Temperature Trajectory Research Articles

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products.

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in dairy products was undertaken in order to identify and appraise the relative effectiveness of control measures and intervention strategies implemented at primary production, processing, retail, and consumer practices. A systematic literature search retrieved 18 QRA models, most of them (9) investigated raw and pasteurized milk cheeses, with the majority covering long supply chains (4 farm-to-table and 3 processing-to-table scopes). On-farm contamination sources, either from shedding animals or from the broad environment, have been demonstrated by different QRA models to impact the risk of listeriosis, in particular for raw milk cheeses. Through scenarios and sensitivity analysis, QRA models demonstrated the importance of the modeled growth rate and lag phase duration and showed that the risk contribution of consumers' practices is greater than in retail conditions. Storage temperature was proven to be more determinant of the final risk than storage time. Despite the pathogen's known ability to reside in damp spots or niches, re-contamination and/or cross-contamination were modeled in only two QRA studies. Future QRA models in dairy products should entail the full farm-to-table scope, should represent cross-contamination and the use of novel technologies, and should estimate L. monocytogenes growth more accurately by means of better-informed kinetic parameters and realistic time-temperature trajectories.

Simulation Study on the Influence of Different Molten Steel Temperatures on Inclusion Distribution under Dual-Channel Induction-Heating Conditions.

Impurity elimination in tundishes is an essential metallurgical function in continuous casting. If inclusions in a tundish cannot be effectively removed, their presence will have a serious impact on the quality of the bloom. As a result, this research investigates the locations of inclusion particles in a six-strand induction-heating tundish in depth, combining the flow, temperature, and inclusion trajectories of molten steel under electromagnetic fields. The results show that a pinch effect occurred in the induction-heating tundish, and a rotating magnetic field formed in the channel, with a maximum value of 0.158 T. The electromagnetic force was directed toward the center of the axis, and its numerical distribution corresponds to the magnetic flux density distribution, with a maximum value of 2.11 × 105 N/m3. The inclusion particles' movement speed accelerated as the molten steel's temperature rose, and their distribution in the channel was identical to the rotating flow field distribution. When the steel's temperature rose from 1750 K to 1850 K, the removal percentage of inclusion particles in the discharge chamber rose by 9.20%, the removal rate at the outlet decreased from 8.00% to 3.00%, and the adhesion percentage of inclusion particles in the channel decreased from 48.40% to 44.40%.

A tutorial on the Bayesian statistical approach to inverse problems

Inverse problems are ubiquitous in science and engineering. Two categories of inverse problems concerning a physical system are (1) estimate parameters in a model of the system from observed input–output pairs and (2) given a model of the system, reconstruct the input to it that caused some observed output. Applied inverse problems are challenging because a solution may (i) not exist, (ii) not be unique, or (iii) be sensitive to measurement noise contaminating the data. Bayesian statistical inversion (BSI) is an approach to tackle ill-posed and/or ill-conditioned inverse problems. Advantageously, BSI provides a “solution” that (i) quantifies uncertainty by assigning a probability to each possible value of the unknown parameter/input and (ii) incorporates prior information and beliefs about the parameter/input. Herein, we provide a tutorial of BSI for inverse problems by way of illustrative examples dealing with heat transfer from ambient air to a cold lime fruit. First, we use BSI to infer a parameter in a dynamic model of the lime temperature from measurements of the lime temperature over time. Second, we use BSI to reconstruct the initial condition of the lime from a measurement of its temperature later in time. We demonstrate the incorporation of prior information, visualize the posterior distributions of the parameter/initial condition, and show posterior samples of lime temperature trajectories from the model. Our Tutorial aims to reach a wide range of scientists and engineers.

