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NASA NTRS2020

Genetic Algorithm for Optimization of Neural Networks for Bayesian Inference of Model Uncertainty

The objective of this work was to develop a genetic optimization algorithm that can design a neural network capable of producing uncertainty estimates along with predictions. This algorithm is necessary because the inclusion of uncertainty modeling in a neural network greatly complicates the network’s design space, making the development of a converging model extremely difficult and time consuming. The genetic algorithm presented in this work uses a number of value ranges for various configurable neural network parameters to create a randomly generated population of network architectures. The initially generated population is then evolved over the course of several generations, with the best performing models breeding to produce novel network configurations. Mutations are randomly applied to the network designs to facilitate the development of adaptations beneficial to the task being performed. An experiment was conducted to validate the proposed algorithm, in which the genetic optimizer was tasked with producing a neural network capable of predicting the sound pressure level (SPL) resulting from jet-surface interaction (JSI) noise. The data used for this task was generated at the NASA Glenn Research Center in the Aero-Acoustic Propulsion Laboratory. Starting with an initial population size of 35 randomly generated networks, and evolved over the course of 10 generations, the genetic algorithm produced a design able to predict SPL as a result of JSI noise within 0.272 dB, on average.

Genetic algorithm↗

DOE OSTI2024

Jet-hadron correlations with respect to the event plane in $\sqrt{s_{NN}}$ = 200 GeV Au + Au collisions in STAR

Angular distributions of charged particles relative to jet axes are studied in $\sqrt{s_{NN}}$ = 200 GeV Au+Au collisions as a function of the jet orientation with respect to the event plane. This differential study tests the expected path-length dependence of energy loss experienced by a hard-scattered parton as it traverses the hot and dense medium formed in heavy-ion collisions. A second-order event plane is used in the analysis as an experimental estimate of the reaction plane formed by the collision impact parameter and the beam direction. Charged-particle jets with 15 < 𝑝 T,jet < 20 and 20 < 𝑝 T,jet < 40 ⁢GeV/𝑐 were reconstructed with the anti-𝑘 T algorithm with radius parameter setting of 𝑅 = 0.4 in the 20–50% centrality bin to maximize the initial-state eccentricity of the interaction region. The reaction plane fit method is implemented to remove the flow-modulated background with better precision than prior methods. Yields and widths of jet-associated charged-hadron distributions are extracted in three angular bins between the jet axis and the event plane. The event-plane (EP) dependence is further quantified by ratios of the associated yields in different EP bins. No dependence on orientation of the jet axis with respect to the event plane is seen within the uncertainties in the kinematic regime studied. This finding is consistent with a similar experimental observation by ALICE in $\sqrt{s_{NN}}$ = 2.76 TeV Pb-Pb collision data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1984

Formation and inflammation of a turbulent jet

The formation and inflammation of a planar, turbulent jet in an incompressible medium is modeled numerically by the use of the random vortex method amended by a flame propagation algorithm. The results demonstrate the dominant influence of turbulent eddies and their interactions upon the development of the jet. Its growth is shown to consist of three stages: formation of small eddies, pairing of eddies with the same sign of circulation, and pairing of eddies of opposite signs. On this basis a number of features of the jet mechanism are revealed, namely penetration, engulfment, entrainment, and intermittency. Two cases of inflammation are considered. In one, the jet is ignited at the center of the orifice, the solution tracing its own inflammation. In the other, combustion is initiated across its full cross section, the results modeling the action of a turbulent torch as it spreads the flame into the combustible surroundings. In both cases the flow field is still dominated by the turbulent eddies and their interactions. However, the coherence among them is encumbered as a consequence of expansion due to the exothermicity of the combustion process.

Ghoniem, A. F.↗

DOE OSTI2026

Recoil-safe subtraction, matching and merging in e$^{+}$e$^{−}$ → hadrons

We present the first next-to-leading order matched and multi-jet merged predictions based on the Alaric parton shower. The components needed for infrared subtraction in the S-Mc@Nlo algorithm are computed analytically for the case of color singlet decays to hadronic final states and validated against existing approaches for up to e$^{−}$e$^{+}$ → 5 jets. Phenomenological results for e$^{−}$e$^{+}$ → hadrons at the Z pole are obtained with up to five jets at next-to-leading order precision, for the first time using an evolution algorithm with NLL-preserving kinematics mapping.

