Search NASA⌕ Search

SEARCH · Search NASA

Results for “core optimization”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Cogging torque minimization in transverse flux machines

This study presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method. A multi-level DOE is proposed as an optimization method to reach an optimum solution with minimum simulations. The case study is analyzed in a two level DOE optimization. Finite element analysis is used to study the different effects. Two prototypes are fabricated for validating the FEA results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. To support such capabilities, significant progress has been made in various components including the deployment of 100 Gbps networks with future 1 Tbps bandwidth, increases in end-host capabilities with multiple cores and buses, capacity improvements in large disk arrays, and deployment of parallel file systems such as Lustre and GPFS. High-performance source-to-sink data flows must be composed of these component systems, which requires significant optimizations of the storage-to-host data and execution paths to match the edge and long-haul network connections. In particular, end systems are currently supported by 10-40 Gbps Network Interface Cards (NIC) and 8-32 Gbps storage Host Channel Adapters (HCAs), which carry the individual flows that collectively must reach network speeds of 100 Gbps and higher. Indeed, such data flows must be synthesized using multicore, multibus hosts connected to high-performance storage systems on one side and to the network on the other side. Current experimental results show that the constituent flows must be optimally composed and preserved from storage systems, across the hosts and the networks with minimal interference. Furthermore, such a capability must be made available transparently to the science users without placing undue demands on them to account for the details of underlying systems and networks. And, this task is expected to become even more complex in the future due to the increasing sophistication of hosts, storage systems, and networks that constitute the high-performance flows. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections. These solutions will be tested using (1) 100 Gbps connection(s) between Oak Ridge National Laboratory (ORNL) and Argonne National Laboratory (ANL) with storage systems supported by Lustre and GPFS file systems with an asymmetric connection to University of Memphis (UM); (2) ORNL testbed with multicore and multibus hosts, switches with OpenFlow capabilities, and network emulators; and (3) 100 Gbps connections from ESnet and their Openflow testbed, and other experimental connections. This proposal brings together the expertise and facilities of the two national laboratories, ORNL and ANL, and UM. It also represents a collaboration between DOE and the Department of Defense (DOD) projects at ORNL by sharing technical expertise and personnel costs, and leveraging the existing DOD Extreme Scale Systems Center (ESSC) facilities at ORNL.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Coronagraphic Observations of Lunar Sodium

This grant supported an investigation of lunar sodium by our coronagraph and spectrograph on nearby Mount Lemmon. We report successful operation and data analysis during International Lunar Atmosphere Week, September 15 - 22, 1995, and submittal of a paper to Icarus. The core of the proposed work was to observe the lunar sodium atmosphere with our classical Lyot coronagraph and specially-built grating spectrograph on Mount Lemmon, a 9400-foot peak about an hour's drive from Tucson. It is optimized for low scattered light and for observing from the Moon's limb to an altitude of approx.1 lunar radius. The grating has 600 lines/mm and a blaze angle of 49 deg., and is used with a somewhat wide slit at a resolving power of about 5000. It is called DARRK for the initials of the people who designed it. The rejection of stray light from the Moon's disk is spectacularly good: when the sky is clear this light is absent right up to a few arcsec from the limb. We use an excellent 1024 by 1024 pixel CCD camera, operated at -100 C; the exposures are 10 to 30 min. Data reduction is done with IRAF running on a Sun Sparcstation.

Hunten, D. M.↗
NASA NTRS1997

Coronagraphic Observations of Lunar Sodium

The core of the proposed work was to observe the lunar sodium atmosphere with our classical Lyot coronagraph and specially-built grating spectrograph on Mount Lemmon, a 9400-foot peak about an hour's drive from Tucson. It is optimized for low scattered light and for observing from the Moon's limb to an altitude of approx. 1 lunar radius. The grating has 600 lines/mm and a blaze angle of 49 deg, and is used with a somewhat wide slit at a resolving power of about 5000. It is called DARRK for the initials of the people who designed it. The rejection of stray light from the Moon's disk is spectacularly good: when the sky is clear this light is absent right up to a few arcsec from the limb. We use an excellent 1024 by 1024 pixel CCD camera, operated at -100 C; the exposures are 10 to 30 min. Data reduction is done with ERAF running on a Sun Sparcstation.

