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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations. Published by the American Physical Society 2025

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Classification of AB O 3 perovskite solids: a machine learning study

Here we explored the use of machine learning methods for classifying whether a particular AB O 3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2–3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Moreover, doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT

Distributed Shared Memory (DSM) machines are a wide class of multi-processor computing systems where a large virtually-shared address space is mapped on a network of physically distributed memories. High memory latency and network contention are two of the main factors that limit performance scaling of such architectures. Modern high-performance computing DSM systems have evolved toward exploitation of massive hardware multi-threading and fine-grained memory hashing to tolerate irregular latencies, avoid network hot-spots and enable high scaling. In order to model the performance of such large-scale machines, parallel simulation has been proved to be a promising approach to achieve good accuracy in reasonable times. One of the most critical factors in solving the simulation speed-accuracy trade-off is network modeling. The Cray XMT is a massively multi-threaded supercomputing architecture that belongs to the DSM class, since it implements a globally-shared address space abstraction on top of a physically distributed memory substrate. In this paper, we discuss the development of a contention-aware network model intended to be integrated in a full-system XMT simulator. We start by measuring the effects of network contention in a 128-processor XMT machine and then investigate the trade-off that exists between simulation accuracy and speed, by comparing three network models which operate at different levels of accuracy. The comparison and model validation is performed by executing a string-matching algorithm on the full-system simulator and on the XMT, using three datasets that generate noticeably different contention patterns.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗
DOE OSTI2019

Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on four generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Zou, Pengfei↗
DOE OSTI2012

NVMalloc: Exposing an Aggregate SSD Store as a Memory Partition in Extreme-Scale Machines

DRAM is a precious resource in extreme-scale machines and is increasingly becoming scarce, mainly due to the growing number of cores per node. On future multi-petaflop and exaflop machines, the memory pressure is likely to be so severe that we need to rethink our memory usage models. Fortunately, the advent of non-volatile memory (NVM) offers a unique opportunity in this space. Current NVM offerings possess several desirable properties, such as low cost and power efficiency, but suffer from high latency and lifetime issues. We need rich techniques to be able to use them alongside DRAM. In this paper, we propose a novel approach for exploiting NVM as a secondary memory partition so that applications can explicitly allocate and manipulate memory regions therein. More specifically, we propose an NVMalloc library with a suite of services that enables applications to access a distributed NVM storage system. We have devised ways within NVMalloc so that the storage system, built from compute node-local NVM devices, can be accessed in a byte-addressable fashion using the memory mapped I/O interface. Our approach has the potential to re-energize out-of-core computations on large-scale machines by having applications allocate certain variables through NVMalloc, thereby increasing the overall memory capacity available. Our evaluation on a 128-core cluster shows that NVMalloc enables applications to compute problem sizes larger than the physical memory in a cost-effective manner. It can bring more performance/efficiency gain with increased computation time between NVM memory accesses or increased data access locality. In addition, our results suggest that while NVMalloc enables transparent access to NVM-resident variables, the explicit control it provides is crucial to optimize application performance.

Computer Science↗
DOE OSTI2016

X: A Comprehensive Analytic Model for Parallel Machines

To continuously comply with Moore’s Law, modern parallel machines become increasingly complex. Effectively tuning application performance for these machines therefore becomes a daunting task. Moreover, identifying performance bottlenecks at application and architecture level, as well as evaluating various optimization strategies, are becoming extremely difficult when the entanglement of numerous correlated factors is being presented. To tackle these challenges, we present a visual analytical model named “X”. It is intuitive and sufficiently flexible to track all the typical features of a parallel machine.

Modeling and Performance Analysis↗
DOE OSTI2015

A Novel Transverse Flux Machine for Vehicle Traction Applications

A novel transverse flux machine topology for electric vehicle traction application using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to Halbach-array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from Finite Element Analysis (FEA) show the motor achieved comparable torque density to conventional rare-earth permanent magnet machines. This machine is a viable candidate for direct drive applications with low cost and high torque density.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dynamic Shaping of Grid Response of Multi-Machine Multi-Inverter Systems Through Grid-Forming IBRs