Höche, Stefan [Fermilab] (ORCID:0000000213706059)↗

DOE OSTI2022

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Accuracy of volumetric flow rate inflow/outflow measurement by integrating PIV velocity fields

In this paper, the usefulness of planar Particle Image Velocimetry (PIV) measurements to determine the volumetric flow rate from a rectangular or round jet exit is assessed. Both two-component PIV (2C–PIV) and through-plane three-component (3C) stereo PIV (SPIV) data sets were acquired at the exit of a turbulent, rectangular nozzle and a round nozzle with Reynolds numbers between 10 000 and 100 000. The PIV data sets were processed using a variety of algorithms. The time-averaged results were then spatially integrated across the jet exits and compared to a calibrated flow meter. Recommendations for each method are developed and discussed with potential drawbacks. The accuracy of the measurement was found to be a weak function of the Reynolds number of the flow. Furthermore, two-component PIV was found to underestimate the volumetric flow rate by 1%–4% depending on the integration scheme and SPIV underestimated volumetric flow rate by 2%.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Jas4pp — A data-analysis framework for physics and detector studies

This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimization, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. Furthermore, the improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1988

Fully-coupled analysis of jet mixing problems. Three-dimensional PNS model, SCIP3D

Numerical procedures formulated for the analysis of 3D jet mixing problems, as incorporated in the computer model, SCIP3D, are described. The overall methodology closely parallels that developed in the earlier 2D axisymmetric jet mixing model, SCIPVIS. SCIP3D integrates the 3D parabolized Navier-Stokes (PNS) jet mixing equations, cast in mapped cartesian or cylindrical coordinates, employing the explicit MacCormack Algorithm. A pressure split variant of this algorithm is employed in subsonic regions with a sublayer approximation utilized for treating the streamwise pressure component. SCIP3D contains both the ks and kW turbulence models, and employs a two component mixture approach to treat jet exhausts of arbitrary composition. Specialized grid procedures are used to adjust the grid growth in accordance with the growth of the jet, including a hybrid cartesian/cylindrical grid procedure for rectangular jets which moves the hybrid coordinate origin towards the flow origin as the jet transitions from a rectangular to circular shape. Numerous calculations are presented for rectangular mixing problems, as well as for a variety of basic unit problems exhibiting overall capabilities of SCIP3D.

Wolf, D. E.↗

NASA NTRS1990

A flight expert system for on-board fault monitoring and diagnosis

An architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing, and recovering from inflight faults is described. A prototype was implemented and an attempt was made to automate the knowledge acquisition process by employing a learning by being told methodology. The scope of acquired knowledge ranges from domain knowledge, including the information about objects and their relationships, to the procedural knowledge associated with the functionality of the mechanisms. AKAS (automatic knowledge acquisition system) is the constructed prototype for demonstration proof of concept, in which the expert directly interfaces with the knowledge acquisition system to ultimately construct the knowledge base for the particular application. The expert talks directly to the system using a natural language restricted only by the extent of the definitions in an analyzer dictionary, i.e., the interface understands a subset of concepts related to a given domain. In this case, the domain is the electrical system of the Boeing 737. Efforts were made to define and employ heuristics as well as algorithmic rules to conceptualize data produced by normal and faulty jet engine behavior examples. These rules were employed in developing the machine learning system (MLS). The input to MLS is examples which contain data of normal and faulty engine behavior and which are obtained from an engine simulation program. MLS first transforms the data into discrete selectors. Partial descriptions formed by those selectors are then generalized or specialized to generate concept descriptions about faults. The concepts are represented in the form of characteristic and discriminant descriptions, which are stored in the knowledge base and are employed to diagnose faults. MLS was successfully tested on jet engine examples.

Ali, Moonis↗

NASA NTRS2010

Evaluating the Performance of Unmanned Ground Vehicle Water Detection

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. During the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.

stereo vision↗

NASA NTRS2011

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Gay, Robert S.↗

DOE OSTI2020

Study of Heavy Flavor Mesons and Flavor-Tagged Jets with the CMS Detector

The goal of this research program is to implement heavy flavor meson triggers in heavy-ion collisions for the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN, including algorithm design, timing studies, offline validation, and online performance monitoring. The physics analyses which can be achieved by data from these new triggers is to address one of the most important questions in the field: parton flavor dependence of jet-quenching for the understanding of the transport properties of the Quark-Gluon Plasma. This program will allow CMS to collect the highest statistics heavy flavor meson and jet data ever recorded in heavy-ion colliders. The program includes two objectives: (1) Build and maintain the heavy flavor meson and jet triggers for heavy-ion collisions and deploy the trigger algorithms for 2015-2018 PbPb and pPb run at the LHC; (2) Perform heavy flavor meson and jet physics analyses, which can be used to study the parton flavor dependence of jet quenching, to extract the elastic energy loss coefficient of the QGP, and to test whether massive quarks also participate in collective expansion dynamics in heavy-ion collisions. With the heavy flavor physics trigger developed in this project, a competitive heavy flavor physics program in heavy-ion collisions has been established in CMS. This program allows studies of the fully reconstructed and flavor identified charm, beauty, and exotic hadrons that cover the widest transverse momentum range. The novel measurements supported by the award provide new constraints on the size of the flavor dependence of parton energy loss, the value of the in-medium charm quark diffusion coefficient, the mechanism of charm and beauty quark hadronization, and provide new insights to the nature of the X(3872) hadron.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1986

