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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 OSTI2021

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Report on local data recovery approaches suitable for weather and climate prediction (Deliverable 1.3) (V.1.0)

Numerical weather and climate prediction rates as one of the scientific applications whose accuracy improvements greatly depend on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increasing average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys approaches for fault-tolerance in numerical algorithms and system resilience in parallel simulations from the perspective of numerical weather and climate prediction systems. A selection of existing strategies is analyzed, featuring interpolation-restart and compressed checkpointing for the numerics, in-memory checkpointing, user-level failure mitigation-based and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analyzed and some recommendations outlined for future developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Voxel Based Three-Dimensional Topology Optimization of Heat Exchanger Fins

Increasing interest in novel aircraft propulsion systems and potential for unwanted heat generation, or capture and reuse of waste heat, may require increasingly lightweight and high performing heat exchangers. Advances in manufacturing technologies have shown potential to create complex designs, but design tools need more flexibility. This study utilizes genetic algorithm-driven topology optimization to develop high performance heat exchanger fins for critical applications such as aerospace. The solid domain is generated using voxel representation where a voxel value of 1 indicates the solid domain and a voxel value of 0 refers to the fluid domain. The use of voxel representation somewhat matches the digitization of a model that is required to fabricate using additive manufacturing, and also allows for a highly unconstrained geometry. To test the topology optimization approach, a three-dimensional(3D)baseline offset strip fin geometry in steady laminar flow(Reynolds number = 215)with conjugate heat transfer(simultaneous solution of solid and fluid temperature fields)is optimized. New designs are generated using the genetic algorithm (GA) and sent to evaluation by the CFD software OpenFOAM; then the GA sorts and selects the reproduction pool for the following generation. This process is repeated for 60 generations. The study also investigates the effect off in material on the performance of the GA and the resulting designs. The results show that the optimal designs have overall performance improvement of 18% relative to the baseline. Additionally, a fin constructed of a lower conductivity material (such as an Inconel super alloy that might be necessary for waste heat recovery applications)results in lower overall performance improvement (11%)and optimal designs with higher pressure drop relative to their baseline, and relative to optimal designs produced using higher conductivity materials.

3D Optimization↗
DOE OSTI2021

Evaluating Automated Face Identity-Masking Methods with Human Perception and a Deep Convolutional Neural Network

Face de-identification (or “masking”) algorithms have been developed in response to the prevalent use of video recordings in public places. Here, we evaluated the success of face identity masking for human perceivers and a deep convolutional neural network (DCNN). Eight de-identification algorithms were applied to videos of drivers’ faces, while they actively operated a motor vehicle. These masks were pre-selected to be applicable to low-quality video and to maintain coarse information about facial actions. Humans studied high-resolution images to learn driver identities and were tested on their recognition of active drivers in low-resolution videos. Faces in the videos were either unmasked or were masked by one of the eight algorithms. When participants were tested immediately after learning (Experiment 1), all masks reduced identification, with six of eight masks reducing identification to extremely poor performance. In a second experiment, two of the most effective masks were tested after a delay of 7 or 28 days. The delay did not further reduce identification of the masked faces. In all masked conditions, participants maintained stringent decision criteria, with low confidence in recognition, further indicating the effectiveness of the masks. Next, the DCNN performed an identity-matching task between high-resolution images and masked videos—a task analogous to that done by humans. The pattern of accuracy for the DCNN mirrored some, but not all, aspects of human performance, highlighting the need to test the effectiveness of identity masking for both humans and machines. The DCNN was also tested on its ability to match identity between masked and unmasked versions of the same video, based only on the face. DCNN performance for the eight masks offers insight into the nature of the information in faces that is coded in these networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗
NASA NTRS1984

A Simple Algorithm for the Metric Traveling Salesman Problem

An algorithm was designed for a wire list net sort problem. A branch and bound algorithm for the metric traveling salesman problem is presented for this. The algorithm is a best bound first recursive descent where the bound is based on the triangle inequality. The bounded subsets are defined by the relative order of the first K of the N cities (i.e., a K city subtour). When K equals N, the bound is the length of the tour. The algorithm is implemented as a one page subroutine written in the C programming language for the VAX 11/750. Average execution times for randomly selected planar points using the Euclidean metric are 0.01, 0.05, 0.42, and 3.13 seconds for ten, fifteen, twenty, and twenty-five cities, respectively. Maximum execution times for a hundred cases are less than eleven times the averages. The speed of the algorithms is due to an initial ordering algorithm that is a N squared operation. The algorithm also solves the related problem where the tour does not return to the starting city and the starting and/or ending cities may be specified. It is possible to extend the algorithm to solve a nonsymmetric problem satisfying the triangle inequality.