Adaptive look-ahead model predictive control strategy of vehicular PEMFC thermal management

This study presents an improved model predictive control (MPC) strategy for the thermal management of vehicular fuel cells, with the incorporation of model adaptation, the utilization of look-ahead information, and temperature trajectory planning. To describe the thermal dynamics of the fuel cell cooling system, a three-volume model of a 60 kW fuel cell system is proposed and validated with dynamic load experiments. Additionally, a fuel cell temperature sensitivity experiment is conducted to identify a comfort temperature reference. The predictive models developed to cover the entire range of power levels are based on the integration of look-ahead power information and weigh adjustment of actuators. Results from the analysis of look-ahead times suggest that a 4-s preview is suitable for mitigating fuel cell dry and flooding risks. Dynamic control results demonstrate a 22%–60% reduction in temperature tracking error compared to MPC-H, MPC-M, MPC-L and MPID, while energy-saving abilities are increased by 50% relative to MPID, contributing to improve the state balance and economy for vehicular fuel cells.

Modeling Improved Li-Ion Cell Capacity through Suppression of Temperature with Internal Electrolyte Flow

Lithium-ion cells are ubiquitous in portable electronic applications and are growing in popularity for electric vehicles including passenger cars. Yet, a dramatic reduction in global emissions to combat climate change calls for significantly broader adoption of carbon neutral technologies in electric vehicles and in stationary energy storage to complement variable renewable sources and growing electricity demand.1 For lithium-ion batteries, a critical barrier to such broader adoption is the inability to sustain high rate while also accessing the full capacity of cells. In addition to mass transport effects, overheating commonly limits the duration of high-rate discharge or charge due to excessive heat generation and inadequate heat removal.Here, we provide a brief illustrative tutorial of these coupled effects in a pseudo two-dimensional electrochemical model and build on our previous modeling and dimensional analyses2-4 of how internal electrolyte flow across Li-ion cells reduces both mass and thermal transport limitations.First, we describe the intentional and sudden onset of internal electrolyte convection to curb or to avert further temperature rise in a Li-ion cell during high-rate discharge. We examine two modes for introducing electrolyte flow: temperature-triggered (“reactive”) and discharge-rate triggered (“proactive”) modes. In the reactive case, flow is introduced in the simulation when a cell under high-rate discharge reaches a high safety cutoff temperature. In the proactive case, flow begins as the discharge rate is step changed from midrate to high rate. A practical application of proactive mode is that a battery thermal management system could be programmed to initiate electrolyte flow in anticipation of greater heat generation at a higher rate.Next, using the aforementioned continuum modeling with circulation through an external tank, we compare and contrast Li-ion cell thermal responses to the onset of flow in reactive mode versus in proactive mode. In both cases, the threshold flow rate (~μm/s) to suppress temperature rise agrees in order of magnitude with dimensionless group estimates reported in our previous work.4 Namely, temperature rise is suppressed when the heat generation rate becomes lower than the heat removal rate. At the highest electrolyte flow rates simulated (>>μm /s), the heat removal by internal electrolyte flow dominates, and the cell achieves nearly full discharge capacity without prematurely crossing temperature or voltage cutoffs. For low flow rates (<μm /s), the proactive and reactive cases show quantitatively different transient temperature trajectories due to the temperature dependences of heat generation and removal. Heat generation rate decreases with elevated cell temperature due to facile mass transfer and resultant concentration uniformity, optimal electrolyte conductivity, and ease of charge transfer kinetics. Also, heat removal using internal electrolyte flow becomes more effective with high cell temperature relative to ambient inlet temperature. Hence, the flow rate needed to cool a cell that has reached a high safety cutoff temperature (in reactive mode) is lower than the flow rate required to curb temperature rise in a cell just above ambient temperature (in proactive mode) as high-rate discharge starts.

What is the best way to keep the patient warm during technical rescue? Results from two prospective randomised controlled studies with healthy volunteers

