Experimental and theoretical thermal performance analysis of additively manufactured polymer vacuum insulation panels
Not Available
Pei, Yong (ORCID:0000000179835913)↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Investigation of fuel film formation and soot emissions in a GDI engine during cold-start with Split-injection strategies
This study investigates the impact of fuel-film formation on engine-out soot emissions in a gasoline direct injection (GDI) engine under cold-start conditions. Split-injection strategies were applied by varying the number of injections, injection duration, and total fuel quantity to affect wall wetting and control the average in-cylinder equivalence ratio. A combined experimental and numerical approach was employed to analyze fuel-film deposition, combustion efficiency, and engine-out soot and unburnt hydrocarbons (UHC) emissions. In particular, fuel film distribution estimated by means of non-reacting, 3-D, computational fluid dynamics (CFD) simulations, together with experimentally measured soot data, were used to investigate fuel film formation and its role in soot generation. In the experiments, a skip-fired engine control strategy was applied to mimic the transient nature of engine cold-start operation. The results indicate that, under the same total number of injections, increasing the average in-cylinder equivalence ratio through longer injection durations improves combustion stability, as indicated by the decrease of the coefficient of variation of IMEP n (Net Indicated Mean Effective Pressure) from 6.0 % to 0.6 %. However, this strategy leads to higher soot emissions, which increased by nearly an order of magnitude, primarily due to enhanced wall-film formation. In contrast, increasing the number of injections while maintaining a constant equivalence ratio significantly impacts fuel-film deposition and, consequently, soot emissions, with a fivefold reduction of the measured engine-out soot, decreasing from 3.5 mg to 0.7 mg. A soot-film correlation was developed and achieved a high coefficient of determination (r 2 = 0.95) and was further extended to account for spark timing effects. These findings confirm the effectiveness of split-injection for avoiding wall film formation and soot emissions, and the critical role of fuel film in soot generation, supporting the hypothesis that pool fires play a crucial role in contributing to soot formation under these cold-start conditions. In conclusion, the study also indicates the value of a predictive soot-film correlation for developing cold-start emission control strategies.
Engine Cold-start↗
Multi-scale signaling and tumor evolution in high-grade gliomas
Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.
60 APPLIED LIFE SCIENCES↗
Optimal design of power constrained bipolar membrane electrodialysis over a wide brine range
Bipolar membrane electrodialysis enables the in-situ production of high value products (e.g., acid and base) from clean brine, which is essential for a sustainable future. A technoeconomic assessment (TEA) was conducted on extraction of value from brine using the WaterTAP framework to identify optimal cost across a wide design space. Here, in the power constrained regime, increasing supplied salt concentration does not necessarily result in reduced cost or increased NaOH concentration. A detailed analysis elucidates the critical roles of water dissociation, limiting currents, and sodium diffusion play in shaping the landscape of levelized cost. Among these, water splitting predominantly influences the TEA outcomes across most of the optimal design space. Sensitivity analysis further demonstrates that membrane properties controlling water dissociation significantly impact the unit cost. The results indicate that innovations targeting improvements in water disassociation should be prioritised to effectively reduce the levelized cost of product production.
Bipolar membrane electrodialysis↗
Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions
We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.
Density functional theory calculations↗
From clutter to clarity: Emergent neural operators via questionnaire metrics
Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.
42 ENGINEERING↗
Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials
Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.
Xu, Pei [Clemson Univ., SC (United States)]↗
Can chemical short-range order be transformed into a practical alloy-engineering tool?
Explore the source record for details and available documents.
Pei, Zongrui (ORCID:0000000307484629)↗
Numerical modeling of electromagnetic field spatiotemporal evolution to evaluate the effects on calcium carbonate crystallization
Calcium carbonate (CaCO 3 ) scaling is a significant impediment to water systems. Electromagnetic field (EMF) treatment is a promising approach to control scaling owing to its simplicity and low or no energy requirements. However, the underlying mechanisms by which EMF impacts CaCO 3 crystallization remain unclear due to the challenges in measuring the EMFs in feed solutions and the lack of a fundamental understanding of the applied EMFs and the observed physicochemical phenomena. To fill this knowledge gap, a high-fidelity COMSOL model was first developed to simulate EMFs in bulk solutions for three alternating current-induced EMF devices with different configurations and properties. These were then integrated with experimental data to unveil the underlying mechanism by which applied EMFs alter the physicochemical processes. The study revealed that even low-strength EMFs (e.g., electric fields <0.15 V/m and magnetic fields <0.03 mT) promoted CaCO 3 precipitation in bulk solutions. The electric fields created by these EMF devices resulted in higher Lorentz force compared to their induced magnetic fields. In conclusion, the methodology of this study offers the capability to predict the effectiveness of different EMF devices in facilitating crystallization processes, and these mechanistic insights lay the foundation for the smart design of EMF devices for diverse water treatment applications.