Hunten, D. M.↗
NASA NTRSDate not supplied

The Doppler Wind Temperature Sensor (DWTS) Flight Evaluation and Experiments (TES-16,17)

The Doppler Wind Temperature Sensor instrument (DWTS), developed by Global Atmospheric Technologies and Sciences (GATS), is a powerful yet simple tool with the potential of becoming a new window to upper atmosphere dynamics. Based around a defense-grade infrared camera peering through a static gas cell used as a scanning spectral filter, the DWTS is designed to measure the fingerprints of wind and thermal waves as they propagate to the mesosphere and lower thermosphere. DWTS achieves this scanning by measuring the induced change in the doppler shift of the emission as it passes through the DWTS field of view. DWTS holds promise to aid in more accurate weather determination (Gordley, et al), and the core technology can be easily adapted to study other atmospheres of interest. In partnership with GATS, NOAA, and other collaborators, NASA Ames and the Nano Orbital Workshop group have been working to evaluate the DWTS instrument on orbit and optimize it as a flexible cube satellite payload. The first mission selected for DWTS evaluation is preparing for flight in early 2024, followed by a more capable science mission in 2025, with both missions part of the TES-n/NOW flight series. The first flight, TES-16/DWTS-A, will incorporate a single DWTS instrument in an approximately 2U payload volume with the imaging aperture perpendicular to the flight velocity vector. With an estimated power consumption of 20 watts, supplied via a custom NOW-designed GaN converter, the instrument will maintain the imaging sensor plane at 80K using an integrated pulse-tube cryocooler during instrument evaluation periods. Data from DWTS will be captured via a NOW-designed custom data processor before being transmitted via S-band radio back to select ground stations, with instrument command and control operated via L-band global-coverage radio. After the TES-16/DWTS-A initial demonstration mission, the subsequent TES-17/DWTS-B mission will be a dedicated science mission equipped with three DWTS instruments, each hosting a different gas cell to obtain full altitude coverage from 20 to 200 km both day and night from a single cube satellite. The intention of the flight series, and one of NASA’s interests in the instrument, is to advance a Martian atmospheric instrument (Colaprete, Gordley, et al) which, if successful, would greatly further understanding of Martian atmospheric behavior. The proposed paper will review the flight series in detail, including challenges from the TES-16 flight tests and the projected challenges and application to Mars study. Additional detail regarding the possible applications of a Cognitive Communication technique in current flight development by NOW collaborators at the NASA Glenn Research Center will also be discussed, including the implications of using an automated User Initiated Service (UIS) protocol to maximize the data collected per orbit.

Doppler Wind Temperature Sensor↗
NASA NTRSDate not supplied

All-sky Retrieval of Atmospheric Temperature, Water Vapor, Clouds, Trace Gases, and Surface Properties from Operational Hyperspectral IR Sounders

Operational IR sounders such AIRS, CrIS, and IASI provide high quality hyperspectral measurements for weather and climate applications. We will describe a new all-sky Single Field-of-view Sounder Atmospheric Product (SiFSAP). The uniqueness of this product is that it uses all available channels from hyperspectral sounders and the optimal estimation retrieval is done at a single FOV spatial resolution. The SiFSAP includes atmospheric temperature, water vapor, clouds, trace gases, surface skin, and surface emissivity and will be produced operationally at NASA GES DISC. We will describe the core component of the SiFSAP algorithm, which is the Principal Component-based Radiative Transfer Model (PCRTM), and will show example applications of the SiFSAP product for various atmospheric weather and dynamics studies. We also describe a new Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides fast and accurate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

pcrtm↗
DOE OSTI2020

Contributions of Solar + Storage to Future Power Needs

The costs of solar photovoltaics (PV) have been dropping in recent years, leading to increasing installations of solar PV systems and growing interest in how the technology will impact the electric grid. Additionally, as battery costs decline, there is increasing interest in understanding the benefits and limitations of battery storage for the grid as well as potential co-benefits of coupling battery storage and photovoltaics. This paper presents recent methodological developments to more accurately represent the value and limitations of coupled PV and battery systems in capacity expansion models. These are demonstrated using the Resource Planning Model (RPM), which co-optimizes capacity investments, transmission investments, and reduced-order dispatch through 2045. We simulate the evolution of the generation and transmission system in the Western Interconnection both with and without a coupled PV and battery technology option under two core scenarios - a baseline scenario and a high renewable penetration scenario - coupled with sensitivities assuming low and mid PV and battery cost projections. We analyze the degree to which a variety of configurations of coupled PV + Battery systems are able to meet future electricity needs, under which system conditions this coupled technology becomes beneficial to the electric grid and identify the displaced technologies when coupled solar and storage is introduced into the model. We find that capturing the benefits of co-locating these systems becomes increasingly important as the penetration of solar PV rises, and that in particular it is important to accurately capture the ability of these technologies to shift energy, provide firm capacity, and reduce expected curtailment.

