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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

On the Mechanisms of Formation of Memory Channels and Development of Negative Differential Resistance in Solid Solutions of the ТlInТe{sup 2}–ТlYbТe{sup 2} System

The behavior of an electronic subsystem is investigated in the course of formation and development of a memory channel in solid solutions of the TlInTe2–TlYbTe2 system. An analysis of the current-voltage characteristics allows getting an insight into the reason for a sharp change in electrical conductance of the specimens under study during their transition from the high-resistance to high-conductance state and the reasons for the well known instability of threshold converters, which makes it possible to design devices with high threshold voltage stability.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Fitting Matérn smoothness parameters using automatic differentiation

The Mat$\acute{e}$rn covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoothness parameter $\nu$ gives complete control over the mean-square differentiability of the process, which has significant implications for the behavior of estimated quantities such as interpolants and forecasts. Unfortunately, derivatives of the Mat$\acute{e}$rn covariance function with respect to $\nu$ require derivatives of the modified second-kind Bessel function $K$ $\nu$ with respect to $\nu$. While closed form expressions of these derivatives do exist, they are prohibitively difficult and expensive to compute. For this reason, many software packages require fixing $\nu$ as opposed to estimating it, and all existing software packages that attempt to offer the functionality of estimating $\nu$ use finite difference estimates for $\partial$ $\nu$ $K$ $\nu$ . In this work, we introduce a new implementation of $K$$\nu$ that has been designed to provide derivatives via automatic differentiation (AD), and whose resulting derivatives are significantly faster and more accurate than those computed using finite differences. Here, we provide comprehensive testing for both speed and accuracy and show that our AD solution can be used to build accurate Hessian matrices for second-order maximum likelihood estimation in settings where Hessians built with finite difference approximations completely fail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability

We report that in a recent numerical study, it was demonstrated that characterizing reservoir permeability in terms of rock’s quality, as observed in lab and field, is the most important step before implementing an enhanced oil recovery operation or drilling a new well in a tight formation. In that study, it was shown that permeable features in shale-like organic matter (OM) and fractures were the only regions that allowed some reasonable movement of fluid, whereas inorganic matter (iOM) that occupies larger pore volume with significant saturation of hydrocarbons has extremely low permeability that did not allow any reasonable fluid movement to affect production. That study demonstrated the importance of characterizing reservoir heterogeneity in shale in order to economically exploit the shale resource. This study proposes a method to predict spatially heterogeneous field-scale permeability of shale in terms of natural fractures, and matrix (iOM and OM). The method developed in Part 1 is combined with a history-matching process that uses only readily available information from lab-scale and outcrop (information from geologists) to predict field-scale permeability. The method also ensures consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance. Optimized parameters of fracture distribution are used to generate multiple realizations of geological model, and the “best-fitting” (most-likely) permeability scenario is chosen by generating production response of each realization of the geological model and comparing them against the observed field production history. Finally, the novelty of the proposed to predict field-scale permeability is that it uses only readily available information while also ensuring consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance.