BackgroundAccidental hypothermia is a manifest problem during the rescue of entrapped victims and results in different subsequent problems as coagulopathy and wound infection. Different warming methods are available for the preclinicial use. However, their effectiveness has hardly been evaluated.MethodsIn a first step a survey among German fire brigades was performed with questions about the most used warming methods. In a second step two crossover studies were conducted. In each study two different warming method were compared with forced air warming – which is the most frequently used and highly effective warming method in operation rooms (Study A: halogen floodlight vs. forced air warming; Study B: forced air warming vs. fleece blanket). In both studies healthy volunteers (Study A: 30 volunteers, Study B: 32 volunteers) were sitting 60 min in a cold store. In the first 21 min there was no subject warming. Afterwards the different warming methods were initiated. Every 3 min parameters like skin temperature, core body temperature and cold perception on a 10-point numeric rating scale were recorded. Linear mixed models were fitted for each parameter to check for differences in temperature trajectories and cold perception with regard to the different warming methods.ResultsOne hundred fifty-one German fire brigades responded to the survey. The most frequently used warming methods were different rescue blankets (gold/silver, wool) and work light (halogen floodlights). Both studies (A and B) showed significantly (p < 0.05) higher values in mean skin temperature, mean body temperature and total body heat for the forced air warming methods compared to halogen floodlight respectively fleece blanket shortly after warming initiation. In contrast, values for the cold perception were significantly lower (less unpleasant cold perception) during the phase the forced air warming methods were used, compared to the fleece blanket or the halogen floodlight was used.ConclusionForced air warming methods used under the standardised experimental setting are an effective method to keep patients warm during technical rescue. Halogen floodlight has an insufficient effect on the patient’s heat preservation. In healthy subjects, fleece blankets will stop heat loss but will not correct heat that has already been lost.Trial registrationThe studies were registered retrospectively on 14/02/2022 on the German Clinical Trials registry (DRKS) with the number DRKS00028079.

Model predictive temperature control of a closed-loop spray cooling system

A dynamic model of a closed-loop spray cooling system was proposed, and a model predictive temperature control algorithm was presented. The presented algorithm could quantitively estimate sudden changes in heat load, and then plan an optimized control path along which the temperature trajectory was numerically predicted to be the most stable. Then, the presented algorithm was integrated into a closed-loop spray cooling system, and its control performance was numerically studied. The results showed that, following a 20 W downward step change in heat load, the temperature was re-stabilized in 4 s, with its oscillation less than 0.14 K. Under identical conditions, the presented algorithm outperformed PID in terms of temperature stability, with its peak time shorter by 25–55%, settling time shorter by 38–51%, and average temperature offset smaller by 50–60%. This advantage was mainly because the presented algorithm compensated for hydraulic and thermal lags, two sources of control nonlinearity, by excessive regulation in advance. Unexpectedly, temperature overshoots of the presented algorithm and PID did not differ significantly, seemingly due to the algorithm-inherent estimation lag. From a system design perspective, the length of pump pipeline should be as short as possible, which proved to help reduce overshoot and settling time. Similarly, addition in heat load’s mass also helped reduce overshoot, but would result in an unfavorable long settling time, and vice versa. The simulation results were validated by experimental data, with error less than 10%.

Climate change impacts on eutrophication in the Po River (Italy): Temperature-mediated reduction in nitrogen export but no effect on phosphorus

Rivers worldwide are under stress from eutrophication and nitrate pollution, but the ecological consequences overlap with climate change, and the resulting interactions may be unexpected and still unexplored. The Po River basin (northern Italy) is one of the most agriculturally productive and densely populated areas in Europe. It remains unclear whether the climate change impacts on the thermal and hydrological regimes are already affecting nutrient dynamics and transport to coastal areas. The present work addresses the long-term trends (1992–2020) of nitrogen and phosphorus export by investigating both the annual magnitude and the seasonal patterns and their relationship with water temperature and discharge trajectories. Despite the constant diffuse and point sources in the basin, a marked decrease (-20%) in nitrogen export, mostly as nitrate, was recorded in the last decade compared to the 1990s, while no significant downward trend was observed for phosphorus. The water temperature of the Po River has warmed, with the most pronounced signals in summer (+0.13°C/year) and autumn (+0.16°C/year), together with the strongest increase in the number of warm days (+70%–80%). An extended seasonal window of warm temperatures and the persistence of low flow periods are likely to create favorable conditions for permanent nitrate removal via denitrification, resulting in a lower delivery of reactive nitrogen to the sea. The present results show that climate change-driven warming may enhance nitrogen processing by increasing respiratory river metabolism, thereby reducing export from spring to early autumn, when the risk of eutrophication in coastal zones is higher.

