Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computational paradigms”

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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Adding intelligent services to an object oriented system

As today's software becomes increasingly complex, the need grows for intelligence of one sort or another to becomes part of the application, often an intelligence that does not readily fit the paradigm of one's software development. There are many methods of developing software, but at this time, the most promising is the object oriented (OO) method. This method involves an analysis to abstract the problem into separate 'objects' that are unique in the data that describe them and the behavior that they exhibit, and eventually to convert this analysis into computer code using a programming language that was designed (or retrofitted) for OO implementation. This paper discusses the creation of three different applications that are analyzed, designed, and programmed using the Shlaer/Mellor method of OO development and C++ as the programming language. All three, however, require the use of an expert system to provide an intelligence that C++ (or any other 'traditional' language) is not directly suited to supply. The flexibility of CLIPS permitted us to make modifications to it that allow seamless integration with any of our applications that require an expert system. We illustrate this integration with the following applications: (1) an after action review (AAR) station that assists a reviewer in watching a simulated tank battle and developing an AAR to critique the performance of the participants in the battle; (2) an embedded training system and over-the-shoulder coach for howitzer crewmen; and (3) a system to identify various chemical compounds from their infrared absorption spectra.

Robideaux, Bret R.↗
DOE OSTI2025

Conflict Detection in Open Radio Access Network (O-RAN) Control

A brief overview of the O-RAN approach to 5G cellular networking, discussion of the problem of conflicts among control functions under this paradigm, and research toward an approach detecting these conflicts using machine learning. This talk provides a high-level overview of academic research associated with an ongoing LDRD.

5G↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Calibrating constitutive models with full‐field data via physics informed neural networks

Abstract The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non‐linear elastic behaviour, elastomer behaviour with finite strain lock‐up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full‐field experimental data are utilized to calibrate constitutive models under finite deformations.

Hamel, Craig M.↗
DOE OSTI2021

Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D

Focal Area(s): 1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: DOE R&D, including DOE’s Basic Energy Research (BER)’s Environmental Systems Science Division (EESSD) program and DOE’s applied energy research (AER) programs (EERE, FE, and NE) are producers and consumers of Earth systems datasets. This white paper focuses on the first topic area from the call in relation to how crosscutting resources and innovations from DOE’s EESSD and AER can be brought to bear to mutual benefit and more efficient energy-water, Earth system data resources through improved. The overarching challenge posed by this call focuses on how DOE can directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability? While stemming from DOE BER’s EESSD program, this is a challenge that is faced and also being addressed by DOE’s AER programs. Over the past decade plus, FE, EERE, and NE programs have made important strides towards addressing this need. These strides are in many ways highly complementary to EESSD’s MODEX efforts. Energy water systems spanning metocean to groundwater to surface water systems all are data driven whether for basic energy or applied energy. These are remote, multi-variate, complex natural, and in many cases engineered, systems. Key needs and challenges of both EESSD and AER include developing data-focused tools to enhance data search and discovery to fill in knowledge gaps (address sparse data challenge), and rapidly transform datasets, including disparate and multi-source data. Leveraging DOE on-premise computing (HPC, exascale) infrastructure supports the computing-intensive algorithms required to execute these data acquisition and transformation processes to derive enriched knowledge and data, driving AI/ML and big data analytics for these systems. The opportunity lies in combining BER and AER efforts to provide a more robust, advanced, efficient and complete computing data fabric to address energy-water data acquisition and assimilation needs which currently pose significant impediments to AI/ML predictions and research.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

New Rover Conops with High-Performance Onboard Computing: Give Up Raw Data to Reduce Ops Cost and Do More Science