We consider the problem of controlling the frequency response of weakly-coupled multi-machine multi-inverter low-inertia power systems via grid-forming inverter-based resources (IBRs). In contrast to existing methods, our approach relies on dividing the larger system into multiple strongly-coupled subsystems, without ignoring either the underlying network or approximating the subsystem response as an aggregate harmonic mean model. Rather, through a structured clustering and recursive dynamic shaping approach, the frequency response of the overall system to load perturbations is shaped appropriately. We demonstrate the proposed approach for a three-node triangular configuration and a small-scale radial network. Furthermore, previous synchronization analysis for heterogeneous systems requires the machines to satisfy certain proportionality property. In our approach, the effective transfer functions for each cluster can be tuned by the IBRs to satisfy such property, enabling us to apply the shaping control to systems with a wider range of heterogeneous machines.

frequency-shaping control↗
DOE OSTI2025

Balancing Trade-offs: Adaptive Differential Privacy in Interpretable Machine Learning Models

In the advancing field of machine learning, balancing accuracy, interpretability, and privacy represents a significant challenge. The problem is exacerbated by the widespread deployment of pre-trained models locally in diverse applications, which could lead to various amounts of privacy leakage. Conventional Differential Privacy strategies, in which uniform noises are applied to model gradients, guarantee data privacy at the expense of accuracy and interpretability. This paper introduces a Feature-Sensitive Adaptive Differential Privacy (FADP) framework with a unique noise-adding strategy. Noises are adaptively added based on feature importance clustering, where important features are considered for interpretability. By employing a unique masking technique, FADP selectively preserves crucial features with minimal noise interference, maintaining accuracy while enhancing interpretability. The FADP framework addresses the limitations of traditional DP methods by preserving critical channels and improving interpretability — a vital requirement in machine learning applications that demand transparency in model decisions. Through comprehensive testing, FADP is shown to balance the trade-offs among accuracy, privacy, and interpretability, marking a substantial advancement in the field of privacy-preserving machine learning.

Farhad Riya, Farhin [University of Tennessee, Knox↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Machinable, high‐conductivity NaSICON through mitigation of humidity effects during solid‐state synthesis

The Na + super ion conductor (NaSICON, Na 1+x Zr 2 Si x P 3-x O 12 ) is a solid electrolyte well-known for fast, selective Na + transport at low temperatures, uniquely enabling sodium-based batteries. Producing high-quality NaSICON from solid-state methods, especially when cost-effective, potentially hygroscopic precursors are used, is not trivial. To understand and eliminate the influence of humidity during processing, a scheme was developed to reproducibly yield a high Na + conductivity (3.75 mS/cm at 25°C, 81.7 mS/cm at 150°C), high density (97%), and machinable NaSICON without the use of binders, sintering aids, or dopants. Controlled humidity studies over 20%–50% RH coupled with thermal, structural, and electrical analysis reveal that calcination temperatures < 1000°C leave NaSICON processing susceptible to water absorption at > 20% RH due to the presence of hygroscopic Na 3 PO 4 and Na 2 CO 3 during shaping, pressing, and sintering. Water absorption results in NaSICON with lower densities, machinability, and Na+ conductivity, due to impaired intergranular Na+ transport. At the other extreme, fully converting precursor to the NaSICON phase at 1230°C before pressing and sintering leads to poor conductivity and density. By calcining at 1000°C, excellent quality NaSICON may be produced under a range of laboratory environments, enabling low-cost production of high-conductivity, machinable NaSICON necessary the ever-growing energy storage market.

NaSICON↗
DOE OSTI2013

Correction factors for ionization chamber dosimetry in CyberKnife: Machine-specific, plan-class, and clinical fields

