2 (W1/W2 = 1/10, and W3 = 1 or 10) dynamically adjusted the set temperature between 25 °C and 27oC, achieving NER values ∼17 %–∼373 % higher than Agent 1 across all reward combinations. The proposed algorithm can be deployed in the field through low-cost microcontrollers and monitors, presenting a potentially deployable solution for healthy indoor environments with minimal environmental impacts
Chemical composition of aerosols over the Arabian Sea based on global reanalyses data and on-board ship measurements
No full textThe knowledge of chemical composition of atmospheric aerosols is key to understand aerosol-cloud-climate interactions and surface water biogeochemistry in the oceanic regions. Despite of strong natural and anthropogenic sources in its upwind regions, the studies on aerosol composition have been very limited over the Arabian Sea. We have comprehensively analyzed the results from global models, ECMWF's CAMS and NASA's MERRA-2 reanalyses, in conjunction with our ship-based measurements. Both models captured the overall spatio-temporal variability in sulphate (SO42−) and sea salt (r = 0.76–0.86). However, there is large scatter in PM10 and dust variability and the concentrations are typically overestimated by MERRA-2 except during winter, but underestimated by CAMS. Despite of difference in magnitudes, these models successfully reproduced the key seasonal features e.g., winter-time maxima in sulphate (9.0 ± 6.5 μg m−3 in MERRA-2, 11.9 ± 6.2 μg m−3 in measurements). While sulphate enhancement is most pronounced along India's west coast, the monsoon-time sea salt spike is strongest near east coast of the Middle-East (>200 μg m−3) region. Trend analysis results from both models indicate a statistically significant increase in sulphate aerosols over the Arabian Sea during 2003–2022 period (0.4 μgm−3y−1 in winter). However, long-term trends in sea salt and dust are not consistent between the two models and underscore a need for further investigations. Insights into aerosol distribution and model performances from this study would aid in planning future expeditions and refining chemistry-climate models, thereby enhancing our understanding of biogeochemical processes in the Indian Ocean
Influence of Particle Size and Packing Density on Combustion of Compacted Nickel-Aluminum Powder Mixtures
No full textAn experimental study is conducted to investigate the effects of particle size and packing density on the combustion of nickel-aluminum pellets. The particle sizes of Al and Ni are varied in the range of 1�25 �m, and the packing density is varied between 63% and 76% of the theoretical maximum density (TMD). The pellets are burned in a quartz tube, and the combustion process is recorded using a high-speed video camera. Temperature evolution during combustion is recorded, and the composition of burned pellets is analyzed. Computer simulations of the pellet combustion process are also conducted using a multiscale model, in which the diffusion process in the particles is coupled to energy transport in the pellet. Two distinct propagation modes are observed�(i) a quasi-steady mode characterized by high combustion velocities and continuous front propagation and (ii) a relay-race mode characterized by low combustion velocities and discrete jumps of the combustion front. The combustion velocity increased with decreasing Ni particle size due to a reduction in the length scale of the atomic diffusion process. Increasing the Al particle size generally increased the combustion velocity due to increased permeability of the pellet and reduced oxide content in the Al powder. The combustion velocity decreased by a factor of 3�4 when the packing density increased from 63% to 76% TMD. Super-adiabatic flame temperatures are not observed and the product analysis revealed the presence of intermediates (such as Ni2Al3) and layered structures in the burned pellets for the relay-race propagation mode. The study suggests that the combustion rate of Ni-Al pellets is strongly dependent on the rate of atomic diffusion processes, the oxide content in the powders, and the ability of the Al melt to wet the surface of Ni particles and preheat the unburned regions of the pellet. � 2024 Elsevier B.V., All rights reserved
Bimetallic MOF-derived CuO-Co3O4 heterostructures as high-capacity electrodes for asymmetric supercapacitors
No full textMetal-organic frameworks (MOFs) offer unique opportunities for designing high-performance supercapacitor electrodes through controlled structural evolution. However, achieving optimal balance between conductivity, redox activity, and stability in MOF-derived oxides remains challenging. Here, we report a controlled room-temperature synthesis strategy for bimetallic Cu-Co MOFs with varying morphologies and tuneable Cu2+/Co2+ ratios, which, upon annealing, resulted in mixed-phase CuO-Co3O4 heterostructures with oxygen vacancy density directly correlated to cobalt content. The CuO-Co₃O₄ (1:1) hybrid exhibits exceptional specific capacitance (1564.4 F g−1 at 1 A g−1), outperforming its parent MOF (333.3 F g−1) and monometallic oxides by >300 %, attributable to synergistic Cu+/Cu2+ and Co2+/Co3+ redox couples and vacancy-enhanced ion diffusion. The electrochemical and structural characteristics were also verified by spin-polarised density functional theory (DFT) calculations. An asymmetric supercapacitor pairing of this hybrid with activated carbon achieves an extended 1.5 V window, delivering 48.7 Wh kg−1 energy density at a power density of 750 W kg−1 while retaining 91.2 % capacitance over 10,000 cycles – surpassing other reported MOF-derived oxides. The CuO-Co3O4 electrodes lead to a combined synergistic effect due to the modified electronic state, higher active sites and improved redox activity, producing high supercapacitive performances. Our work demonstrates how metal ratio tuning in bimetallic MOF precursors can engineer defect-rich oxides for durable high-energy storage
Accelerated parameter estimation of supermassive black hole binaries in LISA using a meshfree approximation