14 SOLAR ENERGY↗
DOE OSTI2018

ATF Transient Prescription Tests in TREAT

EXECUTIVE SUMMARY The Transient Reactor Test facility (TREAT) was constructed in the late 1950’s and provided thousands of transient irradiations before being placed in standby mode in 1994. In 2017, it resumed operation in order to reclaim its crucial role in nuclear-heated safety research. TREAT is a transient reactor where inherently-safe core physics, a nimble transient rod drive system, and a philosophy of continual facility improvement work together to enable flexible power maneuvers for fuel safety research. While the facility’s flexible design and multi-mission nature saw historic experiments for numerous transient types, TREAT was not specifically adapted to optimize or demonstrate its full performance capability for Light Water Reactor (LWR) postulated accidents including Hot Zero Power Reactivity Initiated Accidents (HZP-RIA) and Loss of Coolant Accidents (LOCA). The physics of HZP-RIA drive very brief pulses with fairly large energy excursions. TREAT is currently capable of approaching HZP-RIA pulse widths through transients initiated on a large step insertion of reactivity and terminated shortly thereafter by rapid re-insertion of the transient rods (“clipping”). LOCAs are postulated to occur quite differently, starting from normal power operations followed by a long period of lower power decay heat. TREAT is able to simulate LOCA events with an initial flattop transient for tens of seconds, followed by a sizeable power reduction, to a longer segment where specimen fission heating represents internal decay heat generation. Two reactor physics testing campaigns were undertaken to address the HZP-RIA type transients, neither of which had been performed in any great measure historically to address data gaps in the timing, repeatability of clipping and effect of dynamic transient rod motions on core-to-specimen fission power coupling. The first campaign, referred to as the Accident Tolerant Fuel (ATF) transient prescription tests, employed dosimeters in the core to characterize the effect of rod motions on specimen power coupling for clipped, unclipped, and low-level steady state operations each with approximately the same moderate core energy release. This campaign was accompanied by efforts to measure dosimetry and compare results to analytic predictions. The other HZP-RIA campaign, referred to as Narrow Pulse Width (NPW) transient prescriptions, employed higher step insertions with increased core energy release to demonstrate TREAT’s capability in achieving the narrowest-possible pulse width. Additionally, a series of LOCA transient prescriptions were also performed to demonstrate how well TREAT could perform long-duration shaped transients. Both NPW and LOCA transient prescriptions did not include dosimeters but used existing plant power instrumentation to produce transient power data. All three transient prescription campaigns were successfully carried out in TREAT. The ATF-3 transient prescriptions showed that TREAT can perform clipped transients with remarkable repeatability. Measurement and modeling both showed that the power coupling in small, fissile dosimeters does not change by more than 8% between clipped, unclipped, and steady state runs while uncertainty sources in both the modeling and measurement methods were identified and found to be on the order of 20% in total. Overall, the ATF-3 transient prescription tests were successful in determining repeatability and consistency of dosimeter power coupling, developing processes for modeling and measuring this crucial parameter, and demonstrating the need for continued efforts in transient prescription work at TREAT in support of future experiment design and post test data interpretation. The NPW and LOCA transient prescriptions were also successfully performed to exhibit TREAT’s capability for these important LWR accident categories. The NPW transients showed pulse widths of ~90 ms, which were the narrowest ever achieved in TREAT to date and relevant for the upper end range of HZP-RIA simulation. This effort provided crucial data for calibrating kinetic models to be used in enhanced pulse-narrowing strategies in upcoming irradiations. Similarly, The LOCA transient prescriptions showed that TREAT can simulate the needed nuclear heating and event time scales for LOCA fuel safety research and provided essential data for future experiment design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2017