A major portion of time during the tactical operation of Mars rovers is spent for selecting, prioritizing, and coordinating sciences and engineering activities such that they fit within resource constraints, including the downlink data volume, energy, and time. In particular, the downlink data volume constraint is getting particularly tighter in recent missions because modern instruments produce increasingly high data volume while the communication bandwidth is essentially bounded by the law of physics. Tactical operation would be substantially simplified, hence the operation cost could be reduced, if the data volume constraint is relaxed or even removed. In this abstract, we propose a new operation paradigm for achieving this goal. The key observation is that, both in science and engineering applications, the bit size of raw data is typically much greater than the volume of processed information that is needed for scientific or engineering analysis. For example, a full-resolution image from Mastcam-Z, the main science camera on Perseverance, is about 700 kB in volume and we downlinked 29,685 images up to Sol 243, totaling ~20 GB of data. But of course, scientists do not use every pixel of these images; what they really look for in the images are geological features, typically represented by specific geometric configurations or textures. An end product after processing hundreds of Mascam-Z images could be a single geological map summarizing the spatial distribution of the features. For another example, a 100-meter drive of Perseverance produces 7-12 MB of drive telemetry, which records every detail of the rover's motion at 8 Hz, including position, attitude, steering angles, encoder readings, motor currents and many other information. But what the ground engineers eventually pay attention to is the signs of anomaly, such as excessive motor currents or high slip; if a drive is nominal, the vast majority of this data is unused. What if, then, we process the raw data onboard and only downlink the processed data that is relevant to scientific or engineering analyses, such as a list of detected science features (with cropped images) or a list of potential signs of anomaly while driving? A major roadblock for such onboard, high-level information processing has been the onboard computational resource. RAD750, the main onboard computer of Perseverance, is obviously not sufficient for performing complex image or signal processing such as object detection, semantic segmentation, or anomaly detection. Interestingly, RAD750 is not the best processor that Perseverance has; Qualcomm's Snapdragon 801, a modern mobile processor, is on her Heli Base Station, a device for communicating with Mars Helicopter Ingenuity; also, Intel's Atom E3845 processors are on engineering cameras. In the reminder of this paper, we will introduce two particular uses cases of these high-performance co-processors (meaning auxiliary CPU, GPU, or other types of processors that are separate from the main processor that runs the main flight software) for lowering operation cost and accommodating more science activities for a given communication constraint.

Didier, A.↗
DOE OSTI2023

Non-singlet quark helicity $\mathrm{PDF}$s of the nucleon from pseudo-distributions

The non-singlet helicity quark parton distribution functions (PDFs) of the nucleon are determined from lattice QCD, by jointly leveraging pseudo-distributions and the distillation spatial smearing paradigm. A Lorentz decomposition of appropriately isolated space-like matrix elements reveals pseudo-distributions that contain information on the leading-twist helicity PDFs, as well as an invariant amplitude that induces an additional z 2 contamination of the leading-twist signal. An analysis of the short-distance behavior of the space-like matrix elements using matching coefficients computed to next-to-leading order (NLO) exposes the desired PDF up to this additional z 2 contamination. Due to the non-conservation of the axial current, we elect to isolate the helicity PDFs normalized by the nucleon axial charge at the same scale μ 2 . The leading-twist helicity PDFs as well as several sources of systematic error, including higher-twist effects, discretization errors, and the aforementioned z 2 contaminating amplitude are jointly determined by characterizing the computed pseudo-distribution in a basis of Jacobi polynomials. The Akaike Information Criterion is exploited to effectively average over distinct model parameterizations and cuts on the pseudo-distribution. Encouraging agreement is observed with recent global analyses of each non-singlet quark helicity PDF, notably a rather small non-singlet anti-quark helicity PDF for all quark momentum fractions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Pileup and Infrared Radiation Annihilation (PIRANHA): a paradigm for continuous jet grooming

