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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Numerical Simulations of High Prandtl Number Liquid Jets Impinging on a Flat Plate

In this work, 3D simulations of oil jets impinging on a flat, heated wall are presented. The numerical setup uses the Volume of Fluid (VoF) method to model the two-phase flow. A careful grid definition across the liquid film, along with the use of the Conjugate Heat Transfer (CHT) approach allowed local heat transfer to be solved with fine resolution at the wall. Variations of liquid flow rate, liquid temperature and surface temperature allow to cover a wide range of local Reynolds and Prandtl numbers (226 < Re < 2850, 77 < Pr < 161). Resulting surface-averaged heat transfer compares very well with experimental measurements conducted in a previous study. In-depth analysis of the flow has identified expected features from the literature. In particular, the impact of jet axial velocity profiles on the heat transfer distribution in the stagnation zone was clearly stated. The increase in heat transfer when warming the liquid film was also reproduced and explained by a decrease in oil viscosity and an increase in film velocity. All those effects were taken into account in correlations for stagnation and local values of Nusselt number. A grid sensitivity study was also conducted, showing that if the grid solving the thermal boundary layer in the stagnation zone can be coarsened without impacting local and surface-averaged predictions of heat transfer, a minimum resolution (2 to 3 cells) within the thermal boundary layer is however required for an accurate prediction of heat transfer.

conjugate heat transfer↗
DOE OSTI2024

Alternating conduction and convection drying of paper – an experimental analysis with a continuous data acquisition approach

In conventional multi-cylinder drying of paper and board, both conductive drying from steam-heated dryer cylinders and convective drying by flowing air over the paper surface in the pockets are used. Conductive drying from steam-heated drying cylinders is a critical component in providing the necessary thermal energy to paper and board as they dry. Steam temperature and internal and external resistances at the contacting surface are critical process parameters influencing the conductive drying process. An experimental setup was developed to study the alternating conductive and convective drying of paper and board. Paper sheet moisture, temperature, and temperature distribution within the heated platen and the instantaneous heat flux as the sheet was being dried were measured. The instantaneous heat flux, contact heat transfer coefficient, and drying rates were determined as drying proceeds. Experimental results, as well as comparisons to literature and commercial data, are presented. The conductive heat transfer coefficients determined were compared to traditional correlations normally used in the modeling of paper drying. Similarly, the convective heat and mass transfer coefficients are also determined and compared to literature data. In addition to the evaluation of alternating conductive and convective drying characteristics of paper and board, the potential inclusion of auxiliary energy components will also be included. Experimental results from the conduction and convection drying system are presented. Furthermore, this data will be useful in process development, intensification of manufacturing processes, and modeling and simulation of paper drying processes.

42 ENGINEERING↗
DOE OSTI2020

Assessment of reverse gun taylor cylinder experimental configuration

Experimental efforts for Taylor-anvil impact tests have often been limited to near room temperature. The ‘Reverse Gun’ method proposed by Gust in 1982 allows for the Taylor impact specimen to be uniformly heated without temperature losses before impact. Through the use of finite element analysis, we explore two topics in this work. First, we examine whether the reverse gun experimental configuration is comparable to the traditional Taylor-anvil setup. Second, we assess the accuracy of several commonly employed flow strength models in terms of their ability to predict the reverse gun experimental results which involve dynamic loading conditions and complex thermo-mechanical coupling. The reverse gun simulations are performed for tantalum targets at initial temperatures in the range 295 K to 1295 K and velocities from 135 m/s to 242 m/s. We show that with suitable care in the modeling of the preheated reverse gun experiments one can make valuable assessments of flow strength models. Given the conditions explored, these observations probe the thermal softening, strain hardening, and strain rate sensitivity of the material.