58 GEOSCIENCES↗
DOE OSTI2019

Mathematical Modelling of Gas–Liquid, Two-Phase Flows in a Ladle Shroud

Differential and macroscopic models of argon–steel flows in ladle shroud have been developed. In this, argon–steel, two-phase flow phenomena have been formulated via a transient, three dimensional, turbulent flow model, based on the volume of fluid (VOF) calculation procedure. While realizable k–ε turbulence model has been applied to map turbulence, commercial, CFD software ANSYS-Fluent™ (Version 18), has been applied to carry out numerical calculations. Predictions from the model have been directly assessed against experimental measurements across the range of shroud dimensions and volumetric flow rates typically practiced in the industry. It is demonstrated that the two-phase turbulent flow model captures the general features of gas–liquid flows in ladle shroud providing estimates of free jet length and threshold gas flow rates (required to halt air ingression) which are in agreement with corresponding experimental measurements. In the absence of differential solutions, a macroscopic model has been worked out through dimensional analysis embodying multiple non-linear regression. It is shown that dimensionless free jet length in bloom and slab casting shrouds can be estimated reasonably accurately from the following correlation (in SI unit), viz., ((L{sub jet})/(D{sub CN}))=2.8×10{sup −2}(((Q{sub G})/(Q{sub L}))){sup 1.14}(((gD{sub CN}{sup 5})/(Q{sub L}{sup 2}))){sup 0.8}(((σD{sub CN}{sup 3})/(ρ{sub L}Q{sub L}{sup 2}))){sup −0.9}(((D{sub sh})/(D{sub CN}))){sup 2.0}(((ρ{sub G})/(ρ{sub L}))){sup −0.30}in which, L{sub jet} is the free liquid jet length (m), Q{sub G} is the gas flow rate (m{sup 3}/s), Q{sub L} is the liquid flow rate (m{sup 3}/s), D{sub sh} is shroud diameter (m), D{sub CN} is the collector nozzle diameter (m), σ is the interfacial tension (N/m), and ρ{sub G} as well as ρ{sub L} are respectively density of gas and liquid (kg/m{sup 3}). It is demonstrated that the proposed correlation is consistent with the laws of physical modeling and leads to estimates that are in good agreement with predictions from the differential models, for both air-water as well as argon–steel systems. Numerical simulations as well as macroscopic modeling have indicated that thermo-physical properties of the gas–liquid system are important and exert some influences on the gas–liquid, two-phase, flow in ladle shrouds, albeit not to a large extent. Despite dissimilar thermo-physical properties, full scale water modeling appears to be sufficiently predictive and provides reasonable macroscopic descriptions of the two-phase flow phenomena in industrial ladle shroud systems.

42 ENGINEERING↗
DOE OSTI2017

Precipitation Kinetics of M{sub 23}C{sub 6} Carbides in the Super304H Austenitic Heat-Resistant Steel

The precipitation kinetics of M{sub 23}C{sub 6} carbides in Super304H and TP304H steels were investigated using the selective-etching method, SEM backscattered electron images and Image-Pro-Plus 6.0 software. Precipitation–temperature–time (PTT) diagrams of M{sub 23}C{sub 6} carbides in the as-received Super304H (fine grains), coarsened Super304H (coarse grains) and TP304H (coarse grains) steels all show the typical C-shaped character with nose temperature range from 800 to 850 °C. Compared with the TP304H steel, the same trend is found of the PTT curve of M{sub 23}C{sub 6} carbides for both kinds of Super304H steels, but their start lines move to the right and finish lines to the left. The preferential formation of Nb(C,N) phase at grain boundaries in the Super304H steels inhibited the nucleation of M{sub 23}C{sub 6} carbides in the early stage of precipitation, causing the right shift of the start line of PTT curve. The main reason for the left shift of the finish line of the two Super304H steels was the quicker growing and coarsening rate of M{sub 23}C{sub 6} in the later precipitation stage due to their higher C content than in TP304H. For the difference in PPT curves between the two grain sizes of the Super304H steel, the lower diffusion rate of atoms in the coarse-grained Super304H steel may explain its righter finish line than the fine-grained counterpart, while the reason for its lefter start line is due to the higher solute segregation along coarse-grained boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Pulsed TIG Welding–Brazing of Aluminum–Stainless Steel with an Al-Cu Twin Hot Wire

A TIG welding–brazing process with a twin aluminum hot wire technique was utilized to control the heat input, improve the joint formation, suppress the interfacial intermetallic compounds (IMCs) and increase the joint strength, thereby creating reliable joints between an aluminum alloy and stainless steel with an ER2319 filler. Twin wires with reasonable diameters and filling modes led to a satisfactory weld of parent metals with varying thicknesses. By the hot wire technique, the reasonable range of the welding current was extended and the thickness of the interfacial IMC at the seam bottom was reduced. In addition, both the tensile strength and stability of the joints increased compared with a cold wire. The IMC consisted of θ-(Fe,Cu){sub 4}Al{sub 13} and minor Cr{sub 0.7}Fe{sub 0.3}Al{sub 6}, and the precipitated phases in the weld were Al{sub 2}Cu and Al{sub 2}CuMg. With an increasing welding current, the IMC thickness significantly increased, while the joint strength decreased. The fracture positions of the joints varied with the corresponding welding currents.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Analytical Model of High-Velocity Impact of a Deformable Projectile Against Textile-Based Composites