Jet grooming is an important strategy for analyzing relativistic particle collisions in the presence of contaminating radiation. Most jet grooming techniques introduce hard cutoffs to remove soft radiation, leading to discontinuous behavior and associated experimental and theoretical challenges. In this paper, we introduce Pileup and Infrared Radiation Annihilation (Piranha), a paradigm for continuous jet grooming that overcomes the discontinuity and infrared sensitivity of hard-cutoff grooming procedures. We motivate Piranha from the perspective of optimal transport and the Energy Mover’s Distance and review Apollonius Subtraction and Iterated Voronoi Subtraction as examples of Piranha-style grooming. We then introduce a new tree-based implementation of Piranha, Recursive Subtraction, with reduced computational costs. Finally, we demonstrate the performance of Recursive Subtraction in mitigating sensitivity to soft distortions from hadronization and detector effects, and additive contamination from pileup and the underlying event.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

BioPhotovoltaics: New paradigm towards high-efficiency and high-stability cells

In this project, we demonstrated significant progress in the development of Bio-Photovoltaic (BioPV) technology, with a particular focus on the transition from the initial success with Artemisinin (ART) to the development of the E1 compound. This journey began with the exploration of less conformationally restricted analogs of ART, leading to the discovery of E1. The initial success in the first quarter with ART set a precedent for the project, guiding our approach in molecular selection and design. Our computational studies provided a solid rationale for selecting specific biomolecules, with density functional theory calculations revealing the potential of certain molecules to form beneficial interactions with perovskite. This was a crucial step in narrowing down the candidate molecules from a broader selection. Subsequently, our approach involved simplifying these molecules to refine their properties and enhance their performance in bioPV applications. The ART-MAPbI3 films, for example, showcased not only high carrier mobility and hydrophobicity but also a significant increase in PCE. The evolution from ART to E1 was marked by a thorough understanding of molecular interactions and their impact on the material’s performance. This progression, from the complexity of lead candidates to the modeling and testing of simplified compounds, has culminated in the development of next-generation biomolecules with vastly improved properties. The link between E1 and ART, through this enhanced understanding, has been compelling and instrumental in achieving the milestones set forth in our project. The success in material and device performance underscores the importance of fundamental molecular design parameters, pointing towards future potential in the field of bioPV technology.

14 SOLAR ENERGY↗
NASA NTRS2014

Distributed Prognostics based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

centrifugal pump↗
NASA NTRS1991

Object oriented development of engineering software using CLIPS

Engineering applications involve numeric complexity and manipulations of a large amount of data. Traditionally, numeric computation has been the concern in developing an engineering software. As engineering application software became larger and more complex, management of resources such as data, rather than the numeric complexity, has become the major software design problem. Object oriented design and implementation methodologies can improve the reliability, flexibility, and maintainability of the resulting software; however, some tasks are better solved with the traditional procedural paradigm. The C Language Integrated Production System (CLIPS), with deffunction and defgeneric constructs, supports the procedural paradigm. The natural blending of object oriented and procedural paradigms has been cited as the reason for the popularity of the C++ language. The CLIPS Object Oriented Language's (COOL) object oriented features are more versatile than C++'s. A software design methodology based on object oriented and procedural approaches appropriate for engineering software, and to be implemented in CLIPS was outlined. A method for sensor placement for Space Station Freedom is being implemented in COOL as a sample problem.

Yoon, C. John↗
DOE OSTI2026

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments

Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms.

FOS: Computer and information sciences↗
DOE OSTI2019

Proactive Data Containers for Scientific Storage (Final Report)