42 ENGINEERING↗
DOE OSTI2024

CFD unified approach under Eulerian–Lagrangian framework for methanol and gasoline direct injection sprays in evaporative and flash boiling conditions

Innovative synthetic fuels for advanced propulsion systems, such as methanol and ammonia, and synthetic blended fuels (E00, E10, and E30), known for their high volatility, are often injected directly into combustion chambers. It follows that Eulerian–Lagrangian spray models need to accurately capture the spray collapse as a consequence of flash boiling onset and be capable of proficiently handling the preferential evaporation of multi-component fuels in evaporative scenarios. So, we performed the assessment of an Eulerian–Lagrangian CFD code for simulating methanol and E00 gasoline blend sprays in both early and late injection conditions involving flash boiling conditions and preferential evaporation. The adoption of an effervescent breakup model and of a non-equilibrium phase transition model for the discrete phase allows the adoption of a setup that is almost completely free from specific constant tuning, especially for what concerns the breakup model. We validated the simulations using experimental PLV maps of methanol and E00 sprays issued from the ECN Spray M injector. The results highlight a significantly different morphology of the methanol spray compared to the E00 one under late injection conditions. Under stratified combustion, low-volatile fuels are likely to be ignited first, and the flame propagates toward the high-volatile fuels. In conclusion, the spray collapse was also correctly reproduced, inducing the presence of a low-pressure zone and modifying the spray morphology.

E00↗
DOE OSTI2023

Nonlinear analysis and vibro-impact characteristics of a shaft-bearing assembly

Here this study investigates the nonlinear frequency response of a shaft-bearing assembly with vibro-impacts occurring at the bearing clearances. The formation of nonlinear behavior as system parameters change is examined, along with the effects of asymmetries in the nominal, inherently symmetric system. The primary effect of increasing the forcing magnitude or decreasing the contact gap sizes is the formation of grazing-induced chaotic solution branches occurring over a wide frequency range near each system resonance. The system's nominal setup has very hard contact stiffness and shows no evidence of isolas or superharmonic resonances over the frequency ranges of interest. Moderate contact stiffnesses cause symmetry breaking and introduce superharmonic resonance branches of primary resonances. Even if some primary resonances are not present due to the system's inherent symmetry, their superharmonic resonances still manifest. Branches of quasiperiodic isolas (isolated resonance branches) are also discovered, along with a cloud of isolas near a high-frequency resonance. Parameter asymmetries are found to produce a few significant changes in behavior: asymmetric linear stiffness, contact stiffness, and gap size could affect the behavior of primary resonant frequencies and isolas.

42 ENGINEERING↗
DOE OSTI2026

High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system

Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.

Additive manufacturing↗
DOE OSTI2021

Interprofessional Image Verification Workshop for Physician and Physics Residents: A Multi-Institutional Experience

Verification of patient position through pretreatment setup imaging is crucial in modern radiation therapy. As treatment complexity increases and technology evolves, physicist-physician collaboration becomes imperative for safe and successful radiation delivery. Despite the importance of both, residency programs lack formal interprofessional education (IPE) activities or structured training for image verification. Here we show the impact of an interprofessional image verification workshop for residents in a multi-institutional setting.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Atomic Layer Thermopiles: Comprehensive static calibration, comparison and application in subsonic and supersonic flows

Here, this paper compares the calibration of different heat flux sensors in radiation- and convection-based sub- and supersonic operation. First, four heat flux sensors based on different principles: ALTP (Transverse Seebeck Effect), HFM-8E (differential-layer device), coaxial Thermocouple and a TG-2000 (circular-foil gage) are calibrated in a laser-based radiation setup. In a second step, all heat flux sensors are compared with a slug-calorimeter within a subsonic convection-dominated facility based on stagnation-point measurements of an impinging hot air jet. The obtained results indicate that a sensitivity transfer between a radiative calibrated sensor used in a mainly convective environment is not always possible and can lead to significant, systematic errors. Calibration in a subsonic shear flow with thermocouple readings demonstrate small divergence from the manufacturer provided sensitivity. Finally, supersonic testing with high frequency shock-boundary layer interactions highlight the need for ALTP rather than the conventional use of thermocouple arrays to resolve the high frequency phenomena associated with shock boundary layer interactions.