Textile-based composites are used in manufacturing of ballistic shields due to their favorable combination of mechanical properties which enables weight saving and efficiency while retaining or even improving ballistic performance. Predictive models are an important tool in reducing experimental tests which are still an important step in ballistic shield design. Among those, analytical models provide less accurate results but require far less computational cost than numerical models and can be used for preliminary analyses. Many authors in the literature have developed analytical models for high-velocity impact on textile-based composites based on wave propagation theory and an energy equilibrium approach. The main limitation of these models is the assumption of rigid projectile which leads to the underestimation of the ballistic limit for soft core projectiles, which are a very common type of threat for this kind of protection. For this reason, an enhanced version of these models is reported in this paper, which releases the assumption of rigid projectile and adds erosion and deformation of the projectile to the formulation. Ballistic tests were performed on panels manufactured from layers of Kevlar 29 plain weave fabric embedded in an epoxy matrix. An insight into the different kinds of energy absorption considered by the model shows that projectile deformation plays an important role in the dissipation of the kinetic energy of the projectile in case of intermediate impact velocity. For this reason, the present model delivers more accurate results than the models present in the literature. In particular, the estimation of the ballistic limit is drastically improved which is a key step in ballistic shields optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Evaluating the decisions of glioma patients regarding clinical trial participation: a retrospective single provider review

Clinical trial accrual is vital to advancing care. A single study elucidated demographic data correlating with glioma patients’ clinical trial enrollment. However, it did not investigate the underlying decision-making process for non-participation. In this study, we seek to understand this key aspect of patient accrual. All notes for glioma patients seen by a single neuro-oncologist from July 2010 to May 2017 were examined for mention of clinical trial offerings. When a trial was declined, the patient’s reasoning was recorded along with the following: diagnosis, KPS, extent of resection, age, gender, race, marital status, income group, religion, trial offered at initial visit versus subsequent, and distance from trial site. Of 279 consecutive glioma patients, 88 were eligible for and offered a clinical trial. Fifty-seven accepted (65%), and 31 (35%) declined participation (Fig. 1). Of those offered a clinical trial, patients with glioblastoma (GBM) were significantly more likely to accept (44 out of 57 (77%) vs. 13 out of 57 (23%), p =0.03). After we adjusted for gender and travel distance, GBM was the only significant predictor of clinical trial acceptance, with an odds ratio of 3.18 (95% CI 1.17, 8.61, p =0.02). Reasons cited for non-participation included: travel distance (39%), lack of interest (39%), visit frequency (16%), and fear of randomization (6%). This study clarified for the first time individual glioma patient rationale for non-participation and potential areas for improving enrollment. Allowing off-site treatment centers or telemedicine visits may entice rural patients to participate. Visit frequency should be carefully considered and minimized whenever possible. Further prospective study of rationale for non-participation may improve enrollment over time.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

The Role of Biomass Composition and Steam Treatment on Durability of Pellets

Steam treatment has been reported to improve the durability of wood pellet likely by changing the physical and chemical structures of wood particles, but published literature is inconclusive about which structure change is the major reason for enhanced durability. Here, in this paper, steam treatment was combined either with alkaline or with SO 2 to study. The solids obtained after steam treatments along with a control sample were dried and each was compacted into pellets. The pellets were then tested for durability. Steam treatment alone dominated improvements in durability. The pellet durability increased with the amount of xylose, but xylose performed better in the pellet from raw poplar than did in the pellet from treated poplar. Water-soluble components contributed a maximum 4% of the durability of poplar pellets. The addition of lignin and sugars to substrates after steam treatment did not improve durability significantly. The surface modification that took place as a result of size reduction during steam treatment was the major reason, contributing about 50% of the durability of the pellet from steam-treated poplar. The acidity of steam treatment slightly affected the relative contributions of these structure changes on the durability. Lastly, the new knowledge helps tailor the chemical and/or mechanical pretreatment involved in pelleting biomass to durable pellets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Suppressed oxygen extraction and degradation of LiNixMnyCozO2 cathodes at high charge cut-off voltages