Emerging HPC systems are expected to be deployed with an unprecedented level of complexity, due to a deep system memory/storage hierarchy and heterogeneity of the storage hardware. This hierarchy is expected to range from CPU cache through several levels of volatile memory to nonvolatile memory, traditional hard disks, and tape. Simple and efficient methods of data management and movement through this hierarchy is critical for scientific applications using exascale systems. Existing storage system and I/O (SSIO) technologies face severe challenges in dealing with these requirements. POSIX and MPI I/O standards that are the basis for existing I/O libraries and parallel file systems present fundamental challenges in the areas of scalable metadata operations, semantics-based data movement performance tuning, asynchronous operation, and support for scalable consistency of distributed operations. Moving toward new paradigms for SSIO in the extreme-scale era, we have proposed to investigate novel object-based data abstractions and storage mechanisms that take advantage of the deep storage hierarchy and enable proactive automated performance tuning. In order to achieve these overarching goals, we initiated an effort to develop a fundamental new data abstraction, called Proactive Data Containers (PDC). A PDC is a container within a locus of storage (memory, NVRAM, disk, etc.) that stores science data in an object-centric manner. Managing data as objects enables powerful optimization opportunities for data movement and transformations. The R&D focus of this project are: 1) formulation of object-oriented PDCs and their mapping in different levels of the exascale storage hierarchy; 2) efficient strategies for moving data in deep storage hierarchies using PDCs; 3) techniques for transforming and reorganizing data based on application requirements; and 4) novel analysis paradigms for enabling data transformations and user-defined analysis on data in PDCs. Toward achieving these overarching goals, we designed an object-centric application programing interface (API) for HPC, scalable metadata management for object-centric storage systems, and data movement optimizations such as Data Elevator for moving data between two levels of storage devices and TAPIOCA for efficient aggregation of data on compute nodes. We then implemented several components of the PDC system. They include metadata management, data placement services, remote procedure calls, data aggregation, etc. We have put them together into the overall PDC framework.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Power spectrum, correlation function, and tests for luminosity bias in the CfA redshift survey

We describe and apply a method for directly computing the power spectrum for the galaxy distribution in the extension of the Center for Astrophysics Redshift Survey. Tests show that our technique accurately reproduces the true power spectrum for k greater than 0.03 h Mpc(exp -1). The dense sampling and large spatial coverage of this survey allow accurate measurement of the redshift-space power spectrum on scales from 5 to approximately 200 h(exp -1) Mpc. The power spectrum has slope n approximately equal -2.1 on small scales (lambda less than or equal 25 h(exp -1) Mpc) and n approximately -1.1 on scales 30 less than lambda less than 120 h(exp -1) Mpc. On larger scales the power spectrum flattens somewhat, but we do not detect a turnover. Comparison with N-body simulations of cosmological models shows that an unbiased, open universe CDM model (OMEGA h = 0.2) and a nonzero cosmological constant (CDM) model (OMEGA h = 0.24, lambda(sub zero) = 0.6, b = 1.3) match the CfA power spectrum over the wavelength range we explore. The standard biased CDM model (OMEGA h = 0.5, b = 1.5) fails (99% significance level) because it has insufficient power on scales lambda greater than 30 h(exp -1) Mpc. Biased CDM with a normalization that matches the Cosmic Microwave Background (CMB) anisotropy (OMEGA h = 0.5, b = 1.4, sigma(sub 8) (mass) = 1) has too much power on small scales to match the observed galaxy power spectrum. This model with b = 1 matches both Cosmic Background Explorer Satellite (COBE) and the small-scale power spect rum but has insufficient power on scales lambda approximately 100 h(exp -1) Mpc. We derive a formula for the effect of small-scale peculiar velocities on the power spectrum and combine this formula with the linear-regime amplification described by Kaiser to compute an estimate of the real-space power spectrum. Two tests reveal luminosity bias in the galaxy distribution: First, the amplitude of the pwer spectrum is approximately 40% larger for the brightest 50% of galaxies in volume-limited samples that have M(sub lim) greater than M*. This bias in the power spectrum is independent of scale, consistent with the peaks-bias paradigm for galaxy formation. Second, the distribution of local density around galaxies shows that regions of moderate and high density contain both very bright (M less than M* = -19.2 + 5 log h) and fainter galaxies, but that voids preferentially harbor fainter galaxies (approximately 2 sigma significance level).