Purpose: The aim of this work is the application of the formalism for ionization chamber reference dosimetry of small and nonstandard fields [R. Alfonso, P. Andreo, R. Capote, M. S. Huq, W. Kilby, P. Kjaell, T. R. Mackie, H. Palmans, K. Rosser, J. Seuntjens, W. Ullrich, and S. Vatnitsky, 'A new formalism for reference dosimetry of small and nonstandard fields,' Med. Phys. 35, 5179-5186 (2008)] to the CyberKnife robotic radiosurgery system. Correction factors for intermediate calibration fields, a machine-specific reference field (msr) and two plan-class specific reference fields (pcsr), have been studied. Furthermore, the applicability of the new formalism to clinical dosimetry has been analyzed through the investigation of two clinical treatments. Methods: PTW31014 and Scanditronix-Wellhofer CC13 ionization chamber measurements were performed for the fields under investigation. Absorbed dose to water was determined using alanine reference dosimetry, and experimental correction factors were calculated from alanine to ionization chamber readings ratios. In addition, correction factors were calculated for the intermediate calibration fields and one of the clinical treatment fields using the Monte Carlo method and these were compared with the experimental values. Results: Overall correction factors deviating from unity by approximately 2% were obtained from both measurements and simulations, with values below and above unity for the studied intermediate calibration fields and clinical fields for the ionization chambers under consideration. Monte Carlo simulations yielded correction factors comparable with those obtained from measurements for the machine-specific reference field, although differences from 1% to 3.3% were observed between measured and calculated correction factors for the composite intermediate calibration fields. Dose distribution inhomogeneities are thought to be responsible for such discrepancies. Conclusions: The differences found between overall correction factors associated with the proposed intermediate calibration fields and the clinical fields under investigation show that depending on the clinical field and the detector used, either a machine-specific reference field or a plan-class specific reference field is more representative for the clinical field. Given the experimental and numerical uncertainties and the small number of clinical fields considered in this study the significance of these observations is limited and it remains unclear for the CyberKnife if there would be a significant gain in using a pcsr field rather than a msr field as reference field for relative dosimetry.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Monte Carlo simulation based study of a proposed multileaf collimator for a telecobalt machine

Purpose: The objective of the present work was to propose a design of a secondary multileaf collimator (MLC) for a telecobalt machine and optimize its design features through Monte Carlo simulation. Methods: The proposed MLC design consists of 72 leaves (36 leaf pairs) with additional jaws perpendicular to leaf motion having the capability of shaping a maximum square field size of 35 Multiplication-Sign 35 cm{sup 2}. The projected widths at isocenter of each of the central 34 leaf pairs and 2 peripheral leaf pairs are 10 and 5 mm, respectively. The ends of the leaves and the x-jaws were optimized to obtain acceptable values of dosimetric and leakage parameters. Monte Carlo N-Particle code was used for generating beam profiles and depth dose curves and estimating the leakage radiation through the MLC. A water phantom of dimension 50 Multiplication-Sign 50 Multiplication-Sign 40 cm{sup 3} with an array of voxels (4 Multiplication-Sign 0.3 Multiplication-Sign 0.6 cm{sup 3}= 0.72 cm{sup 3}) was used for the study of dosimetric and leakage characteristics of the MLC. Output files generated for beam profiles were exported to the PTW radiation field analyzer software through locally developed software for analysis of beam profiles in order to evaluate radiation field width, beam flatness, symmetry, and beam penumbra. Results: The optimized version of the MLC can define radiation fields of up to 35 Multiplication-Sign 35 cm{sup 2} within the prescribed tolerance values of 2 mm. The flatness and symmetry were found to be well within the acceptable tolerance value of 3%. The penumbra for a 10 Multiplication-Sign 10 cm{sup 2} field size is 10.7 mm which is less than the generally acceptable value of 12 mm for a telecobalt machine. The maximum and average radiation leakage through the MLC were found to be 0.74% and 0.41% which are well below the International Electrotechnical Commission recommended tolerance values of 2% and 0.75%, respectively. The maximum leakage through the leaf ends in closed condition was observed to be 8.6% which is less than the values reported for other MLCs designed for medical linear accelerators. Conclusions: It is concluded that dosimetric parameters and the leakage radiation of the optimized secondary MLC design are well below their recommended tolerance values. The optimized design of the proposed MLC can be integrated into a telecobalt machine by replacing the existing adjustable secondary collimator for conformal radiotherapy treatment of cancer patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-373: A Motorized Stage for Fast and Accurate QA of Machine Isocenter

Purpose: Precision delivery of radiation dose relies on accurate knowledge of the machine isocenter under a variety of machine motions. This is typically determined by performing a Winston-Lutz test consisting of imaging a known object at multiple gantry/collimator/table angles and ensuring that the maximum offset is within specified tolerance. The first step in the Winston-Lutz test is careful placement of a ball bearing at the machine isocenter as determined by repeated imaging and shifting until accurate placement has been determined. Conventionally this is performed by adjusting a stage manually using vernier scales which carry the limitation that each adjustment must be done inside the treatment room with the risks of inaccurate adjustment of the scale and physical bumping of the table. It is proposed to use a motorized system controlled outside of the room to improve the required time and accuracy of these tests. Methods: The three dimensional vernier scales are replaced by three motors with accuracy of 1 micron and a range of 25.4mm connected via USB to a computer in the control room. Software is designed which automatically detects the motors and assigns them to proper axes and allows for small shifts to be entered and performed. Input values match calculated offsets in magnitude and sign to reduce conversion errors. Speed of setup, number of iterations to setup, and accuracy of final placement are assessed. Results: Automatic BB placement required 2.25 iterations and 13 minutes on average while manual placement required 3.76 iterations and 37.5 minutes. The average final XYZ offsets is 0.02cm, 0.01cm, 0.04cm for automatic setup and 0.04cm, 0.02cm, 0.04cm for manual setup. Conclusion: Automatic placement decreased time and repeat iterations for setup while improving placement accuracy. Automatic placement greatly reduces the time required to perform QA.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