Optimal temperature trajectory for tubular reactor using physics informed neural networks

Numerical approaches to solve partial differential equations (PDEs) governing the physical systems are very time consuming, and it limits their applications in online decision making. These physical systems can be modeled using a Neural network, and a nonlinear mapping can be obtained by capturing the knowledge represented by the PDEs governing the physical system. Such a Physics Informed Neural Network (PINN) generates a solution in a closed analytical form that maps the inputs of the PDE to its solution. In this paper, we exploit the advantage of obtaining this closed analytical form of solution for substantially easing the step of inverting such models for optimization. We demonstrate this capability by obtaining the optimal spatial temperature trajectory of a representative tubular reactor model to maximize the reactor yields. The PINN model was seen to accurately capture the dynamics of the tubular reactor governed by second order PDEs with spatial variables and temperature parameters as inputs. The results of modeling and optimization were compared with the finite difference method (FDM) of solving PDEs and were found to be computationally superior to the latter. The computational time taken by the PINN framework to obtain the optimal trajectory was significantly less than the traditional FDM method without compromising accuracy. Such approaches can supersede traditional methods where the governing physical models are computationally expensive and facilitate online decision making through the use of PINNs.

Solid Boundary Output Feedback Control of the Stefan Problem: The Enthalpy Approach.

By taking enthalpy-an internal energy of a diffusion-type system-as the system state and expressing it in terms of the temperature profile and the phase-change interface position, the output feedback boundary control laws for a fundamentally nonlinear single-phase one-dimensional (1-D) PDE process model with moving boundaries, referred to as the Stefan problem, are developed. The control objective is tracking of the spatiotemporal temperature and temporal interface (solidification front) trajectory generated by the reference model. The external boundaries through which temperature sensing and heat flux actuation are performed are assumed to be solid. First, a full-state single-sided tracking feedback controller is presented. Then, an observer is proposed and proven to provide a stable full-state reconstruction. Finally, by combining a full-state controller with an observer, the output feedback trajectory tracking control laws are presented and the closed-loop convergence of the temperature and the interface errors proven for the single-sided and the two-sided Stefan problems. Simulation shows the exponential-like trajectory convergence attained by the implementable smooth bounded control signals.

A zoned group control of indoor temperature based on MPC for a space heating building

The traditional regulation of district heating (DH) system are mainly based on the central control in heat plants and/or in the substations. But the end-user control is becoming increasingly important in saving DH energy consumption. In view of this, a novel zoned group control method of indoor room temperature based on model predictive control (MPC) for a space heating building is proposed. Firstly, a room temperature simulation model based on IDA Indoor Climate and Energy (IDA-ICE) is developed for the experimental room, where the simulation result is validated by the measurements. But it is still difficult to use this model in the predictive control. Therefore, a resistor capacitor (RC) room temperature simulation model with parameters identified by IDA-ICE is proposed. A room temperature controller based on RC-MPC is developed and tested in the experimental room for model validation. Secondly, a DH building in Dalian, China is chosen for the case study. The IDA-ICE simulation results show that the temperature distribution in different rooms of the building is clearly uneven without end-user control. Corresponding targeted control strategies according to the temperature distribution should be used in different zones (cluster of rooms) to improve indoor thermal environment. Therefore, a zoned group control model based on RC-MPC for DH building room temperature is developed in MATLAB/Simulink, which adopts IDA-ICE for parameter identification in RC simulation model and utilizes RC model to predict the room temperature trajectory in the MPC controller. Results show that the proposed model can achieve active and dynamic room temperature control for different zones more precisely according to the changing heat load distribution. It is not necessary to define every room a group, instead, many rooms with similar conditions can be grouped in the same cluster, and temperature differences between rooms can be controlled within a reasonable range.