Grimm, M. J.↗
NASA NTRS1993

Eigenvalue routines in NASTRAN: A comparison with the Block Lanczos method

The NASA STRuctural ANalysis (NASTRAN) program is one of the most extensively used engineering applications software in the world. It contains a wealth of matrix operations and numerical solution techniques, and they were used to construct efficient eigenvalue routines. The purpose of this paper is to examine the current eigenvalue routines in NASTRAN and to make efficiency comparisons with a more recent implementation of the Block Lanczos algorithm by Boeing Computer Services (BCS). This eigenvalue routine is now available in the BCS mathematics library as well as in several commercial versions of NASTRAN. In addition, CRAY maintains a modified version of this routine on their network. Several example problems, with a varying number of degrees of freedom, were selected primarily for efficiency bench-marking. Accuracy is not an issue, because they all gave comparable results. The Block Lanczos algorithm was found to be extremely efficient, in particular, for very large size problems.

Tischler, V. A.↗
NASA NTRS2000

Evaluation of Various Radar Data Quality Control Algorithms Based on Accumulated Radar Rainfall Statistics

The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. A fundamental and extremely important step in creating high-quality GV products is radar data quality control. Quality control (QC) processing of TRMM GV radar data is based on some automated procedures, but the current QC algorithm is not fully operational and requires significant human interaction to assure satisfactory results. Moreover, the TRMM GV QC algorithm, even with continuous manual tuning, still can not completely remove all types of spurious echoes. In an attempt to improve the current operational radar data QC procedures of the TRMM GV effort, an intercomparison of several QC algorithms has been conducted. This presentation will demonstrate how various radar data QC algorithms affect accumulated radar rainfall products. In all, six different QC algorithms will be applied to two months of WSR-88D radar data from Melbourne, Florida. Daily, five-day, and monthly accumulated radar rainfall maps will be produced for each quality-controlled data set. The QC algorithms will be evaluated and compared based on their ability to remove spurious echoes without removing significant precipitation. Strengths and weaknesses of each algorithm will be assessed based on, their abilit to mitigate both erroneous additions and reductions in rainfall accumulation from spurious echo contamination and true precipitation removal, respectively. Contamination from individual spurious echo categories will be quantified to further diagnose the abilities of each radar QC algorithm. Finally, a cost-benefit analysis will be conducted to determine if a more automated QC algorithm is a viable alternative to the current, labor-intensive QC algorithm employed by TRMM GV.

Robinson, Michael↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
NASA NTRS2003

Using a Portfolio of Algorithms for Planning and Scheduling

The Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles, includes a subsystem that utilizes a portfolio of heuristic algorithms that work synergistically to solve problems. The nature of the synergy of the specific algorithms is that their likelihoods of success are negatively correlated: that is, when a combination of them is used to solve a problem, the probability that at least one of them will succeed is greater than the sum of probabilities of success of the individual algorithms operating independently of each other. In ASPEN, the portfolio of algorithms is used in a planning process of the iterative repair type, in which conflicts are detected and addressed one at a time until either no conflicts exist or a user-defined time limit has been exceeded. At each choice point (e.g., selection of conflict; selection of method of resolution of conflict; or choice of move, addition, or deletion) ASPEN makes a stochastic choice of a combination of algorithms from the portfolio. This approach makes it possible for the search to escape from looping and from solutions that are locally but not globally optimum.

Sherwood, Robert↗
DOE OSTI2026

Cold Climate Field Study of the Effect of Defrost Controls on the Integrated Performance of a Ductless Air-Source Heat Pump

Residential heat pumps have advanced over the past decade to allow for operation at colder temperatures. However, the challenges of frost accumulation and defrosting the outdoor coil remain. The goal of this study was to evaluate the impact of the control algorithms that determine when a heat pump needs to defrost and when the base pan heater runs on the overall heating efficiency of the heat pump. In this study, which occurred during the 2023–2024 heating season, we measured the performance of a ductless air-source heat pump installed in Fairbanks, Alaska, USA. The heat pump was instrumented to measure the electrical input and the thermal output, as well as selected internal variables and indoor and outdoor environmental conditions. The heat pump was first operated with factory default control algorithms associated with the initiation of defrost and control of the base pan heater. These factory default algorithms focused on aggressively defrosting the outdoor coil and keeping the base pan ice-free. In the middle of the winter, these algorithms were changed to focus on reducing defrost cycles and increasing efficiency, while the heat pump continued to be operated and monitored. The results showed that significant increases in efficiency are possible by improving the defrost and base pan heater control algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2005

An Interoperability Consideration in Selecting Domain Parameters for Elliptic Curve Cryptography

Elliptic curve cryptography (ECC) will be an important technology for electronic privacy and authentication in the near future. There are many published specifications for elliptic curve cryptosystems, most of which contain detailed descriptions of the process for the selection of domain parameters. Selecting strong domain parameters ensures that the cryptosystem is robust to attacks. Due to a limitation in several published algorithms for doubling points on elliptic curves, some ECC implementations may produce incorrect, inconsistent, and incompatible results if domain parameters are not carefully chosen under a criterion that we describe. Few documents specify the addition or doubling of points in such a manner as to avoid this problematic situation. The safety criterion we present is not listed in any ECC specification we are aware of, although several other guidelines for domain selection are discussed in the literature. We provide a simple example of how a set of domain parameters not meeting this criterion can produce catastrophic results, and outline a simple means of testing curve parameters for interoperable safety over doubling.