SU-E-T-434: Evaluation of the Machine Performance Check (MPC) Program for Truebeam 2.0

Purpose: Machine Performance Check (MPC) is a software application used to verify that the TrueBeam machine is operating within major specifications prior to treatment. Used in combination with a phantom named Isocal, it verifies beam output, beam uniformity, treatment isocenter size, coincidence of treatment and imaging isocenters, positioning accuracy of kV and MV imaging systems, accuracy of collimator and gantry rotation angle, positioning accuracy of jaws and MLC leafs, and couch positioning. The tests can be performed semi-automatically and requires approximately 10 minutes of machine time. It is the purpose of this study to report the performance of this program. Methods: A pre-release version of the MPC tool was installed on a Truebeam linac with 6D couch at our center. Baseline beam output measurements were taken for 5 photon beams (6–15 MV, 6 FFF, 10 FFF) and 5 electron beams (6–20 MeV). Deviations from the baseline output were subsequently recorded for several days and compared against independent measurements from a PTW farmer chamber and our daily QA device (Fluke Biomedical Tracker) as part of an ongoing evaluation. Results: The beam output deviations between the MPC and the PTW chamber measurements were within ±0.7% for photons beams and ±1.0% for electrons beams. This was similar to the tracker performance. There were some isolated incidents where the MPC measurements had unexplained spikes (>3%) that disappeared on a repeat measurement. MPC was also able to detect maximum positioning errors in the jaws (1.12 mm), MLCs (1.14 mm), and couch roll (0.11°). Conclusion: Overall, the ability of the MPC to monitor linac output stability was comparable to that of ionization chamber-based measurements. MPC also provided fast daily mechanical tests not currently available in the clinic. How best to utilize this previously unavailable data is still under investigation.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-E-T-250: Determining VMAT Machine Limitations of An Elekta Linear Accelerator with Agility MLC for Accurate Modeling in RayStation and Robust Delivery

Purpose: To implement VMAT in RayStation with the Elekta Synergy linac with the new Agility MLC, and to utilize the same vendor softwares to determine the optimum Elekta VMAT machine parameters in RayStation for accurate modeling and robust delivery. Methods: iCOMCat is utilized to create various beam patterns with user defined dose rate, gantry, MLC and jaw speed for each control point. The accuracy and stability of the output and beam profile are qualified for each isolated functional component of VMAT delivery using ion chamber and Profiler2 with isocentric mounting fixture. Service graphing on linac console is used to verify the mechanical motion accuracy. The determined optimum Elekta VMAT machine parameters were configured in RayStation v4.5.1. To evaluate the system overall performance, TG-119 test cases and nine retrospective VMAT patients were planned on RayStation, and validated using both ArcCHECK (with plug and ion chamber) and MapCHECK2. Results: Machine output and profile varies <0.3% when only variable is dose rate (35MU/min-600MU/min). <0.9% output and <0.3% profile variation are observed with additional gantry motion (0.53deg/s–5.8deg/s both directions). The output and profile variation are still <1% with additional slow leaf motion (<1.5cm/s both direction). However, the profile becomes less symmetric, and >1.5% output and 7% profile deviation is seen with >2.5cm/s leaf motion. All clinical cases achieved comparable plan quality as treated IMRT plans. The gamma passing rate is 99.5±0.5% on ArcCheck (<3% iso center dose deviation) and 99.1±0.8% on MapCheck2 using 3%/3mm gamma (10% lower threshold). Mechanical motion accuracy in all VMAT deliveries is <1°/1mm. Conclusion: Accurate RayStation modeling and robust VMAT delivery is achievable on Elekta Agility for <2.5cm/s leaf motion and full range of dose rate and gantry speed determined by the same vendor softwares. Our TG-119 and patient results have provided us with the confidence to use VMAT clinically.

60 APPLIED LIFE SCIENCES↗