Integrated energy and thermal management strategy for extended range electric vehicle based on battery temperature and state-of-charge global planning

There is a coupling relationship between energy and thermal management of extended range electric vehicle (EREV), so developing an integrated energy and thermal management strategy (IETMS) is an effective approach to reduce fuel consumption and battery degradation and further improve vehicle driving economy. Global optimization of EREV energy and thermal management is solved by dynamic programming based on adaptive grid optimization (AGO-DP), which enhances optimization performance by narrowing the feasible domain of battery temperature and state-of-charge (SOC). Meanwhile, within the hierarchical model predictive control (MPC) framework, a novel IETMS based on battery temperature and SOC global planning is proposed in this study. According to the road and weather information, the battery temperature and SOC reference trajectories approximating to the global optimum are generated to guide real-time control. Then MPC is formulated to minimize total operating cost by coordinating APU output power and battery cooling power in the short-term prediction horizon. The simulation results show that the proposed IETMS comprehensively optimizes the vehicle economy from the aspects of tracking reference trajectory, reducing battery peak current and improving fuel efficiency. The total operating cost decreases by 1.77%–14.52% compared with other strategies. Moreover, the proposed IETMS also exhibits desirable versatility and robustness under different driving scenarios, achieving 86.60%—92.18% of the optimal performance.

The microbiome of the endosymbiotic Symbiodiniaceae in corals exposed to thermal stress

The coral reef crisis has influenced research for over two decades, during which time the capacity of corals to withstand and respond to environmental stress has been documented from the cellular to ecosystem level. Over the past decade, research is increasingly working towards uncovering the extent of coral–bacterial interactions, finding that diverse and stable microbial interactions can be indicative of the health of the coral host. However, we have yet to determine at which level of organismal organisation these interactions occur, in particular those with the coral’s photosynthetic dinoflagellate symbionts. This information is critical if we are to understand the impact of stress on meta-organism functioning. Using 16S gene amplicon sequencing, we investigated the bacterial microbiome of endosymbiotic Symbiodiniaceae from thermally stressed Acropora aspera, under 3 ecologically relevant temperature trajectories (defined as protective, repetitive and single) that are expected under a changing climate. We show that endosymbiotic Symbiodiniaceae host a distinct and diverse bacterial assemblage when compared with the A. aspera host. Alphaproteobacteria (mainly Rhodobacteraceae and Bradyrhizobiaceae), from the Rhizobiales order dominated the Symbiodiniaceae microbiome, while Gammaproteobacteria (mainly Endozoicomonadaceae) dominated the coral microbiome. The Symbiodiniaceae core microbiome also reflected the distinct microbiomes of the two partners, specifically, Rhizobiales were not present in the A. aspera core, while Endozoicomonadaceae were not present in the Symbiodiniaceae core. We show the Symbiodiniaceae-associated microbiome was highly responsive to increases in temperature, and the microbial consortium was significantly altered in the Symbiodiniaceae retained in the host exposed to different temperature. Most notably, Myxococcolaes were up to 25-fold higher relative abundance in dinoflagellate partner microbiomes under the single temperature trajectory, compared with the repetitive and control treatments. The distinct composition of bacteria associated with Symbiodiniaceae suggests a previously unrecognised, yet important functional role of these associations to overall coral health, which is increasingly important as reefs decline worldwide. Our study provides the first characterisation of Symbiodiniaceae-associated microbes from a coral host under a range of temperature trajectories occurring on the Great Barrier Reef.

Performance of octane index in LTGC engines from beyond MON to beyond RON

This work analyzes the inability of the Octane Index (OI) to characterize the autoignition resistance of gasoline-like fuels in a Low-Temperature Gasoline Combustion (LTGC) engine. Experimental studies in the Sandia LTGC engine at naturally aspirated ‘beyond-MON’ and high-boost ‘beyond-RON’ conditions showed a poor correlation between OI and autoignition reactivity. These experiments were successfully replicated in CHEMKIN, allowing detailed studies of the factors affecting the OI correlation. Systematic investigations were conducted to determine why the OI performs less well for LTGC combustion compared to end-gas autoignition (i.e. knock) in spark-ignition (SI) engines. A pseudo-MON condition that follows the pressure–temperature (P-T) trajectory of the real MON test was tested numerically, using 10 different fuels, leading to the conclusion that the OI correlation performs very well for an SI engine running at close-to-MON conditions. The effects of each difference between SI and LTGC combustion were then analyzed for four P-T trajectories: ‘beyond-MON’, ‘MON test’, ‘between RON-MON’, and ‘beyond-RON’ conditions. These differences included changes in: equivalence ratio, engine speed, and residence-time history along the P-T trajectory for piston-only compression, as used for LTGC, compared to compression by the piston plus flame propagation for SI combustion. For LTGC combustion, the OI correlation did not characterize the autoignition quality of gasoline-like fuels well for any P-T trajectory, but the factors causing this poor performance varied with conditions. For example, at naturally aspirated LTGC conditions (beyond-MON), changes in equivalence ratio were the primary cause, followed by differences in the P-T trajectory and changes in residence time. Whereas, for intake-boosted beyond-RON conditions, differences in the P-T trajectory had the largest effect, followed by changes in residence time. Finally, the parameter K that characterizes the OI correlation showed values that contradict the OI theory under realistic LTGC conditions, suggesting that K might be meaningless for LTGC engines.

