Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “quantization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,369 records · Page 76

Quantum Gravity and Laser Interferometry: Towards Observable Predictions

Understanding quantum gravity remains one of the deepest challenges in modern physics, as direct experimental access to Planck-scale effects is beyond current technological reach. However, recent theoretical advances indicate that quantum fluctuations of spacetime may produce measurable effects in precision experiments, particularly near causal horizons. This opens new avenues for testing quantum gravity phenomena through high-precision measurement techniques. This dissertation develops multiple theoretical models to characterize these effects and examines their potential observational signatures in future gravitational wave interferometers. We begin by investigating the role of quantum fluctuations in near-horizon geometries through the lens of the AdS/CFT correspondence, which provides a powerful framework for understanding the interplay between quantum field theory and general relativity via holographic principles. By modeling stochastic energy-momentum sources in Rindler-AdS spacetime, we demonstrate that vacuum fluctuations transform the Einstein equations into a Langevin-type stochastic differential equation, leading to potentially observable fluctuations in photon traversal times. Extending this approach to Minkowski spacetime, we establish a correspondence between gravitational shockwaves and fluid dynamics, showing that near-horizon perturbations satisfy an equation analogous to that governing incompressible fluids, thereby reinforcing the membrane paradigm and hydrodynamic analogies in the context of the fluid/gravity duality. Furthermore, we construct the covariant phase space of a spherically symmetric causal diamond in Minkowski spacetime, identifying two fundamental charges that govern its evolution. These results provide a foundation for quantizing causal horizons and understanding their microscopic degrees of freedom. Building upon these theoretical developments, we further examine a related stochastic phenomenon: the gravitational wave memory background arising from the cumulative memory steps produced by supermassive black hole mergers. After reviewing the standard stochastic gravitational wave background, gravitational memory effects, and BMS symmetries, we model the stochastic memory background using a Brownian motion framework. We show that while the cumulative memory background initially appears above the sensitivity curve of space-based interferometers like LISA, the realistic subtraction of individually resolvable merger events substantially suppresses the residual signal, making its detection more challenging. This highlights the critical importance of source subtraction when evaluating the detectability of gravitational memory effects. By bridging fundamental theory with experimental prospects, this dissertation contributes to the ongoing effort to uncover the quantum nature of spacetime through precision measurement techniques. Whether through detecting quantum spacetime fluctuations, gravitational memory backgrounds, or probing the symmetries of causal horizons, the pursuit of observable quantum gravity phenomena continues to expand the frontiers of both theory and experiment.

Zhang, Yiwen [Caltech] (ORCID:0000000323559416)↗

MINING NUCLEAR TRANSIENT DATA THROUGH SYMBOLIC CONVERSION

Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.

97 MATHEMATICS AND COMPUTING↗

Bulk dimensional nanocomposites for thermoelectric applications

Thermoelectric elements may be used for heat sensors, heat pumps, and thermoelectric generators. A quantum-dot or nano-scale grain size polycrystalline material the effects of size-quantization are present inside the nanocrystals. A thermoelectric element composed of densified Groups IV-VI material, such as calcogenide-based materials are doped with metal or chalcogenide to form interference barriers form along grains. The dopant used is either silver or sodium. These chalcogenide materials form nanoparticles of highly crystal grains, and may specifically be between 1- and 100 nm. The compound is densified by spark plasma sintering.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Two-Nucleon Systems in a Finite Volume

I present the formalism and methodology for determining the nucleon-nucleon scattering parameters from the finite volume spectra obtained from lattice quantum chromodynamics calculations. Using the recently derived energy quantization conditions and the experimentally determined scattering parameters, the bound state spectra for finite volume systems with overlap with the 3S1-3D3 channel are predicted for a range of volumes. It is shown that the extractions of the infinite-volume deuteron binding energy and the low-energy scattering parameters, including the S-D mixing angle, are possible from Lattice QCD calculations of two-nucleon systems with boosts of |P| <= 2pi sqrt{3}/L in volumes with spatial extents L satisfying fm <~ L <~ 14 fm.

Briceno, Raul↗

Signal digitizing system and method based on amplitude-to-time optical mapping

A signal digitizing system and method based on analog-to-time optical mapping, optically maps amplitude information of an analog signal of interest first into wavelength information using an amplitude tunable filter (ATF) to impress spectral changes induced by the amplitude of the analog signal onto a carrier signal, i.e. a train of optical pulses, and next from wavelength information to temporal information using a dispersive element so that temporal information representing the amplitude information is encoded in the time domain in the carrier signal. Optical-to-electrical conversion of the optical pulses into voltage waveforms and subsequently digitizing the voltage waveforms into a digital image enables the temporal information to be resolved and quantized in the time domain. The digital image may them be digital signal processed to digitally reconstruct the analog signal based on the temporal information with high fidelity.