Finite Element Modeling and Analysis of Mars Entry Aeroshell Baseline Concept

The structure that is developed and analyzed in this project must be able to survive all the various load conditions that it will encounter along its course to Mars with the minimal amount of weight and material. At this stage, the goal is to study the capability of the structure using a finite element model (FEM). This FEM is created using a python script, and is numerically solved in Nastran. The purpose of the model is to achieve an optimization of mass given specific constraints on launch and entry. The generation and analysis of the baseline Rigid Mid-Range Lift to Drag Ratio Aeroshell model is a continuation and an improvement on previous work done for the FEM. The model is generated using Python programming with the axisymmetric placement of nodes for beam and shell elements. The shells are assigned a honeycomb sandwich material with an aluminum honeycomb core and composite face sheets, and the beams are assigned the same material as the shell face sheets. There are two load cases assigned to the model: Earth launch and Mars entry. The Earth launch case consists of pressure, gravity, and vibration loads, and the Mars entry case consists of just pressure and gravity loads. The Earth launch case was determined to be the driving case, though the analyses are performed for both cases to ensure the constraints are satisfied. The types of analysis performed with the model are design optimization, statics, buckling, normal modes, and frequency response, the last of which is only for the Earth launch load case. The final results indicated that all of the requirements are satisfied except the thermal limits, which could not yet be tested, and the normal modes for the Mars entry. However, the frequency limits during Mars entry are expected to be much higher than the lower frequency limits set for the analysis. In addition, there are still improvements that can be made in order to reduce the weight while still meeting all requirements.

Ahmed, Samee W.↗
DOE OSTI2020

Boosting the Oxygen Reduction Performance via Tuning the Synergy between Metal Core and Oxide Shell of Metal−Organic Frameworks‐Derived Co@CoO x

Abstract Co core@Co oxide shell (Co@CoO x ) catalysts represent a large family with promising oxygen reduction reaction (ORR) catalytic activity. However, inadequate understanding of Co@CoO x synergy prohibits further pursuit of catalytic performance enhancement. Herein, a Co zeolitic−imidazolate framework was converted into metallic Co, followed by controlled air treatment to form Co@CoO x . The composition and structure evolution as a function of air treatment temperature were studied thoroughly through conventional and synchrotron (both ex‐situ and in‐situ) characterizations, confirming the coexistence of CoO and Co 3 O 4 in the shell. The optimal catalyst showed an ORR half‐wave potential of 0.87 V (vs. RHE) in an alkaline half‐cell and delivered high discharge capacity in an aprotic Li−O 2 battery (7,124 mAh g Cat+C −1 ) and an aqueous Zn−air battery (694 mAh g Zn −1 ) with good performance retention after durability test. Modeling simulation and density functional theory calculation confirmed the charge donation from metal core to oxide shell and shed light on new insights of how metal@metal oxide synergy impacted the ORR via tuning the charge conductivity, oxygen affinity and intermediate transfer pathway. This work opens up a venue to boost ORR catalytic activity from an interfacial synergy perspective.

Wang, Hao↗
DOE OSTI2019

Twin-core sunflower-type photonic quasicrystal fibers incorporated gold, silver, and copper microwire: an ultrashort and broad bandwidth polarization splitter

Polarization splitters are beneficial in the field of passive optical devices which are principally used in the coherent optical communication systems. An ideal polarization splitter must possess an efficient coupling with significant optical bandwidth. Hence, we proposed a novel metal-filled polarization splitter based on twin-core sunflower-type photonic quasicrystal fiber (PQF). The characteristics of the proposed splitter such as coupling length, extinction ratio, and available optical bandwidth were investigated by using the commercially available software, COMSOL Multiphysics. These aforementioned characteristics were determined by optimizing the geometrical parameters of proposed PQF—first by using gold-filled PQF (AuPQF). With optimized geometrical parameters, the splitter achieved an ultra-short coupling length of 132 µm and an ultra-broad bandwidth of 301 nm (wavelength range 1.36–1.68 µm) with an extinction ratio below than − 20 dB which comprised E + S+C + L+U band. Secondly, a comparative study to use silver- and copper-filled PQF (AgPQF and CuPQF) was explored. Both AgPQF and CuPQF retained a short length with adequate bandwidth and a significant extinction ratio below than − 20 dB. The AgPQF has a coupling length of 137 µm with a bandwidth of 136 nm which operated at 1.44 µm wavelength, while CuPQF with 134 µm lengths with a bandwidth of 149 nm and operated at 1.50 µm wavelengths.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Plasma Radiation Source on the Basis of the Gas Puff with Outer Plasma Shell in the Circuit of a Mega-Ampere Load Current Doubler