Coupling effect of thermal conducting and low-temperature reaction process on ignition characteristics under diesel-like conditions

The typical behavior of misfiring after low-temperature ignition (LTI) in the cold-start of a diesel engine is mainly caused by thermo-kinetic imbalance and is related to the coupling effect of thermal conducting and low-temperature chemistry. This paper abstractly constructed a two-zone model to characterize such a diesel-like operating condition and investigated the coupling effect of thermal conducting and low-temperature chemistry on ignition characteristics mechanistically. The results show that at relatively low temperature/equivalence ratio environments, thermal conducting has a stronger effect on the ignition process and acts mainly on the chemical reaction rate rather than the reaction pathway. At lower temperature environments (600 K–700 K), the two zones' temperature trajectories almost overlap before LTI. At a higher temperature (700 K–800 K), thermal conducting and low-temperature reactions together affect the ignition process, causing the lower-temperature zone to earlier translate from LTI to high-temperature ignition (HTI), in contrast to the situation without thermal conducting. Thermal conducting induced by fuel-concentration gradient has an effect after the LTI of richer zones, which causes the HTI to delay, but for the leaner zone, it increases the low-temperature reaction rates sharply and advances the ignition. This study provides theoretical support to overcome the unstable ignition problem of cold-start.

Estimating mortality cost and social cost of CO2 emitted by items, applied to passenger vehicles

Over 60% of carbon emissions are from residential consumption. People in developed nations need to reduce their carbon footprints six-fold to stabilize CO2 levels in the atmosphere. This research develops a method of estimating and monetizing mortality costs of items. Mortality Cost of Carbon Rate (MCCR) is the fraction of a climate change related death that would probably occur over 80 yr with a specified temperature trajectory, from the emission of 1 tCO2e. MCCRs are allocated to items on an exponential curve generated from two researched MCCRs corresponding to temperature trajectories. MCC of an item (MCCI) is its MCCR multiplied by its life cycle CO2e emissions. The method was applied to the 2020 Australian passenger vehicle fleet and 6 vehicle types. MCCI was compared to mortalities from crashes and exhaust pollution. Total fleet mortality was estimated at around 62,000/ year. Mortality from 2020 CO2 emissions will probably be around 56,000 - 75 times higher than crash deaths - with uncertainty range 28,000 to 106,000, compared to toxic exhaust emissions 5,600 and crashes 750. A Sustainable Personal CO2 Footprint' of 1.5 t CO2/ person/ year was set as a benchmark for sustainable consumption. Electric buses, E-bicycles/ scooters and micro-EVs are sustainable and if universally adopted, would reduce mortality by 96%. Social Cost of Carbon Mortality Rate (SCCMR) monetizes MCCR using a global Value of a Statistical Life Year. SCCM of items (SCCMI) is calculated as for MCCI. Research estimates of MCCR and SCCMR vary according to assumed causes of mortality, temperature trajectories and discount rates; many are underestimated as they exclude some mortality damage sectors. Toxic exhaust emission and crash mortalities were monetized using the Australian Value of a Statistical Life. Total social mortality cost of a large 4 wd ICE diesel SUV was approximately $6300, current fleet average $4800, and micro-EV $400.