47 OTHER INSTRUMENTATION↗

The Common Elements of Atomic and Hadronic Physics

Atomic physics and hadronic physics are both governed by the Yang Mills gauge theory Lagrangian; in fact, Abelian quantum electrodynamics can be regarded as the zero-color limit of quantum chromodynamics. I review a number of areas where the techniques of atomic physics can provide important insight into hadronic eigenstates in QCD. For example, the Dirac-Coulomb equation, which predicts the spectroscopy and structure of hydrogenic atoms, has an analog in hadron physics in the form of frame-independent light-front relativistic equations of motion consistent with light-front holography which give a remarkable first approximation to the spectroscopy, dynamics, and structure of light hadrons. The production of antihydrogen in flight can provide important insight into the dynamics of hadron production in QCD at the amplitude level. The renormalization scale for the running coupling is unambiguously set in QED; an analogous procedure sets the renormalization scales in QCD, leading to scheme-independent scale-fixed predictions. Conversely, many techniques which have been developed for hadron physics, such as scaling laws, evolution equations, the quark-interchange process and light-front quantization have important applicants for atomic physics and photon science, especially in the relativistic domain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Imaging agents for monitoring changes of dopamine receptors and methods of using thereof

The present invention is related generally to a method for screening subjects to determine those subjects more likely to develop diabetes by quantization of insulin producing cells. The present invention is also related to the diagnosis of diabetes and related to monitor disease progression or treatment efficacy of candidate drugs.

60 APPLIED LIFE SCIENCES↗

Quantum Geometry in the Lab

Standard particle theory is based on quantized matter embedded in a classical geometry. Here, a complementary model is proposed, based on classical matter -- massive bodies, without quantum properties -- embedded in a quantum geometry. It does not describe elementary particles, but may be a better, fully consistent quantum description for position states in laboratory-scale systems. Gravitational theory suggests that the geometrical quantum system has an information density of about one qubit per Planck length squared. If so, the model here predicts that the quantum uncertainty of geometry creates a new form of noise in the position of massive bodies, detectable by interferometers.

Hogan, Craig↗

Cyclotron resonance spectroscopy of a high-mobility two-dimensional electron gas from 0.4 to 100 K at high filling factors

We have studied the cyclotron mobility of a Landau-quantized two-dimensional electron gas as a function of temperature (0.4 --100 K) at a fixed magnetic field (1.25 T) using terahertz time-domain spectroscopy in a sample with a low frequency mobility of μ dc = 3.6 x 10 6 cm 2 V -1 s -1 and a carrier concentration of ns = 2 x 10 6 cm -2 . The low temperature mobility in this sample results from both impurity scattering and acoustic deformation potential scattering, with μ$-1\atop{CR}$ ≈ (2.1 x 10 5 cm 2 V -1 s -1 ) -1 + (3.8 x 10 -8 V sK -1 cm -2 x T) -1 at low temperatures. Above 50 K, the cyclotron oscillations show a strong reduction in both the oscillation amplitude and lifetime that is dominated by the contribution due to polar optical phonons. These results suggest that electron dephasing times as long as ~ 300 ps are possible even at this high lling factor (v = 6:6) in higher mobility samples (> 10 7 cm 2 V -1 s -1 ) that have lower impurity concentrations and where the cyclotron mobility at this carrier concentration would be limited by acoustic deformation potential scattering.

Curtis, Jeremy A.↗

Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms

Scientific simulations in high-performance computing (HPC) environments are producing vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for post-analysis. Unlike the traditional data reduction schemes (such as deduplication or lossless compression), not only can error-controlled lossy compression significantly reduce the data size but it can also hold the promise to satisfy user demand on error control. Point-wise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications in the lossy compression, since error control can adapt to the precision in the dataset automatically. Point-wise relative error bounded compression is complicated and time consuming. In this work, we develop efficient precomputation-based mechanisms in the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error bounded compression with excellent compression ratios. In addition, our mechanisms also help reduce traversing operations for Huffman decoding, and thus significantly accelerate the decompression process in SZ. Experiments with four well-known real-world scientific simulation datasets show that our solution can improve the compression rate by about 30% and decompression rate by about 70% in most of cases, making our designed lossy compression strategy the best choice in class in most cases.

compression rate↗

Frontiers in Thermal Transport and Energy Conversion: Proceedings of a Workshop

Thermal transport and energy conversion has remained an active field for at least 200 years, with numerous opportunities for discoveries and new applications. Recently, experiments have advanced researchers’ understanding of basic physics, and how new discoveries might translate into applications in energy, materials, quantum technologies, and other areas. The National Academies convened a workshop on April 11, 2019 to identify and assess the frontier of current research in the field of thermal transport and energy conversion. Discussions involved topics related to thermal transport and quasi-particle hydrodynamics, thermal transport beyond the quasiparticle paradigm, the thermal hall effect from neutral spin excitations in frustrated quantum magnets, quantization of the thermal hall conductivity at small hall angles, and thermal spin transport, including spin-Seebeck and magnon drag effects. These topics were strategically selected with the goal of uncovering key challenges, opportunities, and issues in order to guide future efforts and investments to advance the field. This publication offers a condensed summary of the discussions and presentations from the workshop, which was unclassified and open to the public.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Three Dimensional Rotations of the Electroweak Interaction