Characteristics of Z-pinch plasma radiation in the form of a double shell neon gas puff with outer plasma shell are investigated in the microsecond implosion mode. Experiments are performed using a GIT-12 mega-joule generator with load current doubler having a ferromagnetic core at implosion currents up to 5 MA. Conditions for matching of the nonlinear load with the mega-ampere current multiplier circuit are determined. The load parameters (plasma shell characteristics and mass and geometry of gas puff shells) are optimized on the energy supplied to the gas puff and n energy characteristics of radiation. It is established that the best modes of K-shell radiation in neon are realized for such radial distribution of the gas-puff material at which the compression velocity of the shell is close to a constant and amounts to 27–30 cm/μs. In these modes, up to 40% of energy supplied to the gas puff is converted into K-shell radiation. The reasons limiting the efficiency of the radiation source with increasing implosion current are analyzed. A modernized version of the energy supply from the current doubler to the Z-pinch is proposed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory

merging High-Performance Computing (HPC) workloads, such as graph analytics, machine learning, and big data science, are data-intensive. Data-intensive workloads usually present fine-grained memory accesses with limited or no data locality, and thus incur frequent cache misses and low utilization of memory bandwidth. 3D-stacked memory devices such as Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM) can provide significantly higher bandwidth than conventional memory modules. However, the traditional interfaces and optimization methods for JEDEC DDR devices do not allow to fully exploit the potential performance of 3D-stacked memory with the massive amount of irregular memory accesses of data-intensive applications. In this paper, we propose a novel Hotspot-Aware Manager (HAM) infrastructure for 3D-stacked memory devices capable of optimizing memory access streams via request aggregation, hotspot detection, and in-memory prefetching. %and an associated hotspot-aware page policy. We present the HAM design and implementation, and simulate it on a system using RISC-V embedded cores with attached HMC devices. We extensively evaluate HAM with over 12 benchmarks and applications representing diverse irregular memory access patterns. The results show that, on average, HAM reduces redundant requests by 37.51\% and increases the prefetch buffer hit rate by 4.2 times, compared to a baseline streaming prefetcher. On the selected benchmark set, HAM provides performance gains of 21.81\% in average (up to 34.28\%) and power savings of 35.07\% over a standard 3D-stacked memory.

Wang, Xi↗
DOE OSTI2019

Scalable Deep Learning via I/O Analysis and Optimization

Scalable deep neural network training has been gaining prominence because of the increasing importance of deep learning in a multitude of scientific and commercial domains. Consequently, a number of researchers have investigated techniques to optimize deep learning systems. Much of the prior work has focused on runtime and algorithmic enhancements to optimize the computation and communication. Despite these enhancements, however, deep learning systems still suffer from scalability limitations, particularly with respect to data I/O. This situation is especially true for training models where the computation can be effectively parallelized, leaving I/O as the major bottleneck. In fact, our analysis shows that I/O can take up to 90% of the total training time. Thus, in this article, we first analyze LMDB, the most widely used I/O subsystem of deep learning frameworks, to understand the causes of this I/O inefficiency. Based on our analysis, we propose LMDBIO—an optimized I/O plugin for scalable deep learning. LMDBIO includes six novel optimizations that together address the various shortcomings in existing I/O for deep learning. Lastly, our experimental results show that LMDBIO significantly outperforms LMDB in all cases and improves overall application performance by up to 65-fold on a 9,216-core system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗
DOE OSTI2025

Comparative study of spectral broadening and few-cycle compression of Yb:KGW laser pulses in gas-filled hollow-core fibers

While industrial-grade Yb-based amplifiers have become very prevalent, their limited gain bandwidth has created a large demand for robust spectral broadening techniques that allow for few-cycle pulse compression. In this work, we perform a comparative study between several atomic and molecular gases as media for spectral broadening in a hollow-core fiber geometry. Exploiting nonlinearities such as self-phase modulation, self-steepening, and stimulated Raman scattering, we explore the extent of spectral broadening and its dependence on gas pressure, the critical power for self-focusing, and the optimal regime for few-cycle pulse compression. Using a 3-mJ, 200-fs input laser pulses, we achieve 17 fs, few-cycle pulses with 80% fiber energy transmission efficiency. The optimal parameters can be scaled for higher or lower input pulse energies with appropriate gas parameters and fiber geometry.

Shalaby, Islam (ORCID:0000000332846636)↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