Experimental study and numerical simulation of the influence of ball milling parameters on granule sizes of mold powder

In this study, the ball milling parameters (solids loading, binder and dispersant content, ball milling time) were optimized to control the granule size of the mold powder during spray drying and obtain good mechanical strength and metallurgical properties of the hollow granule mold powder. The effect of ball milling parameters on the granule size of mold powder was systematically investigated using computational fluid dynamics (CFD). A series of parameters, such as the temperature field, velocity field, granule size and trajectory were varied and the moisture content of the slurry in the spray drying tower was computed. To ensure that the granule size of the mold powder is controlled at 0.2–0.6 mm, the following optimum ball milling parameters were chosen based on the results of the numerical simulation: solid loading, 60 wt%; modified sodium carboxymethyl cellulose binder content, 1.0 wt%; sodium tripolyphosphate dispersant content, 0.5 wt%; ball-milling time, 60 min. • Mold powder slurry was prepared, and the viscosity was tested and analyzed. • A numerical simulation method for spray granulation was established. • The temperature and velocity field, moisture content, and diameter distribution were computed. • The optimum ball milling parameters were determined and applied in production.

Revealing Morphology Evolution of Lithium Dendrites by Large‐Scale Simulation Based on Machine Learning Force Field

AbstractSolving the dendrite growth problem is critical for the development of lithium metal anode for high‐capacity batteries. In this work, a machine learning force field model in combination with a self‐consistent continuum solvation model is used to simulate the morphology evolution of dendrites in a working electrolyte environment. The dynamic evolution of the dendrite morphology can be described in two stages. In the first stage, the energy reduction of the surface atoms induces localized reorientation of the originally single‐crystal dendrite and the formation of multiple domains. In the second stage, the energy reduction of internal atoms drives the migration of grain boundaries and the slipping of crystal domains. The results indicate that the formation of multiple domains might help to stabilize the dendrite, as a higher temperature trajectory in a single crystal dendrite without domains shows a higher dendrite collapsing rate. Several possible modes of morphological evolutions are also investigated, including surface diffusion of adatoms and configuration twists from [100] exposed surfaces to [110] exposed surfaces. In summary, reducing the surface and grain boundary energy drives the morphology evolution. Based on the analysis of these driving forces, some guidelines are suggested for designing a more stable lithium metal anode.

Temperature Trajectory Subphenotypes in Oncology Patients with Neutropenia and Suspected Infection.

Rationale: Despite etiologic and severity heterogeneity in neutropenic sepsis, management is often uniform. Understanding host response clinical subphenotypes might inform treatment strategies for neutropenic sepsis. Objectives: In this retrospective two-hospital study, we analyzed whether temperature trajectory modeling could identify distinct, clinically relevant subphenotypes among oncology patients with neutropenia and suspected infection. Methods: Among adult oncologic admissions with neutropenia and blood cultures within 24 hours, a previously validated model classified patients' initial 72-hour temperature trajectories into one of four subphenotypes. We analyzed subphenotypes' independent relationships with hospital mortality and bloodstream infection using multivariable models. Measurements and Main Results: Patients (primary cohort n = 1,145, validation cohort n = 6,564) fit into one of four temperature subphenotypes. "Hyperthermic slow resolvers" (pooled n = 1,140 [14.8%], mortality n = 104 [9.1%]) and "hypothermic" encounters (n = 1,612 [20.9%], mortality n = 138 [8.6%]) had higher mortality than "hyperthermic fast resolvers" (n = 1,314 [17.0%], mortality n = 47 [3.6%]) and "normothermic" (n = 3,643 [47.3%], mortality n = 196 [5.4%]) encounters (P < 0.001). Bloodstream infections were more common among hyperthermic slow resolvers (n = 248 [21.8%]) and hyperthermic fast resolvers (n = 240 [18.3%]) than among hypothermic (n = 188 [11.7%]) or normothermic (n = 418 [11.5%]) encounters (P < 0.001). Adjusted for confounders, hyperthermic slow resolvers had increased adjusted odds for mortality (primary cohort odds ratio, 1.91 [P = 0.03]; validation cohort odds ratio, 2.19 [P < 0.001]) and bloodstream infection (primary odds ratio, 1.54 [P = 0.04]; validation cohort odds ratio, 2.15 [P < 0.001]). Conclusions: Temperature trajectory subphenotypes were independently associated with important outcomes among hospitalized patients with neutropenia in two independent cohorts.