No full textThe Laser Interferometer Space Antenna (LISA) will be capable of detecting gravitational waves (GWs) in the milli-Hertz band. Among various sources, LISA will detect the coalescence of supermassive black hole binaries (SMBHBs). Accurate and rapid inference of parameters for such sources will be important for potential electromagnetic follow-up efforts. Rapid Bayesian inference with LISA includes additional complexities as compared to current generation terrestrial detectors in terms of time and frequency dependent antenna response functions. In this work, we extend a recently developed, computationally efficient technique that uses meshfree interpolation methods to accelerate Bayesian reconstruction of compact binaries. Originally developed for second-generation terrestrial detectors, this technique is now adapted for LISA parameter estimation. Using the full inspiral, merger, and ringdown waveform (PhenomD) and assuming rigid adiabatic antenna response function, we show faithful inference of SMBHB parameters from GW signals embedded in stationary, Gaussian instrumental noise. We discuss the computational cost and performance of the meshfree approximation method in estimating the GW source parameters
Designing Resilient Multipurpose Reservoir Operation Policies in Presence of Internal Climate Variability
No full textAdaptation planning for water resource systems is fraught with significant challenges, arising from uncertainties associated with diverse climate change scenarios, varying model structures, and Internal Climate Variability (ICV), often captured through multiple initial condition runs. ICV, typically considered irreducible, has received significant attention for state and derived hydrological variables. However, its implications and role in regional decision-making remain elusive. Here, we develop an integrated framework to incorporate uncertainties through hydrological modeling combined with a suite of multi-objective stochastic optimization techniques. This approach is applied to design optimal operating policies for the Sardar Sarovar Dam in Gujarat, India, a multipurpose infrastructure of national importance to meet flood control, hydroelectric generation and domestic, industrial, and irrigation water demands while accounting for two future climate change scenarios, SSP245 and SSP585, with 49 different initializations of each scenario to represent the ICV. We employ Sampling Stochastic Dynamic Programming to incorporate ICV by considering multiple initializations simultaneously, in contrast to Stochastic Dynamic Programming, which evaluates realizations individually. We show that despite the wide range of uncertainties, optimal operating policies can be designed to meet the various demands with reliability of 100%, 59%, and 27% for domestic, irrigation, and industrial water demand, respectively, when all scenarios are considered simultaneously. Our study advocates for the systematic inclusion of a wide array of climate model outputs, emphasizing that such integration is essential not only for crafting robust operating policies, but also for the reliability assessment of current operating policies in light of changing climate and demand scenarios
Natural kaolin-derived ruthenium-supported nanoporous geopolymer: a sustainable catalyst for CO2 methanation
No full textTo address the serious concern of excessive CO2 emissions, the conversion of environmental CO2 into methane via a CO2 methanation reaction is promising. Methane can be used not only as a fuel but also as a hydrogen carrier. In this study, a geopolymer synthesized using natural kaolin (GNK) is explored as a support. This geopolymer support was used to disperse ruthenium (Ru) nanoparticles through a single-step hydrazine reduction method. The catalyst was characterized using various surface and bulk techniques. Furthermore, the catalytic performance of the ruthenium-supported geopolymer (Ru/GNK) for the CO2 methanation process was explored with different Ru loadings (%) and at different flow rates. Catalyst stability was also investigated for 20 h by a time-on-stream isothermal experiment. The spent catalyst was characterized by O2-temperature programmed oxidation (O2-TPO) and X-ray photoelectron spectroscopy (XPS). Overall, the catalyst proved to be cost-effective and free from pretreatment requirements, in addition to exhibiting superior activity, high selectivity, and good stability
DTQ-16T: Double Node Upset Tolerant Quadruple SRAM for Space Applications
No full textThe high-energy particles in space cause SRAM failures. The vulnerability of SRAM increases at lower technology, and it flips the SRAM cell's data due to single-event multi-node-upset. Various state-of-the-art radiation hardened by design SRAMs have been proposed; however, most designs tackle Single Node Upset (SNU). This paper presents DNU Tolerant Quadruple-16T (DTQ-16T) SRAM with no read disturb. The most important feature of the proposed design is its immunity towards radiation, where it recovers from all possible upsets, whether SNU, DNU, Triple Node Upset (TNU), or Quadruple Node Upset (QNU) for storage '1'. On top of it, the proposed design gives very high read stability, Write Access Time, and Wordline Write Trip Voltage (WWTV) than most of the existing radiation-hardened SRAMs. Finally, the post-layout and Monte Carlo simulations validate the efficiency of the proposed SRAM in commercial CMOS 28nm technology
Star formation efficiency and scaling relations in parsec-scale cluster-forming clumps
No full textNumerical simulations predict that clumps (∼1 pc) should form stars at high efficiency to produce bound star clusters. We conducted a statistical study of 17 nearby cluster-forming clumps to examine the star formation rate and gas mass surface density relations (i.e. ΣSFR versus Σgas) at the clump scale. Using near-infrared point sources and Herschel dust continuum analysis, we obtained the radius, age, and stellar mass for most clusters in the ranges 0.5-1.6 pc, 0.5-1.5 Myr, 40-500 M⊙, respectively, and also found that they are associated with Σgas values ranging from 80 to 600 M⊙ pc−2. We obtained the best-fitting scaling relations as ΣSFR ∝ Σgas1.46 and ΣSFR ∝ (Σgas/tff)0.80 for the studied sample of clumps. Comparing our results with existing scaling relations at cloud and extragalactic scales, we found that while the power-law exponent obtained in this work is similar to those found at these scales, the star formation rate surface densities are relatively higher for similar gas mass surface densities. From this work, we obtained instantaneous median star formation efficiency (SFE) and efficiency per free-fall time (ϵff) of ∼20 per cent and ∼13 per cent, respectively, for the studied clumps. We discuss the cause of the obtained high SFE and ϵff in the studied clumps and also discuss the results in the context of bound cluster formation within molecular clouds. We conclude that our results do not favour a universal scaling law with a constant value of ϵff in star-forming systems across different scales