42 ENGINEERING↗
DOE OSTI2024

Tandem pyrolysis evolved gas–gas chromatography–mass spectrometry

Analysis of byproducts from thermal degradation of polymer materials provides a wealth of information about a materials’ composition, thermal stability, degradation mechanisms, and kinetics. However, regardless of the instrumentation used, only limited information is obtainable from a single experiment. Microfurnace technology, when interfaced to a gas chromatography-mass spectrometry (GC-MS), can be used to obtain both thermal and chemical information via evolved gas analysis-MS (EGA-MS) and GC-MS analysis modes. While both EGA-MS and Py-GC-MS are valuable when characterizing polymer materials, at least two experiments on distinct samples are required, which can be a liability for clear interpretation of results from inhomogeneous samples. Here, we seek to overcome this limitation by combining EGA-MS and Py-GC-MS modes in a single experimental setup. Further, this was done by developing new gas line modifications to allow for tandem Pyrolysis Evolved Gas-Gas Chromatography-Mass Spectrometry (Py/EG-GC-MS) analysis. Verification of Py/EG-GC-MS analysis was performed using a polystyrene standard. Results demonstrate successful Py/EG-GC-MS analysis for the first-time showing the potential of these modifications for application in areas where sample is limited or direct correlation of products to the thermal profile is desirable such as in forensics or product-specific kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Novel nonwetting solid-infused surfaces for superior fouling mitigation

Fouling is a ubiquitous issue in several environmental and energy applications. Here we introduce novel nonwetting solid-infused surfaces (SIS) with superior anti-fouling characteristics that are durable than conventional nonwetting surfaces in a dynamic flow environment. A systematic study is presented to elucidate the fouling mitigation performance of SIS in comparison to lubricant-infused surface (LIS) and conventional smooth surface. Copper tubes with SIS, LIS or smooth inner walls are fabricated and subjected to accelerated calcium sulfate fouling in a flow fouling experimental setup. Fouling on the various surface types is quantified in terms of asymptotic fouling resistance, and the fundamental morphological differences in the interactions of the foulant and the various surface types are analyzed. Based on a systematic sweep of the parameter combinations using design of experiments and Taguchi analysis, an analytical dependence of asymptotic fouling resistance on the governing parameters namely, Reynolds number, foulant concentration and temperature is derived. The analytical model is shown to predict the asymptotic fouling resistance to within 20% accuracy with a 95% confidence. In addition, for the first time, the effects of shear durability on the fouling mitigation performance of LIS vis-à-vis SIS are studied. It is shown that the novel nonwetting SIS offers a robust option for superior fouling mitigation over LIS in the long run.

42 ENGINEERING↗
DOE OSTI2024

Solution of the Schrödinger equation for quasi-one-dimensional materials using helical waves

We formulate and implement a spectral method for solving the Schrödinger equation, as it applies to quasi-one-dimensional materials and structures. This allows for computation of the electronic structure of important technological materials such as nanotubes (of arbitrary chirality), nanowires, nanoribbons, chiral nanoassemblies, nanosprings and nanocoils, in an accurate, efficient and systematic manner. Our work is motivated by the observation that one of the most successful methods for carrying out electronic structure calculations of bulk/crystalline systems — the plane-wave method — is a spectral method based on eigenfunction expansion. Our scheme avoids computationally onerous approximations involving periodic supercells often employed in conventional plane-wave calculations of quasi-one-dimensional materials, and also overcomes several limitations of other discretization strategies, e.g., those based on finite differences and atomic orbitals. The basis functions in our method — called helical waves (or twisted waves) — are eigenfunctions of the Laplacian with symmetry adapted boundary conditions, and are expressible in terms of plane waves and Bessel functions in helical coordinates. We describe the setup of fast transforms to carry out discretization of the governing equations using our basis set, and the use of matrix-free iterative diagonalization to obtain the electronic eigenstates. Miscellaneous computational details, including the choice of eigensolvers, use of a preconditioning scheme, evaluation of oscillatory radial integrals and the imposition of a kinetic energy cutoff are discussed. We have implemented these strategies into a computational package called HelicES (Helical Electronic Structure). We demonstrate the utility of our method in carrying out systematic electronic structure calculations of various quasi-one-dimensional materials through numerous examples involving nanotubes, nanoribbons and nanowires. We also explore the convergence properties of our method, and assess its accuracy and computational efficiency by comparison against reference finite difference, transfer matrix method and plane-wave results. We anticipate that our method will find applications in computational nanomechanics and multiscale modeling, for carrying out transport calculations of interest to the field of semiconductor devices, and for the discovery of novel chiral phases of matter that are of relevance to the burgeoning quantum hardware industry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Status and new features of the topography beamline 1-BM after the Advanced Photon Source upgrade