An extension to the standard electroweak model is presented that is different from previous models. This extension involves a three dimensional rotation of a plane containing two charge-neutral current axes. In this model the plane also contains the quantization axes of the three algebraic SU(2) groups that de ne the SU(3) group. The extension differentiates leptonic currents from baryonic currents through hypercolor-charge channels and predicts additional symmetries and currents due to the nature of the coupling of the three SU(2) groups into a SU(3) group format.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Lifting the Garage Door on Spawn, An Open-Source BEM-Controls Engine

Spawn is the latest whole-building energy simulation engine developed by the US Department of Energy, National Labs and industry. Whereas EnergyPlus was designed as a successor to DOE-2, Spawn is not a direct successor of–nor is it intended as an imminent replacement for– EnergyPlus. Instead, Spawn reuses parts of EnergyPlus while supporting new use cases in HVAC and controls. Spawn is intended to provide several capabilities that significantly advance beyond EnergyPlus. It is intended to support the evaluation of novel HVAC and district energy systems in a more physically realistic way. Critically, it can model control in a physically realistic way, using portable specifications that can be compiled for execution on control platforms. Spawn is also intended to support co-simulation in an intrinsic way to enable integration with third-party models. This paper describes the software architecture of Spawn from model authoring to compilation and simulation. It explains how Spawn reuses the envelope and daylighting modules of EnergyPlus and couples them to HVAC and control models from the Modelica Buildings Library using the Functional Mockup Interface (FMI) standard. It presents a number of examples that: i) validate Spawn’s coupled simulation approach by comparing its results to those of EnergyPlus, ii) illustrate the Spawn methodology for modeling and simulating HVAC systems, and iii) evaluate the performance of Spawn’s Quantized State System (QSS) time integration algorithms

Wetter, Michael↗

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

Peak Prediction Using Multi Layer Perceptron (MLP) for Edge Computing ASICs Targeting Scientific Applications

High data rate detectors play an integral part in scientific research and their development is actively pursued at High Energy Physics (HEP) facilities around the world. Edge Machine Learning (ML) offers the ability to reduce data rates by integrating ML algorithms into Application Specific Integrated Circuits (ASICs) on the front end electronics. In this work, we explore a set of neural network architectures for predicting the peak amplitudes in the detector's sensor response. We have designed and synthesized several MLP based neural networks comparing their inference accuracy, power consumption, and area targeting for minimal latency. The neural networks are synthesized in a commercial 65nm process. The effect of quantizing the network's weights and biases on hardware performance and area is reported. We also conduct design space exploration to compare between design alternatives

47 OTHER INSTRUMENTATION↗

An Innovative Secure mmWave M2M Communication Network for Operating Drones

Analyze RF (Radio Frequency) coverage in the sky with the innovative use of mmWave phased arrays aimed towards the drone corridor to harness the power of 5G – multiplicative improvement in data throughput & reduction in latency, and support of massive M2M communications. Apply MA (Multiple Access) technology to increase the spectral efficiency by covering a swarm of drones with a single mmWave beam and associated radio resources. Design energy efficient low-bit quantization to form mmWave beams for drone operation. Use drones’ navigation capabilities to significantly improve security by e.g. evading attackers. Utilize intra-drone communication to improve reliability as well as security. Demonstrate the simulated recovery of a critical infrastructure such as a power grid that requires a black restart with mmWave modeling and experiments .

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Measurements of Magnetic Field Penetration of Materials for Superconducting Radiofrequency Cavities

Superconducting Radio Frequency (SRF) cavities used in particle accelerators are typically formed from or coated with superconducting materials. Currently high purity niobium is the material of choice for SRF cavities which have been optimized to operate near their theoretical field limits. This brings about the need for significant R&D efforts to develop next generation superconducting materials which could outperform Nb and keep up with the demands of new accelerator facilities. To achieve high quality factors and accelerating gradients, the cavity material should be able to remain in the superconducting Meissner state under high RF magnetic field without penetration of quantized magnetic vortices through the cavity wall. Therefore, the magnetic field at which vortices penetrate in a supercon- ductor is one of the key parameters of merit of SRF cavities. Techniques to measure the onset of magnetic field penetration on thin film samples need to be developed to mitigate the issues with the conventional magnetometry measurements which are strongly influenced by the film orientation and shape and edge effects. The applied magnetic field is also needed to be parallel to the one side of the superconductor to resemble the magnetic field profile at the surface of the SRF cavities operating at fundamental accelerating mode. In this work we report the development of an experimental setup called Magnetic Field Penetration (MFP) magnetometer to measure the field of full flux penetration through bulk, thin films and multilayered superconductors. Our system combines a small superconducting solenoid which can generate the magnetic field up to 500 mT at the sample surface and three Hall probes to detect the full flux penetration through the planner superconductor with 2 inch diameter. This setup was used to study alternative materials which could potentially outperform niobium, as well as SIS multilayer coatings on niobium.

Senevirathne, Iresha↗