Park, Changbom↗
NASA NTRSDate not supplied

Saccade-Pursuit Coordination during Ocular Tracking across Different Impairment Sources

Purpose: Sleep loss and alcohol have been shown to impair smooth pursuit and its underlying visual motion processing in humans. This study examines the saccadic compensation for this poor pursuit. Methods: Using an established behavioral ocular-tracking paradigm (Liston & Stone, 2014, doi:10.1167/14.14.12), we examined the dose-response of ground lost (pursuit deficit integrated across our 300-ms steady-state tracking interval) and ground gained (increased saccadic response harnessed to compensate)across three separate studies – acute low-dose alcohol administration (LDA; n = 16 subjects), acute sleep loss(ASL; n = 12), and chronic sleep restriction (CSR; n = 12). We computed dose-responses as the linear regression slopes of ground lost and gained across treatment dose (% blood alcohol concentration [BAC] or hours awake). For the CSR study, we computed the mean effect for a single dose (5-hours nightly sleep for 1week). Results: For LDA, there was significantly increased ground lost (P < 0.001) and gained (P < 0.001) with increased %BAC. In addition, the dose-responses were not significantly different (P = 0.35), indicating effectively complete saccadic compensation due to significant increases in both saccadic rate (P < 0.05) and amplitude (P < 0.001). For ASL, there was significantly increased ground lost with time awake (P < 0.01),however ground gained was significantly lower (P < 0.001), indicating, at best, incomplete compensation due to a significant increase in saccadic rate (P < 0.001) but not amplitude (P = 0.10). With CSR, pursuit was again significantly impaired (P < 0.05), with saccadic rate significantly increased (P < 0.05) but, surprisingly, amplitude was significantly decreased (P < 0.05), effectively eliminating ground gained (P = 0.90). Conclusions: Our analyses show that LDA, ASL, and CSR affect tracking differently, suggesting the involvement of different brain pathways. With LDA, the effect appears largely due to the cortical impairment of visual motion processing with largely healthy brainstem and mid-brain responses (driving effective saccadic compensation). ASL and CSR however appear to affect both cortical and sub-cortical pathways, with at best partial saccadic compensation. Lastly, CSR is associated with an additional compromise due to a maladaptive decrease in saccade amplitude.

saccade↗
NASA NTRSDate not supplied

Fan Noise Predictions of the NASA Source Diagnostic Test using Unsteady Simulations with LAVA Part II - Tonal and Broadband Noise Assessment

The NASA Source Diagnostic Test (SDT) campaign experimental data is used for validation of a sliding mesh technique recently implemented within the Launch, Ascent, and Vehicle Aerodynamics computational framework for time-accurate simulation of rotating fans. The far-field acoustics are analyzed in this work, building upon the aerodynamic validation studies previously published in Part I. Two modeling approaches are explored: the unsteady Reynolds-averaged Navier Stokes (uRANS) with the negative Spalart-Allmaras (SA-neg) turbulence model closure, and a hybrid Reynolds-averaged Navier Stokes/large-eddy simulation (RANS/LES) paradigm employing a zonal detached-eddy simulation (ZDES) closure with enhanced shielding protection. Two convective flux scheme approaches with different dissipation properties are also explored with ZDES. The Ffowcs-Williams and Hawkings (FW-H) permeable surface approach is used for propagation of the near-field acoustics to the far-field microphone locations. This work analyzes the low-speed (approach) condition, characterized by a fan rotation speed of 7808 rotations-per-minute (RPM). Three different grid levels ranging between 200 million and 1.1 billion grid points are considered. Results show good prediction of the broadband noise levels at sideline angles ranging between 70° and 110°. The forward arc observers show an under-prediction of the overall sound pressure levels (OASPL) even at the fine grid level. A breakdown of the inlet and exhaust contributions reveals a steep drop-off in the broadband noise levels past a blade-passing frequency (BPF) of 1.5, potentially caused by a lack of resolved small-scale turbulent fluctuations in the interstage region. Past 110° the OASPL are over-predicted by up to 10 dB due to an over-prediction of the low-frequency broadband noise levels in the aft arc. The detuning of BPF2 caused by small deviations in the blade stagger angle around the wheel is captured, and a corresponding decrease in the sound power level for this cut-on tone is observed.

AATT↗