The beamline 1-BM of the Advanced Photon Source (APS), a bending-magnet beamline with an effective X-ray beam size of ~100×4 mm2, has relatively comprehensive synchrotron topography and rocking curve imaging capabilities for characterization of crystals (particularly wide-bandgap semiconductors SiC, AlN, GaN, Ga2O3 etc). It is equipped with a white-beam topography stage for imaging large wafers up to 8 inches. It also has a double-crystal setup, of which the second stage can be used for monochromatic-beam topography. The first stage has different beam conditioners in the grazing-incidence geometry that can expand the vertical beam size from 4 to ~100 mm for double-crystal rocking curve imaging when it is combined with the second stage. Recently APS has been upgraded to a modern 4th-generation light source, and 1-BM has been recommissioned to its normal operation for general users with better performance. The upgraded APS leads to new features at 1-BM. The much smaller source size and higher X-ray coherence significantly improve the image resolution and contrast. The higher flux and brightness of the new source reduce exposure time, which mitigates the mechanical drifting and vibration issues. Here the main capabilities and status of 1-BM together with these new features are introduced.

X-ray topography↗
DOE OSTI2020

Synthesis and flash sintering of (Hf 1-x Zr x )B 2 solid solution powders

(Hf 1-x Zr x )B 2 solid solution powders were synthesized by two methods. First, solution-based processing of HfCl 4 , ZrCl 4 , sucrose, and H 3 BO 3 was conducted followed by heat treatment in Argon to carry out the carbothermal reduction (CTR) reaction to form the diboride powders. Alternatively, in the so-called borohydride reduction (BHR) method, HfCl 4 , ZrCl 4 and NaBH 4 were mixed in an Argon glove box followed by heat treatment in Argon at 700-1500°C. The synthesized powders were characterized by XRD, SEM, TEM, EDS, and TGA, and the influence of different parameters such as starting composition, heat treatment temperature and time on products characteristics were revealed. Both CTR and BHR solid solution powders were then consolidated within ~5 min in a homemade flash sintering (FS) setup. Here, the composition, microstructure, hardness, and thermal-oxidation properties of flash sintered ceramics were characterized, and the implication of this study and directions for future research were discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Core-scale numerical simulation and comparison of breakdown of shale and resulting fractures using sc-CO 2 and water as injectants

Supercritical carbon dioxide (sc-CO 2 ) is an alternative to water for stimulation of low permeability systems such as shale gas and geothermal resources. Previously core-scale experimental studies have compared the behavior of CO 2 to water injection for sample breakdown. Due to differences in experimental setup and core sample preparation, inconsistent or even apparently contradictory conclusions have resulted. To reconcile this contradiction, a phase-field numerical model is applied to understand hydraulic fracturing experiments using Green River shale found in the literature. The finite element numerical model incorporates a rate-dependent phase-field fracture model developed separately to describe fracture initiation and growth. We investigate the impact of various material and fluid properties on the resulting fractures. Most importantly, we study the effect of fluid properties and boundary conditions on the breakdown pressure, including the direction of the resulting fracture plane. Model results predict that (1) sc-CO 2 injection in the laboratory may result in greater breakdown pressure than that of water under no-flow boundary conditions because lower viscosity sc-CO 2 may result in pressure build up at the core boundary that opposes fracture initiation and (2) lower viscosity sc-CO 2 also produces fast-propagating fractures that are less influenced by the bedding plane on their resulting fracture topology. Here our model offers a straightforward explanation and reconciliation of existing experimental observations, as well as a means to extrapolate to new conditions. Exploration of field-scale conditions suggests less pronounced or no elevation in breakdown pressure when sc-CO 2 is injected because the pressure build up effect at the system boundary is significantly less or absent at field length scales.

42 ENGINEERING↗
DOE OSTI2020

Impact of climate change on adaptive management decisions in the face of water scarcity

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. Here, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers’ responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗
DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