COMSOL simulation↗
Scaling dynamics in low-salt-rejection reverse osmosis for high-salinity produced water desalination: Mechanistic modeling and membrane autopsy
Membrane scaling remains a critical barrier to the reliable operation of desalination systems, particularly for hypersaline produced water (PW) treatment. This study fills the knowledge gap of autopsy-based model validation for PW desalination by elucidating scaling mechanisms in a Low-Salt-Rejection Reverse Osmosis (LSRRO) system through the integration of pilot-scale experimentation and complementary modeling approaches. A semi-empirical modeling framework was developed and applied to a multistage pilot LSRRO system equipped with nanofiltration and RO membranes treating high-salinity PW from the Permian Basin. Water quality analysis showed that total dissolved solids decreased from ~130,000 mg/L to ~1900 mg/L in the permeate, then further reduced to ~300 mg/L by a second-pass RO. Two different thermodynamic modeling approaches were evaluated: the first extends the LSRRO framework by incorporating system complexity and scaling phenomena, whereas the second method explicitly captures concentration polarization in localized supersaturation. Both methods illustrate the tendency for carbonate and sulfate scaling throughout the stages. Membrane autopsies revealed a silica-dominated deposit matrix, localized CaSO 4 at Stage 2, and minor barite/celestite despite their prominence in model predictions. Quantum-chemical calculations indicated silica scaling can be rationalized by favorable adsorption of H 4 SiO 4 on Fe-oxide surfaces (ΔG ≈ −44 kJ/mol), providing a kinetic pathway for interfacial inorganic polymerization even when bulk equilibrium predictions are conservative. Overall, the thermodynamic scaling modeling and membrane autopsy revealed heterogeneous, localized deposits with limited impact on LSRRO performance, while quantum analysis rationalized the thermodynamically unfavorable precipitation formation under bulk equilibrium, reconciling model–autopsy discrepancies. These insights support targeted pretreatment and silica-specific antiscalants to extend membrane lifetime and increase recovery, providing a transferable framework for hypersaline water desalination systems. The combined experimental–computational approach provides new mechanistic insight into scaling in hypersaline membrane systems and establishes a transferable framework for predicting and mitigating scaling in next-generation desalination technologies.
Low-salt-rejection reverse osmosis↗
A comprehensive review of heat pump wood drying technologies
Not Available
Gao, Lei (ORCID:0000000326037787)↗
Hybrid combustion modeling approach for turbulent jet ignition in natural-gas pre-chamber spark-ignition engines at high EGR
Here, this study presented a hybrid modeling approach for simulating turbulent jet ignition and combustion processes in a natural-gas pre-chamber spark-ignition engine operating under exhaust gas recirculation (EGR) diluted conditions. In-depth analyses of experimental data and simulation results from previous work [Chinnathambi et al., ICEF2021-67836; Kim et al., Fuel 409: 137815, 2026] revealed two key findings: (i) the magnitude of pressure difference between the pre-chamber and main chamber ($∆P_{PC-MC}$) was positively correlated with the combustion duration from the moment of $∆P_{PC-MC}=0$ to the point of 5% mass fraction burned, with larger $∆P_{PC-MC}$ associated with longer duration; and (ii) the turbulent combustion regime in the main chamber transitioned from the broken reaction zone to the corrugated flamelet regime, with the Karlovitz number exceeding 100 immediately after turbulent hot jets were ejected from nozzles, coinciding with observed local extinction events. To accurately simulate the entire combustion process, a hybrid approach was developed under Reynolds-Averaged Navier Stokes framework, combining the G-equation model for pre-chamber combustion with the multi-zone well-stirred reactor approach and a turbulence-chemistry interaction (TCI) submodel for main chamber combustion. The TCI submodel accounted for the attenuation of reaction rates due to turbulent strain and modeled local extinction by suppressing reaction rates under certain flow and flame conditions. When applied to three EGR rate conditions toward the dilution limit, the hybrid modeling approach accurately reproduced experimental data in terms of cylinder pressure, apparent heat release rate, and the observed positive correlation, including the delayed onset of main chamber combustion—a feature not captured by existing combustion models.