Computation of multi-dimensional viscous supersonic flow

A method has been developed for two- and three-dimensional computations of viscous supersonic jet flows interacting with an external flow. The approach employs a reduced form of the Navier-Stokes equations which allows solution as an initial-boundary value problem in space, using an efficient noniterative forward marching algorithm. Numerical instability associated with forward marching algorithms for flows with embedded subsonic regions is avoided by approximation of the reduced form of the Navier-Stokes equations in the subsonic regions of the boundary layers. Supersonic and subsonic portions of the flow field are simultaneously calculated by a consistently split linearized block implicit computational algorithm. The results of computations for a series of test cases associated with supersonic jet flow is presented and compared with other calculations for axisymmetric cases. Demonstration calculations indicate that the computational technique has great promise as a tool for calculating a wide range of supersonic flow problems including jet flow. Finally, a User's Manual is presented for the computer code used to perform the calculations.

Buggeln, R. C.↗

DOE OSTI2020

The soft drop groomed jet radius at NLL

We present results for the soft drop groomed jet radius R g at next-to-leading logarithmic accuracy. The radius of a groomed jet which corresponds to the angle between the two branches passing the soft drop criterion is one of the characteristic observables relevant for the precise understanding of groomed jet substructure. We establish a factorization formalism that allows for the resummation of all relevant large logarithms, which is based on demonstrating the all order equivalence to a jet veto in the region between the boundaries of the groomed and ungroomed jet. Non-global logarithms including clustering effects due to the Cambridge/Aachen algorithm are resummed to all orders using a suitable Monte Carlo algorithm. We perform numerical calculations and find a very good agreement with Pythia 8 simulations. We provide theoretical predictions for the LHC and RHIC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2017

Downlink Receiver Algorithms for Deep Space Optical Communications

The goal of the Deep Space Optical Communications project at the Jet Propulsion Laboratory is to demonstrate laser communication links at ranges out to approximately 3 AU. In this paper, we discuss a downlink receiver concept capable of demodulating optical pulse-position modulated (PPM) waveforms with data rates varying from approximately 50 kbps up to 265 Mbps, using a range of PPM orders, slot widths, and code rates. The receiver operates on recorded timestamps corresponding to the times-of-arrival of photons detected by a photon-counting detector array followed by a commercial time-tagger. Algorithms are presented for slot, symbol, and frame synchronization as well as parameter estimation. Estimates of link performance are evaluated through Monte- Carlo simulation for an optical channel that includes optical losses, detector blocking, signal clock dynamics, and pointing-induced downlink fades. Based upon these simulation results, it is expected that link closure may be achieved with at least 3 dB of margin under a variety of relevant conditions.

Tkacenko, Andre↗

NASA NTRS1990

Multigrid Calculations of a Jet in Crossflow

A series of numerical calculations are made of a jet in crossflow using progressive mesh refinement up to 2.4 million grid points. A solution algorithm that combined the features of a point-coupled solution of the momentum and continuity equations with multigrid acceleration demonstrated efficient convergence of the governing equations. In comparisons with experimental data, some features of the jet flow field were found to be predicted reasonably well, but the results still remain affected by numerical diffusion.

Claus, R. W.↗

NASA NTRS2003

Bio-inspired Computing for Robots

Living creatures may provide algorithms to enable active sensing/control systems in robots. Active sensing could enable planetary rovers to feel their way in unknown environments. The surface of Jupiter's moon Europa consists of fractured ice over a liquid sea that may contain microbes similar to those on Earth. To explore such extreme environments, NASA needs robots that autonomously survive, navigate, and gather scientific data. They will be too far away for guidance from Earth. They must sense their environment and control their own movements to avoid obstacles or investigate a science opportunity. To meet this challenge, CICT's Information Technology Strategic Research (ITSR) Project is funding neurobiologists at NASA's Jet Propulsion Laboratory (JPL) and selected universities to search for biologically inspired algorithms that enable robust active sensing and control for exploratory robots. Sources for these algorithms are living creatures, including rats and electric fish.

Laufenberg, Larry↗

DOE OSTI2026

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