The capacity degradation mechanism in lithium nickel-manganese-cobalt oxide (NMC) cathodes (LiNi1/3Mn1/3Co1/3O2 (NMC333) and LiNi0.4Mn0.4Co0.2O2 (NMC442)) during high-voltage (cut-off of 4.8 V) operation has been investigated. In contrast to NMC442, NMC333 exhibits rapid structural changes including severe micro-crack formation and phase transformation from a layered to a disordered rock-salt structure, as well as interfacial degradation during high-voltage cycling, leading to a rapid increase of the electrode resistance and fast capacity decline. The fundamental reason behind the poor structural and interfacial stability of NMC333 was found to be correlated to its high Co content and the significant overlap between the Co3+/4+ t(2g) and O2- 2p bands, resulting in oxygen removal and consequent structural changes at high voltages. In addition, oxidation of the electrolyte solvents by the extracted oxygen species generates acidic species, which then attack the electrode surface and form highly resistive LiF. These findings highlight that both the structural and interfacial stability should be taken into account when tailoring cathode materials for high voltage battery systems

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning

Background: We developed machine-learning (ML) models to estimate a patient’s risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient’s risk to provide insight to clinicians beyond that of a “black box.” Methods: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model’s rationale to facilitate clinical interpretation. Results: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. Conclusions: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Activation of Water by Thorium Cation: A Guided Ion Beam and Quantum Chemical Study

The reaction of atomic thorium cations with deuterated water as a function of kinetic energy from thermal to 10 eV was studied using guided ion beam tandem mass spectrometry. At thermal energies, both ThO{sup +} + D{sub 2} and DThO{sup +} + D are formed in barrierless exothermic processes and reproduce results in the literature obtained using ion cyclotron resonance mass spectrometry. As the energy is increased, the branching ratio between these two channels changes such that the dominant product changes from ThO{sup +} to DThO{sup +} and back to ThO{sup +}, until ThD{sup +} + OD is energetically available and is the dominant product channel. To help understand these experimental results, a variety of theoretical approaches were tried and used to establish a potential energy surface, which compares well with previous theoretical studies. Utilizing the theoretical results, the kinetic energy dependent branching ratio between the ThO{sup +} + D{sub 2} and DThO{sup +} + D channels was calculated using both RRKM and phase space theory (PST). The results indicate that consideration of angular momentum conservation (as in PST) and spin-orbit corrected energies are needed to reproduce experimental results quantitatively. The PST modeling also provides relative energies for the two competing transition states that lead to the primary products, for which theory provides reasonable agreement. .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Silicon Qubits

There are two good reasons to attempt to build quantum bits (qubits) out of silicon. The first is the obvious foundation of classical microelectronics. Although silicon quantum computers would operate in a fundamentally different way from classical computers$-$for example, at cryogenic temperatures$-$still the level of development in material quality, crystal growth, and fabrication methodologies for silicon is unrivaled by any other material in the world. Leveraging even a small fraction of the worldwide investment in silicon for qubit development could potentially put silicon-based qubits far ahead of other solid-state alternatives. The second, less obvious reason for choosing silicon is the remarkably clean magnetic environment witnessed by spins in highly purified and isotopically enriched silicon material. Fortuitously, 95.3% of the naturally occurring isotopes of Si nuclei ( 28 Si and 30 Si) are spin-0. These nuclei therefore have a “closed shell” of nuclear moments, providing no external magnetic field whatsoever. Add to this the possibility of intrinsic silicon with part-per-billion chemical quality and the system is remarkably close to “vacuum” with respect to magnetic noise properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2017

The laboratory environmental algae pond simulator (LEAPS) photobioreactor: Validation using outdoor pond cultures of Chlorella sorokiniana and Nannochloropsis salina