computational fluid dynamics simulation↗
Multicycle large-eddy simulations of a direct-injection hydrogen-fueled optical engine
Hydrogen (H 2 ) is a carbon-free chemical energy carrier and one promising solution for achieving effective decarbonization of the transportation sector, particularly for internal combustion engines (ICEs). With a focus on ICEs, and compared to port-fuel injection, direct injection (DI) of gaseous H 2 during the compression stroke offers potential advantages, which include backfire avoidance and reduction of preignition occurrence. In these last two decades, much research, experimental and numerical, has been devoted to understanding H 2 's mixing and combustion processes in ICEs. Computational fluid dynamics modeling efforts commonly rely on unsteady Reynolds-averaged Navier Stokes (URANS) turbulence frameworks, mostly due to their computational affordability. However, many authors have pointed out the opportunity to perform large-eddy simulations (LESs) to investigate the cyclic variability of H 2 engines and assess potential advantages of using LES in place of URANS, especially for lean operation. This study addresses this knowledge gap and presents a computational fluid dynamics (CFD) study of the H 2 DI process in an optical engine operating at relatively low tumble conditions, using multicycle LESs. In conclusion, the manuscript presents a thorough validation of the results against experimental data available from the literature as well as direct comparison with URANS, demonstrating the feasibility of multicycle LESs for CFD modeling of DI H 2 -fueled ICEs.
Direct injection↗
Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys
Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.
36 MATERIALS SCIENCE↗
In-situ synchrotron X-ray study on microstructure and stress evolutions of electroplated copper upon self-annealing
Background: The self-annealing behavior of electroplated copper (Cu) at room temperature is gaining attention in the microelectronics industry due to its significant impact on reliability issues such as substrate warpage and electrical resistivity. Methods: In this study, in-situ analysis of the microstructure transition and stress relaxation of the electroplated copper upon self-annealing was conducted via synchrotron white X-ray nanodiffraction (beamline 21A, Taiwan Photon Source) and grazing-incidence X-ray diffraction (beamline 17B1, Taiwan Light Source). Significant Findings: Remarkable relaxations of deviatoric stress and absolute strain component along the [002] direction were closely related to the Cu grain growth and crystallographic reorientation at the early stage of selfannealing, and a complete stress/strain relaxation can be achieved with the cessation of microstructure transition. In conclusion, the in-situ synchrotron X-ray studies provided an insight into Cu self-annealing mechanism, offering valuable information for improving Cu interconnect reliability.
Cu Self-annealing↗
Designing complex concentrated alloys with quantum machine learning and language modeling
Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.
36 MATERIALS SCIENCE↗
Microstructural evolution and phase stability in Nb-containing interstitial Fe-Mn-Co-Cr-C high-entropy alloys: An in-situ synchrotron X-ray diffraction study during laser melting
The influence of Nb on phase stability and microstructural evolution in an interstitial Fe-Mn-Co-Cr-C high-entropy alloy was investigated using in-situ synchrotron X-ray diffraction (SXRD) during laser melting. Scheil-Gulliver simulations predict the formation of σ and γ-f.c.c. phases in all three alloys, along with NbC in Nb-containing compositions. SXRD confirmed the presence of most predicted phases, but the σ phase was absent. Nb promotes crystallite refinement and increases dislocation density, though excessive additions reduce refinement efficiency due to solubility limits and secondary phase formation. Furthermore, Nb addition also enhances ε-h.c.p. phase formation by reducing stacking fault energy through NbC-induced carbon depletion. Analysis of intensity peak evolution reveals that Nb alters preferred grain orientations, reducing {111} γ intensity while enhancing {220} γ , leading to a more isotropic grain distribution. Overall, Nb plays a key role in phase selection, microstructure refinement, and preferred orientation evolution, allowing the tailored microstructure of high-entropy alloys via rapid solidification.
Interstitial high entropy alloys↗