Ivancic, Will↗
DOE OSTI2021

The Phase-2 Upgrade of the CMS Data Acquisition

The High Luminosity LHC (HL-LHC) will start operating in 2027 after the third Long Shutdown (LS3), and is designed to provide an ultimate instantaneous luminosity of 7:5 × 10$^{34}$ cm$^{-2}$ s$^{-1}$, at the price of extreme pileup of up to 200 interactions per crossing. The number of overlapping interactions in HL-LHC collisions, their density, and the resulting intense radiation environment, warrant an almost complete upgrade of the CMS detector. The upgraded CMS detector will be read out by approximately fifty thousand highspeed front-end optical links at an unprecedented data rate of up to 80 Tb/s, for an average expected total event size of approximately 8 - 10 MB. Following the present established design, the CMS trigger and data acquisition system will continue to feature two trigger levels, with only one synchronous hardware-based Level-1 Trigger (L1), consisting of custom electronic boards and operating on dedicated data streams, and a second level, the High Level Trigger (HLT), using software algorithms running asynchronously on standard processors and making use of the full detector data to select events for offline storage and analysis. The upgraded CMS data acquisition system will collect data fragments for Level-1 accepted events from the detector back-end modules at a rate up to 750 kHz, aggregate fragments corresponding to individual Level- 1 accepts into events, and distribute them to the HLT processors where they will be filtered further. Events accepted by the HLT will be stored permanently at a rate of up to 7.5 kHz. This paper describes the baseline design of the DAQ and HLT systems for the Phase-2 of CMS.

Badaro, Gilbert↗
DOE OSTI2024

Genomic factors shaping codon usage across the Saccharomycotina subphylum

Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. It has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. We characterized codon usage across 1,154 strains from 1,051 species from the fungal subphylum Saccharomycotina to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns. We found a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is distinct between the 12 orders to such a degree that yeasts can be classified with an accuracy >90% using a machine learning algorithm. We also characterized the degree to which codon usage bias is impacted by translational selection. We found it was influenced by a combination of features, including the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that avoidance of the CGN codons is associated with a decline in arginine tRNA function. Consistent with previous findings, codon usage bias within the Saccharomycotina is shaped by genomic features and GC bias. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Detection of Cyber Attacks in Grid-tied PV Systems Using Dynamic Watermarking

This paper presents of an active detection scheme for detecting cyber attacks on sensors controlling a grid-tied PV systems. Several cyber vulnerabilities in Grid tied PV Systems are discussed. The defense mechanism introduces a private (secret) watermarking signal into the control inputs of the grid-tied inverter system. This will enable the detection of any malicious manipulation of sensor measurements. Based on the measured data, two statistical tests are conducted to identify anomalies in the system using the presence of the watermarking signal. It shown that when a sensor data is compromised and/or replaced by a pre-recorded healthy signal, both test 1 and 2 exhibit high values indicating a possible malicious activity. The robustness of the proposed algorithm is tested and validated with several attack scenarios on a grid tied PV system. Select results from an experimental setup are discussed.

Ibrahim, Hasan↗
DOE OSTI2024

Application of Wireless Charging at Seaports for Range Extension of Drayage Battery Electric Trucks

Even though heavy-duty battery electric trucks (BETs) have become commercially available, their range limitation still hinders widespread adoption. Drayage has been regarded as a suitable application for early BETs due to typically having limited daily mileage. However, drayage operation can vary widely and some form of range extension may still be needed for BETs operating in this application. Here, in this paper, wireless charging at port terminals is proposed for this purpose. Potential wireless charging zones at port terminals are identified, and efficacy of wireless charging to extend BET range in drayage operation is verified by simulating the activity of 20 BETs from a drayage operator serving the ports of Los Angeles and Long Beach, using a microscopic BET energy consumption model. Furthermore, an optimization problem is formulated for optimal wireless charging zone planning from the port authority's perspective, considering subsets of the identified zones, and charging power options to choose from, for different budget ranges. In this context, zone planning means determining which areas of the port terminals should be selected for installing wireless charging systems, and what level of charging power should be for each selected zone's system. For each budget range, the optimization problem is solved using genetic algorithm to determine an optimal zone plan that provides the maximum amount of energy through wireless charging per unit cost of installation. The results show that wireless charging can aid improving activity completion of the simulated fleet by 5%, and further optimizing the zone plan can achieve similar performance with lower cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Differentially Private Adaptive Noise Injection (DP-ANI) v1.0

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This software implements a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.

Peisert, Sean [Lawrence Berkeley National Laborato↗