A bench-scale photobioreactor system, the Laboratory Environmental Algae Pond Simulator (LEAPS), was designed and constructed to simulate outdoor pond cultivation for a wide range of geographical locations and seasons. The LEAPS consists of six well-mixed glass column photobioreactors submerged in a temperature controlled waterbath (-2 °C to >60 °C) and illuminated from above by a programmable multicolor LED lighting system (0 to 2,500 µmol/m 2 -sec). Measured incident light intensities and water temperatures deviated from the respective light and temperature setpoints on average only 2.3% and 0.9%, demonstrating accurate simulation of light and temperature conditions measured in outdoor ponds. In order to determine whether microalgae strains cultured in the LEAPS exhibit the same linear phase biomass productivity as in outdoor ponds, Chlorella sorokiniana and Nannochloropsis salina were cultured in the LEAPS bioreactors using light and temperature scripts measured previously in the respective outdoor pond studies. For Chlorella sorokiniana, the summer season biomass productivity in the LEAPS was 6.6% and 11.3% lower than in the respective outdoor ponds in Rimrock, Arizona, and Delhi, California; however, these differences were not statistically significant. For Nannochloropsis salina, the winter season biomass productivity in the LEAPS was 9.2% higher than in the respective outdoor ponds in Tucson, Arizona, a difference shown to be not statistically significant. Potential reasons for the positive or negative divergence in LEAPS performance, relative to outdoor ponds, are discussed. To demonstrate the utility of the LEAPS in predicting productivity, two other strains – Scenedesmus obliquus and Stichococcus minor – were evaluated using the summer season script for Rimrock, Arizona. For both strains, the productivity was around 11.6 g/m 2 -day at 25 cm culture depth. In conclusion, the LEAPS is an accurate pond simulator and thus offers a reliable, fast, and cost-effective way to screen microalgae strains and operating conditions for high biomass productivity and co-product yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

The development and deployment of advanced reactors, such as the sodium-cooled fast reactor (SFR), relies on sophisticated modeling tools to ensure the safety of the design under various transients. The predictive capability of these advanced modeling tools requires validation to garner trust in supporting the licensing of the advanced reactors. For this reason, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13.In this study, we present and discuss the benchmarking efforts of the modern system code SAM on the FFTF LOFWOS Test #13. Further, the SAM baseline model was developed according to the benchmark specification, which included a detailed core model with reactivity feedback. Generally, good agreement was observed between the baseline results and benchmark measurements; however, discrepancies persisted, particularly in predicted fuel assembly coolant outlet temperatures. Utilizing the baseline model, uncertainty quantification (UQ) and sensitivity analysis (SA) were conducted with the assistance of various statistical learning and machine learning methods, including kernel density estimation, Gaussian processes, and Sobol indices. Following the baseline model prediction and UQ and SA results, we discuss the reasons for the simulation discrepancies and propose further improvements to the model. This benchmarking effort adheres to the best-estimate plus uncertainty approach and can serve as a valuable example for supporting risk-informed licensing of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

The Epstein–Glaser causal approach to the light-front QED{sub 4}. II: Vacuum polarization tensor

In this work we show how to construct the one-loop vacuum polarization for light-front QED{sub 4} in the framework of the perturbative causal theory. Usually, in the canonical approach, it is considered for the fermionic propagator the so-called instantaneous term, but it is known in the literature that this term is controversial because it can be omitted by computational reasons; for instance, by compensation or vanishing by dimensional regularization. In this work we propose a solution to this paradox. First, in the Epstein–Glaser causal theory, it is shown that the fermionic propagator does not have instantaneous term, and with this propagator we calculate the one-loop vacuum polarization, from this calculation it follows the same result as those obtained by the standard approach, but without reclaiming any extra assumptions. Moreover, since the perturbative causal theory is defined in the distributional framework, we can also show the reason behind our obtaining the same result whether we consider or not the instantaneous fermionic propagator term. - Highlights: • We develop the Epstein–Glaser causal approach for light-front field theory. • We evaluate in detail the vacuum polarization at one-loop for the light-front QED. • We discuss the subtle issues of the Instantaneous part of the fermionic propagator in the light-front. • We evaluate the vacuum polarization at one-loop for the light-front QED with the Instantaneous fermionic part.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Macroscopic detection of deformed QM by the harmonic oscillator

Based on the nonperturbative analysis, we show that the classical motion of harmonic oscillator derived from the deformed QM is manifestly in contradiction with observations (to the first order in deformation parameter as it has been pointed out in Bawaj et al. (2015)). For this reason, we take an alternate way for estimating the effect and discuss its possible observational manifestations in macrophysics. - Highlights: • The classical motion of harmonic oscillator is derived from deformed QM. • The classical limit is obviously inconsistent with observations. • The reason for this inconsistency is explained. • An alternate estimate of the effect is provided.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